From 548f6ce5c72d7ab5b77da420e381b995973f90aa Mon Sep 17 00:00:00 2001 From: kacey Date: Tue, 5 Dec 2023 13:21:59 -0800 Subject: [PATCH 01/59] add get method --- fiftyone/plugins/secrets.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/fiftyone/plugins/secrets.py b/fiftyone/plugins/secrets.py index 23f7a21a8f..1bb133e4d4 100644 --- a/fiftyone/plugins/secrets.py +++ b/fiftyone/plugins/secrets.py @@ -223,3 +223,6 @@ def items(self): except Exception as e: logging.error(f"Error when iterating through secrets:\n{e}") logging.debug(traceback.print_exc()) + + def get(self, key, default=None): + return self[key] if key in self.__required_keys else default From a38dcf4f82c90457484c9a3c354eecb85a26471c Mon Sep 17 00:00:00 2001 From: kacey Date: Tue, 5 Dec 2023 13:35:59 -0800 Subject: [PATCH 02/59] update tests --- tests/unittests/plugins/secrets_tests.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/unittests/plugins/secrets_tests.py b/tests/unittests/plugins/secrets_tests.py index 72981fdfb2..a92760a0d1 100644 --- a/tests/unittests/plugins/secrets_tests.py +++ b/tests/unittests/plugins/secrets_tests.py @@ -59,6 +59,7 @@ def test_secret_non_existing_key(self): assert result is None def test_secrets_property(self): + # pylint: disable=no-member context = ExecutionContext( operator_uri=self.operator_uri, required_secrets=self.plugin_secrets, @@ -73,6 +74,7 @@ def test_secrets_property(self): for k, v in context.secrets.items(): assert k in context._secrets assert context._secrets[k] == v + assert context.secrets.get(k) == v except Exception as e: pytest.fail( "secrets proproperty items should be the same as _secrets items" From 29126c1617226d0128561723d27dce46c09add49 Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 11 Dec 2023 15:08:13 -0500 Subject: [PATCH 03/59] don't overwrite dataset metadata with empty values during imports --- fiftyone/utils/data/importers.py | 66 +++++++++++++++++--------------- 1 file changed, 36 insertions(+), 30 deletions(-) diff --git a/fiftyone/utils/data/importers.py b/fiftyone/utils/data/importers.py index 3886e5a25d..d2371e5d9b 100644 --- a/fiftyone/utils/data/importers.py +++ b/fiftyone/utils/data/importers.py @@ -628,34 +628,32 @@ def parse_dataset_info(dataset, info, overwrite=True): overwrite (True): whether to overwrite existing dataset info fields """ tags = info.pop("tags", None) - if tags is not None: - if overwrite: - dataset.tags = tags - else: - _update_no_overwrite(dataset.tags, tags) + if tags: + _update_no_overwrite(dataset.tags, tags) description = info.pop("description", None) - if description is not None: + if description: if overwrite or not dataset.description: dataset.description = str(description) classes = info.pop("classes", None) - if isinstance(classes, dict): - if overwrite: - dataset.classes.update(classes) - else: - _update_no_overwrite(dataset.classes, classes) - elif isinstance(classes, list): - if overwrite or not dataset.default_classes: - dataset.default_classes = classes + if classes: + if isinstance(classes, dict): + if overwrite: + dataset.classes.update(classes) + else: + _update_no_overwrite(dataset.classes, classes) + elif isinstance(classes, list): + if overwrite or not dataset.default_classes: + dataset.default_classes = classes default_classes = info.pop("default_classes", None) - if default_classes is not None: + if default_classes: if overwrite or not dataset.default_classes: dataset.default_classes = default_classes mask_targets = info.pop("mask_targets", None) - if mask_targets is not None: + if mask_targets: mask_targets = dataset._parse_mask_targets(mask_targets) if overwrite: dataset.mask_targets.update(mask_targets) @@ -663,14 +661,14 @@ def parse_dataset_info(dataset, info, overwrite=True): _update_no_overwrite(dataset.mask_targets, mask_targets) default_mask_targets = info.pop("default_mask_targets", None) - if default_mask_targets is not None: + if default_mask_targets: if overwrite or not dataset.default_mask_targets: dataset.default_mask_targets = dataset._parse_default_mask_targets( default_mask_targets ) skeletons = info.pop("skeletons", None) - if skeletons is not None: + if skeletons: skeletons = dataset._parse_skeletons(skeletons) if overwrite: dataset.skeletons.update(skeletons) @@ -678,7 +676,7 @@ def parse_dataset_info(dataset, info, overwrite=True): _update_no_overwrite(dataset.skeletons, skeletons) default_skeleton = info.pop("default_skeleton", None) - if default_skeleton is not None: + if default_skeleton: if overwrite or not dataset.default_skeleton: dataset.default_skeleton = dataset._parse_default_skeleton( default_skeleton @@ -1815,18 +1813,26 @@ def _import_samples(self, dataset, dataset_dict, tags=None): # here because the import may need migration # doc = dataset._doc - dataset_dict.update( - dict( - _id=doc.id, - name=doc.name, - slug=doc.slug, - persistent=doc.persistent, - created_at=doc.created_at, - last_loaded_at=doc.last_loaded_at, - sample_collection_name=doc.sample_collection_name, - frame_collection_name=doc.frame_collection_name, - ) + keep_fields = dict( + _id=doc.id, + name=doc.name, + slug=doc.slug, + persistent=doc.persistent, + created_at=doc.created_at, + last_loaded_at=doc.last_loaded_at, + sample_collection_name=doc.sample_collection_name, + frame_collection_name=doc.frame_collection_name, ) + if doc.description: + keep_fields["description"] = doc.description + + if doc.tags: + tags = doc.tags.copy() + new_tags = dataset_dict.get("tags", None) or [] + tags.extend([t for t in new_tags if t not in tags]) + keep_fields["tags"] = tags + + dataset_dict.update(keep_fields) conn = foo.get_db_conn() conn.datasets.replace_one({"name": name}, dataset_dict) From b50d125303c3ff66d47b6d0638ab35abea551b6f Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 11 Dec 2023 15:31:21 -0500 Subject: [PATCH 04/59] ensure description/tags are included --- tests/unittests/import_export_tests.py | 46 ++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/tests/unittests/import_export_tests.py b/tests/unittests/import_export_tests.py index 6c196cd0fb..2f14b76e2f 100644 --- a/tests/unittests/import_export_tests.py +++ b/tests/unittests/import_export_tests.py @@ -3173,6 +3173,29 @@ def test_fiftyone_dataset(self): dataset.values("filepath2"), dataset2.values("filepath2") ) + # Retain description/tags + + description = "Hello, world!" + tags = ["foo", "bar"] + export_dir = self._new_dir() + + dataset.export( + export_dir=export_dir, + dataset_type=fo.types.FiftyOneDataset, + ) + + dataset2 = fo.Dataset() + dataset2.description = description + dataset2.tags = tags + + dataset2.add_dir( + dataset_dir=export_dir, + dataset_type=fo.types.FiftyOneDataset, + ) + + self.assertEqual(dataset2.description, description) + self.assertListEqual(dataset2.tags, tags) + @skipwindows @drop_datasets def test_legacy_fiftyone_dataset(self): @@ -3431,6 +3454,29 @@ def test_legacy_fiftyone_dataset(self): dataset.values("filepath2"), dataset2.values("filepath2") ) + # Retain description/tags + + description = "Hello, world!" + tags = ["foo", "bar"] + export_dir = self._new_dir() + + dataset.export( + export_dir=export_dir, + dataset_type=fo.types.LegacyFiftyOneDataset, + ) + + dataset2 = fo.Dataset() + dataset2.description = description + dataset2.tags = tags + + dataset2.add_dir( + dataset_dir=export_dir, + dataset_type=fo.types.LegacyFiftyOneDataset, + ) + + self.assertEqual(dataset2.description, description) + self.assertListEqual(dataset2.tags, tags) + class OpenLABELImageDatasetTests(ImageDatasetTests): @drop_datasets From ee2b29b244b4a0018d197ae6ce671aff57f7ae5e Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 11 Dec 2023 15:34:45 -0500 Subject: [PATCH 05/59] overwrite if present --- fiftyone/utils/data/importers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fiftyone/utils/data/importers.py b/fiftyone/utils/data/importers.py index d2371e5d9b..6d5c6d0f79 100644 --- a/fiftyone/utils/data/importers.py +++ b/fiftyone/utils/data/importers.py @@ -1823,7 +1823,7 @@ def _import_samples(self, dataset, dataset_dict, tags=None): sample_collection_name=doc.sample_collection_name, frame_collection_name=doc.frame_collection_name, ) - if doc.description: + if doc.description and not dataset_dict.get("description", None): keep_fields["description"] = doc.description if doc.tags: From 52d4d2d8c0877921702695ce2a0bf66d74e597e4 Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 11 Dec 2023 15:37:44 -0500 Subject: [PATCH 06/59] invalid builtin operators when no action is possible --- fiftyone/operators/builtin.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/fiftyone/operators/builtin.py b/fiftyone/operators/builtin.py index 73cf052a6e..a480e046e0 100644 --- a/fiftyone/operators/builtin.py +++ b/fiftyone/operators/builtin.py @@ -34,11 +34,12 @@ def resolve_input(self, ctx): view=types.Warning(), ) else: - inputs.str( + prop = inputs.str( "msg", label="You must select samples in the grid to clone", view=types.Warning(), ) + prop.invalid = True view = types.View(label="Clone selected samples") return types.Property(inputs, view=view) @@ -78,7 +79,10 @@ def resolve_input(self, ctx): "field_name", field_keys, label="Choose a field", - description="The field to copy. You can use dot notation (embedded.field.name) to clone embedded fields", + description=( + "The field to copy. You can use dot notation " + "(embedded.field.name) to clone embedded fields" + ), view=field_selector, required=True, ) @@ -87,7 +91,10 @@ def resolve_input(self, ctx): "new_field_name", required=True, label="New field", - description="The new field to create. You can use dot notation (embedded.field.name) to create embedded fields", + description=( + "The new field to create. You can use dot notation " + "(embedded.field.name) to create embedded fields" + ), default=f"{field_name}_copy", ) if new_field_name and new_field_name in field_keys: @@ -226,15 +233,16 @@ def resolve_input(self, ctx): sample_text = "sample" if count == 1 else "samples" inputs.str( "msg", - label=f"Delete {count} selected {sample_text}s?", + label=f"Delete {count} selected {sample_text}?", view=types.Warning(), ) else: - inputs.str( + prop = inputs.str( "msg", label="You must select samples in the grid to delete", view=types.Warning(), ) + prop.invalid = True view = types.View(label="Delete selected samples") return types.Property(inputs, view=view) From 9c834a084a0c9eafdee6181004244bceb709ca8a Mon Sep 17 00:00:00 2001 From: Stuart Wheaton Date: Tue, 12 Dec 2023 13:58:54 -0500 Subject: [PATCH 07/59] improvements to exporters.py --- fiftyone/utils/data/exporters.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/fiftyone/utils/data/exporters.py b/fiftyone/utils/data/exporters.py index ebf5729272..86583f119d 100644 --- a/fiftyone/utils/data/exporters.py +++ b/fiftyone/utils/data/exporters.py @@ -1211,7 +1211,7 @@ def export(self, media_or_path, outpath=None): outpath = self._filename_maker.get_output_path(media_path) uuid = self._get_uuid(outpath) - if self.export_mode == True: + if self.export_mode is True: etau.copy_file(media_path, outpath) elif self.export_mode == "move": etau.move_file(media_path, outpath) @@ -1228,9 +1228,9 @@ def export(self, media_or_path, outpath=None): outpath = self._filename_maker.get_output_path() uuid = self._get_uuid(outpath) - if self.export_mode == True: + if self.export_mode is True: self._write_media(media, outpath) - elif self.export_mode != False: + elif self.export_mode is not False: raise ValueError( "Cannot export in-memory media when 'export_mode=%s'" % self.export_mode @@ -2071,7 +2071,7 @@ def export_samples(self, sample_collection): def _prep_sample(sd): filepath = sd["filepath"] - if self.export_media != False: + if self.export_media is not False: # Store relative path _, uuid = self._media_exporter.export(filepath) sd["filepath"] = os.path.join("data", uuid) @@ -2212,7 +2212,7 @@ def _export_media_field(self, d, field_name, key=None): if value is None: return - if self.export_media != False: + if self.export_media is not False: # Store relative path media_exporter = self._get_media_field_exporter(field_name) _, uuid = media_exporter.export(value) @@ -2713,6 +2713,7 @@ def setup(self): self._media_exporter = ImageExporter( self.export_media, supported_modes=(True, "move", "symlink"), + export_path=self.export_dir, ) self._media_exporter.setup() @@ -2820,6 +2821,7 @@ def setup(self): self._media_exporter = VideoExporter( self.export_media, supported_modes=(True, "move", "symlink"), + export_path=self.export_dir, ) self._media_exporter.setup() From 856492aec3a158d9b46d11a206a008c841e5dd18 Mon Sep 17 00:00:00 2001 From: Kacey Date: Wed, 13 Dec 2023 09:54:10 -0800 Subject: [PATCH 08/59] Remove loading from mongoengine cache (#3922) * use db client to get the dataset doc instead of mongoengine queryset cache * use db client to get the dataset doc instead of mongoengine queryset cache --- fiftyone/core/dataset.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/fiftyone/core/dataset.py b/fiftyone/core/dataset.py index 651a58e338..fab886c03d 100644 --- a/fiftyone/core/dataset.py +++ b/fiftyone/core/dataset.py @@ -6831,11 +6831,12 @@ def _load_dataset(obj, name, virtual=False): def _do_load_dataset(obj, name): - try: - # pylint: disable=no-member - dataset_doc = foo.DatasetDocument.objects.get(name=name) - except moe.DoesNotExist: + # pylint: disable=no-member + db = foo.get_db_conn() + res = db.datasets.find_one({"name": name}) + if not res: raise ValueError("Dataset '%s' not found" % name) + dataset_doc = foo.DatasetDocument.from_dict(res) sample_collection_name = dataset_doc.sample_collection_name frame_collection_name = dataset_doc.frame_collection_name From 163bac4ad47a8ee5ce561417d2f4afd34b86b161 Mon Sep 17 00:00:00 2001 From: Trinity Date: Wed, 13 Dec 2023 16:35:45 -0800 Subject: [PATCH 09/59] ability to search multiple fields in a delegated operation list query (#3892) --- fiftyone/factory/repos/delegated_operation.py | 33 +++++--- tests/unittests/delegated_operators_tests.py | 75 +++++++++++++++---- 2 files changed, 82 insertions(+), 26 deletions(-) diff --git a/fiftyone/factory/repos/delegated_operation.py b/fiftyone/factory/repos/delegated_operation.py index 2d1328740f..e326b6de13 100644 --- a/fiftyone/factory/repos/delegated_operation.py +++ b/fiftyone/factory/repos/delegated_operation.py @@ -343,13 +343,7 @@ def list_operations( paging = DelegatedOperationPagingParams(**paging) if search: - for term in search: - for field in search[term]: - if field not in ("operator", "delegated_operation"): - raise ValueError( - "Invalid search field: {}".format(field) - ) - query[field] = {"$regex": term} + query.update(self._extract_search_query(search)) docs = self._collection.find(query) if paging.sort_by: @@ -378,13 +372,30 @@ def get(self, _id: ObjectId) -> DelegatedOperationDocument: def count(self, filters: dict = None, search: dict = None) -> int: if filters is None and search is not None: filters = {} + query = filters + + if "dataset_name" in query: + query["context.request_params.dataset_name"] = query[ + "dataset_name" + ] + del query["dataset_name"] if search: + query.update(self._extract_search_query(search)) + + return self._collection.count_documents(filter=query) + + def _extract_search_query(self, search): + if search: + or_query = {"$or": []} for term in search: for field in search[term]: - if field not in ("operator", "delegated_operation"): + if field not in ( + "operator", + "delegated_operation", + "label", + ): raise ValueError( "Invalid search field: {}".format(field) ) - filters[field] = {"$regex": term} - - return self._collection.count_documents(filter=filters) + or_query["$or"].append({field: {"$regex": term}}) + return or_query diff --git a/tests/unittests/delegated_operators_tests.py b/tests/unittests/delegated_operators_tests.py index 8d02fd8dcf..1c2ff237b6 100644 --- a/tests/unittests/delegated_operators_tests.py +++ b/tests/unittests/delegated_operators_tests.py @@ -742,7 +742,7 @@ def test_search( for j in range(25): doc = self.svc.queue_operation( operator=operator, - label=mock_get_operator.return_value.name, + label=f"test_{i}_{j}", delegation_target=delegation_target, context=ExecutionContext( request_params={ @@ -756,31 +756,76 @@ def test_search( ) # ensure that the queued_at times are different self.docs_to_delete.append(doc) + paging = DelegatedOperationPagingParams( + skip=0, + limit=5000, + sort_by=SortByField.QUEUED_AT, + sort_direction=SortDirection.ASCENDING, + ) + # test paging - get a page of everything docs = self.svc.list_operations( - search={"operator/test": {"operator"}}, - paging=DelegatedOperationPagingParams( - skip=0, - limit=5000, - sort_by=SortByField.QUEUED_AT, - sort_direction=SortDirection.DESCENDING, - ), + search={"operator/test": {"operator"}}, paging=paging ) self.assertEqual(len(docs), 100) docs = self.svc.list_operations( - search={"test_0": {"operator"}}, - paging=DelegatedOperationPagingParams( - skip=0, - limit=5000, - sort_by=SortByField.QUEUED_AT, - sort_direction=SortDirection.ASCENDING, - ), + search={"test_0": {"operator"}}, paging=paging ) self.assertEqual(len(docs), 25) + docs = self.svc.list_operations( + search={"test_0": {"operator", "label"}}, paging=paging + ) + + self.assertEqual(len(docs), 25) + + doc = self.svc.queue_operation( + operator="@voxel51/test/foo_baz", + label=f"I am a label", + delegation_target=delegation_target, + context=ExecutionContext( + request_params={ + "foo": "bar", + "dataset_name": dataset_name, + } + ), + ) + self.docs_to_delete.append(doc) + + doc = self.svc.queue_operation( + operator="@voxel51/test/operator", + label=f"foo_baz", + delegation_target=delegation_target, + context=ExecutionContext( + request_params={ + "foo": "bar", + "dataset_name": dataset_name, + } + ), + ) + self.docs_to_delete.append(doc) + + docs = self.svc.list_operations( + search={"foo_baz": {"operator", "label"}}, paging=paging + ) + + self.assertEqual(len(docs), 2) + + docs = self.svc.list_operations( + search={"foo_baz": {"label"}}, paging=paging + ) + + self.assertEqual(len(docs), 1) + + docs = self.svc.list_operations( + search={"foo_baz": {"operator"}}, paging=paging + ) + + self.assertEqual(len(docs), 1) + @patch( "fiftyone.core.dataset.load_dataset", ) From 14512a1f3566ab8da6ddc422b91a809a8f0a60e5 Mon Sep 17 00:00:00 2001 From: Trinity Date: Thu, 14 Dec 2023 09:01:17 -0800 Subject: [PATCH 10/59] use recoil datasetAppConfig over dataset.appConfig (#3924) --- app/packages/plugins/src/index.ts | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/app/packages/plugins/src/index.ts b/app/packages/plugins/src/index.ts index e58f69d43c..8589a615b4 100644 --- a/app/packages/plugins/src/index.ts +++ b/app/packages/plugins/src/index.ts @@ -349,11 +349,11 @@ export function usePluginSettings( pluginName: string, defaults?: Partial ): T { - const dataset = recoil.useRecoilValue(fos.dataset); + const datasetAppConfig = recoil.useRecoilValue(fos.datasetAppConfig); const appConfig = recoil.useRecoilValue(fos.config); const settings = useMemo(() => { - const datasetPlugins = _.get(dataset, "appConfig.plugins", {}); + const datasetPlugins = _.get(datasetAppConfig, "plugins", {}); const appConfigPlugins = _.get(appConfig, "plugins", {}); return _.merge, Partial>( @@ -361,7 +361,7 @@ export function usePluginSettings( _.get(appConfigPlugins, pluginName, {}), _.get(datasetPlugins, pluginName, {}) ); - }, [dataset, appConfig, pluginName, defaults]); + }, [appConfig, pluginName, defaults, datasetAppConfig]); return settings as T; } From b3664d899467f60a0a876a05f020c4c8e37aefb7 Mon Sep 17 00:00:00 2001 From: brimoor Date: Thu, 14 Dec 2023 17:21:36 -0500 Subject: [PATCH 11/59] documenting extra Milvus connection kwargs --- docs/source/integrations/milvus.rst | 52 +++++++++++++++++++++++++---- 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/docs/source/integrations/milvus.rst b/docs/source/integrations/milvus.rst index d8d2657998..885c8bb2bb 100644 --- a/docs/source/integrations/milvus.rst +++ b/docs/source/integrations/milvus.rst @@ -189,9 +189,19 @@ FiftyOne whenever a connection to Milvus is made. .. code-block:: shell + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_URI=XXXXXX export FIFTYONE_BRAIN_SIMILARITY_MILVUS_USER=XXXXXX export FIFTYONE_BRAIN_SIMILARITY_MILVUS_PASSWORD=XXXXXX - export FIFTYONE_BRAIN_SIMILARITY_MILVUS_URI=XXXXXX + + # also available if necessary + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_SECURE=true + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_TOKEN=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_DB_NAME=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_CLIENT_KEY_PATH=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_CLIENT_PEM_PATH=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_CA_PEM_PATH=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_SERVER_PEM_PATH=XXXXXX + export FIFTYONE_BRAIN_SIMILARITY_MILVUS_SERVER_NAME=XXXXXX **FiftyOne Brain config** @@ -203,9 +213,19 @@ located at `~/.fiftyone/brain_config.json`: { "similarity_backends": { "milvus": { - "user": "XXXXXXXXXXXX", - "password": "XXXXXXXXXXXX", - "uri": "XXXXXXXXXXXX" + "uri": "XXXXXX", + "user": "XXXXXX", + "password": "XXXXXX", + + # also available if necessary + "secure": true, + "token": "XXXXXX", + "db_name": "XXXXXX", + "client_key_path": "XXXXXX", + "client_pem_path": "XXXXXX", + "ca_pem_path": "XXXXXX", + "server_pem_path": "XXXXXX", + "server_name": "XXXXXX" } } } @@ -228,9 +248,19 @@ connections to Milvus: ... backend="milvus", brain_key="milvus_index", + uri="XXXXXX", user="XXXXXX", password="XXXXXX", - uri="XXXXXX", + + # also available if necessary + secure=True, + token="XXXXXX", + db_name="XXXXXX", + client_key_path="XXXXXX", + client_pem_path="XXXXXX", + ca_pem_path="XXXXXX", + server_pem_path="XXXXXX", + server_name="XXXXXX", ) Note that, when using this strategy, you must manually provide the credentials @@ -242,9 +272,19 @@ when loading an index later via milvus_index = dataset.load_brain_results( "milvus_index", + uri="XXXXXX", user="XXXXXX", password="XXXXXX", - uri="XXXXXX", + + # also available if necessary + secure=True, + token="XXXXXX", + db_name="XXXXXX", + client_key_path="XXXXXX", + client_pem_path="XXXXXX", + ca_pem_path="XXXXXX", + server_pem_path="XXXXXX", + server_name="XXXXXX", ) .. _milvus-config-parameters: From 59c5bf9fd01b96a5cbb2542ab91c97751426beab Mon Sep 17 00:00:00 2001 From: Sashank Aryal <66688606+sashankaryal@users.noreply.github.com> Date: Thu, 14 Dec 2023 16:38:18 -0600 Subject: [PATCH 12/59] imavid improvements + e2e (#3893) * more range check validation * fix tagging * remove log * fix imavid regression * upgrade dep versions * add watermarking option in image generator * fix frame one-off frame sync bug * move dynamic groups spec to groups folder * move dynamic groups spec to groups folder * add more data-cy attributes * differentiate grid and modal taggers * add speed controller * add toggleRenderFramesAsVideo * rename vars * add imavid tests * add screenshots * add data-cy to pathvalueentry * add tagging tests * remove unused vars * add modal tagger pom * add label count and sample count getters in siderbar * reset popups before play/pause --- .../core/src/components/Actions/Tagger.tsx | 44 +- .../Sidebar/Entries/PathValueEntry.tsx | 2 +- .../looker/src/elements/imavid/index.ts | 28 +- .../looker/src/elements/imavid/loader-bar.ts | 1 + .../looker/src/elements/imavid/play-button.ts | 4 +- .../src/elements/imavid/playback-rate.ts | 1 + .../looker/src/elements/imavid/seek-bar.ts | 1 + app/packages/looker/src/elements/video.ts | 5 +- .../lookers/imavid/buffer-manager/index.ts | 4 +- .../lookers/imavid/ima-vid-frame-samples.ts | 7 + e2e-pw/package.json | 20 +- .../oss/poms/action-row/display-options.ts | 5 + .../{tagger.ts => tagger/grid-tagger.ts} | 2 +- .../poms/action-row/tagger/modal-tagger.ts | 30 + e2e-pw/src/oss/poms/modal/index.ts | 7 + e2e-pw/src/oss/poms/modal/modal-sidebar.ts | 59 +- e2e-pw/src/oss/poms/modal/video-controls.ts | 61 +- .../specs/color-scheme/color-scheme.spec.ts | 2 - .../oss/specs/dynamic-groups-cifar.spec.ts | 56 -- .../specs/{ => groups}/dynamic-groups.spec.ts | 0 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts | 169 ++++ .../ima-vid-1-3-chromium-darwin.png | Bin 0 -> 6549 bytes .../ima-vid-1-3-chromium-linux.png | Bin 0 -> 6398 bytes .../ima-vid-1-5-chromium-darwin.png | Bin 0 -> 6530 bytes .../ima-vid-1-5-chromium-linux.png | Bin 0 -> 6378 bytes .../src/oss/specs/smoke-tests/tagger.spec.ts | 6 +- e2e-pw/src/shared/media-factory/image.ts | 22 +- e2e-pw/yarn.lock | 896 ++++++++++-------- 28 files changed, 942 insertions(+), 490 deletions(-) rename e2e-pw/src/oss/poms/action-row/{tagger.ts => tagger/grid-tagger.ts} (96%) create mode 100644 e2e-pw/src/oss/poms/action-row/tagger/modal-tagger.ts delete mode 100644 e2e-pw/src/oss/specs/dynamic-groups-cifar.spec.ts rename e2e-pw/src/oss/specs/{ => groups}/dynamic-groups.spec.ts (100%) create mode 100644 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts create mode 100644 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts-snapshots/ima-vid-1-3-chromium-darwin.png create mode 100644 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts-snapshots/ima-vid-1-3-chromium-linux.png create mode 100644 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts-snapshots/ima-vid-1-5-chromium-darwin.png create mode 100644 e2e-pw/src/oss/specs/groups/ima-vid.spec.ts-snapshots/ima-vid-1-5-chromium-linux.png diff --git a/app/packages/core/src/components/Actions/Tagger.tsx b/app/packages/core/src/components/Actions/Tagger.tsx index 549fd97f12..860cfdb961 100644 --- a/app/packages/core/src/components/Actions/Tagger.tsx +++ b/app/packages/core/src/components/Actions/Tagger.tsx @@ -3,7 +3,12 @@ import { PopoutSectionTitle, useTheme, } from "@fiftyone/components"; -import { FrameLooker, ImageLooker, VideoLooker } from "@fiftyone/looker"; +import { + FrameLooker, + ImaVidLooker, + ImageLooker, + VideoLooker, +} from "@fiftyone/looker"; import * as fos from "@fiftyone/state"; import { Lookers, groupId, groupStatistics, refresher } from "@fiftyone/state"; import { getFetchFunction } from "@fiftyone/utilities"; @@ -320,6 +325,7 @@ const useTagCallback = ( ...[ useRecoilRefresher_UNSTABLE(tagStatistics({ modal, labels: false })), useRecoilRefresher_UNSTABLE(tagStatistics({ modal, labels: true })), + useRecoilRefresher_UNSTABLE(fos.activeModalSidebarSample), ], ]; @@ -327,6 +333,13 @@ const useTagCallback = ( ({ snapshot, set, reset }) => async ({ changes }) => { const isGroup = await snapshot.getPromise(fos.isGroup); + const isNonNestedDynamicGroup = await snapshot.getPromise( + fos.isNonNestedDynamicGroup + ); + const isImaVidLookerActive = await snapshot.getPromise( + fos.isImaVidLookerAvailable + ); + const slices = await snapshot.getPromise(fos.currentSlices(modal)); const { samples } = await getFetchFunction()("POST", "/tag", { ...tagParameters({ @@ -338,14 +351,15 @@ const useTagCallback = ( modal ? fos.modalFilters : fos.filters ), hiddenLabels: await snapshot.getPromise(fos.hiddenLabelsArray), - groupData: isGroup - ? { - id: modal ? await snapshot.getPromise(groupId) : null, - slices, - slice: await snapshot.getPromise(fos.groupSlice), - mode: await snapshot.getPromise(groupStatistics(modal)), - } - : null, + groupData: + isGroup && !isNonNestedDynamicGroup + ? { + id: modal ? await snapshot.getPromise(groupId) : null, + slices, + slice: await snapshot.getPromise(fos.groupSlice), + mode: await snapshot.getPromise(groupStatistics(modal)), + } + : null, modal, sampleId: modal ? await snapshot.getPromise(fos.sidebarSampleId) @@ -373,6 +387,18 @@ const useTagCallback = ( } else if (samples) { set(fos.refreshGroupQuery, (cur) => cur + 1); updateSamples(samples.map((sample) => [sample._id, sample])); + + if (isImaVidLookerActive) { + // assuming we're working with only one sample + ( + lookerRef?.current as unknown as ImaVidLooker + )?.frameStoreController.store?.updateSample( + samples[0]._id, + samples[0] + ); + + lookerRef?.current?.updateSample(samples[0]); + } } set(fos.anyTagging, false); diff --git a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx index fc0e853b5f..c837bc4171 100644 --- a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx +++ b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx @@ -167,7 +167,7 @@ const ListValueEntry = ({ heading={ {OVERRIDE[path] ?? path} - + }> {slices ? ( diff --git a/app/packages/looker/src/elements/imavid/index.ts b/app/packages/looker/src/elements/imavid/index.ts index 688e82bb69..c03e4f5a49 100644 --- a/app/packages/looker/src/elements/imavid/index.ts +++ b/app/packages/looker/src/elements/imavid/index.ts @@ -22,6 +22,7 @@ export function withImaVidLookerEvents(): () => Events { if (thumbnail) { return { playing: true, + disableOverlays: true, }; } return {}; @@ -109,6 +110,7 @@ export class ImaVidElement extends BaseElement { this.canvas.height = this.element.naturalHeight; this.ctx = this.canvas.getContext("2d"); + this.ctx.imageSmoothingEnabled = false; this.ctx.drawImage(this.element, 0, 0); this.imageSource = this.canvas; @@ -248,9 +250,9 @@ export class ImaVidElement extends BaseElement { this.canvasFrameNumber = frameNumberToDraw; image.addEventListener("load", () => { - // thisSampleOverlayPrepared.then((overlay) => { - this.ctx.imageSmoothingEnabled = false; - this.ctx.drawImage(image, 0, 0); + if (this.isPlaying || this.isSeeking) { + this.ctx.drawImage(image, 0, 0); + } if (animate && !this.waitingToPause) { if (frameNumberToDraw <= this.framesController.totalFrameCount) { @@ -258,7 +260,7 @@ export class ImaVidElement extends BaseElement { if (playing) { return { currentFrameNumber: Math.min( - frameNumberToDraw + 1, + frameNumberToDraw, this.framesController.totalFrameCount ), }; @@ -278,12 +280,14 @@ export class ImaVidElement extends BaseElement { this.drawFrame(1); return { playing: true, + disableOverlays: true, currentFrameNumber: 1, }; } return { playing: false, + disableOverlays: false, currentFrameNumber: this.framesController.totalFrameCount, }; }); @@ -294,7 +298,6 @@ export class ImaVidElement extends BaseElement { }); }, this.setTimeoutDelay); } - // }); }); image.src = src; } @@ -304,8 +307,6 @@ export class ImaVidElement extends BaseElement { return; } - this.update(() => ({ disabled: true, disableOverlays: true })); - requestAnimationFrame(() => this.drawFrame(this.frameNumber)); } @@ -327,10 +328,6 @@ export class ImaVidElement extends BaseElement { this.framesController.totalFrameCount ); - if (isNaN(frameRangeMax)) { - return [currentFrameNumber, currentFrameNumber + 1] as const; - } - return [currentFrameNumber, frameRangeMax] as const; } @@ -339,10 +336,19 @@ export class ImaVidElement extends BaseElement { * This method is not blocking, it merely enqueues a fetch job. */ private ensureBuffers(state: Readonly) { + if (!this.framesController.totalFrameCount) { + return; + } + let shouldEnqueueFetch = false; const necessaryFrameRange = this.getLookAheadFrameRange( state.currentFrameNumber ); + + if (necessaryFrameRange[1] < necessaryFrameRange[0]) { + return; + } + const rangeAvailable = this.framesController.storeBufferManager.containsRange( necessaryFrameRange diff --git a/app/packages/looker/src/elements/imavid/loader-bar.ts b/app/packages/looker/src/elements/imavid/loader-bar.ts index 5ddc2cd51b..33c9172aa2 100644 --- a/app/packages/looker/src/elements/imavid/loader-bar.ts +++ b/app/packages/looker/src/elements/imavid/loader-bar.ts @@ -13,6 +13,7 @@ export class LoaderBar extends BaseElement { const element = document.createElement("div"); element.classList.add(lookerLoader); element.innerText = "loaderbar"; + element.attributes["data-cy"] = "imavid-loader-bar"; return element; } diff --git a/app/packages/looker/src/elements/imavid/play-button.ts b/app/packages/looker/src/elements/imavid/play-button.ts index 7091594d03..4812018da6 100644 --- a/app/packages/looker/src/elements/imavid/play-button.ts +++ b/app/packages/looker/src/elements/imavid/play-button.ts @@ -107,6 +107,9 @@ export class PlayButtonElement extends BaseElement< element.style.height = "24px"; element.style.width = "24px"; element.style.gridArea = "2 / 2 / 2 / 2"; + + element.setAttribute("data-cy", "looker-video-play-button"); + return element; } @@ -129,7 +132,6 @@ export class PlayButtonElement extends BaseElement< this.element.appendChild(this.play); this.element.title = "Play (space)"; this.element.style.cursor = "pointer"; - this.element.setAttribute("data-cy", "looker-video-play-button"); } this.isPlaying = playing; this.isBuffering = !loaded; diff --git a/app/packages/looker/src/elements/imavid/playback-rate.ts b/app/packages/looker/src/elements/imavid/playback-rate.ts index 924d377a0d..ce028f0764 100644 --- a/app/packages/looker/src/elements/imavid/playback-rate.ts +++ b/app/packages/looker/src/elements/imavid/playback-rate.ts @@ -34,6 +34,7 @@ class PlaybackRateContainerElement extends BaseElement< createHTMLElement() { const element = document.createElement("div"); element.classList.add(lookerPlaybackRate); + element.setAttribute("data-cy", "looker-video-speed-button"); return element; } diff --git a/app/packages/looker/src/elements/imavid/seek-bar.ts b/app/packages/looker/src/elements/imavid/seek-bar.ts index 617448bd33..562aa44b1c 100644 --- a/app/packages/looker/src/elements/imavid/seek-bar.ts +++ b/app/packages/looker/src/elements/imavid/seek-bar.ts @@ -27,6 +27,7 @@ export class SeekBarElement extends BaseElement { element.setAttribute("min", "0"); element.setAttribute("max", "100"); element.classList.add(lookerSeekBar); + element.attributes["data-cy"] = "imavid-seek-bar"; return element; } diff --git a/app/packages/looker/src/elements/video.ts b/app/packages/looker/src/elements/video.ts index 609f4d66aa..e622ae2ffa 100644 --- a/app/packages/looker/src/elements/video.ts +++ b/app/packages/looker/src/elements/video.ts @@ -156,6 +156,9 @@ export class PlayButtonElement extends BaseElement { element.style.height = "24px"; element.style.width = "24px"; element.style.gridArea = "2 / 2 / 2 / 2"; + + element.setAttribute("data-cy", "looker-video-play-button"); + return element; } @@ -196,7 +199,6 @@ export class PlayButtonElement extends BaseElement { this.element.appendChild(this.play); this.element.title = "Play (space)"; this.element.style.cursor = "pointer"; - this.element.setAttribute("data-cy", "looker-video-play-button"); } this.isPlaying = playing; this.isBuffering = buffering || !loaded; @@ -923,6 +925,7 @@ class PlaybackRateContainerElement extends BaseElement< createHTMLElement() { const element = document.createElement("div"); element.classList.add(lookerPlaybackRate); + element.setAttribute("data-cy", "looker-video-speed-button"); return element; } diff --git a/app/packages/looker/src/lookers/imavid/buffer-manager/index.ts b/app/packages/looker/src/lookers/imavid/buffer-manager/index.ts index 567169e3de..35cbda67f6 100644 --- a/app/packages/looker/src/lookers/imavid/buffer-manager/index.ts +++ b/app/packages/looker/src/lookers/imavid/buffer-manager/index.ts @@ -60,7 +60,9 @@ export class BufferManager { } if (range[1] < range[0]) { - throw new Error("invalid range: range[1] must be >= range[0]"); + throw new Error( + `invalid range: range[1] (value = ${range[1]}) must be >= range[0] (value = ${range[0]})` + ); } // add the new range to the buffer diff --git a/app/packages/looker/src/lookers/imavid/ima-vid-frame-samples.ts b/app/packages/looker/src/lookers/imavid/ima-vid-frame-samples.ts index 2d9d99202b..f0da6e9265 100644 --- a/app/packages/looker/src/lookers/imavid/ima-vid-frame-samples.ts +++ b/app/packages/looker/src/lookers/imavid/ima-vid-frame-samples.ts @@ -44,6 +44,13 @@ export class ImaVidFrameSamples { return this.samples.get(sampleId); } + updateSample(id: string, newSample: ModalSample) { + const oldSample = this.samples.get(id); + if (oldSample) { + this.samples.set(id, { ...oldSample, sample: newSample }); + } + } + reset() { this.frameIndex.clear(); this.reverseFrameIndex.clear(); diff --git a/e2e-pw/package.json b/e2e-pw/package.json index fbef6e2c82..b134f4ea83 100644 --- a/e2e-pw/package.json +++ b/e2e-pw/package.json @@ -4,21 +4,21 @@ "main": "index.js", "license": "MIT", "devDependencies": { - "@playwright/test": "^1.38.1", - "@types/fluent-ffmpeg": "^2.1.21", - "@types/wait-on": "^5.3.1", - "@typescript-eslint/eslint-plugin": "^6.7.4", - "@typescript-eslint/parser": "^6.7.4", + "@playwright/test": "^1.40.1", + "@types/fluent-ffmpeg": "^2.1.24", + "@types/wait-on": "^5.3.4", + "@typescript-eslint/eslint-plugin": "^6.13.2", + "@typescript-eslint/parser": "^6.13.2", "dotenv": "^16.3.1", - "eslint": "^8.50.0", - "eslint-plugin-playwright": "^0.16.0", + "eslint": "^8.55.0", + "eslint-plugin-playwright": "^0.19.0", "fluent-ffmpeg": "^2.1.2", "jimp": "^0.22.10", "tree-kill": "^1.2.2", "ts-dedent": "^2.2.0", - "typescript": "^5.2.2", - "vitest": "^0.34.6", - "wait-on": "^7.0.1" + "typescript": "^5.3.2", + "vitest": "^1.0.1", + "wait-on": "^7.2.0" }, "scripts": { "lint": "bash -c 'set +e; eslint --quiet --ignore-pattern *.js .; set -e; tsc --skipLibCheck --sourceMap false'", diff --git a/e2e-pw/src/oss/poms/action-row/display-options.ts b/e2e-pw/src/oss/poms/action-row/display-options.ts index 887aa7ae90..11c3ec6fb9 100644 --- a/e2e-pw/src/oss/poms/action-row/display-options.ts +++ b/e2e-pw/src/oss/poms/action-row/display-options.ts @@ -27,4 +27,9 @@ export class DisplayOptionsPom { const selector = this.page.getByTestId(`tab-option-Sort by ${mode}`); return selector.click(); } + + async toggleRenderFramesAsVideo() { + const selector = this.page.getByTestId("checkbox-Render frames as video"); + return selector.click(); + } } diff --git a/e2e-pw/src/oss/poms/action-row/tagger.ts b/e2e-pw/src/oss/poms/action-row/tagger/grid-tagger.ts similarity index 96% rename from e2e-pw/src/oss/poms/action-row/tagger.ts rename to e2e-pw/src/oss/poms/action-row/tagger/grid-tagger.ts index e8ad9a8626..0a31ccc240 100644 --- a/e2e-pw/src/oss/poms/action-row/tagger.ts +++ b/e2e-pw/src/oss/poms/action-row/tagger/grid-tagger.ts @@ -2,7 +2,7 @@ import { Page } from "src/oss/fixtures"; type TaggerMode = "sample" | "label"; -export class TaggerPom { +export class GridTaggerPom { constructor(private readonly page: Page) {} async setActiveTaggerMode(mode: TaggerMode) { diff --git a/e2e-pw/src/oss/poms/action-row/tagger/modal-tagger.ts b/e2e-pw/src/oss/poms/action-row/tagger/modal-tagger.ts new file mode 100644 index 0000000000..e999cebcb9 --- /dev/null +++ b/e2e-pw/src/oss/poms/action-row/tagger/modal-tagger.ts @@ -0,0 +1,30 @@ +import { Locator, Page } from "src/oss/fixtures"; +import { ModalPom } from "../../modal"; + +export class ModalTaggerPom { + readonly locator: Locator; + + constructor(private readonly page: Page, private readonly modal: ModalPom) { + this.locator = modal.locator.getByTestId("popout"); + } + + async toggleOpen() { + await this.modal.locator.getByTestId("action-tag-sample-labels").click(); + } + + async switchTagMode(mode: "sample" | "label") { + await this.locator.getByTestId(`tagger-switch-${mode}`).click(); + } + + async addSampleTag(tag: string) { + await this.locator.getByTestId("sample-tag-input").fill(tag); + await this.locator.getByTestId("sample-tag-input").press("Enter"); + await this.locator.getByTestId("button-Apply").click(); + } + + async addLabelTag(tag: string) { + await this.locator.getByTestId("label-tag-input").fill(tag); + await this.locator.getByTestId("label-tag-input").press("Enter"); + await this.locator.getByTestId("button-Apply").click(); + } +} diff --git a/e2e-pw/src/oss/poms/modal/index.ts b/e2e-pw/src/oss/poms/modal/index.ts index 33b4c844d8..7c30aacc97 100644 --- a/e2e-pw/src/oss/poms/modal/index.ts +++ b/e2e-pw/src/oss/poms/modal/index.ts @@ -4,6 +4,7 @@ import { Duration } from "../../utils"; import { ModalGroupActionsPom } from "./group-actions"; import { ModalSidebarPom } from "./modal-sidebar"; import { ModalVideoControlsPom } from "./video-controls"; +import { ModalTaggerPom } from "../action-row/tagger/modal-tagger"; export class ModalPom { readonly groupCarousel: Locator; @@ -12,6 +13,7 @@ export class ModalPom { readonly assert: ModalAsserter; readonly sidebar: ModalSidebarPom; + readonly tagger: ModalTaggerPom; readonly locator: Locator; readonly group: ModalGroupActionsPom; readonly video: ModalVideoControlsPom; @@ -28,6 +30,7 @@ export class ModalPom { this.modalContainer = this.locator.getByTestId("modal-looker-container"); this.sidebar = new ModalSidebarPom(page); + this.tagger = new ModalTaggerPom(page, this); this.group = new ModalGroupActionsPom(page, this); this.video = new ModalVideoControlsPom(page, this); } @@ -128,6 +131,10 @@ export class ModalPom { await this.page.mouse.up(); } + async toggleTagSampleOrLabels() { + await this.locator.getByTestId("action-tag-sample-labels").click(); + } + async waitForCarouselToLoad() { await this.groupCarousel .getByTestId("looker") diff --git a/e2e-pw/src/oss/poms/modal/modal-sidebar.ts b/e2e-pw/src/oss/poms/modal/modal-sidebar.ts index aef2f09055..3d9899ab93 100644 --- a/e2e-pw/src/oss/poms/modal/modal-sidebar.ts +++ b/e2e-pw/src/oss/poms/modal/modal-sidebar.ts @@ -1,19 +1,33 @@ import { Locator, Page, expect } from "src/oss/fixtures"; +import { Duration } from "src/oss/utils"; export class ModalSidebarPom { readonly page: Page; - readonly sidebar: Locator; + readonly locator: Locator; readonly assert: SidebarAsserter; constructor(page: Page) { this.page = page; this.assert = new SidebarAsserter(this); - this.sidebar = page.getByTestId("modal").getByTestId("sidebar"); + this.locator = page.getByTestId("modal").getByTestId("sidebar"); } async getSidebarEntryText(key: string) { - return this.sidebar.getByTestId(key).textContent(); + return this.locator.getByTestId(key).textContent(); + } + + async getSampleTagCount() { + return Number(await this.getSidebarEntryText("sidebar-entry-tags")); + } + + async getLabelTagCount() { + return Number( + await this.locator + .getByTestId("sidebar-field-container-_label_tags") + .getByTestId("entry-count-all") + .textContent() + ); } async getSampleId() { @@ -31,7 +45,7 @@ export class ModalSidebarPom { } async toggleSidebarGroup(name: string) { - await this.sidebar.getByTestId(`sidebar-group-entry-${name}`).click(); + await this.locator.getByTestId(`sidebar-group-entry-${name}`).click(); } } @@ -39,7 +53,7 @@ class SidebarAsserter { constructor(private readonly modalSidebarPom: ModalSidebarPom) {} async verifySidebarEntryText(key: string, value: string) { - const text = await this.modalSidebarPom.sidebar + const text = await this.modalSidebarPom.locator .getByTestId(`sidebar-entry-${key}`) .textContent(); expect(text).toBe(value); @@ -52,4 +66,39 @@ class SidebarAsserter { ) ); } + + async verifySampleTagCount(count: number) { + await this.modalSidebarPom.page.waitForFunction( + (count_) => { + return ( + Number( + document.querySelector("#modal [data-cy='sidebar-entry-tags']") + .textContent + ) === count_ + ); + }, + count, + { + timeout: Duration.Seconds(1), + } + ); + } + + async verifyLabelTagCount(count: number) { + await this.modalSidebarPom.page.waitForFunction( + (count_) => { + return ( + Number( + document.querySelector( + "#modal [data-cy='sidebar-field-container-_label_tags'] [data-cy='entry-count-all']" + ).textContent + ) === count_ + ); + }, + count, + { + timeout: Duration.Seconds(1), + } + ); + } } diff --git a/e2e-pw/src/oss/poms/modal/video-controls.ts b/e2e-pw/src/oss/poms/modal/video-controls.ts index 9065a21ad6..84c8bee873 100644 --- a/e2e-pw/src/oss/poms/modal/video-controls.ts +++ b/e2e-pw/src/oss/poms/modal/video-controls.ts @@ -8,12 +8,14 @@ export class ModalVideoControlsPom { readonly optionsPanel: Locator; readonly time: Locator; readonly playPauseButton: Locator; + readonly speedButton: Locator; private readonly modal: ModalPom; constructor(page: Page, modal: ModalPom) { this.page = page; this.modal = modal; + this.assert = new ModalVideoControlsAsserter(this); this.controls = this.modal.locator.getByTestId("looker-video-controls"); this.optionsPanel = this.modal.locator.getByTestId( @@ -23,14 +25,13 @@ export class ModalVideoControlsPom { this.playPauseButton = this.controls.getByTestId( "looker-video-play-button" ); + this.speedButton = this.controls.getByTestId("looker-video-speed-button"); } - private async play() { - return this.playPauseButton.click(); - } - - private async pause() { - return this.playPauseButton.click(); + private async togglePlay() { + // this is to clear popups, if any + this.controls.click(); + await this.modal.locator.press("Space"); } private async clickSettings() { @@ -54,7 +55,7 @@ export class ModalVideoControlsPom { } async playUntilDuration(durationText: string) { - await this.play(); + await this.togglePlay(); await this.page.waitForFunction((durationText_) => { const time = document.querySelector( @@ -63,11 +64,11 @@ export class ModalVideoControlsPom { return time.startsWith(durationText_); }, durationText); - await this.pause(); + await this.togglePlay(); } async playUntilFrames(frameText: string) { - await this.play(); + await this.togglePlay(); await this.page.waitForFunction((frameText_) => { const frames = document.querySelector( @@ -76,15 +77,53 @@ export class ModalVideoControlsPom { return frames.startsWith(frameText_); }, frameText); - await this.pause(); + await this.togglePlay(); + } + + async setSpeedTo(config: "low" | "middle" | "high") { + await this.speedButton.hover(); + const speedSliderInputRange = this.speedButton.locator("input[type=range]"); + const sliderBoundingBox = await speedSliderInputRange.boundingBox(); + + if (!sliderBoundingBox) { + throw new Error("Could not find speed slider bounding box"); + } + + const sliderWidth = sliderBoundingBox.width; + + switch (config) { + case "low": + await this.page.mouse.click( + sliderBoundingBox.x + sliderWidth * 0.05, + sliderBoundingBox.y + ); + break; + case "middle": + await this.page.mouse.click( + sliderBoundingBox.x + sliderWidth * 0.5, + sliderBoundingBox.y + ); + break; + case "high": + await this.page.mouse.click( + sliderBoundingBox.x + sliderWidth * 0.95, + sliderBoundingBox.y + ); + break; + } } } class ModalVideoControlsAsserter { constructor(private readonly videoControlsPom: ModalVideoControlsPom) {} - async assertCurrentTime(time: string) { + async isCurrentTimeEqualTo(time: string) { const currentTime = await this.videoControlsPom.getCurrentTime(); expect(currentTime).toBe(time); } + + async isTimeTextEqualTo(text: string) { + const time = await this.videoControlsPom.time.textContent(); + expect(time).toContain(text); + } } diff --git a/e2e-pw/src/oss/specs/color-scheme/color-scheme.spec.ts b/e2e-pw/src/oss/specs/color-scheme/color-scheme.spec.ts index 4b383b4450..7531ab1a39 100644 --- a/e2e-pw/src/oss/specs/color-scheme/color-scheme.spec.ts +++ b/e2e-pw/src/oss/specs/color-scheme/color-scheme.spec.ts @@ -148,10 +148,8 @@ test.describe("color scheme basic functionality with quickstart", () => { }); test("update color by value - detection - no colorbyattribute", async ({ - grid, gridActionsRow, colorModal, - page, sidebar, }) => { // turn off predictions detections and open color modal diff --git a/e2e-pw/src/oss/specs/dynamic-groups-cifar.spec.ts b/e2e-pw/src/oss/specs/dynamic-groups-cifar.spec.ts deleted file mode 100644 index 8678882f9b..0000000000 --- a/e2e-pw/src/oss/specs/dynamic-groups-cifar.spec.ts +++ /dev/null @@ -1,56 +0,0 @@ -import { test as base, expect } from "src/oss/fixtures"; -import { GridPom } from "src/oss/poms/grid"; -import { ModalPom } from "src/oss/poms/modal"; -import { getUniqueDatasetNameWithPrefix } from "src/oss/utils"; - -const test = base.extend<{ grid: GridPom; modal: ModalPom }>({ - grid: async ({ page, eventUtils }, use) => { - await use(new GridPom(page, eventUtils)); - }, - modal: async ({ page, eventUtils }, use) => { - await use(new ModalPom(page, eventUtils)); - }, -}); - -const datasetName = getUniqueDatasetNameWithPrefix(`dynamic-groups-cifar`); - -test.beforeAll(async ({ fiftyoneLoader }) => { - await fiftyoneLoader.executePythonCode(` - import fiftyone as fo - import fiftyone.zoo as foz - - cifar10_dataset = foz.load_zoo_dataset("cifar10", split="test", max_samples=50, dataset_name="${datasetName}") - cifar10_dataset.persistent = True -`); -}); - -test.beforeEach(async ({ page, fiftyoneLoader }) => { - await fiftyoneLoader.waitUntilGridVisible(page, datasetName); -}); - -test.skip("valid candidates for group-by keys", async ({ grid }) => { - await grid.actionsRow.toggleCreateDynamicGroups(); - await verifyCandidateFields(grid, ["ground_truth.id", "ground_truth.label"]); -}); - -const verifyCandidateFields = async (grid: GridPom, fields: string[]) => { - await grid.actionsRow.gridActionsRow.getByTestId("group by").click(); - const results = grid.actionsRow.gridActionsRow.getByTestId( - "selector-results-container" - ); - const options = grid.actionsRow.gridActionsRow - .getByTestId("selector-results-container") - .locator("div"); - - const count = await options.count(); - expect(count).toBe(fields.length); - - for (let index = 0; index < fields.length; index++) { - const field = fields[index]; - const fieldWithDotEscaped = field.replace(/\./g, "\\."); - const visible = await results - .getByTestId(`selector-result-${fieldWithDotEscaped}`) - .isVisible(); - expect(visible).toBe(true); - } -}; diff --git a/e2e-pw/src/oss/specs/dynamic-groups.spec.ts b/e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts similarity index 100% rename from e2e-pw/src/oss/specs/dynamic-groups.spec.ts rename to e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts diff --git a/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts b/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts new file mode 100644 index 0000000000..eea51aacdb --- /dev/null +++ b/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts @@ -0,0 +1,169 @@ +import { test as base, expect } from "src/oss/fixtures"; +import { DynamicGroupPom } from "src/oss/poms/action-row/dynamic-group"; +import { GridActionsRowPom } from "src/oss/poms/action-row/grid-actions-row"; +import { GridPom } from "src/oss/poms/grid"; +import { ModalPom } from "src/oss/poms/modal"; +import { SidebarPom } from "src/oss/poms/sidebar"; +import { getUniqueDatasetNameWithPrefix } from "src/oss/utils"; +import { createBlankImage } from "src/shared/media-factory/image"; + +const NUM_VIDEOS = 2; +const FRAME_COLORS = { + 0: "#ff0000", + 1: "#00ff00", +}; +const NUM_FRAMES_PER_VIDEO = 150; + +const datasetName = getUniqueDatasetNameWithPrefix(`group-ima-vid`); +const test = base.extend<{ + grid: GridPom; + modal: ModalPom; + gridActionsRow: GridActionsRowPom; + sidebar: SidebarPom; + dynamicGroups: DynamicGroupPom; +}>({ + grid: async ({ eventUtils, page }, use) => { + await use(new GridPom(page, eventUtils)); + }, + modal: async ({ page, eventUtils }, use) => { + await use(new ModalPom(page, eventUtils)); + }, + sidebar: async ({ page }, use) => { + await use(new SidebarPom(page)); + }, + gridActionsRow: async ({ eventUtils, page }, use) => { + await use(new GridActionsRowPom(page, eventUtils)); + }, + dynamicGroups: async ({ eventUtils, page }, use) => { + await use(new DynamicGroupPom(page, eventUtils)); + }, +}); + +const writeFrames = async () => { + const start = performance.now(); + const createPromises = []; + for (let i = 1; i <= NUM_VIDEOS; ++i) { + for (let j = 1; j <= NUM_FRAMES_PER_VIDEO; ++j) { + createPromises.push( + createBlankImage({ + outputPath: `/tmp/ima-vid-${i}-${j}.png`, + width: 50, + height: 50, + fillColor: FRAME_COLORS[i % 2], + watermarkString: `${j}`, + }) + ); + } + } + await Promise.all(createPromises); + const end = performance.now(); + console.log( + `Wrote ${NUM_VIDEOS * NUM_FRAMES_PER_VIDEO} frames in ${ + end - start + } milliseconds` + ); +}; + +test.beforeAll(async ({ fiftyoneLoader }) => { + await writeFrames(); + + await fiftyoneLoader.executePythonCode(` + import fiftyone as fo + dataset = fo.Dataset("${datasetName}") + dataset.persistent = True + samples = [] + + for i in range(1, ${NUM_VIDEOS + 1}): + for j in range(1, ${NUM_FRAMES_PER_VIDEO + 1}): + sample = fo.Sample( + filepath=f"/tmp/ima-vid-{i}-{j}.png", + frame_number=j, + video_id=i, + label=fo.Detection( + label=f"box-{i}-{j}", + bounding_box=[0.1, 0.1, 0.2, 0.2] + ) + ) + samples.append(sample) + dataset.add_samples(samples) + `); +}); + +test.beforeEach( + async ({ page, fiftyoneLoader, gridActionsRow, dynamicGroups, grid }) => { + await fiftyoneLoader.waitUntilGridVisible(page, datasetName); + + await gridActionsRow.toggleCreateDynamicGroups(); + await dynamicGroups.groupBy.openResults(); + await dynamicGroups.groupBy.selectResult("video_id"); + await dynamicGroups.groupBy.closeResults(); + await dynamicGroups.selectTabOption("Ordered"); + await dynamicGroups.orderBy.openResults(); + await dynamicGroups.orderBy.selectResult("frame_number"); + await dynamicGroups.orderBy.closeResults(); + const gridRefreshPromiseCreateDynamicGroups = + grid.getWaitForGridRefreshPromise(); + await dynamicGroups.submit(); + await gridRefreshPromiseCreateDynamicGroups; + + const gridRefreshPromiseSetRenderFramesAsVideo = + grid.getWaitForGridRefreshPromise(); + await grid.actionsRow.toggleDisplayOptions(); + await grid.actionsRow.displayActions.toggleRenderFramesAsVideo(); + await gridRefreshPromiseSetRenderFramesAsVideo; + + await grid.assert.isEntryCountTextEqualTo("2 groups"); + await grid.assert.isLookerCountEqualTo(2); + } +); + +test("check modal playback and tagging behavior", async ({ modal, grid }) => { + await grid.openFirstSample(); + await modal.waitForSampleLoadDomAttribute(); + + await modal.video.assert.isTimeTextEqualTo("1 / 150"); + + // change speed to the low for easy testing + await modal.video.setSpeedTo("low"); + + await modal.video.playUntilFrames("3 / 150"); + + // verify it's the third frame that's rendered + await expect(modal.looker).toHaveScreenshot("ima-vid-1-3.png", { + mask: [modal.video.controls], + animations: "allow", + }); + await modal.sidebar.assert.verifySidebarEntryText("frame_number", "3"); + await modal.sidebar.assert.verifySidebarEntryText("video_id", "1"); + + // tag current frame and ensure sidebar updates + const currentSampleTagCount = await modal.sidebar.getSampleTagCount(); + await modal.tagger.toggleOpen(); + await modal.tagger.switchTagMode("sample"); + await modal.tagger.addSampleTag("tag-1-3"); + await modal.sidebar.assert.verifySampleTagCount(currentSampleTagCount + 1); + + // skip a couple of frames and see that sample tag count is zero + await modal.video.playUntilFrames("5 / 150"); + await modal.sidebar.assert.verifySidebarEntryText("frame_number", "5"); + await modal.sidebar.assert.verifySidebarEntryText("video_id", "1"); + await modal.sidebar.assert.verifySampleTagCount(0); + + // verify label is rendering in this frame, too + await expect(modal.looker).toHaveScreenshot("ima-vid-1-5.png", { + mask: [modal.video.controls], + animations: "allow", + }); + + // tag label and see that sidebar updates + const currentLabelTagCount = await 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const datasetName = getUniqueDatasetNameWithPrefix("smoke-quickstart"); const test = base.extend<{ - tagger: TaggerPom; + tagger: GridTaggerPom; sidebar: SidebarPom; grid: GridPom; gridActionsRow: GridActionsRowPom; }>({ tagger: async ({ page }, use) => { - await use(new TaggerPom(page)); + await use(new GridTaggerPom(page)); }, sidebar: async ({ page }, use) => { await use(new SidebarPom(page)); diff --git a/e2e-pw/src/shared/media-factory/image.ts b/e2e-pw/src/shared/media-factory/image.ts index 5b364be136..2d5c05f18c 100644 --- a/e2e-pw/src/shared/media-factory/image.ts +++ b/e2e-pw/src/shared/media-factory/image.ts @@ -5,13 +5,33 @@ export const createBlankImage = async (options: { width: number; height: number; fillColor?: string; + watermarkString?: string; }) => { const { width, height, outputPath, fillColor } = options; const startTime = performance.now(); console.log(`Creating blank image with options: ${JSON.stringify(options)}`); const image = new Jimp(width, height, fillColor ?? "#00ddff"); + + if (options.watermarkString) { + const font = await Jimp.loadFont(Jimp.FONT_SANS_16_BLACK); + image.print( + font, + 0, + 0, + { + text: options.watermarkString, + alignmentX: Jimp.HORIZONTAL_ALIGN_CENTER, + alignmentY: Jimp.VERTICAL_ALIGN_MIDDLE, + }, + width, + height + ); + } + await image.writeAsync(outputPath); const endTime = performance.now(); const timeTaken = endTime - startTime; - console.log(`Image generation completed in ${timeTaken} milliseconds`); + console.log( + `Image generation, path = ${outputPath}, completed in ${timeTaken} milliseconds` + ); }; diff --git a/e2e-pw/yarn.lock b/e2e-pw/yarn.lock index 9f512652dc..bc1376403e 100644 --- a/e2e-pw/yarn.lock +++ b/e2e-pw/yarn.lock @@ -7,115 +7,115 @@ resolved "https://registry.yarnpkg.com/@aashutoshrathi/word-wrap/-/word-wrap-1.2.6.tgz#bd9154aec9983f77b3a034ecaa015c2e4201f6cf" integrity sha512-1Yjs2SvM8TflER/OD3cOjhWWOZb58A2t7wpE2S9XfBYTiIl+XFhQG2bjy4Pu1I+EAlCNUzRDYDdFwFYUKvXcIA== 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"@esbuild/darwin-arm64" "0.18.20" - "@esbuild/darwin-x64" "0.18.20" - "@esbuild/freebsd-arm64" "0.18.20" - "@esbuild/freebsd-x64" "0.18.20" - "@esbuild/linux-arm" "0.18.20" - "@esbuild/linux-arm64" "0.18.20" - "@esbuild/linux-ia32" "0.18.20" - "@esbuild/linux-loong64" "0.18.20" - "@esbuild/linux-mips64el" "0.18.20" - "@esbuild/linux-ppc64" "0.18.20" - "@esbuild/linux-riscv64" "0.18.20" - "@esbuild/linux-s390x" "0.18.20" - "@esbuild/linux-x64" "0.18.20" - "@esbuild/netbsd-x64" "0.18.20" - "@esbuild/openbsd-x64" "0.18.20" - "@esbuild/sunos-x64" "0.18.20" - "@esbuild/win32-arm64" "0.18.20" - "@esbuild/win32-ia32" "0.18.20" - "@esbuild/win32-x64" "0.18.20" + "@esbuild/android-arm" "0.19.8" + "@esbuild/android-arm64" "0.19.8" + "@esbuild/android-x64" "0.19.8" + "@esbuild/darwin-arm64" "0.19.8" + "@esbuild/darwin-x64" "0.19.8" + "@esbuild/freebsd-arm64" "0.19.8" + "@esbuild/freebsd-x64" "0.19.8" + "@esbuild/linux-arm" "0.19.8" + "@esbuild/linux-arm64" "0.19.8" + "@esbuild/linux-ia32" 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sha512-DcHpF0SLbNeh9MT4pMzUGuUSnJ7q5MWbP8sSEFIMS6j7Ggnduq8ghNlfhURgty4c1YFny7Ge9xYTO1FSAoV2Vw== +eslint-plugin-playwright@^0.19.0: + version "0.19.0" + resolved "https://registry.yarnpkg.com/eslint-plugin-playwright/-/eslint-plugin-playwright-0.19.0.tgz#1ac454889d266f7371f1e1e7442573a5a7a79d20" + integrity sha512-/pk5oDxusdPnsPNo16o7kWk4Pq8e0Jw7vM0xI/G4wDHhYw1ImQpclB3fQGbjKAO1eauGRLWtJAq5l1Xq3oF16A== + dependencies: + globals "^13.23.0" eslint-scope@^7.2.2: version "7.2.2" @@ -978,18 +1034,19 @@ eslint-visitor-keys@^3.3.0, eslint-visitor-keys@^3.4.1, eslint-visitor-keys@^3.4 resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz#0cd72fe8550e3c2eae156a96a4dddcd1c8ac5800" integrity sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag== -eslint@^8.50.0: - version "8.51.0" - resolved "https://registry.yarnpkg.com/eslint/-/eslint-8.51.0.tgz#4a82dae60d209ac89a5cff1604fea978ba4950f3" - integrity sha512-2WuxRZBrlwnXi+/vFSJyjMqrNjtJqiasMzehF0shoLaW7DzS3/9Yvrmq5JiT66+pNjiX4UBnLDiKHcWAr/OInA== +eslint@^8.55.0: + version "8.55.0" + resolved "https://registry.yarnpkg.com/eslint/-/eslint-8.55.0.tgz#078cb7b847d66f2c254ea1794fa395bf8e7e03f8" + integrity sha512-iyUUAM0PCKj5QpwGfmCAG9XXbZCWsqP/eWAWrG/W0umvjuLRBECwSFdt+rCntju0xEH7teIABPwXpahftIaTdA== dependencies: "@eslint-community/eslint-utils" "^4.2.0" "@eslint-community/regexpp" "^4.6.1" - "@eslint/eslintrc" "^2.1.2" - "@eslint/js" "8.51.0" - "@humanwhocodes/config-array" "^0.11.11" + "@eslint/eslintrc" "^2.1.4" + "@eslint/js" "8.55.0" + "@humanwhocodes/config-array" "^0.11.13" "@humanwhocodes/module-importer" "^1.0.1" "@nodelib/fs.walk" "^1.2.8" + "@ungap/structured-clone" "^1.2.0" ajv "^6.12.4" chalk "^4.0.0" cross-spawn "^7.0.2" @@ -1054,6 +1111,21 @@ esutils@^2.0.2: resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64" integrity sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g== +execa@^8.0.1: + version "8.0.1" + resolved "https://registry.yarnpkg.com/execa/-/execa-8.0.1.tgz#51f6a5943b580f963c3ca9c6321796db8cc39b8c" + integrity sha512-VyhnebXciFV2DESc+p6B+y0LjSm0krU4OgJN44qFAhBY0TJ+1V61tYD2+wHusZ6F9n5K+vl8k0sTy7PEfV4qpg== + dependencies: + cross-spawn "^7.0.3" + get-stream "^8.0.1" + human-signals "^5.0.0" + is-stream "^3.0.0" + merge-stream "^2.0.0" + npm-run-path "^5.1.0" + onetime "^6.0.0" + signal-exit "^4.1.0" + strip-final-newline "^3.0.0" + exif-parser@^0.1.12: version "0.1.12" resolved "https://registry.yarnpkg.com/exif-parser/-/exif-parser-0.1.12.tgz#58a9d2d72c02c1f6f02a0ef4a9166272b7760922" @@ -1145,7 +1217,7 @@ fluent-ffmpeg@^2.1.2: async ">=0.2.9" which "^1.1.1" -follow-redirects@^1.14.9: +follow-redirects@^1.15.0: version "1.15.3" resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.3.tgz#fe2f3ef2690afce7e82ed0b44db08165b207123a" integrity sha512-1VzOtuEM8pC9SFU1E+8KfTjZyMztRsgEfwQl44z8A25uy13jSzTj6dyK2Df52iV0vgHCfBwLhDWevLn95w5v6Q== @@ -1169,7 +1241,7 @@ fsevents@2.3.2: resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.2.tgz#8a526f78b8fdf4623b709e0b975c52c24c02fd1a" integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA== -fsevents@~2.3.2: +fsevents@~2.3.2, fsevents@~2.3.3: version "2.3.3" resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.3.tgz#cac6407785d03675a2a5e1a5305c697b347d90d6" integrity sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw== @@ -1179,6 +1251,11 @@ get-func-name@^2.0.1, get-func-name@^2.0.2: resolved "https://registry.yarnpkg.com/get-func-name/-/get-func-name-2.0.2.tgz#0d7cf20cd13fda808669ffa88f4ffc7a3943fc41" integrity sha512-8vXOvuE167CtIc3OyItco7N/dpRtBbYOsPsXCz7X/PMnlGjYjSGuZJgM1Y7mmew7BKf9BqvLX2tnOVy1BBUsxQ== +get-stream@^8.0.1: + version "8.0.1" + resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-8.0.1.tgz#def9dfd71742cd7754a7761ed43749a27d02eca2" + integrity sha512-VaUJspBffn/LMCJVoMvSAdmscJyS1auj5Zulnn5UoYcY531UWmdwhRWkcGKnGU93m5HSXP9LP2usOryrBtQowA== + gifwrap@^0.10.1: version "0.10.1" resolved "https://registry.yarnpkg.com/gifwrap/-/gifwrap-0.10.1.tgz#9ed46a5d51913b482d4221ce9c727080260b681e" @@ -1221,7 +1298,7 @@ global@~4.4.0: min-document "^2.19.0" process "^0.11.10" -globals@^13.19.0: +globals@^13.19.0, globals@^13.23.0: version "13.23.0" resolved "https://registry.yarnpkg.com/globals/-/globals-13.23.0.tgz#ef31673c926a0976e1f61dab4dca57e0c0a8af02" integrity sha512-XAmF0RjlrjY23MA51q3HltdlGxUpXPvg0GioKiD9X6HD28iMjo2dKC8Vqwm7lne4GNr78+RHTfliktR6ZH09wA== @@ -1250,6 +1327,11 @@ has-flag@^4.0.0: resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b" integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ== +human-signals@^5.0.0: + version "5.0.0" + resolved "https://registry.yarnpkg.com/human-signals/-/human-signals-5.0.0.tgz#42665a284f9ae0dade3ba41ebc37eb4b852f3a28" + integrity sha512-AXcZb6vzzrFAUE61HnN4mpLqd/cSIwNQjtNWR0euPm6y0iqx3G4gOXaIDdtdDwZmhwe82LA6+zinmW4UBWVePQ== + ieee754@^1.1.13, ieee754@^1.2.1: version "1.2.1" resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352" @@ -1320,6 +1402,11 @@ is-path-inside@^3.0.3: resolved "https://registry.yarnpkg.com/is-path-inside/-/is-path-inside-3.0.3.tgz#d231362e53a07ff2b0e0ea7fed049161ffd16283" integrity sha512-Fd4gABb+ycGAmKou8eMftCupSir5lRxqf4aD/vd0cD2qc4HL07OjCeuHMr8Ro4CoMaeCKDB0/ECBOVWjTwUvPQ== +is-stream@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-3.0.0.tgz#e6bfd7aa6bef69f4f472ce9bb681e3e57b4319ac" + integrity sha512-LnQR4bZ9IADDRSkvpqMGvt/tEJWclzklNgSw48V5EAaAeDd6qGvN8ei6k5p0tvxSR171VmGyHuTiAOfxAbr8kA== + isexe@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/isexe/-/isexe-2.0.0.tgz#e8fbf374dc556ff8947a10dcb0572d633f2cfa10" @@ -1343,7 +1430,7 @@ jimp@^0.22.10: "@jimp/types" "^0.22.10" regenerator-runtime "^0.13.3" -joi@^17.7.0: +joi@^17.11.0: version "17.11.0" resolved "https://registry.yarnpkg.com/joi/-/joi-17.11.0.tgz#aa9da753578ec7720e6f0ca2c7046996ed04fc1a" integrity sha512-NgB+lZLNoqISVy1rZocE9PZI36bL/77ie924Ri43yEvi9GUUMPeyVIr8KdFTMUlby1p0PBYMk9spIxEUQYqrJQ== @@ -1415,10 +1502,13 @@ load-bmfont@^1.4.1: xhr "^2.0.1" xtend "^4.0.0" -local-pkg@^0.4.3: - version "0.4.3" - resolved "https://registry.yarnpkg.com/local-pkg/-/local-pkg-0.4.3.tgz#0ff361ab3ae7f1c19113d9bb97b98b905dbc4963" - integrity sha512-SFppqq5p42fe2qcZQqqEOiVRXl+WCP1MdT6k7BDEW1j++sp5fIY+/fdRQitvKgB5BrBcmrs5m/L0v2FrU5MY1g== +local-pkg@^0.5.0: + version "0.5.0" + resolved "https://registry.yarnpkg.com/local-pkg/-/local-pkg-0.5.0.tgz#093d25a346bae59a99f80e75f6e9d36d7e8c925c" + integrity sha512-ok6z3qlYyCDS4ZEU27HaU6x/xZa9Whf8jD4ptH5UZTQYZVYeb9bnZ3ojVhiJNLiXK1Hfc0GNbLXcmZ5plLDDBg== + dependencies: + mlly "^1.4.2" + pkg-types "^1.0.3" locate-path@^6.0.0: version "6.0.0" @@ -1437,7 +1527,7 @@ lodash@^4.17.21: resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c" integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg== -loupe@^2.3.6: +loupe@^2.3.6, loupe@^2.3.7: version "2.3.7" resolved "https://registry.yarnpkg.com/loupe/-/loupe-2.3.7.tgz#6e69b7d4db7d3ab436328013d37d1c8c3540c697" integrity sha512-zSMINGVYkdpYSOBmLi0D1Uo7JU9nVdQKrHxC8eYlV+9YKK9WePqAlL7lSlorG/U2Fw1w0hTBmaa/jrQ3UbPHtA== @@ -1451,13 +1541,18 @@ lru-cache@^6.0.0: dependencies: yallist "^4.0.0" -magic-string@^0.30.1: +magic-string@^0.30.5: version "0.30.5" resolved "https://registry.yarnpkg.com/magic-string/-/magic-string-0.30.5.tgz#1994d980bd1c8835dc6e78db7cbd4ae4f24746f9" integrity sha512-7xlpfBaQaP/T6Vh8MO/EqXSW5En6INHEvEXQiuff7Gku0PWjU3uf6w/j9o7O+SpB5fOAkrI5HeoNgwjEO0pFsA== dependencies: "@jridgewell/sourcemap-codec" "^1.4.15" +merge-stream@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/merge-stream/-/merge-stream-2.0.0.tgz#52823629a14dd00c9770fb6ad47dc6310f2c1f60" + integrity sha512-abv/qOcuPfk3URPfDzmZU1LKmuw8kT+0nIHvKrKgFrwifol/doWcdA4ZqsWQ8ENrFKkd67Mfpo/LovbIUsbt3w== + merge2@^1.3.0, merge2@^1.4.1: version "1.4.1" resolved "https://registry.yarnpkg.com/merge2/-/merge2-1.4.1.tgz#4368892f885e907455a6fd7dc55c0c9d404990ae" @@ -1488,6 +1583,11 @@ mime@^1.3.4: resolved "https://registry.yarnpkg.com/mime/-/mime-1.6.0.tgz#32cd9e5c64553bd58d19a568af452acff04981b1" integrity sha512-x0Vn8spI+wuJ1O6S7gnbaQg8Pxh4NNHb7KSINmEWKiPE4RKOplvijn+NkmYmmRgP68mc70j2EbeTFRsrswaQeg== +mimic-fn@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/mimic-fn/-/mimic-fn-4.0.0.tgz#60a90550d5cb0b239cca65d893b1a53b29871ecc" + integrity sha512-vqiC06CuhBTUdZH+RYl8sFrL096vA45Ok5ISO6sE/Mr1jRbGH4Csnhi8f3wKVl7x8mO4Au7Ir9D3Oyv1VYMFJw== + min-document@^2.19.0: version "2.19.0" resolved "https://registry.yarnpkg.com/min-document/-/min-document-2.19.0.tgz#7bd282e3f5842ed295bb748cdd9f1ffa2c824685" @@ -1502,12 +1602,12 @@ minimatch@^3.0.5, minimatch@^3.1.1, minimatch@^3.1.2: dependencies: brace-expansion "^1.1.7" -minimist@^1.2.7: +minimist@^1.2.8: version "1.2.8" resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.8.tgz#c1a464e7693302e082a075cee0c057741ac4772c" integrity sha512-2yyAR8qBkN3YuheJanUpWC5U3bb5osDywNB8RzDVlDwDHbocAJveqqj1u8+SVD7jkWT4yvsHCpWqqWqAxb0zCA== -mlly@^1.2.0, mlly@^1.4.0: +mlly@^1.2.0, mlly@^1.4.2: version "1.4.2" resolved 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@@ node-fetch@^2.6.1: dependencies: whatwg-url "^5.0.0" +npm-run-path@^5.1.0: + version "5.1.0" + resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-5.1.0.tgz#bc62f7f3f6952d9894bd08944ba011a6ee7b7e00" + integrity sha512-sJOdmRGrY2sjNTRMbSvluQqg+8X7ZK61yvzBEIDhz4f8z1TZFYABsqjjCBd/0PUNE9M6QDgHJXQkGUEm7Q+l9Q== + dependencies: + path-key "^4.0.0" + omggif@^1.0.10, omggif@^1.0.9: version "1.0.10" resolved "https://registry.yarnpkg.com/omggif/-/omggif-1.0.10.tgz#ddaaf90d4a42f532e9e7cb3a95ecdd47f17c7b19" @@ -1551,6 +1658,13 @@ once@^1.3.0: dependencies: wrappy "1" +onetime@^6.0.0: + version "6.0.0" + resolved "https://registry.yarnpkg.com/onetime/-/onetime-6.0.0.tgz#7c24c18ed1fd2e9bca4bd26806a33613c77d34b4" + integrity sha512-1FlR+gjXK7X+AsAHso35MnyN5KqGwJRi/31ft6x0M194ht7S+rWAvd7PHss9xSKMzE0asv1pyIHaJYq+BbacAQ== + dependencies: + mimic-fn "^4.0.0" + optionator@^0.9.3: version "0.9.3" resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.9.3.tgz#007397d44ed1872fdc6ed31360190f81814e2c64" @@ -1570,10 +1684,10 @@ p-limit@^3.0.2: dependencies: yocto-queue "^0.1.0" -p-limit@^4.0.0: - version "4.0.0" - resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-4.0.0.tgz#914af6544ed32bfa54670b061cafcbd04984b644" - integrity sha512-5b0R4txpzjPWVw/cXXUResoD4hb6U/x9BH08L7nw+GN1sezDzPdxeRvpc9c433fZhBan/wusjbCsqwqm4EIBIQ== +p-limit@^5.0.0: + version "5.0.0" + resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-5.0.0.tgz#6946d5b7140b649b7a33a027d89b4c625b3a5985" + integrity sha512-/Eaoq+QyLSiXQ4lyYV23f14mZRQcXnxfHrN0vCai+ak9G0pp9iEQukIIZq5NccEvwRB8PUnZT0KsOoDCINS1qQ== dependencies: yocto-queue "^1.0.0" @@ -1634,6 +1748,11 @@ path-key@^3.1.0: resolved "https://registry.yarnpkg.com/path-key/-/path-key-3.1.1.tgz#581f6ade658cbba65a0d3380de7753295054f375" integrity sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q== +path-key@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/path-key/-/path-key-4.0.0.tgz#295588dc3aee64154f877adb9d780b81c554bf18" + integrity sha512-haREypq7xkM7ErfgIyA0z+Bj4AGKlMSdlQE2jvJo6huWD1EdkKYV+G/T4nq0YEF2vgTT8kqMFKo1uHn950r4SQ== + path-type@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/path-type/-/path-type-4.0.0.tgz#84ed01c0a7ba380afe09d90a8c180dcd9d03043b" @@ -1685,17 +1804,17 @@ pkg-types@^1.0.3: mlly "^1.2.0" pathe "^1.1.0" -playwright-core@1.39.0: - version "1.39.0" - resolved "https://registry.yarnpkg.com/playwright-core/-/playwright-core-1.39.0.tgz#efeaea754af4fb170d11845b8da30b2323287c63" - integrity sha512-+k4pdZgs1qiM+OUkSjx96YiKsXsmb59evFoqv8SKO067qBA+Z2s/dCzJij/ZhdQcs2zlTAgRKfeiiLm8PQ2qvw== +playwright-core@1.40.1: + version "1.40.1" + resolved "https://registry.yarnpkg.com/playwright-core/-/playwright-core-1.40.1.tgz#442d15e86866a87d90d07af528e0afabe4c75c05" + integrity 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- version "8.4.31" - resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.31.tgz#92b451050a9f914da6755af352bdc0192508656d" - integrity sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ== +postcss@^8.4.32: + version "8.4.32" + resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.32.tgz#1dac6ac51ab19adb21b8b34fd2d93a86440ef6c9" + integrity sha512-D/kj5JNu6oo2EIy+XL/26JEDTlIbB8hw85G8StOE6L74RQAVVP5rej6wxCNqyMbR4RkPfqvezVbPw81Ngd6Kcw== dependencies: - nanoid "^3.3.6" + nanoid "^3.3.7" picocolors "^1.0.0" source-map-js "^1.0.2" @@ -1723,7 +1842,7 @@ prelude-ls@^1.2.1: resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.2.1.tgz#debc6489d7a6e6b0e7611888cec880337d316396" integrity sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g== -pretty-format@^29.5.0: +pretty-format@^29.7.0: version "29.7.0" resolved 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- resolved "https://registry.yarnpkg.com/rollup/-/rollup-3.29.4.tgz#4d70c0f9834146df8705bfb69a9a19c9e1109981" - integrity sha512-oWzmBZwvYrU0iJHtDmhsm662rC15FRXmcjCk1xD771dFDx5jJ02ufAQQTn0etB2emNk4J9EZg/yWKpsn9BWGRw== +rollup@^4.2.0: + version "4.6.1" + resolved "https://registry.yarnpkg.com/rollup/-/rollup-4.6.1.tgz#351501c86b5b4f976dde8c5837516452b59921f8" + integrity sha512-jZHaZotEHQaHLgKr8JnQiDT1rmatjgKlMekyksz+yk9jt/8z9quNjnKNRoaM0wd9DC2QKXjmWWuDYtM3jfF8pQ== optionalDependencies: + "@rollup/rollup-android-arm-eabi" "4.6.1" + "@rollup/rollup-android-arm64" "4.6.1" + "@rollup/rollup-darwin-arm64" "4.6.1" + "@rollup/rollup-darwin-x64" "4.6.1" + "@rollup/rollup-linux-arm-gnueabihf" "4.6.1" + "@rollup/rollup-linux-arm64-gnu" "4.6.1" + "@rollup/rollup-linux-arm64-musl" "4.6.1" + "@rollup/rollup-linux-x64-gnu" "4.6.1" + "@rollup/rollup-linux-x64-musl" "4.6.1" + "@rollup/rollup-win32-arm64-msvc" "4.6.1" + "@rollup/rollup-win32-ia32-msvc" "4.6.1" + "@rollup/rollup-win32-x64-msvc" "4.6.1" fsevents "~2.3.2" run-parallel@^1.1.9: @@ -1804,7 +1940,7 @@ run-parallel@^1.1.9: dependencies: queue-microtask "^1.2.2" -rxjs@^7.8.0: +rxjs@^7.8.1: version "7.8.1" resolved "https://registry.yarnpkg.com/rxjs/-/rxjs-7.8.1.tgz#6f6f3d99ea8044291efd92e7c7fcf562c4057543" integrity sha512-AA3TVj+0A2iuIoQkWEK/tqFjBq2j+6PO6Y0zJcvzLAFhEFIO3HL0vls9hWLncZbAAbK0mar7oZ4V079I/qPMxg== @@ -1845,6 +1981,11 @@ siginfo@^2.0.0: resolved "https://registry.yarnpkg.com/siginfo/-/siginfo-2.0.0.tgz#32e76c70b79724e3bb567cb9d543eb858ccfaf30" integrity sha512-ybx0WO1/8bSBLEWXZvEd7gMW3Sn3JFlW3TvX1nREbDLRNQNaeNN8WK0meBwPdAaOI7TtRRRJn/Es1zhrrCHu7g== +signal-exit@^4.1.0: + version "4.1.0" + resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-4.1.0.tgz#952188c1cbd546070e2dd20d0f41c0ae0530cb04" + integrity sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw== + slash@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634" @@ -1860,10 +2001,10 @@ stackback@0.0.2: resolved "https://registry.yarnpkg.com/stackback/-/stackback-0.0.2.tgz#1ac8a0d9483848d1695e418b6d031a3c3ce68e3b" integrity sha512-1XMJE5fQo1jGH6Y/7ebnwPOBEkIEnT4QF32d5R1+VXdXveM0IBMJt8zfaxX1P3QhVwrYe+576+jkANtSS2mBbw== -std-env@^3.3.3: - version "3.4.3" - resolved "https://registry.yarnpkg.com/std-env/-/std-env-3.4.3.tgz#326f11db518db751c83fd58574f449b7c3060910" - integrity sha512-f9aPhy8fYBuMN+sNfakZV18U39PbalgjXG3lLB9WkaYTxijru61wb57V9wxxNthXM5Sd88ETBWi29qLAsHO52Q== +std-env@^3.5.0: + version "3.6.0" + resolved "https://registry.yarnpkg.com/std-env/-/std-env-3.6.0.tgz#94807562bddc68fa90f2e02c5fd5b6865bb4e98e" + integrity sha512-aFZ19IgVmhdB2uX599ve2kE6BIE3YMnQ6Gp6BURhW/oIzpXGKr878TQfAQZn1+i0Flcc/UKUy1gOlcfaUBCryg== string_decoder@^1.1.1: version "1.3.0" @@ -1879,12 +2020,17 @@ strip-ansi@^6.0.1: dependencies: ansi-regex "^5.0.1" +strip-final-newline@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/strip-final-newline/-/strip-final-newline-3.0.0.tgz#52894c313fbff318835280aed60ff71ebf12b8fd" + integrity sha512-dOESqjYr96iWYylGObzd39EuNTa5VJxyvVAEm5Jnh7KGo75V43Hk1odPQkNDyXNmUR6k+gEiDVXnjB8HJ3crXw== + strip-json-comments@^3.1.1: version "3.1.1" resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006" integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig== -strip-literal@^1.0.1: +strip-literal@^1.3.0: version "1.3.0" resolved "https://registry.yarnpkg.com/strip-literal/-/strip-literal-1.3.0.tgz#db3942c2ec1699e6836ad230090b84bb458e3a07" integrity sha512-PugKzOsyXpArk0yWmUwqOZecSO0GH0bPoctLcqNDH9J04pVW3lflYE0ujElBGTloevcxF5MofAOZ7C5l2b+wLg== @@ -1916,7 +2062,7 @@ timm@^1.6.1: resolved 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+tinypool@^0.8.1: + version "0.8.1" + resolved "https://registry.yarnpkg.com/tinypool/-/tinypool-0.8.1.tgz#b6c4e4972ede3e3e5cda74a3da1679303d386b03" + integrity sha512-zBTCK0cCgRROxvs9c0CGK838sPkeokNGdQVUUwHAbynHFlmyJYj825f/oRs528HaIJ97lo0pLIlDUzwN+IorWg== -tinyspy@^2.1.1: +tinyspy@^2.2.0: version "2.2.0" resolved "https://registry.yarnpkg.com/tinyspy/-/tinyspy-2.2.0.tgz#9dc04b072746520b432f77ea2c2d17933de5d6ce" integrity sha512-d2eda04AN/cPOR89F7Xv5bK/jrQEhmcLFe6HFldoeO9AJtps+fqEnh486vnT/8y4bw38pSyxDcTCAq+Ks2aJTg== @@ -1993,10 +2139,10 @@ type-fest@^0.20.2: resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-0.20.2.tgz#1bf207f4b28f91583666cb5fbd327887301cd5f4" integrity sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ== -typescript@^5.2.2: - version "5.2.2" - resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.2.2.tgz#5ebb5e5a5b75f085f22bc3f8460fba308310fa78" - integrity 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esbuild "^0.18.10" - postcss "^8.4.27" - rollup "^3.27.1" + esbuild "^0.19.3" + postcss "^8.4.32" + rollup "^4.2.0" optionalDependencies: - fsevents "~2.3.2" + fsevents "~2.3.3" -vitest@^0.34.6: - version "0.34.6" - resolved "https://registry.yarnpkg.com/vitest/-/vitest-0.34.6.tgz#44880feeeef493c04b7f795ed268f24a543250d7" - integrity sha512-+5CALsOvbNKnS+ZHMXtuUC7nL8/7F1F2DnHGjSsszX8zCjWSSviphCb/NuS9Nzf4Q03KyyDRBAXhF/8lffME4Q== - dependencies: - "@types/chai" "^4.3.5" - "@types/chai-subset" "^1.3.3" - "@types/node" "*" - "@vitest/expect" "0.34.6" - "@vitest/runner" "0.34.6" - "@vitest/snapshot" "0.34.6" - "@vitest/spy" "0.34.6" - "@vitest/utils" "0.34.6" - acorn "^8.9.0" - acorn-walk "^8.2.0" +vitest@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/vitest/-/vitest-1.0.1.tgz#3ba1307066842bc801084fa384ce0b23941b91f7" + integrity sha512-MHsOj079S28hDsvdDvyD1pRj4dcS51EC5Vbe0xvOYX+WryP8soiK2dm8oULi+oA/8Xa/h6GoJEMTmcmBy5YM+Q== + dependencies: + "@vitest/expect" "1.0.1" + "@vitest/runner" "1.0.1" + "@vitest/snapshot" "1.0.1" + "@vitest/spy" "1.0.1" + "@vitest/utils" "1.0.1" + acorn-walk "^8.3.0" cac "^6.7.14" chai "^4.3.10" debug "^4.3.4" - local-pkg "^0.4.3" - magic-string "^0.30.1" + execa "^8.0.1" + local-pkg "^0.5.0" + magic-string "^0.30.5" pathe "^1.1.1" picocolors "^1.0.0" - std-env "^3.3.3" - strip-literal "^1.0.1" - tinybench "^2.5.0" - tinypool "^0.7.0" - vite "^3.1.0 || ^4.0.0 || ^5.0.0-0" - vite-node "0.34.6" + std-env "^3.5.0" + strip-literal "^1.3.0" + tinybench "^2.5.1" + tinypool "^0.8.1" + vite "^5.0.0-beta.19 || ^5.0.0" + vite-node "1.0.1" why-is-node-running "^2.2.2" -wait-on@^7.0.1: - version "7.0.1" - resolved "https://registry.yarnpkg.com/wait-on/-/wait-on-7.0.1.tgz#5cff9f8427e94f4deacbc2762e6b0a489b19eae9" - integrity sha512-9AnJE9qTjRQOlTZIldAaf/da2eW0eSRSgcqq85mXQja/DW3MriHxkpODDSUEg+Gri/rKEcXUZHe+cevvYItaog== +wait-on@^7.2.0: + version "7.2.0" + resolved "https://registry.yarnpkg.com/wait-on/-/wait-on-7.2.0.tgz#d76b20ed3fc1e2bebc051fae5c1ff93be7892928" + integrity sha512-wCQcHkRazgjG5XoAq9jbTMLpNIjoSlZslrJ2+N9MxDsGEv1HnFoVjOCexL0ESva7Y9cu350j+DWADdk54s4AFQ== dependencies: - axios "^0.27.2" - joi "^17.7.0" + axios "^1.6.1" + joi "^17.11.0" lodash "^4.17.21" - minimist "^1.2.7" - rxjs "^7.8.0" + minimist "^1.2.8" + rxjs "^7.8.1" webidl-conversions@^3.0.0: version "3.0.1" From 66255d59845838366019cebfb77620b0891e4c74 Mon Sep 17 00:00:00 2001 From: Kacey Date: Fri, 15 Dec 2023 11:31:35 -0800 Subject: [PATCH 13/59] Load datasets by ID when executing delegated operations (#3920) * enable load dataset by id in execution context and remove redundant reloading * fix merge * move load_dataset to odm dir * rvt preventing migration and logging * refactor given previous changes * apply requested changes * only reload if dataset exists --- fiftyone/core/odm/utils.py | 34 ++++ fiftyone/core/utils.py | 3 + fiftyone/factory/repos/delegated_operation.py | 25 +-- fiftyone/operators/delegated.py | 6 + fiftyone/operators/executor.py | 49 ++--- fiftyone/server/view.py | 8 +- tests/unittests/delegated_operators_tests.py | 170 ++++++++++++++---- tests/unittests/utils_tests.py | 82 +++++++++ 8 files changed, 308 insertions(+), 69 deletions(-) diff --git a/fiftyone/core/odm/utils.py b/fiftyone/core/odm/utils.py index aef0fa6e57..84a8a2f961 100644 --- a/fiftyone/core/odm/utils.py +++ b/fiftyone/core/odm/utils.py @@ -657,3 +657,37 @@ class DocumentRegistryError(Exception): _document_registry = DocumentRegistry() + + +def load_dataset(id=None, name=None): + """Loads the dataset from the database by its unique id or name. Throws + an error if neigher id nor name is provided. + + Args: + id (None): the unique id of the dataset + name (None): the name of the dataset + + Returns: + a :class:`fiftyone.core.dataset.Dataset` + """ + import fiftyone.core.odm as foo + import fiftyone.core.dataset as fod + + if name: + return fod.load_dataset(name) + + if not id: + raise ValueError("Must provide either id or name") + + db = foo.get_db_conn() + try: + uid = ObjectId(id) + except: + # Although _id is an ObjectId by default, it's possible to set it to + # something else + uid = id + + res = db.datasets.find_one({"_id": uid}, {"name": True}) + if not res: + raise ValueError(f"Dataset with _id={uid} does not exist") + return fod.load_dataset(res.get("name")) diff --git a/fiftyone/core/utils.py b/fiftyone/core/utils.py index b320c89d34..18969cdda6 100644 --- a/fiftyone/core/utils.py +++ b/fiftyone/core/utils.py @@ -31,6 +31,9 @@ import types from xml.parsers.expat import ExpatError import zlib + +from bson import ObjectId +from bson.errors import InvalidId from matplotlib import colors as mcolors from concurrent.futures import ThreadPoolExecutor diff --git a/fiftyone/factory/repos/delegated_operation.py b/fiftyone/factory/repos/delegated_operation.py index e326b6de13..e1e9b7dc89 100644 --- a/fiftyone/factory/repos/delegated_operation.py +++ b/fiftyone/factory/repos/delegated_operation.py @@ -17,6 +17,7 @@ from fiftyone.factory import DelegatedOperationPagingParams from fiftyone.factory.repos import DelegatedOperationDocument from fiftyone.operators.executor import ( + ExecutionContext, ExecutionProgress, ExecutionResult, ExecutionRunState, @@ -161,17 +162,19 @@ def queue_operation(self, **kwargs: Any) -> DelegatedOperationDocument: if delegation_target: setattr(op, "delegation_target", delegation_target) - dataset_name = None - if isinstance(op.context, dict): - dataset_name = op.context.get("request_params", {}).get( - "dataset_name" - ) - elif "dataset_name" in op.context.request_params: - dataset_name = op.context.request_params["dataset_name"] - - if dataset_name and not op.dataset_id: - dataset = fod.load_dataset(dataset_name) - op.dataset_id = dataset._doc.id + if not op.dataset_id: + # For consistency, set the dataset_id using the ExecutionContext.dataset + # rather than calling load_dataset() on a potentially stale + # dataset_name in the request_params + context = None + if isinstance(op.context, dict): + context = ExecutionContext( + request_params=op.context.get("request_params", {}) + ) + elif isinstance(op.context, ExecutionContext): + context = op.context + if context and context.dataset: + op.dataset_id = context.dataset._doc.id doc = self._collection.insert_one(op.to_pymongo()) op.id = doc.inserted_id diff --git a/fiftyone/operators/delegated.py b/fiftyone/operators/delegated.py index 151a4ae909..d1a493d553 100644 --- a/fiftyone/operators/delegated.py +++ b/fiftyone/operators/delegated.py @@ -366,6 +366,12 @@ async def _execute_operator(self, doc): context = doc.context context.request_params["run_doc"] = doc.id + if not context.request_params.get("dataset_id", None): + # Pass the dataset_id so that the execution context can load the + # dataset by id in case a dataset has been renamed since the task + # was initially queued. However, don't overwrite it if it exists. + context.request_params["dataset_id"] = doc.dataset_id + prepared = await prepare_operator_executor( operator_uri=operator_uri, request_params=context.request_params, diff --git a/fiftyone/operators/executor.py b/fiftyone/operators/executor.py index 2c56d6c019..c313a39659 100644 --- a/fiftyone/operators/executor.py +++ b/fiftyone/operators/executor.py @@ -14,16 +14,15 @@ import fiftyone as fo import fiftyone.core.dataset as fod +import fiftyone.core.odm.utils as focu import fiftyone.core.utils as fou import fiftyone.core.view as fov -import fiftyone.server.view as fosv import fiftyone.operators.types as types +import fiftyone.server.view as fosv from fiftyone.plugins.secrets import PluginSecretsResolver, SecretsDictionary - from .decorators import coroutine_timeout -from .registry import OperatorRegistry from .message import GeneratedMessage, MessageType - +from .registry import OperatorRegistry logger = logging.getLogger(__name__) @@ -212,19 +211,15 @@ async def execute_or_delegate_operator( if should_delegate: if not execution_options.allow_delegated_execution: logger.warning( - ( - "This operation does not support delegated execution; it " - "will be executed immediately" - ) + "This operation does not support delegated " + "execution; it will be executed immediately" ) should_delegate = False else: if not execution_options.allow_immediate_execution: logger.warning( - ( - "This operation does not support immediate execution; it " - "will be delegated" - ) + "This operation does not support immediate " + "execution; it will be delegated" ) should_delegate = True @@ -443,12 +438,22 @@ def dataset(self): """The :class:`fiftyone.core.dataset.Dataset` being operated on.""" if self._dataset is not None: return self._dataset - - dataset_name = self.request_params.get("dataset_name", None) - - self._dataset = fod.load_dataset(dataset_name) - self._dataset.reload() - + # Since dataset may have been renamed, always resolve the dataset by + # id if it is available + uid = self.request_params.get("dataset_id", None) + if uid: + self._dataset = focu.load_dataset(id=uid) + # Set the dataset_name using the dataset object in case the dataset + # has been renamed or changed since the context was created + self.request_params["dataset_name"] = self._dataset.name + else: + uid = self.request_params.get("dataset_name", None) + if uid: + self._dataset = focu.load_dataset(name=uid) + # TODO: refactor so that this additional reload post-load is not + # required + if self._dataset is not None: + self._dataset.reload() return self._dataset @property @@ -471,16 +476,14 @@ def view(self): if self._view is not None: return self._view - # Forces a dataset reload if necessary - _ = self.dataset - - dataset_name = self.request_params.get("dataset_name", None) + # Always derive the view from the context's dataset + dataset = self.dataset stages = self.request_params.get("view", None) filters = self.request_params.get("filters", None) extended = self.request_params.get("extended", None) self._view = fosv.get_view( - dataset_name, + dataset, stages=stages, filters=filters, extended_stages=extended, diff --git a/fiftyone/server/view.py b/fiftyone/server/view.py index ff33ad8d94..2912f062ac 100644 --- a/fiftyone/server/view.py +++ b/fiftyone/server/view.py @@ -72,7 +72,7 @@ def run() -> foc.SampleCollection: def get_view( - dataset_name, + dataset, view_name=None, stages=None, filters=None, @@ -84,7 +84,8 @@ def get_view( """Gets the view defined by the given request parameters. Args: - dataset_name: the dataset name + dataset: the dataset name or :class:`fiftyone.core.dataset.Dataset` + instance view_name (None): the name of a saved view to load stages (None): an optional list of serialized :class:`fiftyone.core.stages.ViewStage` instances @@ -102,7 +103,8 @@ def get_view( Returns: a :class:`fiftyone.core.view.DatasetView` """ - dataset = fod.load_dataset(dataset_name) + if isinstance(dataset, str): + dataset = fod.load_dataset(dataset) if reload: dataset.reload() diff --git a/tests/unittests/delegated_operators_tests.py b/tests/unittests/delegated_operators_tests.py index 1c2ff237b6..27632775d1 100644 --- a/tests/unittests/delegated_operators_tests.py +++ b/tests/unittests/delegated_operators_tests.py @@ -7,8 +7,11 @@ """ import time import unittest +from unittest import mock from unittest.mock import patch +import pytest + import fiftyone from bson import ObjectId @@ -134,7 +137,7 @@ def delete_test_data(self): self.svc.delete_operation(doc_id=doc.id) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_delegate_operation( self, mock_load_dataset, mock_get_operator, mock_operator_exists @@ -168,22 +171,20 @@ def test_delegate_operation( self.assertEqual(doc2.label, "@voxelfiftyone/operator/foo") self.assertEqual(doc2.run_state, ExecutionRunState.QUEUED) - @patch( - "fiftyone.core.dataset.load_dataset", - ) def test_list_queued_operations( - self, mock_load_dataset, mock_get_operator, mock_operator_exists + self, mock_get_operator, mock_operator_exists ): - - dataset_id = ObjectId() - dataset_name = f"test_dataset_{dataset_id}" - mock_load_dataset.return_value.name = dataset_name - mock_load_dataset.return_value._doc.id = dataset_id + dataset_name = f"test_dataset_{ObjectId()}" + dataset = Dataset(dataset_name, _create=True, persistent=True) + dataset.save() + dataset_id = dataset._doc.id self.delete_test_data() - dataset_name = f"test_dataset_{ObjectId()}" dataset_name2 = f"test_dataset_{ObjectId()}" + dataset2 = Dataset(dataset_name2, _create=True, persistent=True) + dataset2.save() + dataset_id2 = dataset2._doc.id operator = "@voxelfiftyone/operator/foo" operator2 = "@voxelfiftyone/operator/bar" @@ -212,6 +213,7 @@ def test_list_queued_operations( request_params={ "foo": "bar", "dataset_name": dataset_name, + "dataset_id": str(dataset_id), }, ), ) @@ -228,6 +230,7 @@ def test_list_queued_operations( request_params={ "foo": "bar", "dataset_name": dataset_name2, + "dataset_id": str(dataset_id2), }, ), ) @@ -251,6 +254,9 @@ def test_list_queued_operations( running = self.svc.list_operations(run_state=ExecutionRunState.RUNNING) self.assertEqual(len(running), 10 + initial_running) + dataset.delete() + dataset2.delete() + def test_set_run_states(self, mock_get_operator, mock_operator_exists): doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/foo", @@ -413,7 +419,7 @@ def test_updates_progress(self, mock_get_operator, mock_operator_exists): self.assertEqual(doc.run_link, "http://run.info") @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_full_run_fail( self, mock_load_dataset, mock_get_operator, mock_operator_exists @@ -449,7 +455,7 @@ def test_full_run_fail( self.assertIsNotNone(doc.failed_at) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_rerun_failed( self, mock_load_dataset, get_op_mock, op_exists_mock @@ -497,23 +503,124 @@ def test_rerun_failed( doc = self.svc.get(doc_id=rerun_doc.id) self.assertEqual(doc.run_state, ExecutionRunState.COMPLETED) - @patch( - "fiftyone.core.dataset.load_dataset", - ) - def test_paging_sorting( - self, mock_load_dataset, mock_get_operator, mock_operator_exists - ): - dataset_id = ObjectId() - dataset_name = f"test_dataset_{dataset_id}" - mock_load_dataset.return_value.name = dataset_name - mock_load_dataset.return_value._doc.id = dataset_id + def test_rerun_with_renamed_dataset(self, get_op_mock, op_exists_mock): + # setup + uid = str(ObjectId()) + dataset_name = f"test_dataset_{uid}" + dataset = Dataset(dataset_name, _create=True, persistent=True) + dataset.save() + + get_op_mock.return_value = MockOperator(success=False) + + ctx = ExecutionContext( + request_params={ + "dataset_id": str(dataset._doc.id), + "dataset_name": dataset.name, + } + ) + # Queue operation using original dataset name + doc = self.svc.queue_operation( + operator="@voxelfiftyone/operator/foo", + label=get_op_mock.return_value.name, + delegation_target=f"test_target", + context=ctx.serialize(), + ) + + self.docs_to_delete.append(doc) + self.assertEqual(doc.run_state, ExecutionRunState.QUEUED) + + # Execute once with original dataset name + self.svc.execute_queued_operations(delegation_target="test_target") + doc = self.svc.get(doc_id=doc.id) + self.assertEqual(doc.run_state, ExecutionRunState.FAILED) + + # set the mock back to a successful operation + get_op_mock.return_value = MockOperator() + + # Rename dataset and save + dataset.name = f"renamed_dataset_{uid}" + dataset.save() + + try: + # Rerun failed operation after the dataset is renamed + rerun_doc = self.svc.rerun_operation(doc.id) + self.docs_to_delete.append(rerun_doc) + self.assertNotEqual(doc.id, rerun_doc.id) + self.assertIsNotNone(rerun_doc.delegation_target) + self.assertEqual( + rerun_doc.delegation_target, doc.delegation_target + ) + self.assertEqual(rerun_doc.run_state, ExecutionRunState.QUEUED) + self.assertIsNotNone(rerun_doc.queued_at) + self.assertIsNone(rerun_doc.started_at) + self.assertIsNone(rerun_doc.completed_at) + self.assertIsNone(rerun_doc.result) + + self.svc.execute_queued_operations(delegation_target="test_target") + + doc = self.svc.get(doc_id=rerun_doc.id) + self.assertEqual(doc.run_state, ExecutionRunState.COMPLETED) + + except: + pytest.fail( + "Should not fail when rerunning failed operation with renamed dataset" + ) + finally: + dataset.delete() + + def test_execute_with_renamed_dataset(self, get_op_mock, op_exists_mock): + # setup + uid = str(ObjectId()) + dataset_name = f"test_dataset_{uid}" + dataset = Dataset(dataset_name, _create=True, persistent=True) + dataset.save() + + get_op_mock.return_value = MockOperator(success=True) + + ctx = ExecutionContext( + request_params={ + "dataset_id": str(dataset._doc.id), + "dataset_name": dataset.name, + } + ) + # Queue operation using original dataset name + doc = self.svc.queue_operation( + operator="@voxelfiftyone/operator/foo", + label=get_op_mock.return_value.name, + delegation_target=f"test_target", + context=ctx.serialize(), + ) + + self.docs_to_delete.append(doc) + self.assertEqual(doc.run_state, ExecutionRunState.QUEUED) + + # Rename dataset + dataset.name = f"renamed_dataset_{uid}" + dataset.save() + + # Execute queued operation after saving the new dataset name + try: + self.svc.execute_queued_operations(delegation_target="test_target") + doc = self.svc.get(doc_id=doc.id) + self.assertEqual(doc.run_state, ExecutionRunState.COMPLETED) + except: + pytest.fail( + "Should not fail when executing queued operation with renamed dataset" + ) + finally: + dataset.delete() + + def test_paging_sorting(self, mock_get_operator, mock_operator_exists): + dataset_name = f"test_dataset_{ObjectId()}" + dataset = Dataset(dataset_name, _create=True, persistent=True) + dataset.save() + dataset_id = dataset._doc.id # create 100 docs, 25 of each state & for each user queued = [] running = [] completed = [] failed = [] - dataset_name = f"test_dataset_{ObjectId()}" for i in range(4): operator = f"@voxelfiftyone/operator/test_{i}" for j in range(25): @@ -524,6 +631,7 @@ def test_paging_sorting( request_params={ "foo": "bar", "dataset_name": dataset_name, + "dataset_id:": str(dataset_id), } ), ) @@ -643,9 +751,10 @@ def test_paging_sorting( self.assertEqual(pages, 4) self.assertEqual(total, 25) + dataset.delete() @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_gets_dataset_id_from_name( self, mock_load_dataset, mock_get_operator, *args @@ -669,7 +778,7 @@ def test_gets_dataset_id_from_name( self.assertEqual(doc.dataset_id, dataset_id) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_deletes_by_dataset_id( self, mock_load_dataset, mock_get_operator, mock_operator_exists @@ -681,7 +790,6 @@ def test_deletes_by_dataset_id( # create 100 docs, 25 of each state & for each user queued = [] - dataset_name = f"test_dataset_{ObjectId()}" operator = f"@voxelfiftyone/operator/test_{ObjectId}" for i in range(25): doc = self.svc.queue_operation( @@ -725,7 +833,7 @@ def test_deletes_by_dataset_id( self.assertEqual(len(ops), 0) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_search( self, mock_load_dataset, mock_get_operator, mock_operator_exists @@ -735,7 +843,6 @@ def test_search( mock_load_dataset.return_value.name = dataset_name mock_load_dataset.return_value._doc.id = dataset_id - dataset_name = f"test_dataset_{ObjectId()}" delegation_target = f"delegation_target{ObjectId()}" for i in range(4): operator = f"@voxelfiftyone/operator/test_{i}" @@ -827,7 +934,7 @@ def test_search( self.assertEqual(len(docs), 1) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_count( self, mock_load_dataset, mock_get_operator, mock_operator_exists @@ -839,7 +946,6 @@ def test_count( mock_get_operator.return_value = MockOperator() - dataset_name = f"test_dataset_{ObjectId()}" delegation_target = f"delegation_target{ObjectId()}" for i in range(4): operator = f"@voxelfiftyone/operator/test_{i}" @@ -879,7 +985,7 @@ def test_count( self.assertEqual(docs, 25) @patch( - "fiftyone.core.dataset.load_dataset", + "fiftyone.core.odm.utils.load_dataset", ) def test_rename_operation( self, mock_load_dataset, mock_get_operator, mock_operator_exists diff --git a/tests/unittests/utils_tests.py b/tests/unittests/utils_tests.py index 86a5fbe4da..4cbe7bc711 100644 --- a/tests/unittests/utils_tests.py +++ b/tests/unittests/utils_tests.py @@ -7,6 +7,7 @@ """ import time import unittest +from unittest.mock import MagicMock, patch import numpy as np @@ -313,6 +314,87 @@ def test_multiple_config_cleanup(self): self.assertEqual(config.id, orig_config.id) +from bson import ObjectId +from fiftyone.core.odm.utils import load_dataset + + +class TestLoadDataset(unittest.TestCase): + @patch("fiftyone.core.odm.get_db_conn") + @patch("fiftyone.core.dataset.Dataset") + def test_load_dataset_by_id(self, mock_dataset, mock_get_db_conn): + # Setup + identifier = ObjectId() + mock_db = MagicMock() + mock_get_db_conn.return_value = mock_db + mock_db.datasets.find_one.return_value = { + "_id": ObjectId(identifier), + "name": "test_dataset", + } + + # Test + result = load_dataset(id=identifier) + + # Assertions + mock_get_db_conn.assert_called_once() + mock_db.datasets.find_one.assert_called_once_with( + {"_id": ObjectId(identifier)}, {"name": True} + ) + + self.assertEqual(result, mock_dataset.return_value) + + @patch("fiftyone.core.odm.get_db_conn") + @patch("fiftyone.core.dataset.Dataset") + def test_load_dataset_by_alt_id(self, mock_dataset, mock_get_db_conn): + # Setup + identifier = "alt_id" + mock_db = MagicMock() + mock_get_db_conn.return_value = mock_db + mock_db.datasets.find_one.return_value = { + "_id": "identifier", + "name": "dataset_name", + } + + # Test + result = load_dataset(id=identifier) + + # Assertions + mock_get_db_conn.assert_called_once() + mock_db.datasets.find_one.assert_called_once_with( + {"_id": identifier}, {"name": True} + ) + self.assertEqual(result, mock_dataset.return_value) + + @patch("fiftyone.core.dataset.Dataset") + def test_load_dataset_by_name(self, mock_dataset): + # Setup + identifier = "test_dataset" + mock_dataset.return_value = {"_id": ObjectId(), "name": identifier} + + # Test + result = load_dataset(name=identifier) + + # Assertions + self.assertEqual(result, mock_dataset.return_value) + + @patch("fiftyone.core.odm.get_db_conn") + def test_load_dataset_nonexistent(self, mock_get_db_conn): + # Setup + identifier = ObjectId() + mock_db = MagicMock() + mock_db.datasets.find_one.return_value = None + mock_get_db_conn.return_value = mock_db + + # Call the function and expect a ValueError + with self.assertRaises(ValueError) as context: + load_dataset(id=identifier) + + # Assertions + mock_get_db_conn.assert_called_once() + mock_db.datasets.find_one.assert_called_once_with( + {"_id": identifier}, {"name": True} + ) + + if __name__ == "__main__": fo.config.show_progress_bars = False unittest.main(verbosity=2) From 070bd78aee9be71a4d28199817f383d68ba71d82 Mon Sep 17 00:00:00 2001 From: Trinity Date: Fri, 15 Dec 2023 12:28:12 -0800 Subject: [PATCH 14/59] ensure line breaks in embeddings legend (#3927) * ensure line breaks in embeddings legend * Update app/packages/embeddings/src/tracesToData.tsx Co-authored-by: Benjamin Kane --------- Co-authored-by: Benjamin Kane --- app/packages/embeddings/src/tracesToData.tsx | 23 ++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/app/packages/embeddings/src/tracesToData.tsx b/app/packages/embeddings/src/tracesToData.tsx index e1a7abfaa8..120cab5329 100644 --- a/app/packages/embeddings/src/tracesToData.tsx +++ b/app/packages/embeddings/src/tracesToData.tsx @@ -4,6 +4,10 @@ import { Color, cssColorNames } from "./Color"; import { getPointIndex } from "./getPointIndex"; import { sortStringsAlphabetically } from "./sortStringsAlphabetically"; +const maxLegendLineLength = 35; +const wordMatch = new RegExp(`.{1,${maxLegendLineLength}}(\\s|$)`, "g"); +const charMatch = new RegExp(`.{1,${maxLegendLineLength}}`, "g"); + export function tracesToData( traces, style, @@ -18,6 +22,7 @@ export function tracesToData( const isUncolored = style === "uncolored"; return Object.entries(traces) .sort((a, b) => sortStringsAlphabetically(a[0], b[0])) + .map(addLineBreaks) .map(([key, trace]) => { const selectedpoints = plotSelection?.length ? plotSelection @@ -78,6 +83,24 @@ export function tracesToData( }); } +const addLineBreaks = ([key, trace]) => { + // split the key into chunks of characters or less, respecting word boundaries. + // (\s|$) forces match termination on whitespace or the end of the string. + // {1,35}(\s|$) will match up to 35 characters followed by a whitespace character, + // then we can join the result on
to get a line break. + if (key && key.length > maxLegendLineLength) { + let lines = key.match(wordMatch); + + // if there was no whitespace to split it on, brute force split it at chars + if (lines.length === 1) { + lines = key.match(charMatch); + } + + key = lines.join("
"); + } + return [key, trace]; +}; + const getLabelColor = (key: string, setting: CustomizeColor): Color | null => { if (!setting || !setting.valueColors) { return null; From 0400384ada925e328a1cabcf6d40817e8427fc78 Mon Sep 17 00:00:00 2001 From: Kacey Date: Fri, 15 Dec 2023 17:24:56 -0800 Subject: [PATCH 15/59] Set dataset_id in params from op doc (#3934) --- fiftyone/factory/repos/delegated_operation.py | 36 ++++++--- tests/unittests/delegated_operators_tests.py | 75 ++++++++++++++++--- 2 files changed, 88 insertions(+), 23 deletions(-) diff --git a/fiftyone/factory/repos/delegated_operation.py b/fiftyone/factory/repos/delegated_operation.py index e1e9b7dc89..254da853ec 100644 --- a/fiftyone/factory/repos/delegated_operation.py +++ b/fiftyone/factory/repos/delegated_operation.py @@ -5,6 +5,7 @@ | `voxel51.com `_ | """ +import logging from datetime import datetime from typing import Any, List @@ -13,7 +14,6 @@ from pymongo import IndexModel from pymongo.collection import Collection -import fiftyone.core.dataset as fod from fiftyone.factory import DelegatedOperationPagingParams from fiftyone.factory.repos import DelegatedOperationDocument from fiftyone.operators.executor import ( @@ -23,6 +23,8 @@ ExecutionRunState, ) +logger = logging.getLogger(__name__) + class DelegatedOperationRepo(object): """Base Class for a delegated operation repository.""" @@ -118,7 +120,6 @@ def __init__(self, collection: Collection = None): self._create_indexes() def _get_collection(self) -> Collection: - import fiftyone as fo import fiftyone.core.odm as foo database: pymongo.database.Database = foo.get_db_conn() @@ -162,19 +163,32 @@ def queue_operation(self, **kwargs: Any) -> DelegatedOperationDocument: if delegation_target: setattr(op, "delegation_target", delegation_target) + context = None + if isinstance(op.context, dict): + context = ExecutionContext( + request_params=op.context.get("request_params", {}) + ) + elif isinstance(op.context, ExecutionContext): + context = op.context if not op.dataset_id: - # For consistency, set the dataset_id using the ExecutionContext.dataset + # For consistency, set the dataset_id using the + # ExecutionContext.dataset # rather than calling load_dataset() on a potentially stale # dataset_name in the request_params - context = None - if isinstance(op.context, dict): - context = ExecutionContext( - request_params=op.context.get("request_params", {}) - ) - elif isinstance(op.context, ExecutionContext): - context = op.context - if context and context.dataset: + try: op.dataset_id = context.dataset._doc.id + except: + # If we can't resolve the dataset_id, it is possible the + # dataset doesn't exist (deleted/being created). However, + # it's also possible that future operators can run + # dataset-less, so don't raise an error here and just log it + # in case we need to debug later. + logger.debug("Could not resolve dataset_id for operation. ") + elif op.dataset_id: + # If the dataset_id is provided, we set it in the request_params + # to ensure that the operation is executed on the correct dataset + context.request_params["dataset_id"] = str(op.dataset_id) + context.request_params["dataset_name"] = context.dataset.name doc = self._collection.insert_one(op.to_pymongo()) op.id = doc.inserted_id diff --git a/tests/unittests/delegated_operators_tests.py b/tests/unittests/delegated_operators_tests.py index 27632775d1..54c6a28182 100644 --- a/tests/unittests/delegated_operators_tests.py +++ b/tests/unittests/delegated_operators_tests.py @@ -31,6 +31,19 @@ from fiftyone.operators.operator import Operator, OperatorConfig +class MockDataset: + def __init__(self, **kwargs): + self.name = kwargs.get("name", "test_dataset") + self._doc = mock.MagicMock() + self._doc.id = kwargs.get("id", ObjectId()) + + def save(self): + pass + + def delete(self): + pass + + class MockOperator(Operator): def __init__(self, success=True, sets_progress=False, **kwargs): self.success = success @@ -257,12 +270,20 @@ def test_list_queued_operations( dataset.delete() dataset2.delete() - def test_set_run_states(self, mock_get_operator, mock_operator_exists): + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) + def test_set_run_states( + self, mock_load_dataset, mock_get_operator, mock_operator_exists + ): + mock_load_dataset.return_value = MockDataset() doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/foo", label=mock_get_operator.return_value.name, delegation_target=f"test_target", - context=ExecutionContext(request_params={"foo": "bar"}), + context=ExecutionContext( + request_params={"foo": "bar", "dataset_id": str(ObjectId())} + ), ) original_updated_at = doc.updated_at @@ -291,13 +312,21 @@ def test_set_run_states(self, mock_get_operator, mock_operator_exists): self.assertIsNotNone(doc.result.error) self.assertNotEqual(doc.updated_at, original_updated_at) - def test_sets_progress(self, mock_get_operator, mock_operator_exists): + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) + def test_sets_progress( + self, mock_load_dataset, mock_get_operator, mock_operator_exists + ): + mock_load_dataset.return_value = MockDataset() mock_get_operator.return_value = MockOperator(sets_progress=True) doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/foo", delegation_target=f"test_target", - context=ExecutionContext(request_params={"foo": "bar"}), + context=ExecutionContext( + request_params={"foo": "bar", "dataset_id": str(ObjectId())} + ), ) self.docs_to_delete.append(doc) @@ -312,12 +341,20 @@ def test_sets_progress(self, mock_get_operator, mock_operator_exists): self.assertEqual(doc.status.label, "halfway there") self.assertIsNotNone(doc.status.updated_at) - def test_full_run_success(self, mock_get_operator, mock_operator_exists): + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) + def test_full_run_success( + self, mock_load_dataset, mock_get_operator, mock_operator_exists + ): + mock_load_dataset.return_value = MockDataset() doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/foo", label=mock_get_operator.return_value.name, delegation_target=f"test_target", - context=ExecutionContext(request_params={"foo": "bar"}), + context=ExecutionContext( + request_params={"foo": "bar", "dataset_id": str(ObjectId())} + ), ) self.docs_to_delete.append(doc) @@ -336,17 +373,22 @@ def test_full_run_success(self, mock_get_operator, mock_operator_exists): self.assertEqual(doc.result.result, {"executed": True}) + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) def test_generator_run_success( - self, mock_get_operator, mock_operator_exists + self, mock_load_dataset, mock_get_operator, mock_operator_exists ): - + mock_load_dataset.return_value = MockDataset() mock_get_operator.return_value = MockGeneratorOperator() doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/generator_op", label=mock_get_operator.return_value.name, delegation_target=f"test_target_generator", - context=ExecutionContext(request_params={"foo": "bar"}), + context=ExecutionContext( + request_params={"foo": "bar", "dataset_id": str(ObjectId())} + ), ) self.docs_to_delete.append(doc) @@ -364,9 +406,13 @@ def test_generator_run_success( self.assertIsNone(doc.result) self.assertIsNone(doc.failed_at) + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) def test_generator_sets_progress( - self, mock_get_operator, mock_operator_exists + self, mock_load_dataset, mock_get_operator, mock_operator_exists ): + mock_load_dataset.return_value = MockDataset() mock_get_operator.return_value = MockGeneratorOperator( sets_progress=True ) @@ -389,9 +435,14 @@ def test_generator_sets_progress( self.assertEqual(doc.status.label, "halfway there") self.assertIsNotNone(doc.status.updated_at) - def test_updates_progress(self, mock_get_operator, mock_operator_exists): + @patch( + "fiftyone.core.odm.utils.load_dataset", + ) + def test_updates_progress( + self, mock_load_dataset, mock_get_operator, mock_operator_exists + ): mock_get_operator.return_value = MockProgressiveOperator() - + mock_load_dataset.return_value = MockDataset() doc = self.svc.queue_operation( operator="@voxelfiftyone/operator/foo", delegation_target=f"test_target", From 6a4d043d987756ba0c70192e9d6f666c2f8861ab Mon Sep 17 00:00:00 2001 From: manivoxel51 Date: Fri, 8 Dec 2023 17:55:02 -0800 Subject: [PATCH 16/59] Adds a toggle to show/hide fields in sample modal that have undefined/null values review comments --- .../core/src/components/Actions/Options.tsx | 31 +++++++++++++++++++ .../core/src/components/Modal/Modal.tsx | 15 +++++++-- .../Sidebar/Entries/PathValueEntry.tsx | 17 ++++++++-- app/packages/state/src/recoil/atoms.ts | 15 +++++++++ 4 files changed, 73 insertions(+), 5 deletions(-) diff --git a/app/packages/core/src/components/Actions/Options.tsx b/app/packages/core/src/components/Actions/Options.tsx index 7a8c533e6b..3a3b6fbb0e 100644 --- a/app/packages/core/src/components/Actions/Options.tsx +++ b/app/packages/core/src/components/Actions/Options.tsx @@ -269,6 +269,36 @@ const Lightning = () => { ); }; +const HideFieldSetting = () => { + const [hideNone, setHideNone] = useRecoilState(fos.hideNoneValuedFields); + const theme = useTheme(); + + return ( + <> + + ({ + text: value, + title: value, + onClick: () => setHideNone(value === "enable" ? true : false), + }))} + /> + + ); +}; + type OptionsProps = { modal: boolean; anchorRef: RefObject; @@ -282,6 +312,7 @@ const Options = ({ modal, anchorRef }: OptionsProps) => { return ( + {modal && } {isNonNestedDynamicGroup && } {isGroup && !isDynamicGroup && } diff --git a/app/packages/core/src/components/Modal/Modal.tsx b/app/packages/core/src/components/Modal/Modal.tsx index 2ac958fc36..72ebf13e29 100644 --- a/app/packages/core/src/components/Modal/Modal.tsx +++ b/app/packages/core/src/components/Modal/Modal.tsx @@ -66,6 +66,7 @@ const SampleModal = () => { : { width: "95%", height: "90%", borderRadius: "3px" }; const isGroup = useRecoilValue(fos.isGroup); const isPcd = useRecoilValue(fos.isPointcloudDataset); + const sampleId = useRecoilValue(fos.currentSampleId); const clearModal = fos.useClearModal(); const { jsonPanel, helpPanel, onNavigate } = usePanels(); @@ -91,6 +92,9 @@ const SampleModal = () => { [eventHandler] ); + const noneValuedPaths = useRecoilValue(fos.noneValuedPaths)?.[sampleId]; + const hideNoneFields = useRecoilValue(fos.hideNoneValuedFields); + const renderEntry = useCallback( ( key: string, @@ -104,13 +108,18 @@ const SampleModal = () => { ) => void ) => { switch (entry.kind) { - case fos.EntryKind.PATH: + case fos.EntryKind.PATH: { const isTag = entry.path === "tags"; const isLabelTag = entry.path === "_label_tags"; const isLabel = labelPaths.includes(entry.path); const isOther = disabled.has(entry.path); const isFieldPrimitive = !isLabelTag && !isLabel && !isOther && !(isTag && mode === "group"); + + if (hideNoneFields && noneValuedPaths?.has(entry?.path)) { + return { children: null }; + } + return { children: ( <> @@ -146,7 +155,7 @@ const SampleModal = () => { ), disabled: isTag || isOther, }; - + } case fos.EntryKind.GROUP: { return { children: ( @@ -180,7 +189,7 @@ const SampleModal = () => { throw new Error("invalid entry"); } }, - [disabled, labelPaths, mode] + [disabled, hideNoneFields, labelPaths, mode, noneValuedPaths] ); useEffect(() => { diff --git a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx index c837bc4171..c5024344a5 100644 --- a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx +++ b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx @@ -11,8 +11,8 @@ import { } from "@fiftyone/utilities"; import { KeyboardArrowDown, KeyboardArrowUp } from "@mui/icons-material"; import { useSpring } from "@react-spring/core"; -import React, { Suspense, useMemo, useState } from "react"; -import { useRecoilValue, useRecoilValueLoadable } from "recoil"; +import React, { Suspense, useEffect, useMemo, useState } from "react"; +import { useRecoilState, useRecoilValue, useRecoilValueLoadable } from "recoil"; import styled from "styled-components"; import LoadingDots from "../../../../../components/src/components/Loading/LoadingDots"; import { prettify } from "../../../utils/generic"; @@ -354,6 +354,19 @@ const Loadable = ({ path }: { path: string }) => { const color = useRecoilValue(fos.pathColor(path)); const timeZone = useRecoilValue(fos.timeZone); const formatted = format({ ftype, value, timeZone }); + const [noneValuedPaths, setNoneValued] = useRecoilState(fos.noneValuedPaths); + const sampleId = useRecoilValue(fos.currentSampleId); + const noneValued = noneValuedPaths?.[sampleId]; + + useEffect(() => { + if (none && path && !noneValued?.has(path) && sampleId) { + setNoneValued({ + [sampleId]: noneValued + ? new Set([...noneValued]).add(path) + : new Set([]), + }); + } + }, [noneValued, none, path, setNoneValued, sampleId]); return (
diff --git a/app/packages/state/src/recoil/atoms.ts b/app/packages/state/src/recoil/atoms.ts index c862704549..aa0ce54feb 100644 --- a/app/packages/state/src/recoil/atoms.ts +++ b/app/packages/state/src/recoil/atoms.ts @@ -346,3 +346,18 @@ export const isSidebarFilterMode = atom({ key: "isSidebarFilterMode", default: true, }); + +export const hideNoneValuedFields = atom({ + key: "hideNoneValuedFields", + default: false, + effects: [ + getBrowserStorageEffectForKey("hideNoneValuedFields", { + valueClass: "boolean", + }), + ], +}); + +export const noneValuedPaths = atom>>({ + key: "noneValuedPaths", + default: {}, +}); From 5fce10920920b9ea448fa0cc3d72acc72fe3533e Mon Sep 17 00:00:00 2001 From: manivoxel51 Date: Thu, 7 Dec 2023 13:00:04 -0800 Subject: [PATCH 17/59] Fixes user selection of Scalar fields so copy/paste of feilds and values are easier --- .../core/src/components/Sidebar/Entries/PathValueEntry.tsx | 2 ++ 1 file changed, 2 insertions(+) diff --git a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx index c837bc4171..14abe0094a 100644 --- a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx +++ b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx @@ -28,6 +28,8 @@ const ScalarDiv = styled.div` padding: 0 3px; overflow: hidden; text-overflow: ellipsis; + user-select: all; + white-space: nowrap; } `; From f16c1f6b33417bb5799d328a32aa27fe0a973891 Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Fri, 8 Dec 2023 09:26:54 -0600 Subject: [PATCH 18/59] list item selection --- .../components/Sidebar/Entries/PathValueEntry.tsx | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx index 14abe0094a..a2784a541a 100644 --- a/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx +++ b/app/packages/core/src/components/Sidebar/Entries/PathValueEntry.tsx @@ -23,7 +23,6 @@ import { makePseudoField } from "./utils"; const ScalarDiv = styled.div` & > div { - user-select: text; font-weight: bold; padding: 0 3px; overflow: hidden; @@ -115,7 +114,7 @@ const ScalarValueEntry = ({ ); }; -const ListContainer = styled.div` +const ListContainer = styled(ScalarDiv)` background: ${({ theme }) => theme.background.level2}; border: 1px solid var(--fo-palette-divider); border-radius: 2px; @@ -230,7 +229,9 @@ const ListLoadable = ({ path }: { path: string }) => { return ( {values.map((v, i) => ( -
{v}
+
+ {v} +
))} {values.length == 0 && <>No results} @@ -358,7 +359,11 @@ const Loadable = ({ path }: { path: string }) => { const formatted = format({ ftype, value, timeZone }); return ( -
+
{none ? "None" : formatted}
); From 0a840e959b9c9e0df3fcfc3a233ed8db6fe53290 Mon Sep 17 00:00:00 2001 From: Lanny W Date: Mon, 18 Dec 2023 16:06:57 -0600 Subject: [PATCH 19/59] bugfix: similarity search only works on default_group_slice (#3912) * passing in active group slice as a parameter for similarity sort run * Better similarity search UX experience for grouped dataset when not all slices have similarity computed * attempt to reset similarity parameters * update session slice during page change on similarity reset * reset selection --------- Co-authored-by: manivoxel51 Co-authored-by: Benjamin Kane --- .../src/components/Actions/similar/utils.ts | 2 + app/packages/state/src/recoil/groups.ts | 38 +++++++++++++++++-- app/packages/state/src/recoil/selectors.ts | 23 +++++++++-- app/packages/state/src/session.ts | 4 ++ fiftyone/server/routes/sort.py | 7 ++++ 5 files changed, 67 insertions(+), 7 deletions(-) diff --git a/app/packages/core/src/components/Actions/similar/utils.ts b/app/packages/core/src/components/Actions/similar/utils.ts index 99b4c9bb46..39f97ce0d6 100644 --- a/app/packages/core/src/components/Actions/similar/utils.ts +++ b/app/packages/core/src/components/Actions/similar/utils.ts @@ -72,6 +72,7 @@ export const useSortBySimilarity = (close) => { const view = await snapshot.getPromise(fos.view); const subscription = await snapshot.getPromise(fos.stateSubscription); + const slice = await snapshot.getPromise(fos.sessionGroupSlice); const { query, ...commonParams } = parameters; @@ -95,6 +96,7 @@ export const useSortBySimilarity = (close) => { subscription, filters, extended: toSnakeCase(combinedParameters), + slice, }); set(fos.similarityParameters, combinedParameters); close(); diff --git a/app/packages/state/src/recoil/groups.ts b/app/packages/state/src/recoil/groups.ts index 1186b7caf1..f5f0caf631 100644 --- a/app/packages/state/src/recoil/groups.ts +++ b/app/packages/state/src/recoil/groups.ts @@ -15,11 +15,22 @@ import { } from "@fiftyone/utilities"; import { get as getPath } from "lodash"; import { VariablesOf } from "react-relay"; -import { atom, atomFamily, selector, selectorFamily } from "recoil"; +import { + DefaultValue, + atom, + atomFamily, + selector, + selectorFamily, +} from "recoil"; import { graphQLSelectorFamily } from "recoil-relay"; -import { sessionAtom } from "../session"; +import { getSessionRef, sessionAtom } from "../session"; import type { ResponseFrom } from "../utils"; -import { mediaType } from "./atoms"; +import { + mediaType, + selectedLabels, + selectedSamples, + similarityParameters, +} from "./atoms"; import { getBrowserStorageEffectForKey } from "./customEffects"; import { dataset } from "./dataset"; import { ModalSample, modalLooker, modalSample } from "./modal"; @@ -116,7 +127,26 @@ export const groupSlice = selector({ get: ({ get }) => { return get(isGroup) && get(hasGroupSlices) ? get(sessionGroupSlice) : null; }, - set: ({ set }, slice) => set(sessionGroupSlice, slice), + set: ({ get, reset, set }, slice) => { + const defaultSlice = get(defaultGroupSlice); + if (get(similarityParameters)) { + // avoid this pattern + const unsubscribe = foq.subscribeBefore(() => { + const session = getSessionRef(); + session.sessionGroupSlice = + slice instanceof DefaultValue ? defaultSlice : slice; + session.selectedSamples = new Set(); + session.selectedLabels = []; + + unsubscribe(); + }); + reset(similarityParameters); + } else { + set(sessionGroupSlice, slice); + set(selectedLabels, []); + set(selectedSamples, new Set()); + } + }, }); export const defaultGroupSlice = graphQLSyncFragmentAtom< diff --git a/app/packages/state/src/recoil/selectors.ts b/app/packages/state/src/recoil/selectors.ts index 98df083413..84aacbef52 100644 --- a/app/packages/state/src/recoil/selectors.ts +++ b/app/packages/state/src/recoil/selectors.ts @@ -8,14 +8,14 @@ import { datasetFragment$key, graphQLSyncFragmentAtom, } from "@fiftyone/relay"; -import { fieldVisibilityStage } from "@fiftyone/state"; +import { currentSlice, fieldVisibilityStage, isGroup } from "@fiftyone/state"; import { toSnakeCase } from "@fiftyone/utilities"; import { DefaultValue, atomFamily, selector, selectorFamily } from "recoil"; import { v4 as uuid } from "uuid"; import * as atoms from "./atoms"; import { config } from "./config"; import { dataset as datasetAtom } from "./dataset"; -import { currentModalSample, modalSample } from "./modal"; +import { currentModalSample, isModalActive, modalSample } from "./modal"; import { pathFilter } from "./pathFilters"; import { State } from "./types"; import { isPatchesView } from "./view"; @@ -384,7 +384,24 @@ export const similarityMethods = selector<{ }>({ key: "similarityMethods", get: ({ get }) => { - const methods = get(datasetAtom)?.brainMethods || []; + let methods = get(datasetAtom)?.brainMethods || []; + const isGroupDataset = get(isGroup); + const activeSlice = get(currentSlice(get(isModalActive))); + + if (isGroupDataset && activeSlice) { + methods = methods.filter(({ viewStages }) => { + return viewStages.some((vs) => { + const { _cls, kwargs } = JSON.parse(vs); + if (_cls === "fiftyone.core.stages.SelectGroupSlices") { + const sliceValue = kwargs.filter( + (kwarg: string[]) => kwarg[0] === "slices" + )?.[0]?.[1]; + if (sliceValue && sliceValue !== activeSlice) return false; + } + return true; + }); + }); + } return methods .filter( diff --git a/app/packages/state/src/session.ts b/app/packages/state/src/session.ts index 108b020ba1..2e7638fceb 100644 --- a/app/packages/state/src/session.ts +++ b/app/packages/state/src/session.ts @@ -84,6 +84,10 @@ export const useSessionRef = () => { return sessionRef; }; +export const getSessionRef = () => { + return sessionRef; +}; + export const useSessionSetter = () => { return useCallback((key: K, value: Session[K]) => { const setter = setters[key]; diff --git a/fiftyone/server/routes/sort.py b/fiftyone/server/routes/sort.py index adba1038d7..4ae97fd2ad 100644 --- a/fiftyone/server/routes/sort.py +++ b/fiftyone/server/routes/sort.py @@ -17,6 +17,7 @@ import fiftyone.server.events as fose from fiftyone.server.query import serialize_dataset import fiftyone.server.view as fosv +from fiftyone.server.filters import GroupElementFilter, SampleFilter class Sort(HTTPEndpoint): @@ -26,6 +27,7 @@ async def post(self, request: Request, data: dict): filters = data.get("filters", {}) stages = data.get("view", None) subscription = data.get("subscription", None) + slice = data.get("slice", None) await run_sync_task( lambda: fosv.get_view( @@ -35,6 +37,11 @@ async def post(self, request: Request, data: dict): extended_stages={ "fiftyone.core.stages.SortBySimilarity": data["extended"] }, + sample_filter=SampleFilter( + group=GroupElementFilter(slice=slice) + ) + if slice is not None + else None, ) ) From 66ed798b51fdcf6faefc515ef99fbed6e60fa9ea Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Mon, 18 Dec 2023 18:25:08 -0600 Subject: [PATCH 20/59] fix lightning reset (#3933) --- app/packages/state/src/recoil/lightning.ts | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/app/packages/state/src/recoil/lightning.ts b/app/packages/state/src/recoil/lightning.ts index 0213fa0a1e..ba9efa7ce5 100644 --- a/app/packages/state/src/recoil/lightning.ts +++ b/app/packages/state/src/recoil/lightning.ts @@ -5,7 +5,7 @@ import { STRING_FIELD, VALID_PRIMITIVE_TYPES, } from "@fiftyone/utilities"; -import { atomFamily, selector, selectorFamily } from "recoil"; +import { DefaultValue, atomFamily, selector, selectorFamily } from "recoil"; import { graphQLSelectorFamily } from "recoil-relay"; import { ResponseFrom } from "../utils"; import { config } from "./config"; @@ -219,7 +219,10 @@ export const lightningThreshold = selector({ return Number(setting); }, set: ({ get, reset, set }, value) => { - set(lightningThresholdAtom(get(datasetId)), String(value)); + set( + lightningThresholdAtom(get(datasetId)), + value instanceof DefaultValue ? value : String(value) + ); reset(granularSidebarExpandedStore); reset(sidebarExpandedStore(false)); reset(filters); From 2b4e41c614be6ef1c3cca80fcd4ca9182cad7ff6 Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Tue, 19 Dec 2023 09:18:00 -0600 Subject: [PATCH 21/59] Update selected sample operators (#3932) * updates selection operators * lint --- .../operators/src/built-in-operators.ts | 20 ++----------------- 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/app/packages/operators/src/built-in-operators.ts b/app/packages/operators/src/built-in-operators.ts index f8905f4f39..b5a603b2a7 100644 --- a/app/packages/operators/src/built-in-operators.ts +++ b/app/packages/operators/src/built-in-operators.ts @@ -58,14 +58,7 @@ class ClearSelectedSamples extends Operator { label: "Clear selected samples", }); } - useHooks() { - return { - setSelected: fos.useSetSelected(), - }; - } - async execute({ hooks, state }: ExecutionContext) { - // needs to mutate the server / session - hooks.setSelected([]); + async execute({ state }: ExecutionContext) { state.reset(fos.selectedSamples); } } @@ -364,7 +357,6 @@ class ClearAllStages extends Operator { } useHooks(): {} { return { - setSelected: fos.useSetSelected(), resetExtended: fos.useResetExtendedSelection(), }; } @@ -373,7 +365,6 @@ class ClearAllStages extends Operator { state.reset(fos.filters); hooks.resetExtended(); state.reset(fos.selectedSamples); - hooks.setSelected([]); } } @@ -399,12 +390,7 @@ class ShowSelectedSamples extends Operator { label: "Show selected samples", }); } - useHooks(): {} { - return { - setSelected: fos.useSetSelected(), - }; - } - async execute({ hooks, state }: ExecutionContext) { + async execute({ state }: ExecutionContext) { const selectedSamples = await state.snapshot.getPromise( fos.selectedSamples ); @@ -425,7 +411,6 @@ class ConvertExtendedSelectionToSelectedSamples extends Operator { } useHooks(): {} { return { - setSelected: fos.useSetSelected(), resetExtended: fos.useResetExtendedSelection(), }; } @@ -435,7 +420,6 @@ class ConvertExtendedSelectionToSelectedSamples extends Operator { ); state.set(fos.selectedSamples, new Set(extendedSelection.selection)); state.set(fos.extendedSelection, { selection: null }); - hooks.setSelected(extendedSelection.selection); hooks.resetExtended(); } } From a20e03bc965bb55a7ea55ee1271f3eac765bb2f9 Mon Sep 17 00:00:00 2001 From: brimoor Date: Tue, 19 Dec 2023 11:17:18 -0500 Subject: [PATCH 22/59] optimize docs reloads when performing bulk writes --- fiftyone/core/collections.py | 36 ++++++++++++++++++++++++------------ fiftyone/core/dataset.py | 12 +++++++----- fiftyone/core/singletons.py | 36 +++++++++++++++++++++++------------- 3 files changed, 54 insertions(+), 30 deletions(-) diff --git a/fiftyone/core/collections.py b/fiftyone/core/collections.py index dfb06c210a..8eee92aab4 100644 --- a/fiftyone/core/collections.py +++ b/fiftyone/core/collections.py @@ -1537,11 +1537,14 @@ def untag_samples(self, tags): view._edit_sample_tags(update) def _edit_sample_tags(self, update): + ids = [] ops = [] - for ids in fou.iter_batches(self.values("_id"), 100000): - ops.append(UpdateMany({"_id": {"$in": ids}}, update)) + for _ids in fou.iter_batches(self.values("_id"), 100000): + ids.extend(_ids) + ops.append(UpdateMany({"_id": {"$in": _ids}}, update)) - self._dataset._bulk_write(ops) + if ops: + self._dataset._bulk_write(ops, ids=ids) def count_sample_tags(self): """Counts the occurrences of sample tags in this collection. @@ -1669,7 +1672,7 @@ def _edit_label_tags( ) if ops: - self._dataset._bulk_write(ops, frames=is_frame_field) + self._dataset._bulk_write(ops, ids=ids, frames=is_frame_field) return ids, label_ids @@ -2574,7 +2577,8 @@ def _set_doc_values( ops.append(UpdateOne({"_id": _id}, {"$set": {field_name: value}})) - self._dataset._bulk_write(ops, frames=frames) + if ops: + self._dataset._bulk_write(ops, ids=ids, frames=frames) def _set_list_values_by_id( self, @@ -2628,7 +2632,8 @@ def _set_list_values_by_id( ) ) - self._dataset._bulk_write(ops, frames=frames) + if ops: + self._dataset._bulk_write(ops, ids=ids, frames=frames) def _set_label_list_values( self, @@ -2645,6 +2650,7 @@ def _set_label_list_values( leaf = field_name[len(root) + 1 :] path = root + ".$[label]." + leaf + ids = [] ops = [] for label_id, value in values.items(): if value is None and skip_none: @@ -2655,16 +2661,18 @@ def _set_label_list_values( field_name, field, value, validate=validate ) + _id = id_map[label_id] + ids.append(_id) ops.append( UpdateOne( - {"_id": id_map[label_id]}, + {"_id": _id}, {"$set": {path: value}}, array_filters=[{"label._id": ObjectId(label_id)}], ) ) if ops: - self._dataset._bulk_write(ops, frames=frames) + self._dataset._bulk_write(ops, ids=ids, frames=frames) def _set_labels(self, field_name, sample_ids, label_docs): if self._is_group_field(field_name): @@ -2676,6 +2684,7 @@ def _set_labels(self, field_name, sample_ids, label_docs): root, is_list_field = self._get_label_field_root(field_name) field_name, is_frame_field = self._handle_frame_field(field_name) + ids = [] ops = [] if is_list_field: elem_id = root + "._id" @@ -2691,6 +2700,7 @@ def _set_labels(self, field_name, sample_ids, label_docs): if not isinstance(_docs, (list, tuple)): _docs = [_docs] + ids.append(_id) for doc in _docs: ops.append( UpdateOne( @@ -2705,6 +2715,7 @@ def _set_labels(self, field_name, sample_ids, label_docs): if etau.is_str(_id): _id = ObjectId(_id) + ids.append(_id) ops.append( UpdateOne( {"_id": _id, elem_id: doc["_id"]}, @@ -2712,7 +2723,8 @@ def _set_labels(self, field_name, sample_ids, label_docs): ) ) - self._dataset._bulk_write(ops, frames=is_frame_field) + if ops: + self._dataset._bulk_write(ops, ids=ids, frames=is_frame_field) def _delete_labels(self, ids, fields=None): self._dataset.delete_labels(ids=ids, fields=fields) @@ -10562,9 +10574,9 @@ def _parse_frame_values_dicts(sample_collection, sample_ids, values): # Insert frame documents for new frame numbers if dicts: - sample_collection._dataset._bulk_write( - [InsertOne(d) for d in dicts], frames=True - ) # adds `_id` to each dict + # adds `_id` to each dict + ops = [InsertOne(d) for d in dicts] + sample_collection._dataset._bulk_write(ops, ids=[], frames=True) for d in dicts: id_map[(str(d["_sample_id"]), d["frame_number"])] = d["_id"] diff --git a/fiftyone/core/dataset.py b/fiftyone/core/dataset.py index fab886c03d..6f9cdbf8ff 100644 --- a/fiftyone/core/dataset.py +++ b/fiftyone/core/dataset.py @@ -2642,7 +2642,7 @@ def _make_dict(self, sample, include_id=False): return d - def _bulk_write(self, ops, frames=False, ordered=False): + def _bulk_write(self, ops, ids=None, frames=False, ordered=False): if frames: coll = self._frame_collection else: @@ -2651,9 +2651,11 @@ def _bulk_write(self, ops, frames=False, ordered=False): foo.bulk_write(ops, coll, ordered=ordered) if frames: - fofr.Frame._reload_docs(self._frame_collection_name) + fofr.Frame._reload_docs(self._frame_collection_name, frame_ids=ids) else: - fos.Sample._reload_docs(self._sample_collection_name) + fos.Sample._reload_docs( + self._sample_collection_name, sample_ids=ids + ) def _merge_doc( self, @@ -7545,7 +7547,7 @@ def _add_collection_with_new_ids( ) ops.append(op) - dataset._bulk_write(ops) + dataset._bulk_write(ops, ids=[]) if not contains_videos: return new_ids @@ -7586,7 +7588,7 @@ def _add_collection_with_new_ids( for old_id, new_id in zip(old_ids, new_ids) ] - dataset._bulk_write(ops, frames=True) + dataset._bulk_write(ops, ids=[], frames=True) return new_ids diff --git a/fiftyone/core/singletons.py b/fiftyone/core/singletons.py index fb54621544..78b7d8f1da 100644 --- a/fiftyone/core/singletons.py +++ b/fiftyone/core/singletons.py @@ -158,7 +158,7 @@ def _reload_doc(cls, collection_name, sample_id, hard=False): if collection_name not in cls._instances: return - sample = cls._instances[collection_name].get(sample_id, None) + sample = cls._instances[collection_name].get(str(sample_id), None) if sample is not None: sample.reload(hard=hard) @@ -174,7 +174,7 @@ def _reload_docs(cls, collection_name, sample_ids=None, hard=False): samples = cls._instances[collection_name] if sample_ids is not None: - sample_ids = set(sample_ids) + sample_ids = set(str(_id) for _id in sample_ids) for sample in samples.values(): if sample.id in sample_ids: sample.reload(hard=hard) @@ -194,7 +194,7 @@ def _sync_docs(cls, collection_name, sample_ids, hard=False): samples = cls._instances[collection_name] - sample_ids = set(sample_ids) + sample_ids = set(str(_id) for _id in sample_ids) reset_ids = set() for sample in samples.values(): if sample.id in sample_ids: @@ -220,7 +220,7 @@ def _reset_docs(cls, collection_name, sample_ids=None): if sample_ids is not None: samples = cls._instances[collection_name] for sample_id in sample_ids: - sample = samples.pop(sample_id, None) + sample = samples.pop(str(sample_id), None) if sample is not None: sample._reset_backing_doc() else: @@ -270,7 +270,9 @@ def _get_instances(cls, collection_name, sample_id): """Returns a frame number -> Frame dict containing all in-memory frame instances for the specified sample. """ - return dict(cls._instances.get(collection_name, {}).get(sample_id, {})) + return dict( + cls._instances.get(collection_name, {}).get(str(sample_id), {}) + ) def _rename_fields(cls, collection_name, field_names, new_field_names): """Renames the field on all in-memory frames in the collection.""" @@ -312,7 +314,7 @@ def _reload_doc(cls, collection_name, sample_id, frame_number, hard=False): if collection_name not in cls._instances: return - frames = cls._instances[collection_name].get(sample_id, {}) + frames = cls._instances[collection_name].get(str(sample_id), {}) frame = frames.get(frame_number, None) if frame is not None: frame.reload(hard=hard) @@ -330,7 +332,7 @@ def _sync_docs_for_sample( return samples = cls._instances[collection_name] - frames = samples.get(sample_id, {}) + frames = samples.get(str(sample_id), {}) if not frames: return @@ -365,7 +367,7 @@ def _sync_docs(cls, collection_name, sample_ids, hard=False): samples = cls._instances[collection_name] - sample_ids = set(sample_ids) + sample_ids = set(str(_id) for _id in sample_ids) reset_ids = set() for sample_id, frames in samples.items(): if sample_id in sample_ids: @@ -379,7 +381,9 @@ def _sync_docs(cls, collection_name, sample_ids, hard=False): for sample_id in reset_ids: frames = samples.pop(sample_id) - def _reload_docs(cls, collection_name, sample_ids=None, hard=False): + def _reload_docs( + cls, collection_name, sample_ids=None, frame_ids=None, hard=False + ): """Reloads the backing documents for in-memory frames in the collection. @@ -392,11 +396,17 @@ def _reload_docs(cls, collection_name, sample_ids=None, hard=False): samples = cls._instances[collection_name] if sample_ids is not None: - sample_ids = set(sample_ids) + sample_ids = set(str(_id) for _id in sample_ids) for sample_id in sample_ids: frames = samples.get(sample_id, {}) for frame in frames.values(): frame.reload(hard=hard) + elif frame_ids is not None: + frame_ids = set(str(_id) for _id in frame_ids) + for frames in samples.values(): + for frame in frames.values(): + if frame.id in frame_ids: + frame.reload(hard=hard) else: for frames in samples.values(): for frame in frames.values(): @@ -414,7 +424,7 @@ def _reset_docs(cls, collection_name, sample_ids=None): if sample_ids is not None: samples = cls._instances[collection_name] for sample_id in sample_ids: - frames = samples.pop(sample_id, {}) + frames = samples.pop(str(sample_id), {}) for frame in frames.values(): frame._reset_backing_doc() else: @@ -436,7 +446,7 @@ def _reset_docs_for_sample( return samples = cls._instances[collection_name] - frames = samples.get(sample_id, {}) + frames = samples.get(str(sample_id), {}) if not frames: return @@ -473,7 +483,7 @@ def _reset_docs_by_frame_id(cls, collection_name, frame_ids, keep=False): samples = cls._instances[collection_name] - frame_ids = set(frame_ids) + frame_ids = set(str(_id) for _id in frame_ids) reset = [] if keep: From 66bdfad4c23ec8f47ed187f96064b9c334723aa7 Mon Sep 17 00:00:00 2001 From: Sashank Aryal <66688606+sashankaryal@users.noreply.github.com> Date: Tue, 19 Dec 2023 18:14:54 -0600 Subject: [PATCH 23/59] add support for frames-as-videos in nested groups (#3935) * more range check validation * remove log * add speed controller * reset popups before play/pause * show 'render as video' option when it is ordered dynamic group * remove isImaVidAvailable and replace with isOrderedDynamicGroup * rename nonNestedDynamicGroupsViewMode to nonNestedDynamicGroupsViewMode * pass slice info if nested group * stringify groupByFieldValue * rename nonNestedDynamicGroupsViewMode to nonNestedDynamicGroupsViewMode in dynamicgroup * set default mode to pagination * differentiate groupMedia3dVisibleSetting and groupMediaIsCarouselVisible * sync currentframenumber with dynamicgroupindex * cleanup DynamicGroupsFlashlightWrapper * fix visibility settings * implement sync between pagination and video player * partition by groupby value and not 'postersample' * fix bug where aggregations query were misfiring * update dep versions for e2e * add check-flaky directions in readme * fix check-flaky script * make image-gen less verbose * fix imavid key bug * fix keyboard navigation * fix e2e - use 'pagination' as default mode * fix import * wait for carousel to load in sparse test * fix regressions * render 3d below if main not visible * linting --------- Co-authored-by: Benjamin Kane --- .../Actions/GroupMediaVisibilityContainer.tsx | 21 +- .../core/src/components/Actions/Options.tsx | 47 ++-- .../core/src/components/Actions/Tagger.tsx | 2 +- .../Group/DynamicGroup/NestedGroup/index.tsx | 18 +- .../DynamicGroup/NonNestedGroup/index.tsx | 10 +- .../DynamicGroupsFlashlightWrapper.tsx | 73 +------ .../Modal/Group/DynamicGroup/index.tsx | 20 +- .../DynamicGroup/useDynamicGroupSamples.ts | 8 +- .../src/components/Modal/Group/GroupView.tsx | 19 +- .../core/src/components/Modal/Modal.tsx | 46 +++- .../core/src/components/Modal/Sample3d.tsx | 1 - .../core/src/components/Modal/hooks.ts | 2 +- .../core/src/components/Sidebar/Sidebar.tsx | 2 +- .../looker/src/elements/imavid/index.ts | 6 + .../looker/src/elements/imavid/seek-bar.ts | 6 + .../looker/src/lookers/imavid/controller.ts | 23 +- .../looker/src/lookers/imavid/index.ts | 7 +- .../looker/src/lookers/imavid/store.ts | 12 +- .../looker/src/lookers/imavid/types.ts | 2 +- app/packages/looker/src/state.ts | 1 + .../state/src/hooks/useCreateLooker.ts | 45 ++-- .../state/src/hooks/useExpandSample.ts | 10 +- app/packages/state/src/recoil/groups.ts | 56 +++-- app/packages/state/src/recoil/modal.ts | 16 +- app/packages/state/src/recoil/options.ts | 10 +- e2e-pw/README.md | 13 ++ e2e-pw/package.json | 10 +- e2e-pw/scripts/check-flaky.sh | 9 +- e2e-pw/src/oss/poms/modal/group-actions.ts | 1 + e2e-pw/src/oss/poms/modal/video-controls.ts | 20 +- .../oss/specs/groups/dynamic-groups.spec.ts | 11 +- e2e-pw/src/oss/specs/groups/ima-vid.spec.ts | 1 + .../specs/groups/sparse-groups-scenes.spec.ts | 1 + .../specs/regression-tests/grid-page.spec.ts | 1 + .../group-video/group-video-label.spec.ts | 2 +- e2e-pw/src/shared/media-factory/image.ts | 15 +- e2e-pw/yarn.lock | 204 +++++++++--------- 37 files changed, 448 insertions(+), 303 deletions(-) diff --git a/app/packages/core/src/components/Actions/GroupMediaVisibilityContainer.tsx b/app/packages/core/src/components/Actions/GroupMediaVisibilityContainer.tsx index 85e11d10d3..544c726e83 100644 --- a/app/packages/core/src/components/Actions/GroupMediaVisibilityContainer.tsx +++ b/app/packages/core/src/components/Actions/GroupMediaVisibilityContainer.tsx @@ -38,8 +38,17 @@ const GroupMediaVisibilityPopout = ({ const [isMainVisible, setIsMainVisible] = useRecoilState( fos.groupMediaIsMainVisibleSetting ); + const isNestedDynamicGroup = useRecoilValue(fos.isNestedDynamicGroup); + const shouldRenderImaVid = useRecoilValue(fos.shouldRenderImaVidLooker); + const dynamicGroupsViewMode = useRecoilValue(fos.dynamicGroupsViewMode); + const hasGroupSlices = useRecoilValue(fos.hasGroupSlices); + const isSequentialAccessAllowed = - useRecoilValue(fos.nonNestedDynamicGroupsViewMode) === "carousel"; + isNestedDynamicGroup || + dynamicGroupsViewMode === "carousel" || + hasGroupSlices; + + const isImavidInNestedGroup = isNestedDynamicGroup && shouldRenderImaVid; const checkboxes = useMemo(() => { const toReturn: React.ReactNode[] = []; @@ -50,7 +59,9 @@ const GroupMediaVisibilityPopout = ({ key="checkbox-3d-viewer" name={"3D Viewer"} value={isSlotVisible} - muted={!isMainVisible && !isCarouselVisible} + muted={ + isImavidInNestedGroup || (!isMainVisible && !isCarouselVisible) + } setValue={(value) => setIsSlotVisible(value)} /> ); @@ -62,7 +73,10 @@ const GroupMediaVisibilityPopout = ({ key="checkbox-carousel" name={"Carousel"} value={isCarouselVisible} - muted={!(isSlotVisible && pointCloudSliceExists) && !isMainVisible} + muted={ + isImavidInNestedGroup || + (!(isSlotVisible && pointCloudSliceExists) && !isMainVisible) + } setValue={(value) => setIsCarouselVisible(value)} /> ); @@ -74,6 +88,7 @@ const GroupMediaVisibilityPopout = ({ name={"Viewer"} value={isMainVisible} muted={ + isImavidInNestedGroup || toReturn.length === 0 || (!(isSlotVisible && pointCloudSliceExists) && !isCarouselVisible) } diff --git a/app/packages/core/src/components/Actions/Options.tsx b/app/packages/core/src/components/Actions/Options.tsx index 3a3b6fbb0e..513919a1eb 100644 --- a/app/packages/core/src/components/Actions/Options.tsx +++ b/app/packages/core/src/components/Actions/Options.tsx @@ -82,7 +82,7 @@ const MediaFields = ({ modal }) => { ); const mediaFields = useRecoilValue(fos.mediaFields); - if (mediaFields.length <= 1) return null; + if (!mediaFields || mediaFields?.length <= 1) return null; return ( <> @@ -148,16 +148,19 @@ const SidebarMode = () => { }; const DynamicGroupsViewMode = ({ modal }: { modal: boolean }) => { - const [mode, setMode] = useRecoilState(fos.nonNestedDynamicGroupsViewMode); - const isImaVidLookerAvailable = useRecoilValue(fos.isImaVidLookerAvailable); + const isOrderedDynamicGroup = useRecoilValue(fos.isOrderedDynamicGroup); + const hasGroupSlices = useRecoilValue(fos.hasGroupSlices); + + const [mode, setMode] = useRecoilState(fos.dynamicGroupsViewMode); + const setIsCarouselVisible = useSetRecoilState( + fos.groupMediaIsCarouselVisibleSetting + ); + const setIsMainVisible = useSetRecoilState( + fos.groupMediaIsMainVisibleSetting + ); const tabOptions = useMemo(() => { const options = [ - { - text: "carousel", - title: "Sequential Access", - onClick: () => setMode("carousel"), - }, { text: "pagination", title: "Random Access", @@ -165,23 +168,36 @@ const DynamicGroupsViewMode = ({ modal }: { modal: boolean }) => { }, ]; - if (isImaVidLookerAvailable) { + if (!hasGroupSlices) { + options.push({ + text: "carousel", + title: "Sequential Access", + onClick: () => { + setMode("carousel"); + setIsCarouselVisible(true); + }, + }); + } + + if (isOrderedDynamicGroup) { options.push({ text: "video", title: "Video", - onClick: () => setMode("video"), + onClick: () => { + setMode("video"); + setIsMainVisible(true); + }, }); } return options; - }, [isImaVidLookerAvailable]); + }, [isOrderedDynamicGroup, hasGroupSlices]); return ( <> Dynamic Groups Navigation - {modal ? ( - - ) : ( + {modal && } + {isOrderedDynamicGroup && !modal && ( { const isGroup = useRecoilValue(fos.isGroup); const isDynamicGroup = useRecoilValue(fos.isDynamicGroup); - const isNonNestedDynamicGroup = useRecoilValue(fos.isNonNestedDynamicGroup); const view = useRecoilValue(fos.view); return ( {modal && } - {isNonNestedDynamicGroup && } + {isDynamicGroup && } {isGroup && !isDynamicGroup && } diff --git a/app/packages/core/src/components/Actions/Tagger.tsx b/app/packages/core/src/components/Actions/Tagger.tsx index 860cfdb961..9561065064 100644 --- a/app/packages/core/src/components/Actions/Tagger.tsx +++ b/app/packages/core/src/components/Actions/Tagger.tsx @@ -337,7 +337,7 @@ const useTagCallback = ( fos.isNonNestedDynamicGroup ); const isImaVidLookerActive = await snapshot.getPromise( - fos.isImaVidLookerAvailable + fos.isOrderedDynamicGroup ); const slices = await snapshot.getPromise(fos.currentSlices(modal)); diff --git a/app/packages/core/src/components/Modal/Group/DynamicGroup/NestedGroup/index.tsx b/app/packages/core/src/components/Modal/Group/DynamicGroup/NestedGroup/index.tsx index bf1ce2769f..91cd523f88 100644 --- a/app/packages/core/src/components/Modal/Group/DynamicGroup/NestedGroup/index.tsx +++ b/app/packages/core/src/components/Modal/Group/DynamicGroup/NestedGroup/index.tsx @@ -1,6 +1,8 @@ import * as foq from "@fiftyone/relay"; +import * as fos from "@fiftyone/state"; import React, { Suspense } from "react"; import { PreloadedQuery } from "react-relay"; +import { useRecoilValue } from "recoil"; import { GroupSuspense } from "../../GroupSuspense"; import { GroupView } from "../../GroupView"; import { GroupElementsLinkBar } from "../pagination"; @@ -8,16 +10,24 @@ import { GroupElementsLinkBar } from "../pagination"; export const NestedGroup = ({ queryRef, }: { - queryRef: PreloadedQuery; + queryRef?: PreloadedQuery | null; }) => { + const shouldRenderImaVid = useRecoilValue(fos.shouldRenderImaVidLooker); + + if (!queryRef && !shouldRenderImaVid) { + throw new Error("no queryRef provided"); + } + return ( <> - - - + {!shouldRenderImaVid && queryRef && ( + + + + )} ); }; diff --git a/app/packages/core/src/components/Modal/Group/DynamicGroup/NonNestedGroup/index.tsx b/app/packages/core/src/components/Modal/Group/DynamicGroup/NonNestedGroup/index.tsx index 2054169b71..1076cba869 100644 --- a/app/packages/core/src/components/Modal/Group/DynamicGroup/NonNestedGroup/index.tsx +++ b/app/packages/core/src/components/Modal/Group/DynamicGroup/NonNestedGroup/index.tsx @@ -29,7 +29,7 @@ const ElementsContainer = styled.div` export const NonNestedDynamicGroup = ({ queryRef, }: { - queryRef: PreloadedQuery; + queryRef: PreloadedQuery | null; }) => { const { lookerRefCallback } = useGroupContext(); const lookerRef = useRef(); @@ -38,7 +38,7 @@ export const NonNestedDynamicGroup = ({ const [isBigLookerVisible, setIsBigLookerVisible] = useRecoilState( fos.groupMediaIsMainVisibleSetting ); - const viewMode = useRecoilValue(fos.nonNestedDynamicGroupsViewMode); + const viewMode = useRecoilValue(fos.dynamicGroupsViewMode); const isCarouselVisible = useRecoilValue( fos.groupMediaIsCarouselVisibleSetting ); @@ -54,6 +54,10 @@ export const NonNestedDynamicGroup = ({ return null; } + if (viewMode !== "video" && !queryRef) { + throw new Error("no queryRef provided"); + } + return ( {/* weird conditional rendering of the bar because lookerControls messes up positioning of the bar in firefox in inexplicable ways */} @@ -81,7 +85,7 @@ export const NonNestedDynamicGroup = ({ )} - {viewMode === "pagination" && ( + {viewMode === "pagination" && queryRef && ( )} diff --git a/app/packages/core/src/components/Modal/Group/DynamicGroup/carousel/DynamicGroupsFlashlightWrapper.tsx b/app/packages/core/src/components/Modal/Group/DynamicGroup/carousel/DynamicGroupsFlashlightWrapper.tsx index 383f7f9209..1b6cfa2c91 100644 --- a/app/packages/core/src/components/Modal/Group/DynamicGroup/carousel/DynamicGroupsFlashlightWrapper.tsx +++ b/app/packages/core/src/components/Modal/Group/DynamicGroup/carousel/DynamicGroupsFlashlightWrapper.tsx @@ -3,7 +3,7 @@ import { Sample, freeVideos } from "@fiftyone/looker"; import * as fos from "@fiftyone/state"; import { selectedSamples } from "@fiftyone/state"; import { get } from "lodash"; -import { +import React, { useCallback, useEffect, useId, @@ -11,12 +11,7 @@ import { useRef, useState, } from "react"; -import { - selector, - useRecoilCallback, - useRecoilValue, - useRecoilValueLoadable, -} from "recoil"; +import { selector, useRecoilValue, useRecoilValueLoadable } from "recoil"; import useFlashlightPager from "../../../../../useFlashlightPager"; import useSetDynamicGroupSample from "./useSetDynamicGroupSample"; @@ -28,10 +23,16 @@ export const DYNAMIC_GROUPS_FLASHLIGHT_ELEMENT_ID = const pageParams = selector({ key: "paginateDynamicGroupVariables", get: ({ get }) => { + const dataset = get(fos.datasetName); + if (!dataset) { + throw new Error("no dataset"); + } + const params = { - dataset: get(fos.datasetName), + dataset, view: get(fos.dynamicGroupViewQuery({})), }; + return (page: number, pageSize: number) => { return { ...params, @@ -55,8 +56,6 @@ export const DynamicGroupsFlashlightWrapper = () => { [modalSampleId] ); - const cursor = useRecoilValue(fos.dynamicGroupIndex); - const createLooker = fos.useCreateLooker( false, true, @@ -73,62 +72,8 @@ export const DynamicGroupsFlashlightWrapper = () => { const flashlightRef = useRef>(); selectSample.current = select; - const getScrollParams = useCallback(() => { - const flashlight = flashlightRef.current; - - if (!flashlight) { - return; - } - - const containerWidth = flashlight.element.clientWidth; - // elementWidth represents the width of the first element in the flashlight - const elementWidth = - flashlight.element.firstElementChild?.firstElementChild?.clientWidth ?? - 100; - - const elementsCount = Math.ceil(containerWidth / elementWidth!); - - return { elementWidth, elementsCount, containerWidth }; - }, []); - const setSample = useSetDynamicGroupSample(); const { init, deferred } = fos.useDeferrer(); - const navigationCallback = useRecoilCallback( - ({ snapshot }) => - async (isPrevious) => { - const flashlight = flashlightRef.current; - - if (!flashlight) { - return; - } - - const id = await snapshot.getPromise(fos.modalSampleId); - const currentSampleIndex = flashlight.itemIndexes[id]; - const nextSampleIndex = currentSampleIndex + (isPrevious ? -1 : 1); - const nextSampleId = store.indices.get(nextSampleIndex); - - if (!nextSampleId) { - return; - } - - setSample(id); - - // todo: implement better scrolling logic - if (flashlightRef.current) { - const { elementWidth } = getScrollParams()!; - - const newLeft = isPrevious - ? flashlightRef.current?.element.scrollLeft - elementWidth - : flashlightRef.current?.element.scrollLeft + elementWidth; - - flashlightRef.current?.element.scroll({ - left: newLeft, - behavior: "smooth", - }); - } - }, - [store, store, setSample] - ); const { page, reset } = useFlashlightPager(store, pageParams); diff --git a/app/packages/core/src/components/Modal/Group/DynamicGroup/index.tsx b/app/packages/core/src/components/Modal/Group/DynamicGroup/index.tsx index a91b0f7bd1..9af19891f0 100644 --- a/app/packages/core/src/components/Modal/Group/DynamicGroup/index.tsx +++ b/app/packages/core/src/components/Modal/Group/DynamicGroup/index.tsx @@ -1,6 +1,6 @@ import * as fos from "@fiftyone/state"; -import React from "react"; -import { useRecoilValue } from "recoil"; +import React, { useEffect } from "react"; +import { useRecoilValue, useSetRecoilState } from "recoil"; import { NestedGroup } from "./NestedGroup"; import { NonNestedDynamicGroup } from "./NonNestedGroup"; import { useDynamicGroupSamples } from "./useDynamicGroupSamples"; @@ -10,6 +10,22 @@ export const DynamicGroup = () => { const { queryRef } = useDynamicGroupSamples(); + const shouldRenderImaVid = useRecoilValue(fos.shouldRenderImaVidLooker); + const setDynamicGroupCurrentElementIndex = useSetRecoilState( + fos.dynamicGroupCurrentElementIndex + ); + const imaVidIndex = useRecoilValue( + fos.imaVidLookerState("currentFrameNumber") + ); + + useEffect(() => { + // checking for integer because it is initialized to a float random value + // in useInitializeImaVidSubscriptions + if (shouldRenderImaVid && Number.isInteger(imaVidIndex)) { + setDynamicGroupCurrentElementIndex(imaVidIndex); + } + }, [shouldRenderImaVid, imaVidIndex, setDynamicGroupCurrentElementIndex]); + return ( <> {hasGroupSlices ? ( diff --git a/app/packages/core/src/components/Modal/Group/DynamicGroup/useDynamicGroupSamples.ts b/app/packages/core/src/components/Modal/Group/DynamicGroup/useDynamicGroupSamples.ts index 9f96a4513f..c2f194674a 100644 --- a/app/packages/core/src/components/Modal/Group/DynamicGroup/useDynamicGroupSamples.ts +++ b/app/packages/core/src/components/Modal/Group/DynamicGroup/useDynamicGroupSamples.ts @@ -10,6 +10,7 @@ export const useDynamicGroupSamples = () => { const view = useRecoilValue(fos.dynamicGroupViewQuery({})); const dataset = useRecoilValue(fos.datasetName); const dynamicGroupIndex = useRecoilValue(fos.dynamicGroupIndex); + const shouldRenderImavid = useRecoilValue(fos.shouldRenderImaVidLooker); const loadDynamicGroupSamples = useCallback( (cursor?: number) => { @@ -17,6 +18,11 @@ export const useDynamicGroupSamples = () => { throw new Error("No dataset"); } + // imavid has its own fetching logic + if (shouldRenderImavid) { + return null; + } + return loadQuery( environment, foq.paginateSamples, @@ -32,7 +38,7 @@ export const useDynamicGroupSamples = () => { } ); }, - [dataset, environment, slice, view] + [dataset, environment, slice, shouldRenderImavid, view] ); const queryRef = useMemo( diff --git a/app/packages/core/src/components/Modal/Group/GroupView.tsx b/app/packages/core/src/components/Modal/Group/GroupView.tsx index e9316cefdf..120f9a28e6 100644 --- a/app/packages/core/src/components/Modal/Group/GroupView.tsx +++ b/app/packages/core/src/components/Modal/Group/GroupView.tsx @@ -3,7 +3,7 @@ import { VideoLooker } from "@fiftyone/looker"; import * as fos from "@fiftyone/state"; import { groupId, useBrowserStorage } from "@fiftyone/state"; import { Resizable } from "re-resizable"; -import React, { useRef } from "react"; +import React, { useMemo, useRef } from "react"; import { useRecoilValue } from "recoil"; import { GroupBar } from "../Bars"; import EnsureGroupSample from "./EnsureGroupSample"; @@ -19,9 +19,7 @@ export const GroupView = () => { const theme = useTheme(); const key = useRecoilValue(groupId); const mediaField = useRecoilValue(fos.selectedMediaField(true)); - const isCarouselVisible = useRecoilValue( - fos.groupMediaIsCarouselVisibleSetting - ); + const isCarouselVisible = useRecoilValue(fos.groupMediaIsCarouselVisible); const is3dVisible = useRecoilValue(fos.groupMediaIs3dVisible); const isMainVisible = useRecoilValue(fos.groupMediaIsMainVisible); const [width, setWidth] = useBrowserStorage( @@ -29,6 +27,10 @@ export const GroupView = () => { DEFAULT_SPLIT_VIEW_LEFT_WIDTH ); + const shouldRender3DBelow = useMemo(() => { + return isCarouselVisible && is3dVisible && !isMainVisible; + }, [is3dVisible, isCarouselVisible, isMainVisible]); + return (
@@ -37,13 +39,13 @@ export const GroupView = () => { { )} + {shouldRender3DBelow && } )} - {is3dVisible && } + {!shouldRender3DBelow && is3dVisible && }
); diff --git a/app/packages/core/src/components/Modal/Modal.tsx b/app/packages/core/src/components/Modal/Modal.tsx index 72ebf13e29..82b4bb1fd7 100644 --- a/app/packages/core/src/components/Modal/Modal.tsx +++ b/app/packages/core/src/components/Modal/Modal.tsx @@ -1,5 +1,4 @@ import { ErrorBoundary, HelpPanel, JSONPanel } from "@fiftyone/components"; -import { AbstractLooker } from "@fiftyone/looker"; import * as fos from "@fiftyone/state"; import { Controller } from "@react-spring/core"; import React, { @@ -10,7 +9,7 @@ import React, { useRef, } from "react"; import ReactDOM from "react-dom"; -import { useRecoilValue } from "recoil"; +import { useRecoilState, useRecoilValue, useSetRecoilState } from "recoil"; import styled from "styled-components"; import Sidebar, { Entries } from "../Sidebar"; import Group from "./Group"; @@ -54,7 +53,7 @@ const ContentColumn = styled.div` `; const SampleModal = () => { - const lookerRef = useRef(); + const lookerRef = useRef(); const wrapperRef = useRef(null); const disabled = useRecoilValue(fos.disabledPaths); @@ -85,7 +84,7 @@ const SampleModal = () => { * note: cannot use `useEventHandler()` hook since there's no direct reference to looker in Modal */ const lookerRefCallback = useCallback( - (looker: AbstractLooker) => { + (looker: fos.Lookers) => { lookerRef.current = looker; looker.addEventListener("tooltip", eventHandler); }, @@ -199,6 +198,45 @@ const SampleModal = () => { }; }, [eventHandler]); + const isNestedDynamicGroup = useRecoilValue(fos.isNestedDynamicGroup); + const isOrderedDynamicGroup = useRecoilValue(fos.isOrderedDynamicGroup); + const isLooker3DVisible = useRecoilValue(fos.groupMedia3dVisibleSetting); + const isCarouselVisible = useRecoilValue( + fos.groupMediaIsCarouselVisibleSetting + ); + + const [dynamicGroupsViewMode, setDynamicGroupsViewMode] = useRecoilState( + fos.dynamicGroupsViewMode + ); + const setIsMainLookerVisible = useSetRecoilState( + fos.groupMediaIsMainVisibleSetting + ); + + useEffect(() => { + // if it is unordered nested dynamic group and mode is not pagination, set to pagination + if ( + isNestedDynamicGroup && + !isOrderedDynamicGroup && + dynamicGroupsViewMode !== "pagination" + ) { + setDynamicGroupsViewMode("pagination"); + } + + // hide 3d looker and carousel if `hasGroupSlices` + if ( + dynamicGroupsViewMode === "video" && + (isLooker3DVisible || isCarouselVisible) + ) { + setIsMainLookerVisible(true); + } + }, [ + dynamicGroupsViewMode, + isNestedDynamicGroup, + isOrderedDynamicGroup, + isLooker3DVisible, + isCarouselVisible, + ]); + return ReactDOM.createPortal( { () => { // note: resetRecoilState is not triggering `onSet` in effect, // see https://github.com/facebookexperimental/Recoil/issues/2183 - // replace with `useResetRecoileState` when fixed + // replace with `useResetRecoilState` when fixed // this setter is to trigger onSet effect that kicks-off the subscription to frame number // the supplied random value is placeholder so that the onSet effect is triggered in the atom diff --git a/app/packages/core/src/components/Sidebar/Sidebar.tsx b/app/packages/core/src/components/Sidebar/Sidebar.tsx index 214919a089..f2f228ba6d 100644 --- a/app/packages/core/src/components/Sidebar/Sidebar.tsx +++ b/app/packages/core/src/components/Sidebar/Sidebar.tsx @@ -402,7 +402,7 @@ type RenderEntry = ( key: string, cb: () => void ) => void -) => { children: React.ReactNode; disabled: boolean }; +) => { children: React.ReactNode; disabled?: boolean }; const InteractiveSidebar = ({ render, diff --git a/app/packages/looker/src/elements/imavid/index.ts b/app/packages/looker/src/elements/imavid/index.ts index c03e4f5a49..1ec5ebc525 100644 --- a/app/packages/looker/src/elements/imavid/index.ts +++ b/app/packages/looker/src/elements/imavid/index.ts @@ -254,6 +254,12 @@ export class ImaVidElement extends BaseElement { this.ctx.drawImage(image, 0, 0); } + // this is when frame number changed through methods like keyboard navigation + if (!this.isPlaying && !this.isSeeking && !animate) { + this.ctx.drawImage(image, 0, 0); + this.update(() => ({ currentFrameNumber: frameNumberToDraw })); + } + if (animate && !this.waitingToPause) { if (frameNumberToDraw <= this.framesController.totalFrameCount) { this.update(({ playing }) => { diff --git a/app/packages/looker/src/elements/imavid/seek-bar.ts b/app/packages/looker/src/elements/imavid/seek-bar.ts index 562aa44b1c..d4d93cc0a5 100644 --- a/app/packages/looker/src/elements/imavid/seek-bar.ts +++ b/app/packages/looker/src/elements/imavid/seek-bar.ts @@ -56,6 +56,12 @@ export class SeekBarElement extends BaseElement { const currentBufferRange = storeBuffer.buffers.at( storeBuffer.getRangeIndexForFrame(currentFrameNumber) ); + + if (!currentBufferRange) { + this.element.style.display = "none"; + return this.element; + } + const bufferValue = (currentBufferRange[1] / totalFrames) * 100; this.element.style.setProperty( diff --git a/app/packages/looker/src/lookers/imavid/controller.ts b/app/packages/looker/src/lookers/imavid/controller.ts index 9d61207634..074f10ff95 100644 --- a/app/packages/looker/src/lookers/imavid/controller.ts +++ b/app/packages/looker/src/lookers/imavid/controller.ts @@ -1,6 +1,5 @@ import * as foq from "@fiftyone/relay"; import { Environment, Subscription, fetchQuery } from "relay-runtime"; -import { Sample } from "../.."; import { BufferRange, ImaVidState, StateUpdate } from "../../state"; import { BufferManager } from "./buffer-manager"; import { BUFFERS_REFRESH_TIMEOUT_YIELD, DEFAULT_FRAME_RATE } from "./constants"; @@ -10,27 +9,29 @@ import { ImaVidStore } from "./store"; const BUFFER_METADATA_FETCHING = "fetching"; export class ImaVidFramesController { - public storeBufferManager = new BufferManager([[1, 1]]); public fetchBufferManager = new BufferManager(); private frameRate = DEFAULT_FRAME_RATE; public totalFrameCount: number; private timeoutId: number; public isFetching = false; + public storeBufferManager: BufferManager; private subscription: Subscription; private updateImaVidState: StateUpdate; constructor( private readonly config: { environment: Environment; - // todo: see if we can do without it - orderBy: string; + firstFrameNumber: number; // todo: remove any page: any; - posterSample: Sample; + key: string; totalFrameCountPromise: Promise; } ) { + this.storeBufferManager = new BufferManager([ + [config.firstFrameNumber, config.firstFrameNumber], + ]); config.totalFrameCountPromise.then((frameCount) => { this.totalFrameCount = frameCount; }); @@ -155,19 +156,19 @@ export class ImaVidFramesController { return this.config.page; } - private get posterSampleId() { - return this.config.posterSample._id; + private get key() { + return this.config.key; } public get store() { - if (!ImaVidStore.has(this.posterSampleId)) { + if (!ImaVidStore.has(this.key)) { ImaVidStore.set( - this.posterSampleId, + this.key, new ImaVidFrameSamples(this.storeBufferManager) ); } - return ImaVidStore.get(this.posterSampleId); + return ImaVidStore.get(this.key); } public setFrameRate(newFrameRate: number) { @@ -185,7 +186,7 @@ export class ImaVidFramesController { public async fetchMore(cursor: number, count: number) { const variables = this.page(cursor, count); - const fetchUid = `${this.posterSampleId}-${cursor}-${variables.count}`; + const fetchUid = `${this.key}-${cursor}-${variables.count}`; return new Promise((resolve, _reject) => { // do a gql query here, get samples, update store diff --git a/app/packages/looker/src/lookers/imavid/index.ts b/app/packages/looker/src/lookers/imavid/index.ts index 98a8a890ac..4e822377a6 100644 --- a/app/packages/looker/src/lookers/imavid/index.ts +++ b/app/packages/looker/src/lookers/imavid/index.ts @@ -32,10 +32,6 @@ export class ImaVidLooker extends AbstractLooker { return this.frameStoreController.store.getSampleAtFrame(this.frameNumber); } - get posterSample() { - return this.sample; - } - get frameNumber() { return this.state.currentFrameNumber; } @@ -54,6 +50,7 @@ export class ImaVidLooker extends AbstractLooker { destroy() { this.unsubscribe && this.unsubscribe(); + this.frameStoreController.pauseFetch(); this.pause(); super.destroy(); } @@ -87,7 +84,7 @@ export class ImaVidLooker extends AbstractLooker { config: ImaVidState["config"], options: ImaVidState["options"] ): ImaVidState { - const firstFrame = 1; + const firstFrame = config.firstFrameNumber ?? 1; return { ...this.getInitialBaseState(), diff --git a/app/packages/looker/src/lookers/imavid/store.ts b/app/packages/looker/src/lookers/imavid/store.ts index 2104d487fb..3bc1a2e3ad 100644 --- a/app/packages/looker/src/lookers/imavid/store.ts +++ b/app/packages/looker/src/lookers/imavid/store.ts @@ -1,24 +1,24 @@ import LRUCache from "lru-cache"; -import { ImaVidFrameSamples } from "./ima-vid-frame-samples"; -import { PartitionSampleId } from "./types"; import { ImaVidFramesController } from "./controller"; +import { ImaVidFrameSamples } from "./ima-vid-frame-samples"; +import { PartitionId } from "./types"; /** * Each entry in the cache stores frames for ONE ordered dynamic group. */ -export const ImaVidStore = new LRUCache({ +export const ImaVidStore = new LRUCache({ max: 20, - dispose: (_partitionSampleId, sampleFrames) => { + dispose: (_partitionId, sampleFrames) => { sampleFrames.reset(); }, }); export const ImaVidFramesControllerStore = new LRUCache< - PartitionSampleId, + PartitionId, ImaVidFramesController >({ max: 20, - dispose: (_partitionSampleId, framesController) => { + dispose: (_partitionId, framesController) => { framesController.destroy(); }, }); diff --git a/app/packages/looker/src/lookers/imavid/types.ts b/app/packages/looker/src/lookers/imavid/types.ts index 9c7084715f..f13f39e97b 100644 --- a/app/packages/looker/src/lookers/imavid/types.ts +++ b/app/packages/looker/src/lookers/imavid/types.ts @@ -4,7 +4,7 @@ import { ImaVidFrameSamples } from "./ima-vid-frame-samples"; export type SampleId = string; export type SampleResponse = foq.paginateSamplesQuery$data["samples"]["edges"][number]["node"]; -export type PartitionSampleId = string; +export type PartitionId = string; export type ImaVidStore = { [partitionSampleId: string]: ImaVidFrameSamples; diff --git a/app/packages/looker/src/state.ts b/app/packages/looker/src/state.ts index e51808fce1..a097902635 100644 --- a/app/packages/looker/src/state.ts +++ b/app/packages/looker/src/state.ts @@ -232,6 +232,7 @@ export interface ImaVidConfig extends BaseConfig { * "C"ontroller for [ImaVidStore](./lookers/imavid/store.ts) */ frameStoreController: ImaVidFramesController; + firstFrameNumber: number; } export type PcdConfig = BaseConfig; diff --git a/app/packages/state/src/hooks/useCreateLooker.ts b/app/packages/state/src/hooks/useCreateLooker.ts index 95be1c0176..1534121051 100644 --- a/app/packages/state/src/hooks/useCreateLooker.ts +++ b/app/packages/state/src/hooks/useCreateLooker.ts @@ -129,7 +129,7 @@ export default ( } } - const config: ConstructorParameters[1] = { + let config: ConstructorParameters[1] = { fieldSchema: { ...fieldSchema, frames: { @@ -161,13 +161,13 @@ export default ( } if (constructor === ImaVidLooker) { - const { groupBy, orderBy } = snapshot + const { groupBy } = snapshot .getLoadable(groupAtoms.dynamicGroupParameters) .valueMaybe(); - const groupByFieldValue = get( - sample, - getSanitizedGroupByExpression(groupBy) - ) as string; + const groupByFieldValue = String( + get(sample, getSanitizedGroupByExpression(groupBy)) + ); + const totalFrameCountPromise = getPromise( dynamicGroupsElementCount(groupByFieldValue) ); @@ -175,27 +175,42 @@ export default ( .getLoadable(groupAtoms.dynamicGroupPageSelector(groupByFieldValue)) .valueMaybe(); + const firstFrameNumber = isModal + ? snapshot + .getLoadable(groupAtoms.dynamicGroupCurrentElementIndex) + .valueMaybe() ?? 1 + : 1; + + const imavidKey = snapshot + .getLoadable( + groupAtoms.imaVidStoreKey({ groupByFieldValue, modal: isModal }) + ) + .valueOrThrow(); + const thisSampleId = sample._id as string; if (!ImaVidFramesControllerStore.has(thisSampleId)) { ImaVidFramesControllerStore.set( thisSampleId, new ImaVidFramesController({ environment, - orderBy, + firstFrameNumber, page, totalFrameCountPromise, - posterSample: sample, + key: imavidKey, }) ); } - (config as ImaVidConfig).frameStoreController = ( - config as ImaVidConfig - ).frameStoreController = - ImaVidFramesControllerStore.get(thisSampleId); - - // todo - (config as ImaVidConfig).frameRate = 24; + config = { + ...config, + frameStoreController: ImaVidFramesControllerStore.get(thisSampleId), + frameRate: 24, + firstFrameNumber: isModal + ? snapshot + .getLoadable(groupAtoms.dynamicGroupCurrentElementIndex) + .valueMaybe() ?? 1 + : 1, + } as ImaVidConfig; } const looker = new constructor(sample, config, { diff --git a/app/packages/state/src/hooks/useExpandSample.ts b/app/packages/state/src/hooks/useExpandSample.ts index 68120f2f7f..faad8a4f2c 100644 --- a/app/packages/state/src/hooks/useExpandSample.ts +++ b/app/packages/state/src/hooks/useExpandSample.ts @@ -117,13 +117,15 @@ export default (store: LookerStore) => { groupAtoms.dynamicGroupParameters ); - const getIndex = async (index: number) => { + const getItemAtIndex = async (index: number) => { if (!store.indices.has(index)) await next(); const id = store.indices.get(index); if (!id) { - throw new Error("unable to paginate to next sample"); + throw new Error( + `unable to paginate to next sample, index = ${index}` + ); } const sample = store.samples.get(id); @@ -146,7 +148,9 @@ export default (store: LookerStore) => { return { id, groupId, groupByFieldValue }; }; - setModalState(getIndex).then(() => setExpandedSample(clickedIndex)); + setModalState(getItemAtIndex).then(() => + setExpandedSample(clickedIndex) + ); }, [setExpandedSample, setModalState, store] ); diff --git a/app/packages/state/src/recoil/groups.ts b/app/packages/state/src/recoil/groups.ts index f5f0caf631..640f6d537c 100644 --- a/app/packages/state/src/recoil/groups.ts +++ b/app/packages/state/src/recoil/groups.ts @@ -34,7 +34,7 @@ import { import { getBrowserStorageEffectForKey } from "./customEffects"; import { dataset } from "./dataset"; import { ModalSample, modalLooker, modalSample } from "./modal"; -import { nonNestedDynamicGroupsViewMode } from "./options"; +import { dynamicGroupsViewMode } from "./options"; import { RelayEnvironmentKey } from "./relay"; import { fieldPaths } from "./schema"; import { datasetName, parentMediaTypeSelector } from "./selectors"; @@ -53,6 +53,16 @@ export const groupMediaIsCarouselVisibleSetting = atom({ ], }); +export const groupMediaIsCarouselVisible = selector({ + key: "groupMediaIsCarouselVisible", + get: ({ get }) => { + const isImaVidInNestedGroup = + get(shouldRenderImaVidLooker) && get(isNestedDynamicGroup); + + return get(groupMediaIsCarouselVisibleSetting) && !isImaVidInNestedGroup; + }, +}); + export const groupMedia3dVisibleSetting = atom({ key: "groupMediaIs3dVisibleSetting", default: true, @@ -69,7 +79,9 @@ export const groupMediaIs3dVisible = selector({ get: ({ get }) => { const set = get(groupMediaTypesSet); const hasPcd = set.has("point_cloud"); - return get(groupMedia3dVisibleSetting) && hasPcd; + const isImaVidInNestedGroup = + get(shouldRenderImaVidLooker) && get(isNestedDynamicGroup); + return get(groupMedia3dVisibleSetting) && hasPcd && !isImaVidInNestedGroup; }, }); @@ -447,22 +459,32 @@ export const isNonNestedDynamicGroup = selector({ }, }); -export const isImaVidLookerAvailable = selector({ - key: "isImaVidLookerAvailable", +export const isNestedDynamicGroup = selector({ + key: "isNestedDynamicGroup", get: ({ get }) => { - const isOrderedDynamicGroup_ = get(isOrderedDynamicGroup); - const isNonNestedDynamicGroup_ = get(isNonNestedDynamicGroup); - return isOrderedDynamicGroup_ && isNonNestedDynamicGroup_; + return get(isDynamicGroup) && get(hasGroupSlices); }, }); +export const imaVidStoreKey = selectorFamily< + string, + { modal: boolean; groupByFieldValue: string } +>({ + key: "imaVidStoreKey", + get: + ({ modal, groupByFieldValue }) => + ({ get }) => { + const { groupBy, orderBy } = get(dynamicGroupParameters); + const slice = get(currentSlice(modal)) ?? "UNSLICED"; + + return `$${groupBy}-${orderBy}-${groupByFieldValue}-${slice}`; + }, +}); + export const shouldRenderImaVidLooker = selector({ key: "shouldRenderImaVidLooker", get: ({ get }) => { - return ( - get(isImaVidLookerAvailable) && - get(nonNestedDynamicGroupsViewMode) === "video" - ); + return get(isOrderedDynamicGroup) && get(dynamicGroupsViewMode) === "video"; }, }); @@ -501,9 +523,17 @@ export const dynamicGroupPageSelector = selectorFamily< ), }; + const filter = get(hasGroupSlices) + ? { + group: { + slice: get(groupSlice), + }, + } + : {}; + return (cursor: number, pageSize: number) => ({ ...params, - filter: {}, + filter, after: cursor ? String(cursor) : null, count: pageSize, }); @@ -553,7 +583,7 @@ export const imaVidLookerState = atomFamily({ ({ setSelf, getPromise, onSet }) => { let unsubscribe; - onSet((_newValue, oldValue, isReset) => { + onSet((_newValue) => { // note: resetRecoilState is not triggering `onSet` in effect, // see https://github.com/facebookexperimental/Recoil/issues/2183 // replace with `useResetRecoileState` when fixed diff --git a/app/packages/state/src/recoil/modal.ts b/app/packages/state/src/recoil/modal.ts index 675a83259a..643a4a4b4b 100644 --- a/app/packages/state/src/recoil/modal.ts +++ b/app/packages/state/src/recoil/modal.ts @@ -1,4 +1,4 @@ -import { AbstractLooker, ImaVidLooker, type Sample } from "@fiftyone/looker"; +import { AbstractLooker, type Sample } from "@fiftyone/looker"; import { BaseState } from "@fiftyone/looker/src/state"; import { mainSample, mainSampleQuery } from "@fiftyone/relay"; import { atom, selector } from "recoil"; @@ -7,6 +7,7 @@ import { VariablesOf } from "relay-runtime"; import { Nullable } from "vitest"; import { ResponseFrom } from "../utils"; import { + activeModalSidebarSample, groupId, groupSlice, hasGroupSlices, @@ -36,18 +37,17 @@ export const sidebarSampleId = selector({ const isPlaying = get(imaVidLookerState("playing")); const isSeeking = get(imaVidLookerState("seeking")); - const thisLooker = get(modalLooker) as ImaVidLooker; + const sample = get(activeModalSidebarSample); - if (!isPlaying && !isSeeking && thisFrameNumber && thisLooker) { - const sample = thisLooker.thisFrameSample; - const id = sample?.id || sample?.sample?._id; + if (!isPlaying && !isSeeking && thisFrameNumber && sample) { + // is the type incorrect? fix me + const id = sample?.id || sample?._id || sample?.sample?._id; if (id) { return id; } - } else { - // suspend - return new Promise(() => {}); } + // suspend + return new Promise(() => null); } const override = get(pinned3DSampleSlice); diff --git a/app/packages/state/src/recoil/options.ts b/app/packages/state/src/recoil/options.ts index 944ad94dfe..d3f91b4837 100644 --- a/app/packages/state/src/recoil/options.ts +++ b/app/packages/state/src/recoil/options.ts @@ -63,12 +63,10 @@ export const sidebarMode = atomFamily<"all" | "best" | "fast" | null, boolean>({ default: null, }); -export const nonNestedDynamicGroupsViewMode = atom< - "carousel" | "pagination" | "video" ->({ - key: "nonNestedDynamicGroupsViewMode", - default: "carousel", - effects: [getBrowserStorageEffectForKey("nonNestedDynamicGroupsViewMode")], +export const dynamicGroupsViewMode = atom<"carousel" | "pagination" | "video">({ + key: "dynamicGroupsViewMode", + default: "pagination", + effects: [getBrowserStorageEffectForKey("dynamicGroupsViewMode")], }); export const configuredSidebarModeDefault = selectorFamily< diff --git a/e2e-pw/README.md b/e2e-pw/README.md index 546d151c5d..98b2e922ed 100644 --- a/e2e-pw/README.md +++ b/e2e-pw/README.md @@ -33,6 +33,19 @@ - Refrain from using `page.waitForTimeout()`. There is always a better alternative. +#### Check for flakiness + +If you suspect a test is flaky, you can run it multiple times to see if it +fails consistently. In the following example, the test will be run 10 times and +a summary of the results will be printed describing how many times it passed +and how many times it failed. + +You may either pass the name of the spec file or the test title. + +``` +yarn check-flaky -r 10 -s "video plays with correct label for each slice" +``` + #### Template for POMs ```typescript diff --git a/e2e-pw/package.json b/e2e-pw/package.json index b134f4ea83..9651a360ec 100644 --- a/e2e-pw/package.json +++ b/e2e-pw/package.json @@ -7,17 +7,17 @@ "@playwright/test": "^1.40.1", "@types/fluent-ffmpeg": "^2.1.24", "@types/wait-on": "^5.3.4", - "@typescript-eslint/eslint-plugin": "^6.13.2", - "@typescript-eslint/parser": "^6.13.2", + "@typescript-eslint/eslint-plugin": "^6.14.0", + "@typescript-eslint/parser": "^6.14.0", "dotenv": "^16.3.1", "eslint": "^8.55.0", - "eslint-plugin-playwright": "^0.19.0", + "eslint-plugin-playwright": "^0.20.0", "fluent-ffmpeg": "^2.1.2", "jimp": "^0.22.10", "tree-kill": "^1.2.2", "ts-dedent": "^2.2.0", - "typescript": "^5.3.2", - "vitest": "^1.0.1", + "typescript": "^5.3.3", + "vitest": "^1.0.4", "wait-on": "^7.2.0" }, "scripts": { diff --git a/e2e-pw/scripts/check-flaky.sh b/e2e-pw/scripts/check-flaky.sh index e6bad1782d..164c714662 100755 --- a/e2e-pw/scripts/check-flaky.sh +++ b/e2e-pw/scripts/check-flaky.sh @@ -25,7 +25,14 @@ trap cleanup SIGINT # Function to run the Playwright test spec run_test() { - npx playwright test -c playwright.config.ts $spec_name + # if $spec_file ends in .ts, it's a spec name and not test title + + if [[ $spec_name == *.ts ]]; then + npx playwright test -c playwright.config.ts $spec_name + return + fi + + npx playwright test -c playwright.config.ts -g "$spec_name" } # Default values for keyword arguments diff --git a/e2e-pw/src/oss/poms/modal/group-actions.ts b/e2e-pw/src/oss/poms/modal/group-actions.ts index 22fc06d355..5a0bb98346 100644 --- a/e2e-pw/src/oss/poms/modal/group-actions.ts +++ b/e2e-pw/src/oss/poms/modal/group-actions.ts @@ -80,6 +80,7 @@ export class ModalGroupActionsPom { default: throw new Error(`Unknown mode: ${mode}`); } + await this.modal.toggleDisplayOptionsButton.click(); } } diff --git a/e2e-pw/src/oss/poms/modal/video-controls.ts b/e2e-pw/src/oss/poms/modal/video-controls.ts index 84c8bee873..5548323b3d 100644 --- a/e2e-pw/src/oss/poms/modal/video-controls.ts +++ b/e2e-pw/src/oss/poms/modal/video-controls.ts @@ -67,15 +67,21 @@ export class ModalVideoControlsPom { await this.togglePlay(); } - async playUntilFrames(frameText: string) { + async playUntilFrames(frameText: string, matchBeginning = false) { await this.togglePlay(); - await this.page.waitForFunction((frameText_) => { - const frames = document.querySelector( - "[data-cy=looker-video-time]" - )?.textContent; - return frames.startsWith(frameText_); - }, frameText); + await this.page.waitForFunction( + ({ frameText_, matchBeginning_ }) => { + const frameTextDom = document.querySelector( + "[data-cy=looker-video-time]" + )?.textContent; + if (matchBeginning_) { + return frameTextDom?.startsWith(frameText_); + } + return frameTextDom === frameText_; + }, + { frameText_: frameText, matchBeginning_: matchBeginning } + ); await this.togglePlay(); } diff --git a/e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts b/e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts index 4f3a89bb7d..5b266d80b0 100644 --- a/e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts +++ b/e2e-pw/src/oss/specs/groups/dynamic-groups.spec.ts @@ -60,6 +60,7 @@ extensionDatasetNamePairs.forEach(([extension, datasetName]) => { await grid.assert.isEntryCountTextEqualTo("10 groups"); await grid.openFirstSample(); + await modal.group.setDynamicGroupsNavigationMode("carousel"); await modal.sidebar.assert.verifySidebarEntryText("dynamic_group", "0"); await modal.scrollCarousel(); await modal.navigateCarousel(4, true); @@ -77,15 +78,15 @@ extensionDatasetNamePairs.forEach(([extension, datasetName]) => { }); await grid.openFirstSample(); - await modal.group.assert.assertIsCarouselVisible(); - await modal.group.assert.assertIsPaginationBarNotVisible(); - - await modal.group.setDynamicGroupsNavigationMode("pagination"); - await modal.group.assert.assertIsPaginationBarVisible(); await modal.group.assert.assertIsCarouselNotVisible(); await modal.group.dynamicGroupPagination.assert.verifyPage(1); await modal.group.dynamicGroupPagination.assert.verifyPage(10); + + await modal.group.setDynamicGroupsNavigationMode("carousel"); + + await modal.group.assert.assertIsCarouselVisible(); + await modal.group.assert.assertIsPaginationBarNotVisible(); }); }); diff --git a/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts b/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts index eea51aacdb..638fbee791 100644 --- a/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts +++ b/e2e-pw/src/oss/specs/groups/ima-vid.spec.ts @@ -51,6 +51,7 @@ const writeFrames = async () => { height: 50, fillColor: FRAME_COLORS[i % 2], watermarkString: `${j}`, + hideLogs: true, }) ); } diff --git a/e2e-pw/src/oss/specs/groups/sparse-groups-scenes.spec.ts b/e2e-pw/src/oss/specs/groups/sparse-groups-scenes.spec.ts index eb61b2a325..e73a7683b2 100644 --- a/e2e-pw/src/oss/specs/groups/sparse-groups-scenes.spec.ts +++ b/e2e-pw/src/oss/specs/groups/sparse-groups-scenes.spec.ts @@ -65,6 +65,7 @@ test(`ego default group slice transitions`, async ({ grid, modal }) => { await modal.clickOnLooker3d(); await modal.sidebar.assert.verifySidebarEntryText("group.name", "ego"); await modal.navigateNextSample(true); + await modal.waitForCarouselToLoad(); await modal.assert.verifyCarouselLength(1); await modal.sidebar.assert.verifySidebarEntryText("group.name", "ego"); await modal.groupLooker.click(); diff --git a/e2e-pw/src/oss/specs/regression-tests/grid-page.spec.ts b/e2e-pw/src/oss/specs/regression-tests/grid-page.spec.ts index c71b57474e..b339b17b06 100644 --- a/e2e-pw/src/oss/specs/regression-tests/grid-page.spec.ts +++ b/e2e-pw/src/oss/specs/regression-tests/grid-page.spec.ts @@ -72,6 +72,7 @@ test("modal dynamic group carousel has correct second page (all 21 samples)", as savedView: "group", }); await grid.openFirstSample(); + await modal.group.setDynamicGroupsNavigationMode("carousel"); await modal.waitForCarouselToLoad(); await modal.scrollCarousel(); await modal.navigateSlice("i", "20", true); diff --git a/e2e-pw/src/oss/specs/regression-tests/group-video/group-video-label.spec.ts b/e2e-pw/src/oss/specs/regression-tests/group-video/group-video-label.spec.ts index ba163b92b9..2afeb7e623 100644 --- a/e2e-pw/src/oss/specs/regression-tests/group-video/group-video-label.spec.ts +++ b/e2e-pw/src/oss/specs/regression-tests/group-video/group-video-label.spec.ts @@ -104,7 +104,7 @@ test.describe("groups video labels", () => { animations: "allow", }); - await modal.video.playUntilFrames("5"); + await modal.video.playUntilFrames("5", true); await modal.looker.hover(); // check screenshot after video is played diff --git a/e2e-pw/src/shared/media-factory/image.ts b/e2e-pw/src/shared/media-factory/image.ts index 2d5c05f18c..4a4b2f4b2b 100644 --- a/e2e-pw/src/shared/media-factory/image.ts +++ b/e2e-pw/src/shared/media-factory/image.ts @@ -6,10 +6,14 @@ export const createBlankImage = async (options: { height: number; fillColor?: string; watermarkString?: string; + hideLogs?: boolean; }) => { - const { width, height, outputPath, fillColor } = options; + const { width, height, outputPath, fillColor, hideLogs } = options; const startTime = performance.now(); - console.log(`Creating blank image with options: ${JSON.stringify(options)}`); + !hideLogs && + console.log( + `Creating blank image with options: ${JSON.stringify(options)}` + ); const image = new Jimp(width, height, fillColor ?? "#00ddff"); if (options.watermarkString) { @@ -31,7 +35,8 @@ export const createBlankImage = async (options: { await image.writeAsync(outputPath); const endTime = performance.now(); const timeTaken = endTime - startTime; - console.log( - `Image generation, path = ${outputPath}, completed in ${timeTaken} milliseconds` - ); + !hideLogs && + console.log( + `Image generation, path = ${outputPath}, completed in ${timeTaken} milliseconds` + ); }; diff --git a/e2e-pw/yarn.lock b/e2e-pw/yarn.lock index bc1376403e..2a9e56a6c8 100644 --- a/e2e-pw/yarn.lock +++ b/e2e-pw/yarn.lock @@ -600,16 +600,16 @@ dependencies: "@types/node" "*" -"@typescript-eslint/eslint-plugin@^6.13.2": - version "6.13.2" - resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-6.13.2.tgz#2e03506c5362a65e43cb132c37c9ce2d3cb51470" - integrity sha512-3+9OGAWHhk4O1LlcwLBONbdXsAhLjyCFogJY/cWy2lxdVJ2JrcTF2pTGMaLl2AE7U1l31n8Py4a8bx5DLf/0dQ== +"@typescript-eslint/eslint-plugin@^6.14.0": + version "6.14.0" + resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-6.14.0.tgz#fc1ab5f23618ba590c87e8226ff07a760be3dd7b" + integrity sha512-1ZJBykBCXaSHG94vMMKmiHoL0MhNHKSVlcHVYZNw+BKxufhqQVTOawNpwwI1P5nIFZ/4jLVop0mcY6mJJDFNaw== dependencies: "@eslint-community/regexpp" "^4.5.1" - 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version "1.0.1" - resolved "https://registry.yarnpkg.com/vitest/-/vitest-1.0.1.tgz#3ba1307066842bc801084fa384ce0b23941b91f7" - integrity sha512-MHsOj079S28hDsvdDvyD1pRj4dcS51EC5Vbe0xvOYX+WryP8soiK2dm8oULi+oA/8Xa/h6GoJEMTmcmBy5YM+Q== - dependencies: - "@vitest/expect" "1.0.1" - "@vitest/runner" "1.0.1" - "@vitest/snapshot" "1.0.1" - "@vitest/spy" "1.0.1" - "@vitest/utils" "1.0.1" +vitest@^1.0.4: + version "1.0.4" + resolved "https://registry.yarnpkg.com/vitest/-/vitest-1.0.4.tgz#c4b39ba4fcba674499c90e28f4d8dd16fa1d4eb3" + integrity sha512-s1GQHp/UOeWEo4+aXDOeFBJwFzL6mjycbQwwKWX2QcYfh/7tIerS59hWQ20mxzupTJluA2SdwiBuWwQHH67ckg== + dependencies: + "@vitest/expect" "1.0.4" + "@vitest/runner" "1.0.4" + "@vitest/snapshot" "1.0.4" + "@vitest/spy" "1.0.4" + "@vitest/utils" "1.0.4" acorn-walk "^8.3.0" cac "^6.7.14" chai "^4.3.10" @@ -2218,8 +2218,8 @@ vitest@^1.0.1: strip-literal "^1.3.0" tinybench "^2.5.1" tinypool "^0.8.1" - vite "^5.0.0-beta.19 || ^5.0.0" - vite-node "1.0.1" + vite "^5.0.0" + vite-node "1.0.4" why-is-node-running "^2.2.2" wait-on@^7.2.0: From e1481fa2c32f88608c1439f887a6dee29c905df1 Mon Sep 17 00:00:00 2001 From: Stuart Wheaton Date: Tue, 19 Dec 2023 22:28:05 -0500 Subject: [PATCH 24/59] configurable batch size for bulk_write --- docs/source/user_guide/config.rst | 2 ++ fiftyone/core/config.py | 6 ++++++ fiftyone/core/odm/database.py | 3 ++- 3 files changed, 10 insertions(+), 1 deletion(-) diff --git a/docs/source/user_guide/config.rst b/docs/source/user_guide/config.rst index 93fd967f4a..da22d0392e 100644 --- a/docs/source/user_guide/config.rst +++ b/docs/source/user_guide/config.rst @@ -55,6 +55,8 @@ FiftyOne supports the configuration options described below: +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_batch_size` | `FIFTYONE_DEFAULT_BATCH_SIZE` | `None` | A default batch size to use when :ref:`applying models to datasets `. | +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `bulk_write_batch_size` | `FIFTYONE_BULK_WRITE_BATCH_SIZE` | `100_000` | Batch size to use for bulk writing MongoDB operations; cannot be > 100,000 | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_sequence_idx` | `FIFTYONE_DEFAULT_SEQUENCE_IDX` | `%06d` | The default numeric string pattern to use when writing sequential lists of | | | | | files. | +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ diff --git a/fiftyone/core/config.py b/fiftyone/core/config.py index 9c4cc3027e..89209b121d 100644 --- a/fiftyone/core/config.py +++ b/fiftyone/core/config.py @@ -154,6 +154,12 @@ def __init__(self, d=None): env_var="FIFTYONE_DEFAULT_BATCH_SIZE", default=None, ) + self.bulk_write_batch_size = self.parse_int( + d, + "bulk_write_batch_size", + env_var="FIFTYONE_BULK_WRITE_BATCH_SIZE", + default=100_000, # mongodb limit + ) self.default_sequence_idx = self.parse_string( d, "default_sequence_idx", diff --git a/fiftyone/core/odm/database.py b/fiftyone/core/odm/database.py index 2a7b91980f..dbbb7d0ae0 100644 --- a/fiftyone/core/odm/database.py +++ b/fiftyone/core/odm/database.py @@ -788,7 +788,8 @@ def bulk_write(ops, coll, ordered=False): ordered (False): whether the operations must be performed in order """ try: - for ops_batch in fou.iter_batches(ops, 100000): # mongodb limit + batch_size = fo.config.bulk_write_batch_size + for ops_batch in fou.iter_batches(ops, batch_size): coll.bulk_write(list(ops_batch), ordered=ordered) except BulkWriteError as bwe: msg = bwe.details["writeErrors"][0]["errmsg"] From 700a03422efa4ab5720c31c9a4ababd53830aa19 Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 18 Dec 2023 12:52:43 -0500 Subject: [PATCH 25/59] tweaks to support direct delegated execution --- fiftyone/core/cli.py | 2 +- fiftyone/operators/executor.py | 16 +++++++++++++--- fiftyone/utils/data/exporters.py | 3 +++ fiftyone/utils/data/importers.py | 3 +++ fiftyone/zoo/datasets/__init__.py | 5 ++++- 5 files changed, 24 insertions(+), 5 deletions(-) diff --git a/fiftyone/core/cli.py b/fiftyone/core/cli.py index bbcba44f54..02638629a7 100644 --- a/fiftyone/core/cli.py +++ b/fiftyone/core/cli.py @@ -2172,9 +2172,9 @@ def execute(parser, args): splits=splits, dataset_name=dataset_name, dataset_dir=dataset_dir, + persistent=True, **kwargs, ) - dataset.persistent = True class DatasetZooDeleteCommand(Command): diff --git a/fiftyone/operators/executor.py b/fiftyone/operators/executor.py index c313a39659..2794b0dad2 100644 --- a/fiftyone/operators/executor.py +++ b/fiftyone/operators/executor.py @@ -139,14 +139,21 @@ def execute_operator(operator_uri, ctx=None, **kwargs): Returns: an :class:`ExecutionResult` + + Raises: + ExecutionError: if an error occurred while immediately executing an + operation or scheduling a delegated operation """ request_params = _parse_ctx(ctx=ctx, **kwargs) - return asyncio.run( + result = asyncio.run( execute_or_delegate_operator( operator_uri, request_params, exhaust=True ) ) + result.raise_exceptions() + + return result def _parse_ctx(ctx=None, **kwargs): @@ -164,9 +171,11 @@ def _parse_ctx(ctx=None, **kwargs): if isinstance(dataset, str): dataset = fod.load_dataset(dataset) - view = dataset.view() + if dataset is not None: + view = dataset.view() - view = view._serialize() + if view is not None: + view = view._serialize() if isinstance(dataset, fod.Dataset): dataset_name = dataset.name @@ -677,6 +686,7 @@ class ExecutionResult(object): executor (None): an :class:`Executor` error (None): an error message validation_ctx (None): a :class:`ValidationContext` + delegated (False): whether execution was delegated """ def __init__( diff --git a/fiftyone/utils/data/exporters.py b/fiftyone/utils/data/exporters.py index 86583f119d..5fce0eb30e 100644 --- a/fiftyone/utils/data/exporters.py +++ b/fiftyone/utils/data/exporters.py @@ -471,6 +471,9 @@ def build_dataset_exporter( "exporter" ) + if etau.is_str(dataset_type): + dataset_type = etau.get_class(dataset_type) + if inspect.isclass(dataset_type): dataset_type = dataset_type() diff --git a/fiftyone/utils/data/importers.py b/fiftyone/utils/data/importers.py index 6d5c6d0f79..999eeb01bf 100644 --- a/fiftyone/utils/data/importers.py +++ b/fiftyone/utils/data/importers.py @@ -563,6 +563,9 @@ def build_dataset_importer( "importer" ) + if etau.is_str(dataset_type): + dataset_type = etau.get_class(dataset_type) + if inspect.isclass(dataset_type): dataset_type = dataset_type() diff --git a/fiftyone/zoo/datasets/__init__.py b/fiftyone/zoo/datasets/__init__.py index c886e1830c..0a68df2a71 100644 --- a/fiftyone/zoo/datasets/__init__.py +++ b/fiftyone/zoo/datasets/__init__.py @@ -185,6 +185,7 @@ def load_zoo_dataset( dataset_dir=None, download_if_necessary=True, drop_existing_dataset=False, + persistent=False, overwrite=False, cleanup=True, **kwargs, @@ -228,6 +229,8 @@ def load_zoo_dataset( not found in the specified dataset directory drop_existing_dataset (False): whether to drop an existing dataset with the same name if it exists + persistent (False): whether the dataset should persist in the database + after the session terminates overwrite (False): whether to overwrite any existing files if the dataset is to be downloaded cleanup (True): whether to cleanup any temporary files generated during @@ -323,7 +326,7 @@ def load_zoo_dataset( if splits is None and zoo_dataset.has_splits: splits = zoo_dataset.supported_splits - dataset = fo.Dataset(dataset_name) + dataset = fo.Dataset(dataset_name, persistent=persistent) if splits: for split in splits: From 4eea964546c7219947fd42ed3595d5c0be3a2d54 Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 18 Dec 2023 12:53:15 -0500 Subject: [PATCH 26/59] documenting delegate operator --- docs/source/plugins/using_plugins.rst | 68 +++++++++++++++++++++++++-- 1 file changed, 64 insertions(+), 4 deletions(-) diff --git a/docs/source/plugins/using_plugins.rst b/docs/source/plugins/using_plugins.rst index 56d8c65bad..fc0b1ab18e 100644 --- a/docs/source/plugins/using_plugins.rst +++ b/docs/source/plugins/using_plugins.rst @@ -556,8 +556,9 @@ Some Operators perform an immediate action when executed, while other Operators Executing operators via SDK ___________________________ -Many operators are intended to be executed programmatically via the SDK in -addition to executing them by filling out their input form in the App. +Many operators are intended to be executed programmatically via the SDK rather +than (or in addition to) executing them by filling out their input form in the +App. .. _calling-operators: @@ -582,7 +583,7 @@ operator can be invoked like so: dataset = foz.load_zoo_dataset("quickstart") compute_metadata = foo.get_operator("@voxel51/utils/compute_metadata") - # Schedule a delegated operation to (re)compute metadata for the dataset + # Schedule a delegated operation to (re)compute metadata compute_metadata(dataset, overwrite=True, delegate=True) .. note:: @@ -617,12 +618,71 @@ for the operator's `ctx.params` and then passes them to :func:`execute_operator() `, which performs the execution. +.. _delegating-function-calls: + +Delegating function calls +------------------------- + +The +`@voxel51/utils/delegate `_ +operator provides a general purpose utility for +:ref:`delegating execution ` of an arbitrary function +call that can be expressed in any of the following forms: + +- Execute an arbitrary function: `fcn(*args, **kwargs)` +- Apply a function to a dataset or view: + `fcn(dataset_or_view, *args, **kwargs)` +- Call an instance method of a dataset or view: + `dataset_or_view.fcn(*args, **kwargs)` + +Here's some examples of delegating common tasks that can be expressed in the +above forms: + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.operators as foo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset("quickstart") + delegate = foo.get_operator("@voxel51/utils/delegate") + + # Compute metadata + delegate("compute_metadata", dataset=dataset) + + # Compute visualization + delegate( + "fiftyone.brain.compute_visualization", + dataset=dataset, + brain_key="img_viz", + ) + + # Export a view + delegate( + "export", + view=dataset.to_patches("ground_truth"), + export_dir="/tmp/patches", + dataset_type="fiftyone.types.ImageClassificationDirectoryTree", + label_field="ground_truth", + ) + + # Load the exported patches into a new dataset + delegate( + "fiftyone.Dataset.from_dir", + dataset_dir="/tmp/patches", + dataset_type="fiftyone.types.ImageClassificationDirectoryTree", + label_field="ground_truth", + name="patches", + persistent=True, + ) + .. _direct-operator-execution: Direct execution ---------------- -You can also programmatically execute any operator by calling +You can also programmatically execute any operator by directly calling :func:`execute_operator() `: .. code-block:: python From ee47a062ddd225432a03fee62036c25044bb4205 Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 18 Dec 2023 12:53:52 -0500 Subject: [PATCH 27/59] bumping eta version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 7aa967cdc0..9064dd7b1c 100644 --- a/setup.py +++ b/setup.py @@ -77,7 +77,7 @@ def get_version(): # internal packages "fiftyone-brain>=0.14,<0.15", "fiftyone-db>=0.4,<2.0", - "voxel51-eta>=0.12,<0.13", + "voxel51-eta>=0.12.1,<0.13", ] From 6d8334259ad38a4aa1c88978b4c68dea7be3f6da Mon Sep 17 00:00:00 2001 From: brimoor Date: Mon, 18 Dec 2023 12:54:29 -0500 Subject: [PATCH 28/59] bumping fiftyone-brain version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9064dd7b1c..70e2ead88e 100644 --- a/setup.py +++ b/setup.py @@ -75,7 +75,7 @@ def get_version(): "xmltodict", "universal-analytics-python3>=1.0.1,<2", # internal packages - "fiftyone-brain>=0.14,<0.15", + "fiftyone-brain>=0.14.2,<0.15", "fiftyone-db>=0.4,<2.0", "voxel51-eta>=0.12.1,<0.13", ] From 9433cd6803b29be0198a6f054acc53f2406ce1ec Mon Sep 17 00:00:00 2001 From: ArvindKumarS Date: Fri, 15 Dec 2023 10:53:53 +0100 Subject: [PATCH 29/59] Update iou.py --- fiftyone/utils/iou.py | 188 ++++++++++++++++++++++++++++-------------- 1 file changed, 124 insertions(+), 64 deletions(-) diff --git a/fiftyone/utils/iou.py b/fiftyone/utils/iou.py index 50ffa9fad9..9692f462f5 100644 --- a/fiftyone/utils/iou.py +++ b/fiftyone/utils/iou.py @@ -495,81 +495,141 @@ def _compute_bbox_ious(preds, gts, iscrowd=None, classwise=False): return ious - -def _compute_polyline_ious( - preds, gts, error_level, iscrowd=None, classwise=False, gt_crowds=None -): - is_symmetric = preds is gts - +def polygon_iou_matrix(preds, gts, error_level, is_symmetric, iscrowd=None, classwise=False, gt_crowds=None): with contextlib.ExitStack() as context: - # We're ignoring errors, so suppress shapely logging that occurs when - # invalid geometries are encountered - if error_level > 1: - # pylint: disable=no-member - context.enter_context( - fou.LoggingLevel(logging.CRITICAL, logger="shapely") - ) + # We're ignoring errors, so suppress shapely logging that occurs when + # invalid geometries are encountered + if error_level > 1: + # pylint: disable=no-member + context.enter_context( + fou.LoggingLevel(logging.CRITICAL, logger="shapely") + ) - num_pred = len(preds) - pred_polys = _polylines_to_shapely(preds, error_level) - pred_labels = [pred.label for pred in preds] - pred_areas = [pred_poly.area for pred_poly in pred_polys] + num_pred = len(preds) - if is_symmetric: - num_gt = num_pred - gt_polys = pred_polys - gt_labels = pred_labels - gt_areas = pred_areas - else: - num_gt = len(gts) - gt_polys = _polylines_to_shapely(gts, error_level) - gt_labels = [gt.label for gt in gts] - gt_areas = [gt_poly.area for gt_poly in gt_polys] + pred_polys = _polylines_to_shapely(preds, error_level) + pred_labels = [pred.label for pred in preds] + pred_areas = [pred_poly.area for pred_poly in pred_polys] - if iscrowd is not None: - gt_crowds = [iscrowd(gt) for gt in gts] - elif gt_crowds is None: - gt_crowds = [False] * num_gt + if is_symmetric: + num_gt = num_pred + gt_polys = pred_polys + gt_labels = pred_labels + gt_areas = pred_areas + else: + num_gt = len(gts) + gt_polys = _polylines_to_shapely(gts, error_level) + gt_labels = [gt.label for gt in gts] + gt_areas = [gt_poly.area for gt_poly in gt_polys] - ious = np.zeros((num_pred, num_gt)) + if iscrowd is not None: + gt_crowds = [iscrowd(gt) for gt in gts] + elif gt_crowds is None: + gt_crowds = [False] * num_gt - for j, (gt_poly, gt_label, gt_area, gt_crowd) in enumerate( - zip(gt_polys, gt_labels, gt_areas, gt_crowds) - ): - for i, (pred_poly, pred_label, pred_area) in enumerate( - zip(pred_polys, pred_labels, pred_areas) + ious = np.zeros((num_pred, num_gt)) + + for j, (gt_poly, gt_label, gt_area, gt_crowd) in enumerate( + zip(gt_polys, gt_labels, gt_areas, gt_crowds) ): - if is_symmetric and i < j: - iou = ious[j, i] - elif is_symmetric and i == j: - iou = 1 - elif classwise and pred_label != gt_label: - continue - else: - try: - inter = gt_poly.intersection(pred_poly).area - except Exception as e: - inter = 0.0 - fou.handle_error( - ValueError( - "Failed to compute intersection of predicted " - "object '%s' and ground truth object '%s'" - % (preds[i].id, gts[j].id) - ), - error_level, - base_error=e, - ) - - if gt_crowd: - union = pred_area + for i, (pred_poly, pred_label, pred_area) in enumerate( + zip(pred_polys, pred_labels, pred_areas) + ): + if is_symmetric and i < j: + iou = ious[j, i] + elif is_symmetric and i == j: + iou = 1 + elif classwise and pred_label != gt_label: + continue else: - union = pred_area + gt_area - inter + try: + inter = gt_poly.intersection(pred_poly).area + except Exception as e: + inter = 0.0 + fou.handle_error( + ValueError( + "Failed to compute intersection of predicted " + "object '%s' and ground truth object '%s'" + % (preds[i].id, gts[j].id) + ), + error_level, + base_error=e, + ) + + if gt_crowd: + union = pred_area + else: + union = pred_area + gt_area - inter + + iou = min(etan.safe_divide(inter, union), 1) + + ious[i, j] = iou + return ious - iou = min(etan.safe_divide(inter, union), 1) +def polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=None): + num_pred = len(preds) + pred_labels = [pred.label for pred in preds] + # if is_symmetric: + # num_gt = num_pred + # gt_labels = pred_labels + # else: + num_gt = len(gts) + gt_labels = [gt.label for gt in gts] + + if iscrowd is not None: + gt_crowds = [iscrowd(gt) for gt in gts] + elif gt_crowds is None: + gt_crowds = [False] * num_gt + + ious = np.zeros((num_pred, num_gt)) + + for j, (gt, gt_label, gt_crowd) in enumerate( + zip(gts, gt_labels, gt_crowds) + ): + for i, (pred, pred_label) in enumerate( + zip(preds, pred_labels) + ): + # if is_symmetric and i < j: + # iou = ious[j, i] + # elif is_symmetric and i == j: + # iou = 1 + if classwise and pred_label != gt_label: + continue + ious[i, j] = _compute_object_polyline_similarity(gt, pred) + return ious + +def _compute_object_polyline_similarity(gt, pred): + gtp = np.array(gt.points, dtype=float)[0] + predp = np.array(pred.points, dtype=float)[0] + each_dist =[] + # Use extent of GT points as proxy for box area + scale = np.sqrt(np.prod(np.nanmax(gtp, axis=0) - np.nanmin(gtp, axis=0))) + scale = np.maximum(0.0, np.minimum(scale, 1.0)) + for ind in np.arange(len(gtp)): - ious[i, j] = iou + if not np.isfinite(gtp[ind]).all(): + continue + if np.isfinite(predp[ind]).all(): + dists = np.linalg.norm(gtp[ind]-predp[ind]) + else: + dists = 1 + each_dist.append(np.exp(-(dists**2) / (2 * (scale**2)))) + return np.mean(each_dist) + +def _compute_polyline_ious( + preds, gts, error_level, iscrowd=None, classwise=False, gt_crowds=None +): + is_symmetric = preds is gts - return ious + are_all_closed = np.sum([p ["closed"] for p in preds]) + if are_all_closed > 0: + # all closed polylines = True for atleast one polyline instance, i.e., polygons + ious = polygon_iou_matrix(preds, gts, error_level, is_symmetric, iscrowd=None, classwise=False, gt_crowds=None) + if are_all_closed == 0: + # all closed polylines = False, i.e., Lines not polygons + ious = polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=None) + + return ious def _compute_mask_ious( From a831fa30e57924620191203efd7e6e4eac8dd57e Mon Sep 17 00:00:00 2001 From: ArvindKumarS Date: Fri, 15 Dec 2023 22:03:25 +0100 Subject: [PATCH 30/59] Update iou.py 1) Precommit hooks ran 2) Added filled as a check than close 3) Added the function tag as reccomended --- fiftyone/utils/iou.py | 175 ++++++++++++++++++++++++------------------ 1 file changed, 101 insertions(+), 74 deletions(-) diff --git a/fiftyone/utils/iou.py b/fiftyone/utils/iou.py index 9692f462f5..9b0c74dded 100644 --- a/fiftyone/utils/iou.py +++ b/fiftyone/utils/iou.py @@ -40,6 +40,11 @@ def compute_ious( For polylines, IoUs are computed assuming the shapes are solid (filled), regardless of their ``filled`` attributes. + For :class:`fiftyone.core.labels.Polyline` objects, IoUs are computed + as solid shapes when ``filled=True` and are computed using + `object keypoint similarity `_ + when ``filled=False``. + For keypoints, "IoUs" are computed via `object keypoint similarity `_. @@ -495,78 +500,90 @@ def _compute_bbox_ious(preds, gts, iscrowd=None, classwise=False): return ious -def polygon_iou_matrix(preds, gts, error_level, is_symmetric, iscrowd=None, classwise=False, gt_crowds=None): + +def polygon_iou_matrix( + preds, + gts, + error_level, + is_symmetric, + iscrowd=None, + classwise=False, + gt_crowds=None, +): with contextlib.ExitStack() as context: - # We're ignoring errors, so suppress shapely logging that occurs when - # invalid geometries are encountered - if error_level > 1: - # pylint: disable=no-member - context.enter_context( - fou.LoggingLevel(logging.CRITICAL, logger="shapely") - ) + # We're ignoring errors, so suppress shapely logging that occurs when + # invalid geometries are encountered + if error_level > 1: + # pylint: disable=no-member + context.enter_context( + fou.LoggingLevel(logging.CRITICAL, logger="shapely") + ) - num_pred = len(preds) + num_pred = len(preds) - pred_polys = _polylines_to_shapely(preds, error_level) - pred_labels = [pred.label for pred in preds] - pred_areas = [pred_poly.area for pred_poly in pred_polys] + pred_polys = _polylines_to_shapely(preds, error_level) + pred_labels = [pred.label for pred in preds] + pred_areas = [pred_poly.area for pred_poly in pred_polys] - if is_symmetric: - num_gt = num_pred - gt_polys = pred_polys - gt_labels = pred_labels - gt_areas = pred_areas - else: - num_gt = len(gts) - gt_polys = _polylines_to_shapely(gts, error_level) - gt_labels = [gt.label for gt in gts] - gt_areas = [gt_poly.area for gt_poly in gt_polys] + if is_symmetric: + num_gt = num_pred + gt_polys = pred_polys + gt_labels = pred_labels + gt_areas = pred_areas + else: + num_gt = len(gts) + gt_polys = _polylines_to_shapely(gts, error_level) + gt_labels = [gt.label for gt in gts] + gt_areas = [gt_poly.area for gt_poly in gt_polys] - if iscrowd is not None: - gt_crowds = [iscrowd(gt) for gt in gts] - elif gt_crowds is None: - gt_crowds = [False] * num_gt + if iscrowd is not None: + gt_crowds = [iscrowd(gt) for gt in gts] + elif gt_crowds is None: + gt_crowds = [False] * num_gt - ious = np.zeros((num_pred, num_gt)) + ious = np.zeros((num_pred, num_gt)) - for j, (gt_poly, gt_label, gt_area, gt_crowd) in enumerate( - zip(gt_polys, gt_labels, gt_areas, gt_crowds) + for j, (gt_poly, gt_label, gt_area, gt_crowd) in enumerate( + zip(gt_polys, gt_labels, gt_areas, gt_crowds) + ): + for i, (pred_poly, pred_label, pred_area) in enumerate( + zip(pred_polys, pred_labels, pred_areas) ): - for i, (pred_poly, pred_label, pred_area) in enumerate( - zip(pred_polys, pred_labels, pred_areas) - ): - if is_symmetric and i < j: - iou = ious[j, i] - elif is_symmetric and i == j: - iou = 1 - elif classwise and pred_label != gt_label: - continue + if is_symmetric and i < j: + iou = ious[j, i] + elif is_symmetric and i == j: + iou = 1 + elif classwise and pred_label != gt_label: + continue + else: + try: + inter = gt_poly.intersection(pred_poly).area + except Exception as e: + inter = 0.0 + fou.handle_error( + ValueError( + "Failed to compute intersection of predicted " + "object '%s' and ground truth object '%s'" + % (preds[i].id, gts[j].id) + ), + error_level, + base_error=e, + ) + + if gt_crowd: + union = pred_area else: - try: - inter = gt_poly.intersection(pred_poly).area - except Exception as e: - inter = 0.0 - fou.handle_error( - ValueError( - "Failed to compute intersection of predicted " - "object '%s' and ground truth object '%s'" - % (preds[i].id, gts[j].id) - ), - error_level, - base_error=e, - ) - - if gt_crowd: - union = pred_area - else: - union = pred_area + gt_area - inter - - iou = min(etan.safe_divide(inter, union), 1) - - ious[i, j] = iou + union = pred_area + gt_area - inter + + iou = min(etan.safe_divide(inter, union), 1) + + ious[i, j] = iou return ious -def polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=None): + +def polyline2d_iou_matrix( + preds, gts, iscrowd=None, classwise=False, gt_crowds=None +): num_pred = len(preds) pred_labels = [pred.label for pred in preds] # if is_symmetric: @@ -586,9 +603,7 @@ def polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=N for j, (gt, gt_label, gt_crowd) in enumerate( zip(gts, gt_labels, gt_crowds) ): - for i, (pred, pred_label) in enumerate( - zip(preds, pred_labels) - ): + for i, (pred, pred_label) in enumerate(zip(preds, pred_labels)): # if is_symmetric and i < j: # iou = ious[j, i] # elif is_symmetric and i == j: @@ -598,10 +613,11 @@ def polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=N ious[i, j] = _compute_object_polyline_similarity(gt, pred) return ious + def _compute_object_polyline_similarity(gt, pred): gtp = np.array(gt.points, dtype=float)[0] predp = np.array(pred.points, dtype=float)[0] - each_dist =[] + each_dist = [] # Use extent of GT points as proxy for box area scale = np.sqrt(np.prod(np.nanmax(gtp, axis=0) - np.nanmin(gtp, axis=0))) scale = np.maximum(0.0, np.minimum(scale, 1.0)) @@ -610,24 +626,35 @@ def _compute_object_polyline_similarity(gt, pred): if not np.isfinite(gtp[ind]).all(): continue if np.isfinite(predp[ind]).all(): - dists = np.linalg.norm(gtp[ind]-predp[ind]) + dists = np.linalg.norm(gtp[ind] - predp[ind]) else: dists = 1 each_dist.append(np.exp(-(dists**2) / (2 * (scale**2)))) return np.mean(each_dist) - + + def _compute_polyline_ious( preds, gts, error_level, iscrowd=None, classwise=False, gt_crowds=None ): is_symmetric = preds is gts - are_all_closed = np.sum([p ["closed"] for p in preds]) - if are_all_closed > 0: - # all closed polylines = True for atleast one polyline instance, i.e., polygons - ious = polygon_iou_matrix(preds, gts, error_level, is_symmetric, iscrowd=None, classwise=False, gt_crowds=None) - if are_all_closed == 0: - # all closed polylines = False, i.e., Lines not polygons - ious = polyline2d_iou_matrix(preds, gts, iscrowd=None, classwise=False, gt_crowds=None) + are_all_filled = np.sum([p["filled"] for p in preds]) + if are_all_filled > 0: + # are_all_filled = True for atleast one polyline instance, i.e., polygons/solid shape + ious = polygon_iou_matrix( + preds, + gts, + error_level, + is_symmetric, + iscrowd=None, + classwise=False, + gt_crowds=None, + ) + if are_all_filled == 0: + # are_all_filled = False, i.e., Lines not polygons + ious = polyline2d_iou_matrix( + preds, gts, iscrowd=None, classwise=False, gt_crowds=None + ) return ious From 65df02f15f9fbb43dfe08fba5f10639ccc1ba89b Mon Sep 17 00:00:00 2001 From: ArvindKumarS Date: Fri, 15 Dec 2023 22:19:11 +0100 Subject: [PATCH 31/59] Update iou.py I check the gts are all filled instead of predictions, because what if the model has all false negatives. THen there is no way to find filled is true or false, but an image sample should have some detections to verify if filled is true or false. --- fiftyone/utils/iou.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fiftyone/utils/iou.py b/fiftyone/utils/iou.py index 9b0c74dded..86b062cfa3 100644 --- a/fiftyone/utils/iou.py +++ b/fiftyone/utils/iou.py @@ -638,7 +638,7 @@ def _compute_polyline_ious( ): is_symmetric = preds is gts - are_all_filled = np.sum([p["filled"] for p in preds]) + are_all_filled = np.sum([gt["filled"] for gt in gts]) if are_all_filled > 0: # are_all_filled = True for atleast one polyline instance, i.e., polygons/solid shape ious = polygon_iou_matrix( From c21d559ae3e19269700c4fcb2952b1e88b67f584 Mon Sep 17 00:00:00 2001 From: Stuart Wheaton Date: Wed, 20 Dec 2023 10:43:24 -0500 Subject: [PATCH 32/59] add to viewing config section; alphabetize --- docs/source/user_guide/config.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/user_guide/config.rst b/docs/source/user_guide/config.rst index da22d0392e..bd1997a9db 100644 --- a/docs/source/user_guide/config.rst +++ b/docs/source/user_guide/config.rst @@ -138,6 +138,7 @@ and the CLI: .. code-block:: text { + "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", "database_name": "fiftyone", @@ -145,9 +146,8 @@ and the CLI: "database_validation": true, "dataset_zoo_dir": "~/fiftyone", "dataset_zoo_manifest_paths": null, - "default_app_config_path": "~/.fiftyone/app_config.json", - "default_app_port": 5151, "default_app_address": null, + "default_app_port": 5151, "default_batch_size": null, "default_dataset_dir": "~/fiftyone", "default_image_ext": ".jpg", @@ -157,14 +157,14 @@ and the CLI: "desktop_app": false, "do_not_track": false, "logging_level": "INFO", - "max_thread_pool_workers": null, "max_process_pool_workers": null, + "max_thread_pool_workers": null, "model_zoo_dir": "~/fiftyone/__models__", "model_zoo_manifest_paths": null, "module_path": null, "operator_timeout": 600, - "plugins_dir": null, "plugins_cache_enabled": false, + "plugins_dir": null, "requirement_error_level": 0, "show_progress_bars": true, "timezone": null @@ -185,6 +185,7 @@ and the CLI: .. code-block:: text { + "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", "database_name": "fiftyone", @@ -192,9 +193,8 @@ and the CLI: "database_validation": true, "dataset_zoo_dir": "~/fiftyone", "dataset_zoo_manifest_paths": null, - "default_app_config_path": "~/.fiftyone/app_config.json", - "default_app_port": 5151, "default_app_address": null, + "default_app_port": 5151, "default_batch_size": null, "default_dataset_dir": "~/fiftyone", "default_image_ext": ".jpg", @@ -204,14 +204,14 @@ and the CLI: "desktop_app": false, "do_not_track": false, "logging_level": "INFO", - "max_thread_pool_workers": null, "max_process_pool_workers": null, + "max_thread_pool_workers": null, "model_zoo_dir": "~/fiftyone/__models__", "model_zoo_manifest_paths": null, "module_path": null, "operator_timeout": 600, - "plugins_dir": null, "plugins_cache_enabled": false, + "plugins_dir": null, "requirement_error_level": 0, "show_progress_bars": true, "timezone": null From 9d1f34c065e1e9fdc811e46b754352e0f714599a Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 11:08:27 -0500 Subject: [PATCH 33/59] cleanup --- fiftyone/utils/iou.py | 120 +++++++++++------------------------------- 1 file changed, 30 insertions(+), 90 deletions(-) diff --git a/fiftyone/utils/iou.py b/fiftyone/utils/iou.py index 86b062cfa3..89c92cbe4c 100644 --- a/fiftyone/utils/iou.py +++ b/fiftyone/utils/iou.py @@ -6,6 +6,7 @@ | """ import contextlib +import itertools import logging import numpy as np @@ -37,11 +38,8 @@ def compute_ious( """Computes the pairwise IoUs between the predicted and ground truth objects. - For polylines, IoUs are computed assuming the shapes are solid (filled), - regardless of their ``filled`` attributes. - - For :class:`fiftyone.core.labels.Polyline` objects, IoUs are computed - as solid shapes when ``filled=True` and are computed using + For polylines, IoUs are computed as solid shapes when ``filled=True` and + "IoUs" are computed using `object keypoint similarity `_ when ``filled=False``. @@ -92,17 +90,20 @@ def compute_ious( if etau.is_str(iscrowd): iscrowd = lambda l: bool(l.get_attribute_value(iscrowd, False)) - if isinstance(preds[0], fol.Polyline): + if isinstance(gts[0], fol.Polyline): if use_boxes: return _compute_bbox_ious( preds, gts, iscrowd=iscrowd, classwise=classwise ) - return _compute_polyline_ious( - preds, gts, error_level, iscrowd=iscrowd, classwise=classwise - ) + if any(gt.filled for gt in gts): + return _compute_polygon_ious( + preds, gts, error_level, iscrowd=iscrowd, classwise=classwise + ) + + return _compute_polyline_similarities(preds, gts, classwise=classwise) - if isinstance(preds[0], fol.Keypoint): + if isinstance(gts[0], fol.Keypoint): return _compute_keypoint_similarities(preds, gts, classwise=classwise) if use_masks: @@ -501,15 +502,16 @@ def _compute_bbox_ious(preds, gts, iscrowd=None, classwise=False): return ious -def polygon_iou_matrix( +def _compute_polygon_ious( preds, gts, error_level, - is_symmetric, iscrowd=None, classwise=False, gt_crowds=None, ): + is_symmetric = preds is gts + with contextlib.ExitStack() as context: # We're ignoring errors, so suppress shapely logging that occurs when # invalid geometries are encountered @@ -581,82 +583,18 @@ def polygon_iou_matrix( return ious -def polyline2d_iou_matrix( - preds, gts, iscrowd=None, classwise=False, gt_crowds=None -): - num_pred = len(preds) - pred_labels = [pred.label for pred in preds] - # if is_symmetric: - # num_gt = num_pred - # gt_labels = pred_labels - # else: - num_gt = len(gts) - gt_labels = [gt.label for gt in gts] - - if iscrowd is not None: - gt_crowds = [iscrowd(gt) for gt in gts] - elif gt_crowds is None: - gt_crowds = [False] * num_gt - - ious = np.zeros((num_pred, num_gt)) - - for j, (gt, gt_label, gt_crowd) in enumerate( - zip(gts, gt_labels, gt_crowds) - ): - for i, (pred, pred_label) in enumerate(zip(preds, pred_labels)): - # if is_symmetric and i < j: - # iou = ious[j, i] - # elif is_symmetric and i == j: - # iou = 1 - if classwise and pred_label != gt_label: +def _compute_polyline_similarities(preds, gts, classwise=False): + sims = np.zeros((len(preds), len(gts))) + for j, gt in enumerate(gts): + for i, pred in enumerate(preds): + if classwise and pred.label != gt.label: continue - ious[i, j] = _compute_object_polyline_similarity(gt, pred) - return ious + gtp = list(itertools.chain.from_iterable(gt.points)) + predp = list(itertools.chain.from_iterable(pred.points)) + sims[i, j] = _compute_object_keypoint_similarity(gtp, predp) -def _compute_object_polyline_similarity(gt, pred): - gtp = np.array(gt.points, dtype=float)[0] - predp = np.array(pred.points, dtype=float)[0] - each_dist = [] - # Use extent of GT points as proxy for box area - scale = np.sqrt(np.prod(np.nanmax(gtp, axis=0) - np.nanmin(gtp, axis=0))) - scale = np.maximum(0.0, np.minimum(scale, 1.0)) - for ind in np.arange(len(gtp)): - - if not np.isfinite(gtp[ind]).all(): - continue - if np.isfinite(predp[ind]).all(): - dists = np.linalg.norm(gtp[ind] - predp[ind]) - else: - dists = 1 - each_dist.append(np.exp(-(dists**2) / (2 * (scale**2)))) - return np.mean(each_dist) - - -def _compute_polyline_ious( - preds, gts, error_level, iscrowd=None, classwise=False, gt_crowds=None -): - is_symmetric = preds is gts - - are_all_filled = np.sum([gt["filled"] for gt in gts]) - if are_all_filled > 0: - # are_all_filled = True for atleast one polyline instance, i.e., polygons/solid shape - ious = polygon_iou_matrix( - preds, - gts, - error_level, - is_symmetric, - iscrowd=None, - classwise=False, - gt_crowds=None, - ) - if are_all_filled == 0: - # are_all_filled = False, i.e., Lines not polygons - ious = polyline2d_iou_matrix( - preds, gts, iscrowd=None, classwise=False, gt_crowds=None - ) - - return ious + return sims def _compute_mask_ious( @@ -685,7 +623,7 @@ def _compute_mask_ious( else: gt_crowds = [False] * len(gts) - return _compute_polyline_ious( + return _compute_polygon_ious( pred_polys, gt_polys, error_level, @@ -734,14 +672,16 @@ def _compute_keypoint_similarities(preds, gts, classwise=False): if classwise and pred.label != gt.label: continue - sims[i, j] = _compute_object_keypoint_similarity(gt, pred) + gtp = gt.points + predp = pred.points + sims[i, j] = _compute_object_keypoint_similarity(gtp, predp) return sims -def _compute_object_keypoint_similarity(gt, pred): - gtp = np.array(gt.points, dtype=float) - predp = np.array(pred.points, dtype=float) +def _compute_object_keypoint_similarity(gtp, predp): + gtp = np.asarray(gtp, dtype=float) + predp = np.asarray(predp, dtype=float) # Use extent of GT points as proxy for box area scale = np.sqrt(np.prod(np.nanmax(gtp, axis=0) - np.nanmin(gtp, axis=0))) From 4f938144fca303d490f649f8c71e410d348525c3 Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 11:10:19 -0500 Subject: [PATCH 34/59] cleanup --- fiftyone/utils/iou.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fiftyone/utils/iou.py b/fiftyone/utils/iou.py index 89c92cbe4c..d75b0e8314 100644 --- a/fiftyone/utils/iou.py +++ b/fiftyone/utils/iou.py @@ -522,7 +522,6 @@ def _compute_polygon_ious( ) num_pred = len(preds) - pred_polys = _polylines_to_shapely(preds, error_level) pred_labels = [pred.label for pred in preds] pred_areas = [pred_poly.area for pred_poly in pred_polys] @@ -580,6 +579,7 @@ def _compute_polygon_ious( iou = min(etan.safe_divide(inter, union), 1) ious[i, j] = iou + return ious From e795dcf24347c7c3a4633b7979a668e8f9d58f41 Mon Sep 17 00:00:00 2001 From: Stuart Date: Wed, 20 Dec 2023 14:16:01 -0500 Subject: [PATCH 35/59] Toggle dynamic batcher for add_samples(), others (#3923) * configure-ize batcher used for add_samples and kin; add static sized batcher * bson size based dynamic batcher * add configs to docs; shorten config names * docstring for get_default_batcher * docs cleanup * minor naming / consistency updates * sentence about default changing for teams sdk * missed reference * fix apply_backpressure * batcher_target_size->batcher_target_size_bytes --------- Co-authored-by: brimoor --- docs/source/user_guide/config.rst | 34 ++- fiftyone/core/config.py | 24 ++ fiftyone/core/dataset.py | 41 ++- fiftyone/core/odm/database.py | 14 +- fiftyone/core/utils.py | 443 +++++++++++++++++++++++++----- tests/unittests/utils_tests.py | 60 ++++ 6 files changed, 513 insertions(+), 103 deletions(-) diff --git a/docs/source/user_guide/config.rst b/docs/source/user_guide/config.rst index bd1997a9db..460dd53b69 100644 --- a/docs/source/user_guide/config.rst +++ b/docs/source/user_guide/config.rst @@ -55,7 +55,29 @@ FiftyOne supports the configuration options described below: +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_batch_size` | `FIFTYONE_DEFAULT_BATCH_SIZE` | `None` | A default batch size to use when :ref:`applying models to datasets `. | +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ -| `bulk_write_batch_size` | `FIFTYONE_BULK_WRITE_BATCH_SIZE` | `100_000` | Batch size to use for bulk writing MongoDB operations; cannot be > 100,000 | +| `bulk_write_batch_size` | `FIFTYONE_BULK_WRITE_BATCH_SIZE` | `100_000` | Batch size to use for bulk writing MongoDB operations; cannot be > 100,000. | +| | | | | +| | | | Default changes to `10_000` for | +| | | | :ref:`Teams SDK in API connection mode `. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `default_batcher` | `FIFTYONE_DEFAULT_BATCHER` | `latency` | Batching implementation to use in some batched database operations such as | +| | | | :meth:`add_samples() `. Supported values | +| | | | are `latency`, `size`, and `static`. | +| | | | | +| | | | `latency` is the default, which uses a dynamic batch size to achieve a target latency | +| | | | of `batcher_target_latency` between calls. `size` is the default for the | +| | | | FiftyOne Teams SDK, which targets a size of `batcher_target_size` bytes for | +| | | | each call. `static` uses a fixed batch size of `batcher_static_size`. | +| | | | | +| | | | Default changes to `size` for | +| | | | :ref:`Teams SDK in API connection mode `. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_static_size` | `FIFTYONE_BATCHER_STATIC_SIZE` | `100` | Fixed size of batches. Ignored if `default_batcher` is not `static`. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_target_size_bytes` | `FIFTYONE_BATCHER_TARGET_SIZE_BYTES`| `2 ** 20` | Target content size of batches, in bytes. Ignored if `default_batcher` is not `size`. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_target_latency` | `FIFTYONE_BATCHER_TARGET_LATENCY` | `0.2` | Target latency between batches, in seconds. Ignored if `default_batcher` is not | +| | | | `latency`. | +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_sequence_idx` | `FIFTYONE_DEFAULT_SEQUENCE_IDX` | `%06d` | The default numeric string pattern to use when writing sequential lists of | | | | | files. | @@ -138,6 +160,9 @@ and the CLI: .. code-block:: text { + "batcher_static_size": 100, + "batcher_target_latency": 0.2, + "batcher_target_size": 1048576, "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", @@ -149,6 +174,7 @@ and the CLI: "default_app_address": null, "default_app_port": 5151, "default_batch_size": null, + "default_batcher": "latency", "default_dataset_dir": "~/fiftyone", "default_image_ext": ".jpg", "default_ml_backend": "torch", @@ -185,6 +211,9 @@ and the CLI: .. code-block:: text { + "batcher_static_size": 100, + "batcher_target_latency": 0.2, + "batcher_target_size": 1048576, "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", @@ -196,6 +225,7 @@ and the CLI: "default_app_address": null, "default_app_port": 5151, "default_batch_size": null, + "default_batcher": "latency", "default_dataset_dir": "~/fiftyone", "default_image_ext": ".jpg", "default_ml_backend": "torch", @@ -975,4 +1005,4 @@ code snippet shows how to configure the proxy based on your instance. # before launching the App, configure a proxy_url fo.app_config.proxy_url = "https://.notebook..sagemaker.aws/proxy//" - session = fo.launch_app(port=) \ No newline at end of file + session = fo.launch_app(port=) diff --git a/fiftyone/core/config.py b/fiftyone/core/config.py index 89209b121d..32b5529775 100644 --- a/fiftyone/core/config.py +++ b/fiftyone/core/config.py @@ -160,6 +160,30 @@ def __init__(self, d=None): env_var="FIFTYONE_BULK_WRITE_BATCH_SIZE", default=100_000, # mongodb limit ) + self.default_batcher = self.parse_string( + d, + "default_batcher", + env_var="FIFTYONE_DEFAULT_BATCHER", + default="latency", + ) + self.batcher_static_size = self.parse_int( + d, + "batcher_static_size", + env_var="FIFTYONE_BATCHER_STATIC_SIZE", + default=100, + ) + self.batcher_target_size_bytes = self.parse_int( + d, + "batcher_target_size_bytes", + env_var="FIFTYONE_BATCHER_TARGET_SIZE_BYTES", + default=2**20, + ) + self.batcher_target_latency = self.parse_number( + d, + "batcher_target_latency", + env_var="FIFTYONE_BATCHER_TARGET_LATENCY", + default=0.2, + ) self.default_sequence_idx = self.parse_string( d, "default_sequence_idx", diff --git a/fiftyone/core/dataset.py b/fiftyone/core/dataset.py index 6f9cdbf8ff..9afc213331 100644 --- a/fiftyone/core/dataset.py +++ b/fiftyone/core/dataset.py @@ -2472,22 +2472,15 @@ def add_samples( except: pass - # Dynamically size batches so that they are as large as possible while - # still achieving a nice frame rate on the progress bar - batcher = fou.DynamicBatcher( - samples, - target_latency=0.2, - init_batch_size=1, - max_batch_beta=2.0, - progress=True, - total=num_samples, + batcher = fou.get_default_batcher( + samples, progress=True, total=num_samples ) sample_ids = [] with batcher: for batch in batcher: _ids = self._add_samples_batch( - batch, expand_schema, dynamic, validate + batch, expand_schema, dynamic, validate, batcher=batcher ) sample_ids.extend(_ids) @@ -2510,7 +2503,7 @@ def add_collection( samples refer to the same source media. Args: - samples: a :class:`fiftyone.core.collections.SampleCollection` + sample_collection: a :class:`fiftyone.core.collections.SampleCollection` include_info (True): whether to merge dataset-level information such as ``info`` and ``classes`` overwrite_info (False): whether to overwrite existing dataset-level @@ -2540,7 +2533,9 @@ def add_collection( ) return self.skip(num_samples).values("id") - def _add_samples_batch(self, samples, expand_schema, dynamic, validate): + def _add_samples_batch( + self, samples, expand_schema, dynamic, validate, batcher=None + ): samples = [s.copy() if s._in_db else s for s in samples] if self.media_type is None and samples: @@ -2567,6 +2562,9 @@ def _add_samples_batch(self, samples, expand_schema, dynamic, validate): if sample.media_type == fom.VIDEO: sample.frames.save() + if batcher is not None and batcher.manual_backpressure: + batcher.apply_backpressure(dicts) + return [str(d["_id"]) for d in dicts] def _upsert_samples( @@ -2583,23 +2581,17 @@ def _upsert_samples( except: pass - # Dynamically size batches so that they are as large as possible while - # still achieving a nice frame rate on the progress bar - batcher = fou.DynamicBatcher( - samples, - target_latency=0.2, - init_batch_size=1, - max_batch_beta=2.0, - progress=True, - ) + batcher = fou.get_default_batcher(samples, progress=True) with batcher: for batch in batcher: self._upsert_samples_batch( - batch, expand_schema, dynamic, validate + batch, expand_schema, dynamic, validate, batcher=batcher ) - def _upsert_samples_batch(self, samples, expand_schema, dynamic, validate): + def _upsert_samples_batch( + self, samples, expand_schema, dynamic, validate, batcher=None + ): if self.media_type is None and samples: self.media_type = _get_media_type(samples[0]) @@ -2630,6 +2622,9 @@ def _upsert_samples_batch(self, samples, expand_schema, dynamic, validate): if sample.media_type == fom.VIDEO: sample.frames.save() + if batcher is not None and batcher.manual_backpressure: + batcher.apply_backpressure(dicts) + def _make_dict(self, sample, include_id=False): d = sample.to_mongo_dict(include_id=include_id) diff --git a/fiftyone/core/odm/database.py b/fiftyone/core/odm/database.py index dbbb7d0ae0..3b24dacaac 100644 --- a/fiftyone/core/odm/database.py +++ b/fiftyone/core/odm/database.py @@ -755,23 +755,17 @@ def insert_documents(docs, coll, ordered=False, progress=False, num_docs=None): a list of IDs of the inserted documents """ ids = [] + batcher = fou.get_default_batcher(docs, progress=progress, total=num_docs) try: - batcher = fou.DynamicBatcher( - docs, - target_latency=0.2, - init_batch_size=1, - max_batch_beta=2.0, - max_batch_size=100000, # mongodb limit - progress=progress, - total=num_docs, - ) - with batcher: for batch in batcher: batch = list(batch) coll.insert_many(batch, ordered=ordered) ids.extend(b["_id"] for b in batch) + if batcher.manual_backpressure: + batcher.apply_backpressure(batch) + except BulkWriteError as bwe: msg = bwe.details["writeErrors"][0]["errmsg"] raise ValueError(msg) from bwe diff --git a/fiftyone/core/utils.py b/fiftyone/core/utils.py index 18969cdda6..5a29730c12 100644 --- a/fiftyone/core/utils.py +++ b/fiftyone/core/utils.py @@ -5,7 +5,9 @@ | `voxel51.com `_ | """ +import abc import atexit +from bson import json_util from base64 import b64encode, b64decode from collections import defaultdict from contextlib import contextmanager @@ -955,68 +957,14 @@ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) -class DynamicBatcher(object): - """Class for iterating over the elements of an iterable with a dynamic - batch size to achieve a desired latency. - - The batch sizes emitted when iterating over this object are dynamically - scaled such that the latency between ``next()`` calls is as close as - possible to a specified target latency. - - This class is often used in conjunction with a :class:`ProgressBar` to keep - the user appraised on the status of a long-running task. - - Example usage:: - - import fiftyone.core.utils as fou - - elements = range(int(1e7)) - - batcher = fou.DynamicBatcher( - elements, target_latency=0.1, max_batch_beta=2.0 - ) - - for batch in batcher: - print("batch size: %d" % len(batch)) +class Batcher(abc.ABC): + """Base class for iterating over the elements of an iterable in batches.""" - batcher = fou.DynamicBatcher( - elements, - target_latency=0.1, - max_batch_beta=2.0, - progress=True, - ) - - with batcher: - for batch in batcher: - print("batch size: %d" % len(batch)) - - Args: - iterable: an iterable - target_latency (0.2): the target latency between ``next()`` - calls, in seconds - init_batch_size (1): the initial batch size to use - min_batch_size (1): the minimum allowed batch size - max_batch_size (None): an optional maximum allowed batch size - max_batch_beta (None): an optional lower/upper bound on the ratio - between successive batch sizes - return_views (False): whether to return each batch as a - :class:`fiftyone.core.view.DatasetView`. Only applicable when the - iterable is a :class:`fiftyone.core.collections.SampleCollection` - progress (False): whether to render a progress bar tracking the - consumption of the batches - total (None): the length of ``iterable``. Only applicable when - ``progress=True``. If not provided, it is computed via - ``len(iterable)``, if possible - """ + manual_backpressure = False def __init__( self, iterable, - target_latency=0.2, - init_batch_size=1, - min_batch_size=1, - max_batch_size=None, - max_batch_beta=None, return_views=False, progress=False, total=None, @@ -1027,22 +975,17 @@ def __init__( return_views = False self.iterable = iterable - self.target_latency = target_latency - self.init_batch_size = init_batch_size - self.min_batch_size = min_batch_size - self.max_batch_size = max_batch_size - self.max_batch_beta = max_batch_beta self.return_views = return_views self.progress = progress self.total = total self._iter = None - self._last_time = None self._last_batch_size = None self._pb = None self._in_context = False self._last_offset = None self._num_samples = None + self._manually_applied_backpressure = True def __enter__(self): self._in_context = True @@ -1077,14 +1020,23 @@ def __iter__(self): self._pb.__enter__() else: logger.warning( - "DynamicBatcher must be invoked as a context manager in " - "order to print progress" + "Batcher must be invoked as a context manager in order to " + "print progress" ) self.progress = False return self def __next__(self): + if ( + self.manual_backpressure + and not self._manually_applied_backpressure + ): + raise ValueError( + "Backpressure value not registered for this batcher" + ) + self._manually_applied_backpressure = False + if self.progress and self._last_batch_size is not None: self._pb.update(count=self._last_batch_size) @@ -1117,15 +1069,63 @@ def __next__(self): return batch + def apply_backpressure(self, *args, **kwargs): + """Apply backpressure needed to rightsize the next batch. + + Required to be implemented and called every iteration, if + ``self.manual_backpressure == True``. + + Subclass defines arguments and behavior of this method. + """ + + @abc.abstractmethod def _compute_batch_size(self): - current_time = timeit.default_timer() + """Return next batch size. Concrete classes must implement.""" + + +class BaseDynamicBatcher(Batcher): + """Class for iterating over the elements of an iterable with a dynamic + batch size to achieve a desired target measurement. + + The batch sizes emitted when iterating over this object are dynamically + scaled such that the measurement between ``next()`` calls is as close as + possible to a specified target. + + Concrete base classes define the target measurement and method of + calculation. + """ + + def __init__( + self, + iterable, + target_measurement, + init_batch_size=1, + min_batch_size=1, + max_batch_size=None, + max_batch_beta=None, + return_views=False, + progress=False, + total=None, + ): + super().__init__( + iterable, return_views=return_views, progress=progress, total=total + ) + + self.target_measurement = target_measurement + self.init_batch_size = init_batch_size + self.min_batch_size = min_batch_size + self.max_batch_size = max_batch_size + self.max_batch_beta = max_batch_beta + + def _compute_batch_size(self): + current_measurement = self._get_measurement() if self._last_batch_size is None: batch_size = self.init_batch_size else: # Compute optimal batch size try: - beta = self.target_latency / (current_time - self._last_time) + beta = self.target_measurement / current_measurement except ZeroDivisionError: beta = 1e6 @@ -1144,10 +1144,317 @@ def _compute_batch_size(self): batch_size = min(batch_size, self.max_batch_size) self._last_batch_size = batch_size - self._last_time = current_time return batch_size + @abc.abstractmethod + def _get_measurement(self): + """Get backpressure measurement for current batch.""" + + +class LatencyDynamicBatcher(BaseDynamicBatcher): + """Class for iterating over the elements of an iterable with a dynamic + batch size to achieve a desired latency. + + The batch sizes emitted when iterating over this object are dynamically + scaled such that the latency between ``next()`` calls is as close as + possible to a specified target latency. + + This class is often used in conjunction with a :class:`ProgressBar` to keep + the user appraised on the status of a long-running task. + + Example usage:: + + import fiftyone.core.utils as fou + + elements = range(int(1e7)) + + batcher = fou.LatencyDynamicBatcher( + elements, target_latency=0.1, max_batch_beta=2.0 + ) + + for batch in batcher: + print("batch size: %d" % len(batch)) + + batcher = fou.LatencyDynamicBatcher( + elements, + target_latency=0.1, + max_batch_beta=2.0, + progress=True, + ) + + with batcher: + for batch in batcher: + print("batch size: %d" % len(batch)) + + Args: + iterable: an iterable + target_latency (0.2): the target latency between ``next()`` + calls, in seconds + init_batch_size (1): the initial batch size to use + min_batch_size (1): the minimum allowed batch size + max_batch_size (None): an optional maximum allowed batch size + max_batch_beta (None): an optional lower/upper bound on the ratio + between successive batch sizes + return_views (False): whether to return each batch as a + :class:`fiftyone.core.view.DatasetView`. Only applicable when the + iterable is a :class:`fiftyone.core.collections.SampleCollection` + progress (False): whether to render a progress bar tracking the + consumption of the batches + total (None): the length of ``iterable``. Only applicable when + ``progress=True``. If not provided, it is computed via + ``len(iterable)``, if possible + """ + + def __init__( + self, + iterable, + target_latency=0.2, + init_batch_size=1, + min_batch_size=1, + max_batch_size=None, + max_batch_beta=None, + return_views=False, + progress=False, + total=None, + ): + super().__init__( + iterable, + target_latency, + init_batch_size=init_batch_size, + min_batch_size=min_batch_size, + max_batch_size=max_batch_size, + max_batch_beta=max_batch_beta, + return_views=return_views, + progress=progress, + total=total, + ) + + self._last_time = None + + def _get_measurement(self): + current_time = timeit.default_timer() + time_delta = 0 + if self._last_time is not None: + time_delta = current_time - self._last_time + + self._last_time = current_time + return time_delta + + +# Define this for backwards compatibility in case someone was using this +# batcher directly +DynamicBatcher = LatencyDynamicBatcher + + +class BSONSizeDynamicBatcher(BaseDynamicBatcher): + """Class for iterating over the elements of an iterable with a dynamic + batch size to achieve a desired BSON content size. + + The batch sizes emitted when iterating over this object are dynamically + scaled such that the total BSON size of the batch is as close as + possible to a specified target size. + + This batcher requires that backpressure feedback be manually provided, + so that the provided list is BSON-able in order to calculate an accurate + estimate of total batch content size to adjust next batch size. + + This class is often used in conjunction with a :class:`ProgressBar` to keep + the user appraised on the status of a long-running task. + + Example usage:: + + import fiftyone.core.utils as fou + + elements = range(int(1e7)) + + batcher = fou.BSONSizeDynamicBatcher( + elements, target_size=2**20, max_batch_beta=2.0 + ) + + # Raises ValueError after first batch, we forgot to apply backpressure + for batch in batcher: + print("batch size: %d" % len(batch)) + + # Now it works + for batch in batcher: + print("batch size: %d" % len(batch)) + batcher.apply_backpressure(batch) + + batcher = fou.BSONSizeDynamicBatcher( + elements, + target_size=2**20, + max_batch_beta=2.0, + progress=True + ) + + with batcher: + for batch in batcher: + print("batch size: %d" % len(batch)) + batcher.apply_backpressure(batch) + + Args: + iterable: an iterable + target_size (1048576): the target batch bson content size, in bytes + init_batch_size (1): the initial batch size to use + min_batch_size (1): the minimum allowed batch size + max_batch_size (None): an optional maximum allowed batch size + max_batch_beta (None): an optional lower/upper bound on the ratio + between successive batch sizes + return_views (False): whether to return each batch as a + :class:`fiftyone.core.view.DatasetView`. Only applicable when the + iterable is a :class:`fiftyone.core.collections.SampleCollection` + progress (False): whether to render a progress bar tracking the + consumption of the batches + total (None): the length of ``iterable``. Only applicable when + ``progress=True``. If not provided, it is computed via + ``len(iterable)``, if possible + """ + + manual_backpressure = True + + def __init__( + self, + iterable, + target_size=2**20, + init_batch_size=1, + min_batch_size=1, + max_batch_size=None, + max_batch_beta=None, + return_views=False, + progress=False, + total=None, + ): + super().__init__( + iterable, + target_size, + init_batch_size=init_batch_size, + min_batch_size=min_batch_size, + max_batch_size=max_batch_size, + max_batch_beta=max_batch_beta, + return_views=return_views, + progress=progress, + total=total, + ) + self._last_batch_content_size = 0 + + def apply_backpressure(self, bsonable_batch): + batch_content_size = sum( + len(json_util.dumps(obj)) for obj in bsonable_batch + ) + self._last_batch_content_size = batch_content_size + self._manually_applied_backpressure = True + + def _get_measurement(self): + return self._last_batch_content_size + + +class StaticBatcher(Batcher): + """Class for iterating over the elements of an iterable with a static + batch size. + + This class is often used in conjunction with a :class:`ProgressBar` to keep + the user appraised on the status of a long-running task. + + Example usage:: + + import fiftyone.core.utils as fou + + elements = range(int(1e7)) + + batcher = fou.StaticBatcher(elements, batch_size=10000) + + for batch in batcher: + print("batch size: %d" % len(batch)) + + batcher = fou.StaticBatcher(elements, batch_size=10000, progress=True) + + with batcher: + for batch in batcher: + print("batch size: %d" % len(batch)) + + Args: + iterable: an iterable + batch_size: size of batches to generate + return_views (False): whether to return each batch as a + :class:`fiftyone.core.view.DatasetView`. Only applicable when the + iterable is a :class:`fiftyone.core.collections.SampleCollection` + progress (False): whether to render a progress bar tracking the + consumption of the batches + total (None): the length of ``iterable``. Only applicable when + ``progress=True``. If not provided, it is computed via + ``len(iterable)``, if possible + """ + + def __init__( + self, + iterable, + batch_size, + return_views=False, + progress=False, + total=None, + ): + super().__init__( + iterable, return_views=return_views, progress=progress, total=total + ) + + self.batch_size = batch_size + + def _compute_batch_size(self): + return self.batch_size + + +def get_default_batcher(iterable, progress=True, total=None): + """Returns a :class:`Batcher` over ``iterable`` using defaults from your + FiftyOne config. + + Uses ``fiftyone.config.default_batcher`` to determine the implementation + to use, and related configuration values as needed for each. + + Args: + iterable: an iterable to batch over + progress (True): whether to render a progress bar tracking the + consumption of the batches + total (None): the length of ``iterable``. Only applicable when + ``progress=True``. If not provided, it is computed via + ``len(iterable)``, if possible + + Returns: + a :class:`Batcher` + """ + default_batcher = fo.config.default_batcher + if default_batcher == "latency": + target_latency = fo.config.batcher_target_latency + return LatencyDynamicBatcher( + iterable, + target_latency=target_latency, + init_batch_size=1, + max_batch_beta=2.0, + max_batch_size=100000, + progress=progress, + total=total, + ) + elif default_batcher == "size": + target_content_size = fo.config.batcher_target_size_bytes + return BSONSizeDynamicBatcher( + iterable, + target_size=target_content_size, + init_batch_size=1, + max_batch_beta=8.0, + max_batch_size=100000, + progress=progress, + total=total, + ) + elif default_batcher == "static": + batch_size = fo.config.batcher_static_size + return StaticBatcher( + iterable, batch_size=batch_size, progress=progress, total=total + ) + else: + raise ValueError( + f"Invalid fo.config.default_batcher: '{default_batcher}'" + ) + @contextmanager def disable_progress_bars(): diff --git a/tests/unittests/utils_tests.py b/tests/unittests/utils_tests.py index 4cbe7bc711..17a40dd270 100644 --- a/tests/unittests/utils_tests.py +++ b/tests/unittests/utils_tests.py @@ -21,6 +21,66 @@ from decorators import drop_datasets +class BatcherTests(unittest.TestCase): + def test_get_default_batcher(self): + iterable = list(range(100)) + + target_latency = 0.25 + with patch.object(fo.config, "default_batcher", "latency"): + with patch.object( + fo.config, + "batcher_target_latency", + target_latency, + ): + batcher = fou.get_default_batcher(iterable) + self.assertTrue(isinstance(batcher, fou.DynamicBatcher)) + self.assertEqual(batcher.target_measurement, target_latency) + + static_batch_size = 1000 + with patch.object(fo.config, "default_batcher", "static"): + with patch.object( + fo.config, + "batcher_static_size", + static_batch_size, + ): + batcher = fou.get_default_batcher(iterable) + self.assertTrue(isinstance(batcher, fou.StaticBatcher)) + self.assertEqual(batcher.batch_size, static_batch_size) + + target_size = 2**16 + with patch.object(fo.config, "default_batcher", "size"): + with patch.object( + fo.config, + "batcher_target_size_bytes", + target_size, + ): + batcher = fou.get_default_batcher(iterable) + self.assertTrue( + isinstance(batcher, fou.BSONSizeDynamicBatcher) + ) + self.assertEqual(batcher.target_measurement, target_size) + + with patch.object(fo.config, "default_batcher", "invalid"): + self.assertRaises(ValueError, fou.get_default_batcher, iterable) + + def test_static_batcher(self): + iterable = list(range(105)) + batcher = fou.StaticBatcher(iterable, batch_size=10, progress=False) + with batcher: + batches = [batch for batch in batcher] + expected = [list(range(i, i + 10)) for i in range(0, 95, 10)] + [ + iterable[100:] + ] + self.assertListEqual(batches, expected) + + def test_static_batcher_covered(self): + iterable = list(range(105)) + batcher = fou.StaticBatcher(iterable, batch_size=200, progress=False) + with batcher: + batches = [batch for batch in batcher] + self.assertListEqual(batches, [iterable]) + + class CoreUtilsTests(unittest.TestCase): def test_validate_hex_color(self): # Valid colors From 502a9caf83ee0966bd17e541acfe6f68d01d0718 Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Wed, 20 Dec 2023 13:29:10 -0600 Subject: [PATCH 36/59] add ubuntu 23 (#3936) --- package/db/setup.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/package/db/setup.py b/package/db/setup.py index 5c2997e590..ee4aa4a307 100644 --- a/package/db/setup.py +++ b/package/db/setup.py @@ -78,6 +78,10 @@ "aarch64": "https://fastdl.mongodb.org/linux/mongodb-linux-aarch64-ubuntu2204-6.0.5.tgz", "x86_64": "https://fastdl.mongodb.org/linux/mongodb-linux-x86_64-ubuntu2204-6.0.5.tgz", }, + "23": { + "aarch64": "https://fastdl.mongodb.org/linux/mongodb-linux-aarch64-ubuntu2204-7.0.4.tgz", + "x86_64": "https://fastdl.mongodb.org/linux/mongodb-linux-x86_64-ubuntu2204-7.0.4.tgz", + }, }, } @@ -120,7 +124,7 @@ def _get_download(): MONGODB_BINARIES = ["mongod"] -VERSION = "1.0" +VERSION = "1.1" def get_version(): From 63b26667dc578098901b9767d81644520a0c30b9 Mon Sep 17 00:00:00 2001 From: Ritchie Martori Date: Wed, 20 Dec 2023 11:29:43 -0800 Subject: [PATCH 37/59] v0.23.2 release notes --- docs/source/release-notes.rst | 63 +++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 214d2d3c80..8b40b13366 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -3,6 +3,69 @@ FiftyOne Release Notes .. default-role:: code +.. _release-notes-teams-v1.5.2: + +FiftyOne Teams 1.5.2 +-------------------- +*Released December 20, 2023* + +Includes all updates from :ref:`FiftyOne 0.23.2 `, plus: + +General + +- Added 'label' as searchable field for delegated operations +- Fixed issue where invalid tokens were not causing redirects +- Re-running a delegated operation now uses dataset id instead of name + +.. _release-notes-v0.23.2: + +FiftyOne 0.23.2 +--------------- +*Released December 20, 2023* + +Core + +- Fixed an issue where exporting patches would have incorrect path names + `#3921 `_ +- Removed loading from mongoengine cache + `#3922 `_ +- Fixed overwriting dataset metadata with empty values during import + `#3913 `_ +- Fixed issue where users could not select scalar fields in the sidebar + `#3938 `_ +- Optimized reloading data from mongodb when performing bulk writes + `#3942 `_ +- Added configurable batch sizes to `bulk_write()` + `#3944 `_ + +App + +- Added support for frames-as-videos in nested groups + `#3935 `_ +- Fixed an issue where embeddigns legend did not display full names + `#3927 `_ +- Added a toggle to show/hide fields in the sample modal that have undefined values + `#3937 `_ +- Fixed an issue with the Lightning threshold Reset button + `#3933 `_ +- Fixed an issue where similarity search only worked on the default group slice + `#3912 `_ + +Plugins + +- Added support for the new `@voxel51/utils/delegate` operator + `#3939 `_ +- Added the ability to to search multiple fields in a delegated operation list query + `#3892 `_ +- Fixed backwards compatibility issue with `ctx.secrets` + `#3908 `_ +- Fixed issue with JS plugin app configs + `#3924 `_ +- Improved validation for `delete_selected_samples` and `clone_selected_samples` builtin operators + `#3914 `_ +- Python Operators now load datasets by ID instead of name + `#3920 `_ + .. _release-notes-teams-v1.5.1: FiftyOne Teams 1.5.1 From 515cbcfc617667240f2d039bc539cf9661c14256 Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Wed, 20 Dec 2023 13:35:11 -0600 Subject: [PATCH 38/59] add 3936 --- docs/source/release-notes.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 8b40b13366..06d536b0d6 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -37,6 +37,8 @@ Core `#3942 `_ - Added configurable batch sizes to `bulk_write()` `#3944 `_ +- Added built-in Ubuntu 23 support + `#3936 `_ App From ace82f155cd49efc13ded0aaf7835b446659bd45 Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 14:55:52 -0500 Subject: [PATCH 39/59] fixing docs errors --- docs/source/user_guide/config.rst | 31 +++++++++++++++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/docs/source/user_guide/config.rst b/docs/source/user_guide/config.rst index bd1997a9db..a62664afc5 100644 --- a/docs/source/user_guide/config.rst +++ b/docs/source/user_guide/config.rst @@ -55,7 +55,28 @@ FiftyOne supports the configuration options described below: +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_batch_size` | `FIFTYONE_DEFAULT_BATCH_SIZE` | `None` | A default batch size to use when :ref:`applying models to datasets `. | +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ -| `bulk_write_batch_size` | `FIFTYONE_BULK_WRITE_BATCH_SIZE` | `100_000` | Batch size to use for bulk writing MongoDB operations; cannot be > 100,000 | +| `bulk_write_batch_size` | `FIFTYONE_BULK_WRITE_BATCH_SIZE` | `100,000` | Batch size to use for bulk writing MongoDB operations. Must be <= 100,000. | +| | | | | +| | | | Default changes to 10,000 for the FiftyOne Teams SDK in | +| | | | :ref:`API connection mode `. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `default_batcher` | `FIFTYONE_DEFAULT_BATCHER` | `latency` | Batching implementation to use in some batched database operations such as | +| | | | :meth:`add_samples() `. Supported values | +| | | | are `latency`, `size`, and `static`. | +| | | | | +| | | | `latency` is the default, which uses a dynamic batch size to achieve a target latency | +| | | | of `batcher_target_latency` between calls. The default changes to `size` for the | +| | | | FiftyOne Teams SDK in :ref:`API connection mode `, which targets | +| | | | a size of `batcher_target_size_bytes` for each call. `static` uses a fixed batch size | +| | | | of `batcher_static_size`. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_static_size` | `FIFTYONE_BATCHER_STATIC_SIZE` | `100` | Fixed size of batches. Only used when `default_batcher` is `static`. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_target_size_bytes` | `FIFTYONE_BATCHER_TARGET_SIZE_BYTES`| `2 ** 20` | Target content size of batches, in bytes. Only used when `default_batcher` is `size`. | ++-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ +| `batcher_target_latency` | `FIFTYONE_BATCHER_TARGET_LATENCY` | `0.2` | Target latency between batches, in seconds. Only used when `default_batcher` is | +| | | | `latency`. | +>>>>>>> Stashed changes +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_sequence_idx` | `FIFTYONE_DEFAULT_SEQUENCE_IDX` | `%06d` | The default numeric string pattern to use when writing sequential lists of | | | | | files. | @@ -138,6 +159,9 @@ and the CLI: .. code-block:: text { + "batcher_static_size": 100, + "batcher_target_latency": 0.2, + "batcher_target_size_bytes": 1048576, "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", @@ -185,6 +209,9 @@ and the CLI: .. code-block:: text { + "batcher_static_size": 100, + "batcher_target_latency": 0.2, + "batcher_target_size_bytes": 1048576, "bulk_write_batch_size": 100000, "database_admin": true, "database_dir": "~/.fiftyone/var/lib/mongo", @@ -975,4 +1002,4 @@ code snippet shows how to configure the proxy based on your instance. # before launching the App, configure a proxy_url fo.app_config.proxy_url = "https://.notebook..sagemaker.aws/proxy//" - session = fo.launch_app(port=) \ No newline at end of file + session = fo.launch_app(port=) From 80a9e78a2b087cf481225aa8886c5c57e66a4d5c Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 14:57:36 -0500 Subject: [PATCH 40/59] bad merge --- docs/source/user_guide/config.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/user_guide/config.rst b/docs/source/user_guide/config.rst index a62664afc5..3dce5622ea 100644 --- a/docs/source/user_guide/config.rst +++ b/docs/source/user_guide/config.rst @@ -76,7 +76,6 @@ FiftyOne supports the configuration options described below: +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `batcher_target_latency` | `FIFTYONE_BATCHER_TARGET_LATENCY` | `0.2` | Target latency between batches, in seconds. Only used when `default_batcher` is | | | | | `latency`. | ->>>>>>> Stashed changes +-------------------------------+-------------------------------------+-------------------------------+----------------------------------------------------------------------------------------+ | `default_sequence_idx` | `FIFTYONE_DEFAULT_SEQUENCE_IDX` | `%06d` | The default numeric string pattern to use when writing sequential lists of | | | | | files. | From 75466ade866e9e5de00b7c61f52a6092b579d86d Mon Sep 17 00:00:00 2001 From: Ritchie Martori Date: Wed, 20 Dec 2023 12:00:29 -0800 Subject: [PATCH 41/59] added 3930 --- docs/source/release-notes.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 06d536b0d6..6fea4580b4 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -39,6 +39,8 @@ Core `#3944 `_ - Added built-in Ubuntu 23 support `#3936 `_ +- Fixed evaluation of unclosed polylines + `#3930 `_ App From 3135c7b6dc6a7d5363bfb3be95e8f62ce82ca1a0 Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Thu, 14 Dec 2023 13:22:13 -0600 Subject: [PATCH 42/59] Fix in tokenizer --- fiftyone/utils/open_clip/__init__.py | 70 ++ fiftyone/utils/open_clip/big_vision.py | 239 ++++ .../open_clip/bpe_simple_vocab_16e6.txt.gz | Bin 0 -> 1356917 bytes fiftyone/utils/open_clip/coca_model.py | 593 +++++++++ fiftyone/utils/open_clip/constants.py | 6 + fiftyone/utils/open_clip/factory.py | 543 ++++++++ fiftyone/utils/open_clip/generation_utils.py | 0 fiftyone/utils/open_clip/hf_configs.py | 67 + fiftyone/utils/open_clip/hf_model.py | 249 ++++ fiftyone/utils/open_clip/loss.py | 520 ++++++++ fiftyone/utils/open_clip/model.py | 761 ++++++++++++ .../model_configs/EVA01-g-14-plus.json | 18 + .../open_clip/model_configs/EVA01-g-14.json | 18 + .../open_clip/model_configs/EVA02-B-16.json | 18 + .../model_configs/EVA02-E-14-plus.json | 18 + 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.../model_configs/convnext_base_w_320.json | 19 + .../model_configs/convnext_large.json | 19 + .../model_configs/convnext_large_d.json | 19 + .../model_configs/convnext_large_d_320.json | 19 + .../model_configs/convnext_small.json | 19 + .../model_configs/convnext_tiny.json | 19 + .../model_configs/convnext_xlarge.json | 19 + .../model_configs/convnext_xxlarge.json | 19 + .../model_configs/convnext_xxlarge_320.json | 19 + .../model_configs/mt5-base-ViT-B-32.json | 14 + .../model_configs/mt5-xl-ViT-H-14.json | 15 + .../model_configs/nllb-clip-base-siglip.json | 18 + .../model_configs/nllb-clip-base.json | 15 + .../model_configs/nllb-clip-large-siglip.json | 18 + .../model_configs/nllb-clip-large.json | 16 + .../model_configs/roberta-ViT-B-32.json | 15 + .../swin_base_patch4_window7_224.json | 17 + .../vit_medium_patch16_gap_256.json | 17 + .../vit_relpos_medium_patch16_cls_224.json | 17 + .../xlm-roberta-base-ViT-B-32.json | 14 + .../xlm-roberta-large-ViT-H-14.json | 15 + fiftyone/utils/open_clip/modified_resnet.py | 225 ++++ fiftyone/utils/open_clip/openai.py | 104 ++ fiftyone/utils/open_clip/pos_embed.py | 113 ++ fiftyone/utils/open_clip/pretrained.py | 752 +++++++++++ fiftyone/utils/open_clip/push_to_hf_hub.py | 353 ++++++ fiftyone/utils/open_clip/timm_model.py | 171 +++ fiftyone/utils/open_clip/tokenizer.py | 573 +++++++++ fiftyone/utils/open_clip/transform.py | 485 ++++++++ fiftyone/utils/open_clip/transformer.py | 960 ++++++++++++++ fiftyone/utils/open_clip/utils.py | 105 ++ fiftyone/utils/open_clip/version.py | 1 + .../utils/open_clip/zero_shot_classifier.py | 135 ++ .../utils/open_clip/zero_shot_metadata.py | 1099 +++++++++++++++++ fiftyone/utils/open_clip/zoo.py | 233 ++++ fiftyone/zoo/models/manifest-torch.json | 52 + test.py | 56 + 112 files changed, 10119 insertions(+) create mode 100644 fiftyone/utils/open_clip/__init__.py create mode 100644 fiftyone/utils/open_clip/big_vision.py create mode 100644 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mode 100644 fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json create mode 100644 fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json create mode 100644 fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json create mode 100644 fiftyone/utils/open_clip/modified_resnet.py create mode 100644 fiftyone/utils/open_clip/openai.py create mode 100644 fiftyone/utils/open_clip/pos_embed.py create mode 100644 fiftyone/utils/open_clip/pretrained.py create mode 100644 fiftyone/utils/open_clip/push_to_hf_hub.py create mode 100644 fiftyone/utils/open_clip/timm_model.py create mode 100644 fiftyone/utils/open_clip/tokenizer.py create mode 100644 fiftyone/utils/open_clip/transform.py create mode 100644 fiftyone/utils/open_clip/transformer.py create mode 100644 fiftyone/utils/open_clip/utils.py create mode 100644 fiftyone/utils/open_clip/version.py create mode 100644 fiftyone/utils/open_clip/zero_shot_classifier.py create mode 100644 fiftyone/utils/open_clip/zero_shot_metadata.py create mode 100644 fiftyone/utils/open_clip/zoo.py create mode 100644 test.py diff --git a/fiftyone/utils/open_clip/__init__.py b/fiftyone/utils/open_clip/__init__.py new file mode 100644 index 0000000000..911d8509ea --- /dev/null +++ b/fiftyone/utils/open_clip/__init__.py @@ -0,0 +1,70 @@ +"""CLIP model utils. + +| Copyright 2017-2023, Voxel51, Inc. +| `voxel51.com `_ +| +""" +import types +from .coca_model import CoCa +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import ( + create_model, + create_model_and_transforms, + create_model_from_pretrained, + get_tokenizer, + create_loss, +) +from .factory import ( + list_models, + add_model_config, + get_model_config, + load_checkpoint, +) +from .loss import ClipLoss, DistillClipLoss, CoCaLoss +from .model import ( + CLIP, + CustomTextCLIP, + CLIPTextCfg, + CLIPVisionCfg, + convert_weights_to_lp, + convert_weights_to_fp16, + trace_model, + get_cast_dtype, + get_input_dtype, + get_model_tokenize_cfg, + get_model_preprocess_cfg, + set_model_preprocess_cfg, +) +from .openai import load_openai_model, list_openai_models +from .pretrained import ( + list_pretrained, + list_pretrained_models_by_tag, + list_pretrained_tags_by_model, + get_pretrained_url, + download_pretrained_from_url, + is_pretrained_cfg, + get_pretrained_cfg, + download_pretrained, +) +from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub +from .tokenizer import SimpleTokenizer, tokenize, decode +from .transform import image_transform, AugmentationCfg +from .zero_shot_classifier import ( + build_zero_shot_classifier, + build_zero_shot_classifier_legacy, +) +from .zero_shot_metadata import ( + OPENAI_IMAGENET_TEMPLATES, + SIMPLE_IMAGENET_TEMPLATES, + IMAGENET_CLASSNAMES, +) + + +from .zoo import TorchOpenClipModelConfig, TorchOpenClipModel + +# This enables Sphinx refs to directly use paths imported here +__all__ = [ + k + for k, v in globals().items() + if not k.startswith("_") and not isinstance(v, types.ModuleType) +] diff --git a/fiftyone/utils/open_clip/big_vision.py b/fiftyone/utils/open_clip/big_vision.py new file mode 100644 index 0000000000..a169d9568a --- /dev/null +++ b/fiftyone/utils/open_clip/big_vision.py @@ -0,0 +1,239 @@ +import torch +import numpy as np + +from .model import CustomTextCLIP +from .transformer import TextTransformer, Transformer + + +@torch.no_grad() +def load_big_vision_weights(model: CustomTextCLIP, checkpoint_path: str): + """Load weights from .npz checkpoints for official Google big_vision image-text models + + Currently the SigLIP source models are supported and a CustomTextCLIP destination model + w/ timm image encoder. + """ + from timm.layers import resample_patch_embed, resample_abs_pos_embed + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + interpolation = "bilinear" + antialias = False + + def _convert_timm_img(module, prefix): + embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"]) + if ( + embed_conv_w.shape[-2:] + != module.patch_embed.proj.weight.shape[-2:] + ): + embed_conv_w = resample_patch_embed( + embed_conv_w, + module.patch_embed.proj.weight.shape[-2:], + interpolation=interpolation, + antialias=antialias, + verbose=True, + ) + module.patch_embed.proj.weight.copy_(embed_conv_w) + module.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"])) + + if module.cls_token is not None: + module.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False)) + + pos_embed_w = _n2p(w[f"{prefix}pos_embedding"], t=False) + if pos_embed_w.shape != module.pos_embed.shape: + assert False, f"{pos_embed_w.shape}, {module.pos_embed.shape}" + num_prefix_tokens = ( + 0 + if getattr(module, "no_embed_class", False) + else getattr(module, "num_prefix_tokens", 1) + ) + pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, + new_size=module.patch_embed.grid_size, + num_prefix_tokens=num_prefix_tokens, + interpolation=interpolation, + antialias=antialias, + verbose=True, + ) + module.pos_embed.copy_(pos_embed_w) + + mha_sub, b_sub, ln1_sub = (0, 0, 1) + for i, block in enumerate(module.blocks.children()): + block_prefix = f"{prefix}Transformer/encoderblock_{i}/" + mha_prefix = ( + block_prefix + f"MultiHeadDotProductAttention_{mha_sub}/" + ) + block.norm1.weight.copy_( + _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) + ) + block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"])) + block.attn.qkv.weight.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) + .flatten(1) + .T + for n in ("query", "key", "value") + ] + ) + ) + block.attn.qkv.bias.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) + for n in ("query", "key", "value") + ] + ) + ) + block.attn.proj.weight.copy_( + _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) + ) + block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) + for r in range(2): + getattr(block.mlp, f"fc{r + 1}").weight.copy_( + _n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel"]) + ) + getattr(block.mlp, f"fc{r + 1}").bias.copy_( + _n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias"]) + ) + block.norm2.weight.copy_( + _n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/scale"]) + ) + block.norm2.bias.copy_( + _n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/bias"]) + ) + + module.norm.weight.copy_( + _n2p(w[f"{prefix}Transformer/encoder_norm/scale"]) + ) + module.norm.bias.copy_( + _n2p(w[f"{prefix}Transformer/encoder_norm/bias"]) + ) + + if module.attn_pool is not None: + block_prefix = f"{prefix}MAPHead_0/" + mha_prefix = block_prefix + f"MultiHeadDotProductAttention_0/" + module.attn_pool.latent.copy_( + _n2p(w[f"{block_prefix}probe"], t=False) + ) + module.attn_pool.q.weight.copy_( + _n2p(w[f"{mha_prefix}query/kernel"], t=False).flatten(1).T + ) + module.attn_pool.q.bias.copy_( + _n2p(w[f"{mha_prefix}query/bias"], t=False).reshape(-1) + ) + module.attn_pool.kv.weight.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) + .flatten(1) + .T + for n in ("key", "value") + ] + ) + ) + module.attn_pool.kv.bias.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) + for n in ("key", "value") + ] + ) + ) + module.attn_pool.proj.weight.copy_( + _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) + ) + module.attn_pool.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) + module.attn_pool.norm.weight.copy_( + _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) + ) + module.attn_pool.norm.bias.copy_( + _n2p(w[f"{block_prefix}LayerNorm_0/bias"]) + ) + for r in range(2): + getattr(module.attn_pool.mlp, f"fc{r + 1}").weight.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/kernel"]) + ) + getattr(module.attn_pool.mlp, f"fc{r + 1}").bias.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/bias"]) + ) + + def _convert_openclip_transformer(module: Transformer, prefix): + for i, block in enumerate(module.resblocks.children()): + block_prefix = f"{prefix}encoderblock_{i}/" + mha_prefix = block_prefix + f"MultiHeadDotProductAttention_0/" + block.ln_1.weight.copy_( + _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) + ) + block.ln_1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"])) + block.attn.in_proj_weight.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) + .flatten(1) + .T + for n in ("query", "key", "value") + ] + ) + ) + block.attn.in_proj_bias.copy_( + torch.cat( + [ + _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) + for n in ("query", "key", "value") + ] + ) + ) + block.attn.out_proj.weight.copy_( + _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) + ) + block.attn.out_proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) + block.ln_2.weight.copy_( + _n2p(w[f"{block_prefix}LayerNorm_1/scale"]) + ) + block.ln_2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_1/bias"])) + block.mlp.c_fc.weight.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_0/kernel"]) + ) + block.mlp.c_fc.bias.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_0/bias"]) + ) + block.mlp.c_proj.weight.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_1/kernel"]) + ) + block.mlp.c_proj.bias.copy_( + _n2p(w[f"{block_prefix}MlpBlock_0/Dense_1/bias"]) + ) + + def _convert_openclip_txt(module: TextTransformer, prefix): + module.token_embedding.weight.copy_( + _n2p(w[f"{prefix}Embed_0/embedding"], t=False) + ) + pos_embed_w = _n2p(w[f"{prefix}pos_embedding"], t=False).squeeze(0) + module.positional_embedding.copy_(pos_embed_w) + _convert_openclip_transformer( + module.transformer, prefix=prefix + "Encoder_0/" + ) + module.ln_final.weight.copy_( + _n2p(w[f"{prefix}Encoder_0/encoder_norm/scale"]) + ) + module.ln_final.bias.copy_( + _n2p(w[f"{prefix}Encoder_0/encoder_norm/bias"]) + ) + module.text_projection.weight.copy_(_n2p(w[f"{prefix}head/kernel"])) + module.text_projection.bias.copy_(_n2p(w[f"{prefix}head/bias"])) + + _convert_timm_img(model.visual.trunk, "params/img/") + _convert_openclip_txt(model.text, "params/txt/") + model.logit_bias.copy_(_n2p(w["params/b"])[0]) + model.logit_scale.copy_(_n2p(w["params/t"])[0]) diff --git a/fiftyone/utils/open_clip/bpe_simple_vocab_16e6.txt.gz b/fiftyone/utils/open_clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..7b5088a527f720063f044eb928eee315f63b2fc0 GIT binary patch literal 1356917 zcmV(nK=QvIiwFQl6I)*Z19ZK~vMkwgB)E^SaG(|_PzuTJUep5J0`N~DK81(h@F{(W zxN)TxRpd|fvY8l2bh9uNNe~1;Qsm*`zuHufsU3f;?gfyU@7){Weg+%V)YQIRE$xrC zeq4t3M~}HKs~`QZ|GE9oU+wSve|WU(*3Z-Ti~r@T|LxKj(`7Gim(u>Z7Onkry|nhf z{Z_R9$DcocaOtO_C?efA9V8_G0Eg*J8Fm 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ztxONk0vixS8S>(hzAbw(#0>;C#ks|_ha+((8DqrW_Ad=hHlbPG7JCqoQYc=?dg}X> z=@SQQ4dhuRpq4|p(g#`?$z1JCMe_-s9ZCq=j%b8zcK3ZHG1D=BRYyW{d_f?@sE3m= z&|HmgkeI;Mhm!J^35$8OxjG^dfASN<0q3^NpF2JSh2Ov5RXTanenuCT&t8oT^u^mh zg}2{*uTwPnR^NXf-ae+l{Il@(HKu7_1qjU1RQlcj|G(kSeL{bx0Jc0m-n()VY;oHw zIzVB?O@_32+^NgTKrF&CdZi=_#;fQd&@66bKGZ$rBa(EUG?_*5Y5sg0q!@<>3)C3x zDHjQ08Yct5byJU)RZx}jpJ%P1PloKt@FZl9Bj8yPOMy-m){oxJYiEr>nJ)qwWT{Qp zj_mW)R04a=pMh1%Dv(aj1Q1nfowhih;WqMv}`9*1Gi{T$c5f9??B^HHhv{z(}TVD%q4)no;HK6^>A)q~X hSpdDrox+^4cgglrz7rRzDPZO0{{oPT@#kp3FaTBF64n3! literal 0 HcmV?d00001 diff --git a/fiftyone/utils/open_clip/coca_model.py b/fiftyone/utils/open_clip/coca_model.py new file mode 100644 index 0000000000..84b4781d67 --- /dev/null +++ b/fiftyone/utils/open_clip/coca_model.py @@ -0,0 +1,593 @@ +from typing import Optional + +import torch +from torch import nn +from torch.nn import functional as F +import numpy as np +from dataclasses import dataclass + +from .transformer import ( + LayerNormFp32, + LayerNorm, + QuickGELU, + MultimodalTransformer, +) +from .model import ( + CLIPTextCfg, + CLIPVisionCfg, + _build_vision_tower, + _build_text_tower, +) + +try: + from transformers import ( + BeamSearchScorer, + LogitsProcessorList, + TopPLogitsWarper, + TopKLogitsWarper, + RepetitionPenaltyLogitsProcessor, + MinLengthLogitsProcessor, + MaxLengthCriteria, + StoppingCriteriaList, + ) + + GENERATION_TYPES = { + "top_k": TopKLogitsWarper, + "top_p": TopPLogitsWarper, + "beam_search": "beam_search", + } + _has_transformers = True +except ImportError as e: + GENERATION_TYPES = { + "top_k": None, + "top_p": None, + "beam_search": "beam_search", + } + _has_transformers = False + + +@dataclass +class MultimodalCfg(CLIPTextCfg): + mlp_ratio: int = 4 + dim_head: int = 64 + heads: int = 8 + n_queries: int = 256 + attn_pooler_heads: int = 8 + + +def _build_text_decoder_tower( + embed_dim, + multimodal_cfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + multimodal_cfg = ( + MultimodalCfg(**multimodal_cfg) + if isinstance(multimodal_cfg, dict) + else multimodal_cfg + ) + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = ( + LayerNormFp32 + if cast_dtype in (torch.float16, torch.bfloat16) + else LayerNorm + ) + + decoder = MultimodalTransformer( + context_length=multimodal_cfg.context_length, + width=multimodal_cfg.width, + heads=multimodal_cfg.heads, + layers=multimodal_cfg.layers, + ls_init_value=multimodal_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return decoder + + +class CoCa(nn.Module): + def __init__( + self, + embed_dim, + multimodal_cfg: MultimodalCfg, + text_cfg: CLIPTextCfg, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + init_logit_scale: float = np.log(1 / 0.07), + init_logit_bias: Optional[float] = None, + cast_dtype: Optional[torch.dtype] = None, + pad_id: int = 0, + ): + super().__init__() + multimodal_cfg = ( + MultimodalCfg(**multimodal_cfg) + if isinstance(multimodal_cfg, dict) + else multimodal_cfg + ) + text_cfg = ( + CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg + ) + vision_cfg = ( + CLIPVisionCfg(**vision_cfg) + if isinstance(vision_cfg, dict) + else vision_cfg + ) + + self.text = _build_text_tower( + embed_dim=embed_dim, + text_cfg=text_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + vocab_size = ( + text_cfg.vocab_size # for hf models + if hasattr(text_cfg, "hf_model_name") + and text_cfg.hf_model_name is not None + else text_cfg.vocab_size + ) + + self.visual = _build_vision_tower( + embed_dim=embed_dim, + vision_cfg=vision_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.text_decoder = _build_text_decoder_tower( + vocab_size, + multimodal_cfg=multimodal_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) + if init_logit_bias is not None: + self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) + else: + self.logit_bias = None + self.pad_id = pad_id + + self.context_length = multimodal_cfg.context_length + + @torch.jit.ignore + def set_grad_checkpointing(self, enable: bool = True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + self.text_decoder.set_grad_checkpointing(enable) + + def _encode_image(self, images, normalize: bool = True): + image_latent, tokens_embs = self.visual(images) + image_latent = ( + F.normalize(image_latent, dim=-1) if normalize else image_latent + ) + return image_latent, tokens_embs + + def _encode_text(self, text, normalize: bool = True): + text_latent, token_emb = self.text(text) + text_latent = ( + F.normalize(text_latent, dim=-1) if normalize else text_latent + ) + return text_latent, token_emb + + def encode_image(self, images, normalize: bool = True): + image_latent, _ = self._encode_image(images, normalize=normalize) + return image_latent + + def encode_text(self, text, normalize: bool = True): + text_latent, _ = self._encode_text(text, normalize=normalize) + return text_latent + + def forward( + self, + image, + text: Optional[torch.Tensor] = None, + image_latent: Optional[torch.Tensor] = None, + image_embs: Optional[torch.Tensor] = None, + ): + if image_latent is None or image_embs is None: + image_latent, image_embs = self._encode_image(image) + + if text is None: + return {"image_features": image_latent, "image_embs": image_embs} + + text_latent, token_embs = self._encode_text(text) + + # TODO: add assertion to avoid bugs? + labels = text[:, -token_embs.shape[1] :] + + logits = self.text_decoder(image_embs, token_embs) + out_dict = { + "image_features": image_latent, + "text_features": text_latent, + "logits": logits, + "labels": labels, + "logit_scale": self.logit_scale.exp(), + } + if self.logit_bias is not None: + out_dict["logit_bias"] = self.logit_bias + return out_dict + + def generate( + self, + image, + text=None, + seq_len=30, + max_seq_len=77, + temperature=1.0, + generation_type="beam_search", + top_p=0.1, # keep tokens in the 1 - top_p quantile + top_k=1, # keeps the top_k most probable tokens + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + repetition_penalty=1.0, + fixed_output_length=False, # if True output.shape == (batch_size, seq_len) + ): + # taking many ideas and components from HuggingFace GenerationMixin + # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation + assert ( + _has_transformers + ), "Please install transformers for generate functionality. `pip install transformers`." + assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len" + + with torch.no_grad(): + sot_token_id = 49406 if sot_token_id is None else sot_token_id + eos_token_id = 49407 if eos_token_id is None else eos_token_id + pad_token_id = ( + self.pad_id if pad_token_id is None else pad_token_id + ) + logit_processor = LogitsProcessorList( + [ + MinLengthLogitsProcessor(min_seq_len, eos_token_id), + RepetitionPenaltyLogitsProcessor(repetition_penalty), + ] + ) + + if stopping_criteria is None: + stopping_criteria = [MaxLengthCriteria(max_length=seq_len)] + + stopping_criteria = StoppingCriteriaList(stopping_criteria) + + device = image.device + + if generation_type == "beam_search": + output = self._generate_beamsearch( + image_inputs=image, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + sot_token_id=sot_token_id, + num_beams=num_beams, + num_beam_groups=num_beam_groups, + min_seq_len=min_seq_len, + stopping_criteria=stopping_criteria, + logit_processor=logit_processor, + ) + if fixed_output_length and output.shape[1] < seq_len: + return torch.cat( + ( + output, + torch.ones( + output.shape[0], + seq_len - output.shape[1], + device=device, + dtype=output.dtype, + ) + * self.pad_id, + ), + dim=1, + ) + return output + + elif generation_type == "top_p": + logit_warper = GENERATION_TYPES[generation_type](top_p) + elif generation_type == "top_k": + logit_warper = GENERATION_TYPES[generation_type](top_k) + else: + raise ValueError( + f"generation_type has to be one of " + f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}." + ) + + image_latent, image_embs = self._encode_image(image) + + if text is None: + text = ( + torch.ones( + (image.shape[0], 1), device=device, dtype=torch.long + ) + * sot_token_id + ) + + was_training = self.training + num_dims = len(text.shape) + + if num_dims == 1: + text = text[None, :] + + cur_len = text.shape[1] + self.eval() + out = text + + while True: + x = out[:, -max_seq_len:] + cur_len = x.shape[1] + logits = self( + image, x, image_latent=image_latent, image_embs=image_embs + )["logits"][:, -1] + mask = (out[:, -1] == eos_token_id) | ( + out[:, -1] == pad_token_id + ) + sample = ( + torch.ones( + (out.shape[0], 1), device=device, dtype=torch.long + ) + * pad_token_id + ) + + if mask.all(): + if not fixed_output_length: + break + else: + logits = logits[~mask, :] + filtered_logits = logit_processor(x[~mask, :], logits) + filtered_logits = logit_warper( + x[~mask, :], filtered_logits + ) + probs = F.softmax(filtered_logits / temperature, dim=-1) + + if cur_len + 1 == seq_len: + sample[~mask, :] = ( + torch.ones( + (sum(~mask), 1), + device=device, + dtype=torch.long, + ) + * eos_token_id + ) + else: + sample[~mask, :] = torch.multinomial(probs, 1) + + out = torch.cat((out, sample), dim=-1) + + cur_len += 1 + + if stopping_criteria(out, None): + break + + if num_dims == 1: + out = out.squeeze(0) + + self.train(was_training) + return out + + def _generate_beamsearch( + self, + image_inputs, + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + logit_processor=None, + logit_warper=None, + ): + device = image_inputs.device + batch_size = image_inputs.shape[0] + image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0) + image_latent, image_embs = self._encode_image(image_inputs) + + input_ids = torch.ones( + (batch_size * num_beams, 1), device=device, dtype=torch.long + ) + input_ids = input_ids * sot_token_id + beam_scorer = BeamSearchScorer( + batch_size=batch_size, + num_beams=num_beams, + device=device, + num_beam_groups=num_beam_groups, + ) + # instantiate logits processors + logits_processor = ( + LogitsProcessorList( + [ + MinLengthLogitsProcessor( + min_seq_len, eos_token_id=eos_token_id + ) + ] + ) + if logit_processor is None + else logit_processor + ) + + num_beams = beam_scorer.num_beams + num_beam_groups = beam_scorer.num_beam_groups + num_sub_beams = num_beams // num_beam_groups + batch_size = len(beam_scorer._beam_hyps) // num_beam_groups + batch_beam_size, cur_len = input_ids.shape + beam_indices = None + + if num_beams * batch_size != batch_beam_size: + raise ValueError( + f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." + ) + + beam_scores = torch.full( + (batch_size, num_beams), -1e9, dtype=torch.float, device=device + ) + # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in + # the same group don't produce same tokens everytime. + beam_scores[:, ::num_sub_beams] = 0 + beam_scores = beam_scores.view((batch_size * num_beams,)) + + while True: + + # predicted tokens in cur_len step + current_tokens = torch.zeros( + batch_size * num_beams, dtype=input_ids.dtype, device=device + ) + + # indices which will form the beams in the next time step + reordering_indices = torch.zeros( + batch_size * num_beams, dtype=torch.long, device=device + ) + + # do one decoder step on all beams of all sentences in batch + model_inputs = prepare_inputs_for_generation( + input_ids=input_ids, image_inputs=image_inputs + ) + outputs = self( + model_inputs["images"], + model_inputs["text"], + image_latent=image_latent, + image_embs=image_embs, + ) + + for beam_group_idx in range(num_beam_groups): + group_start_idx = beam_group_idx * num_sub_beams + group_end_idx = min(group_start_idx + num_sub_beams, num_beams) + group_size = group_end_idx - group_start_idx + + # indices of beams of current group among all sentences in batch + batch_group_indices = [] + + for batch_idx in range(batch_size): + batch_group_indices.extend( + [ + batch_idx * num_beams + idx + for idx in range(group_start_idx, group_end_idx) + ] + ) + group_input_ids = input_ids[batch_group_indices] + + # select outputs of beams of currentg group only + next_token_logits = outputs["logits"][ + batch_group_indices, -1, : + ] + vocab_size = next_token_logits.shape[-1] + + next_token_scores_processed = logits_processor( + group_input_ids, + next_token_logits, + current_tokens=current_tokens, + beam_group_idx=beam_group_idx, + ) + next_token_scores = next_token_scores_processed + beam_scores[ + batch_group_indices + ].unsqueeze(-1) + next_token_scores = next_token_scores.expand_as( + next_token_scores_processed + ) + + # reshape for beam search + next_token_scores = next_token_scores.view( + batch_size, group_size * vocab_size + ) + + next_token_scores, next_tokens = torch.topk( + next_token_scores, + 2 * group_size, + dim=1, + largest=True, + sorted=True, + ) + + next_indices = torch.div( + next_tokens, vocab_size, rounding_mode="floor" + ) + next_tokens = next_tokens % vocab_size + + # stateless + process_beam_indices = ( + sum(beam_indices, ()) if beam_indices is not None else None + ) + beam_outputs = beam_scorer.process( + group_input_ids, + next_token_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + beam_indices=process_beam_indices, + group_index=beam_group_idx, + ) + beam_scores[batch_group_indices] = beam_outputs[ + "next_beam_scores" + ] + beam_next_tokens = beam_outputs["next_beam_tokens"] + beam_idx = beam_outputs["next_beam_indices"] + + input_ids[batch_group_indices] = group_input_ids[beam_idx] + group_input_ids = torch.cat( + [ + group_input_ids[beam_idx, :], + beam_next_tokens.unsqueeze(-1), + ], + dim=-1, + ) + current_tokens[batch_group_indices] = group_input_ids[:, -1] + + # (beam_idx // group_size) -> batch_idx + # (beam_idx % group_size) -> offset of idx inside the group + reordering_indices[batch_group_indices] = ( + num_beams + * torch.div(beam_idx, group_size, rounding_mode="floor") + + group_start_idx + + (beam_idx % group_size) + ) + + input_ids = torch.cat( + [input_ids, current_tokens.unsqueeze(-1)], dim=-1 + ) + + # increase cur_len + cur_len = cur_len + 1 + if beam_scorer.is_done or stopping_criteria(input_ids, None): + break + + final_beam_indices = ( + sum(beam_indices, ()) if beam_indices is not None else None + ) + sequence_outputs = beam_scorer.finalize( + input_ids, + beam_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + max_length=stopping_criteria.max_length, + beam_indices=final_beam_indices, + ) + return sequence_outputs["sequences"] + + +def prepare_inputs_for_generation( + input_ids, image_inputs, past=None, **kwargs +): + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + else: + position_ids = None + return { + "text": input_ids, + "images": image_inputs, + "past_key_values": past, + "position_ids": position_ids, + "attention_mask": attention_mask, + } diff --git a/fiftyone/utils/open_clip/constants.py b/fiftyone/utils/open_clip/constants.py new file mode 100644 index 0000000000..599c48c03f --- /dev/null +++ b/fiftyone/utils/open_clip/constants.py @@ -0,0 +1,6 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) +INCEPTION_MEAN = (0.5, 0.5, 0.5) +INCEPTION_STD = (0.5, 0.5, 0.5) diff --git a/fiftyone/utils/open_clip/factory.py b/fiftyone/utils/open_clip/factory.py new file mode 100644 index 0000000000..92816a9670 --- /dev/null +++ b/fiftyone/utils/open_clip/factory.py @@ -0,0 +1,543 @@ +import json +import logging +import os +import re +from copy import deepcopy +from dataclasses import asdict +from pathlib import Path +from typing import Any, Dict, Optional, Tuple, Union + +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import ( + CLIP, + CustomTextCLIP, + convert_weights_to_lp, + convert_to_custom_text_state_dict, + resize_pos_embed, + get_cast_dtype, + resize_text_pos_embed, + set_model_preprocess_cfg, +) +from .coca_model import CoCa +from .loss import ClipLoss, DistillClipLoss, CoCaLoss, SigLipLoss +from .openai import load_openai_model +from .pretrained import ( + is_pretrained_cfg, + get_pretrained_cfg, + download_pretrained, + list_pretrained_tags_by_model, + download_pretrained_from_hf, +) +from .transform import ( + image_transform_v2, + AugmentationCfg, + PreprocessCfg, + merge_preprocess_dict, + merge_preprocess_kwargs, +) +from .tokenizer import HFTokenizer, SimpleTokenizer, DEFAULT_CONTEXT_LENGTH + +HF_HUB_PREFIX = "hf-hub:" +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = ( + {} +) # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [ + int(s) if s.isdigit() else s + for s in re.split(r"(\d+)", string_.lower()) + ] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = (".json",) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f"*{ext}")) + + for cf in config_files: + with open(cf, "r") as f: + model_cfg = json.load(f) + if all( + a in model_cfg for a in ("embed_dim", "vision_cfg", "text_cfg") + ): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = { + k: v + for k, v in sorted( + _MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]) + ) + } + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """enumerate available model architectures based on config files""" + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """add model config path or file and update registry""" + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def _get_hf_config(model_id, cache_dir=None): + config_path = download_pretrained_from_hf( + model_id, filename="open_clip_config.json", cache_dir=cache_dir + ) + with open(config_path, "r", encoding="utf-8") as f: + config = json.load(f) + return config + + +def get_tokenizer( + model_name: str = "", + context_length: Optional[int] = None, + **kwargs, +): + if model_name.startswith(HF_HUB_PREFIX): + model_name = model_name[len(HF_HUB_PREFIX) :] + try: + config = _get_hf_config(model_name)["model_cfg"] + except Exception: + tokenizer = HFTokenizer( + model_name, + context_length=context_length or DEFAULT_CONTEXT_LENGTH, + **kwargs, + ) + return tokenizer + else: + config = get_model_config(model_name) + assert ( + config is not None + ), f"No valid model config found for {model_name}." + + text_config = config.get("text_cfg", {}) + if "tokenizer_kwargs" in text_config: + tokenizer_kwargs = dict(text_config["tokenizer_kwargs"], **kwargs) + else: + tokenizer_kwargs = kwargs + + if context_length is None: + context_length = text_config.get( + "context_length", DEFAULT_CONTEXT_LENGTH + ) + + if "hf_tokenizer_name" in text_config: + tokenizer = HFTokenizer( + text_config["hf_tokenizer_name"], + context_length=context_length, + **tokenizer_kwargs, + ) + else: + tokenizer = SimpleTokenizer( + context_length=context_length, + **tokenizer_kwargs, + ) + + return tokenizer + + +def load_state_dict(checkpoint_path: str, map_location="cpu"): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + if isinstance(checkpoint, dict) and "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + elif isinstance(checkpoint, torch.jit.ScriptModule): + state_dict = checkpoint.state_dict() + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith("module"): + state_dict = {k[7:]: v for k, v in state_dict.items()} + return state_dict + + +def load_checkpoint(model, checkpoint_path, strict=True): + if Path(checkpoint_path).suffix in (".npz", ".npy"): + from .big_vision import load_big_vision_weights + + load_big_vision_weights(model, checkpoint_path) + return {} + + state_dict = load_state_dict(checkpoint_path) + # detect old format and make compatible with new format + if "positional_embedding" in state_dict and not hasattr( + model, "positional_embedding" + ): + state_dict = convert_to_custom_text_state_dict(state_dict) + # If loading a non-SigLIP model for SigLIP training. See https://github.com/mlfoundations/open_clip/issues/712 + if "logit_bias" not in state_dict and model.logit_bias is not None: + state_dict["logit_bias"] = torch.zeros_like(state_dict["logit_scale"]) + # Certain text transformers no longer expect position_ids after transformers==4.31 + position_id_key = "text.transformer.embeddings.position_ids" + if position_id_key in state_dict and not hasattr(model, position_id_key): + del state_dict[position_id_key] + resize_pos_embed(state_dict, model) + resize_text_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + return incompatible_keys + + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = "fp32", + device: Union[str, torch.device] = "cpu", + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + force_preprocess_cfg: Optional[Dict[str, Any]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + cache_dir: Optional[str] = None, + output_dict: Optional[bool] = None, + require_pretrained: bool = False, + **model_kwargs, +): + force_preprocess_cfg = force_preprocess_cfg or {} + preprocess_cfg = asdict(PreprocessCfg()) + has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX) + if has_hf_hub_prefix: + model_id = model_name[len(HF_HUB_PREFIX) :] + checkpoint_path = download_pretrained_from_hf( + model_id, cache_dir=cache_dir + ) + config = _get_hf_config(model_id, cache_dir) + preprocess_cfg = merge_preprocess_dict( + preprocess_cfg, config["preprocess_cfg"] + ) + model_cfg = config["model_cfg"] + pretrained_hf = ( + False # override, no need to load original HF text weights + ) + else: + model_name = model_name.replace( + "/", "-" + ) # for callers using old naming with / in ViT names + checkpoint_path = None + model_cfg = None + + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == "openai": + logging.info(f"Loading pretrained {model_name} from OpenAI.") + model = load_openai_model( + model_name, + precision=precision, + device=device, + cache_dir=cache_dir, + ) + else: + model_cfg = model_cfg or get_model_config(model_name) + if model_cfg is not None: + logging.info(f"Loaded {model_name} model config.") + else: + logging.error( + f"Model config for {model_name} not found; available models {list_models()}." + ) + raise RuntimeError(f"Model config for {model_name} not found.") + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout + + if force_image_size is not None: + # override model config's image size + model_cfg["vision_cfg"]["image_size"] = force_image_size + + is_timm_model = "timm_model_name" in model_cfg.get("vision_cfg", {}) + if pretrained_image: + if is_timm_model: + # pretrained weight loading for timm models set via vision_cfg + model_cfg["vision_cfg"]["timm_model_pretrained"] = True + else: + assert ( + False + ), "pretrained image towers currently only supported for timm models" + + # cast_dtype set for fp16 and bf16 (manual mixed-precision), not set for 'amp' or 'pure' modes + cast_dtype = get_cast_dtype(precision) + is_hf_model = "hf_model_name" in model_cfg.get("text_cfg", {}) + if is_hf_model: + # load pretrained weights for HF text model IFF no CLIP weights being loaded + model_cfg["text_cfg"]["hf_model_pretrained"] = ( + pretrained_hf and not pretrained + ) + custom_text = ( + model_cfg.pop("custom_text", False) + or force_custom_text + or is_hf_model + ) + + model_cfg = dict( + model_cfg, **model_kwargs + ) # merge cfg dict w/ kwargs (kwargs overrides cfg) + if custom_text: + if "multimodal_cfg" in model_cfg: + model = CoCa(**model_cfg, cast_dtype=cast_dtype) + else: + model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + if precision in ("fp16", "bf16"): + dtype = torch.float16 if "fp16" in precision else torch.bfloat16 + # manual mixed precision that matches original OpenAI behaviour + if is_timm_model: + # FIXME this is a bit janky, create timm based model in low-precision and + # then cast only LayerNormFp32 instances back to float32 so they don't break. + # Why? The convert_weights_to_lp fn only works with native models. + model.to(device=device, dtype=dtype) + from .transformer import LayerNormFp32 + + def _convert_ln(m): + if isinstance(m, LayerNormFp32): + m.weight.data = m.weight.data.to(torch.float32) + m.bias.data = m.bias.data.to(torch.float32) + + model.apply(_convert_ln) + else: + model.to(device=device) + convert_weights_to_lp(model, dtype=dtype) + elif precision in ("pure_fp16", "pure_bf16"): + dtype = torch.float16 if "fp16" in precision else torch.bfloat16 + model.to(device=device, dtype=dtype) + else: + model.to(device=device) + + pretrained_loaded = False + if pretrained: + checkpoint_path = "" + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained( + pretrained_cfg, cache_dir=cache_dir + ) + preprocess_cfg = merge_preprocess_dict( + preprocess_cfg, pretrained_cfg + ) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info( + f"Loading pretrained {model_name} weights ({pretrained})." + ) + load_checkpoint(model, checkpoint_path) + else: + error_str = ( + f"Pretrained weights ({pretrained}) not found for model {model_name}." + f" Available pretrained tags ({list_pretrained_tags_by_model(model_name)}." + ) + logging.warning(error_str) + raise RuntimeError(error_str) + pretrained_loaded = True + elif has_hf_hub_prefix: + logging.info( + f"Loading pretrained {model_name} weights ({checkpoint_path})." + ) + load_checkpoint(model, checkpoint_path) + pretrained_loaded = True + + if require_pretrained and not pretrained_loaded: + # callers of create_model_from_pretrained always expect pretrained weights + raise RuntimeError( + f"Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded." + ) + + if output_dict and hasattr(model, "output_dict"): + model.output_dict = True + + if jit: + model = torch.jit.script(model) + + # set image preprocessing configuration in model attributes for convenience + if getattr(model.visual, "image_size", None) is not None: + # use image_size set on model creation (via config or force_image_size arg) + force_preprocess_cfg["size"] = model.visual.image_size + set_model_preprocess_cfg( + model, merge_preprocess_dict(preprocess_cfg, force_preprocess_cfg) + ) + + return model + + +def create_loss(args): + if args.distill: + return DistillClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + elif "coca" in args.model.lower(): + return CoCaLoss( + caption_loss_weight=args.coca_caption_loss_weight, + clip_loss_weight=args.coca_contrastive_loss_weight, + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + elif args.siglip: + assert not args.horovod, "Horovod not currently supported for SigLip" + return SigLipLoss( + rank=args.rank, + world_size=args.world_size, + ) + return ClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = "fp32", + device: Union[str, torch.device] = "cpu", + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + image_interpolation: Optional[str] = None, + image_resize_mode: Optional[str] = None, # only effective for inference + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + cache_dir: Optional[str] = None, + output_dict: Optional[bool] = None, + **model_kwargs, +): + force_preprocess_cfg = merge_preprocess_kwargs( + {}, + mean=image_mean, + std=image_std, + interpolation=image_interpolation, + resize_mode=image_resize_mode, + ) + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_patch_dropout=force_patch_dropout, + force_image_size=force_image_size, + force_preprocess_cfg=force_preprocess_cfg, + pretrained_image=pretrained_image, + pretrained_hf=pretrained_hf, + cache_dir=cache_dir, + output_dict=output_dict, + **model_kwargs, + ) + + pp_cfg = PreprocessCfg(**model.visual.preprocess_cfg) + + preprocess_train = image_transform_v2( + pp_cfg, + is_train=True, + aug_cfg=aug_cfg, + ) + preprocess_val = image_transform_v2( + pp_cfg, + is_train=False, + ) + + return model, preprocess_train, preprocess_val + + +def create_model_from_pretrained( + model_name: str, + pretrained: Optional[str] = None, + precision: str = "fp32", + device: Union[str, torch.device] = "cpu", + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + image_interpolation: Optional[str] = None, + image_resize_mode: Optional[str] = None, # only effective for inference + return_transform: bool = True, + cache_dir: Optional[str] = None, + **model_kwargs, +): + force_preprocess_cfg = merge_preprocess_kwargs( + {}, + mean=image_mean, + std=image_std, + interpolation=image_interpolation, + resize_mode=image_resize_mode, + ) + + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_image_size=force_image_size, + force_preprocess_cfg=force_preprocess_cfg, + cache_dir=cache_dir, + require_pretrained=True, + **model_kwargs, + ) + + if not return_transform: + return model + + preprocess = image_transform_v2( + PreprocessCfg(**model.visual.preprocess_cfg), + is_train=False, + ) + + return model, preprocess diff --git a/fiftyone/utils/open_clip/generation_utils.py b/fiftyone/utils/open_clip/generation_utils.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/fiftyone/utils/open_clip/hf_configs.py b/fiftyone/utils/open_clip/hf_configs.py new file mode 100644 index 0000000000..c012d1a795 --- /dev/null +++ b/fiftyone/utils/open_clip/hf_configs.py @@ -0,0 +1,67 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings", + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings", + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens", + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/bert + "bert": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + }, + "pooler": "cls_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/m2m_100 + "m2m_100": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "encoder_attention_heads", + "layers": "encoder_layers", + }, + "pooler": "cls_pooler", + }, +} diff --git a/fiftyone/utils/open_clip/hf_model.py b/fiftyone/utils/open_clip/hf_model.py new file mode 100644 index 0000000000..e192e9a713 --- /dev/null +++ b/fiftyone/utils/open_clip/hf_model.py @@ -0,0 +1,249 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" +import re + +import torch +import torch.nn as nn + +try: + import transformers + from transformers import ( + AutoModel, + AutoTokenizer, + AutoConfig, + PretrainedConfig, + ) + from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + BaseModelOutputWithPoolingAndCrossAttentions, + ) +except ImportError as e: + transformers = None + + class BaseModelOutput: + pass + + class PretrainedConfig: + pass + + +from .hf_configs import arch_dict + + +# utils +def _camel2snake(s): + return re.sub(r"(? torch.Tensor: + # calculated ground-truth and cache if enabled + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + return labels + + def get_logits(self, image_features, text_features, logit_scale): + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, + text_features, + self.local_loss, + self.gather_with_grad, + self.rank, + self.world_size, + self.use_horovod, + ) + + if self.local_loss: + logits_per_image = ( + logit_scale * image_features @ all_text_features.T + ) + logits_per_text = ( + logit_scale * text_features @ all_image_features.T + ) + else: + logits_per_image = ( + logit_scale * all_image_features @ all_text_features.T + ) + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + + return logits_per_image, logits_per_text + + def forward( + self, image_features, text_features, logit_scale, output_dict=False + ): + device = image_features.device + logits_per_image, logits_per_text = self.get_logits( + image_features, text_features, logit_scale + ) + + labels = self.get_ground_truth(device, logits_per_image.shape[0]) + + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + return {"contrastive_loss": total_loss} if output_dict else total_loss + + +class CoCaLoss(ClipLoss): + def __init__( + self, + caption_loss_weight, + clip_loss_weight, + pad_id=0, # pad_token for open_clip custom tokenizer + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + ): + super().__init__( + local_loss=local_loss, + gather_with_grad=gather_with_grad, + cache_labels=cache_labels, + rank=rank, + world_size=world_size, + use_horovod=use_horovod, + ) + + self.clip_loss_weight = clip_loss_weight + self.caption_loss_weight = caption_loss_weight + self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) + + def forward( + self, + image_features, + text_features, + logits, + labels, + logit_scale, + output_dict=False, + ): + + clip_loss = torch.tensor(0) + + if self.clip_loss_weight: + clip_loss = super().forward( + image_features, text_features, logit_scale + ) + clip_loss = self.clip_loss_weight * clip_loss + + caption_loss = self.caption_loss( + logits.permute(0, 2, 1), + labels, + ) + caption_loss = caption_loss * self.caption_loss_weight + + if output_dict: + return { + "contrastive_loss": clip_loss, + "caption_loss": caption_loss, + } + + return clip_loss, caption_loss + + +class DistillClipLoss(ClipLoss): + def dist_loss(self, teacher_logits, student_logits): + return ( + -( + teacher_logits.softmax(dim=1) + * student_logits.log_softmax(dim=1) + ) + .sum(dim=1) + .mean(dim=0) + ) + + def forward( + self, + image_features, + text_features, + logit_scale, + dist_image_features, + dist_text_features, + dist_logit_scale, + output_dict=False, + ): + logits_per_image, logits_per_text = self.get_logits( + image_features, text_features, logit_scale + ) + + dist_logits_per_image, dist_logits_per_text = self.get_logits( + dist_image_features, dist_text_features, dist_logit_scale + ) + + labels = self.get_ground_truth( + image_features.device, logits_per_image.shape[0] + ) + + contrastive_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + distill_loss = ( + self.dist_loss(dist_logits_per_image, logits_per_image) + + self.dist_loss(dist_logits_per_text, logits_per_text) + ) / 2 + + if output_dict: + return { + "contrastive_loss": contrastive_loss, + "distill_loss": distill_loss, + } + + return contrastive_loss, distill_loss + + +def neighbour_exchange(from_rank, to_rank, tensor, group=None): + tensor_recv = torch.zeros_like(tensor) + send_op = torch.distributed.P2POp( + torch.distributed.isend, + tensor, + to_rank, + group=group, + ) + recv_op = torch.distributed.P2POp( + torch.distributed.irecv, + tensor_recv, + from_rank, + group=group, + ) + reqs = torch.distributed.batch_isend_irecv([send_op, recv_op]) + for req in reqs: + req.wait() + return tensor_recv + + +def neighbour_exchange_bidir( + left_rank, right_rank, tensor_to_left, tensor_to_right, group=None +): + tensor_from_left = torch.zeros_like(tensor_to_right) + tensor_from_right = torch.zeros_like(tensor_to_left) + send_op_left = torch.distributed.P2POp( + torch.distributed.isend, + tensor_to_left, + left_rank, + group=group, + ) + send_op_right = torch.distributed.P2POp( + torch.distributed.isend, + tensor_to_right, + right_rank, + group=group, + ) + recv_op_left = torch.distributed.P2POp( + torch.distributed.irecv, + tensor_from_left, + left_rank, + group=group, + ) + recv_op_right = torch.distributed.P2POp( + torch.distributed.irecv, + tensor_from_right, + right_rank, + group=group, + ) + reqs = torch.distributed.batch_isend_irecv( + [send_op_right, send_op_left, recv_op_right, recv_op_left] + ) + for req in reqs: + req.wait() + return tensor_from_right, tensor_from_left + + +class NeighbourExchange(torch.autograd.Function): + @staticmethod + def forward(ctx, from_rank, to_rank, group, tensor): + ctx.group = group + ctx.from_rank = from_rank + ctx.to_rank = to_rank + return neighbour_exchange(from_rank, to_rank, tensor, group=group) + + @staticmethod + def backward(ctx, grad_output): + return (None, None, None) + ( + NeighbourExchange.apply( + ctx.to_rank, ctx.from_rank, ctx.group, grad_output + ), + ) + + +def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None): + return NeighbourExchange.apply(from_rank, to_rank, group, tensor) + + +class NeighbourExchangeBidir(torch.autograd.Function): + @staticmethod + def forward( + ctx, left_rank, right_rank, group, tensor_to_left, tensor_to_right + ): + ctx.group = group + ctx.left_rank = left_rank + ctx.right_rank = right_rank + return neighbour_exchange_bidir( + left_rank, right_rank, tensor_to_left, tensor_to_right, group=group + ) + + @staticmethod + def backward(ctx, *grad_outputs): + return (None, None, None) + NeighbourExchangeBidir.apply( + ctx.right_rank, ctx.left_rank, ctx.group, *grad_outputs + ) + + +def neighbour_exchange_bidir_with_grad( + left_rank, right_rank, tensor_to_left, tensor_to_right, group=None +): + return NeighbourExchangeBidir.apply( + left_rank, right_rank, group, tensor_to_left, tensor_to_right + ) + + +class SigLipLoss(nn.Module): + """Sigmoid Loss for Language Image Pre-Training (SigLIP) - https://arxiv.org/abs/2303.15343 + + @article{zhai2023sigmoid, + title={Sigmoid loss for language image pre-training}, + author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, + journal={arXiv preprint arXiv:2303.15343}, + year={2023} + } + """ + + def __init__( + self, + cache_labels=False, + rank=0, + world_size=1, + bidir=True, + use_horovod=False, + ): + super().__init__() + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + assert ( + not use_horovod + ) # FIXME need to look at hvd ops for ring transfers + self.use_horovod = use_horovod + self.bidir = bidir + + # cache state FIXME cache not currently used, worthwhile? + self.prev_num_logits = 0 + self.labels = {} + + def get_ground_truth( + self, device, dtype, num_logits, negative_only=False + ) -> torch.Tensor: + labels = -torch.ones( + (num_logits, num_logits), device=device, dtype=dtype + ) + if not negative_only: + labels = ( + 2 * torch.eye(num_logits, device=device, dtype=dtype) + labels + ) + return labels + + def get_logits( + self, image_features, text_features, logit_scale, logit_bias=None + ): + logits = logit_scale * image_features @ text_features.T + if logit_bias is not None: + logits += logit_bias + return logits + + def _loss( + self, + image_features, + text_features, + logit_scale, + logit_bias=None, + negative_only=False, + ): + logits = self.get_logits( + image_features, text_features, logit_scale, logit_bias + ) + labels = self.get_ground_truth( + image_features.device, + image_features.dtype, + image_features.shape[0], + negative_only=negative_only, + ) + loss = -F.logsigmoid(labels * logits).sum() / image_features.shape[0] + return loss + + def forward( + self, + image_features, + text_features, + logit_scale, + logit_bias, + output_dict=False, + ): + loss = self._loss( + image_features, text_features, logit_scale, logit_bias + ) + + if self.world_size > 1: + # exchange text features w/ neighbour world_size - 1 times + right_rank = (self.rank + 1) % self.world_size + left_rank = (self.rank - 1 + self.world_size) % self.world_size + if self.bidir: + text_features_to_right = text_features_to_left = text_features + num_bidir, remainder = divmod(self.world_size - 1, 2) + for i in range(num_bidir): + text_features_recv = neighbour_exchange_bidir_with_grad( + left_rank, + right_rank, + text_features_to_left, + text_features_to_right, + ) + + for f in text_features_recv: + loss += self._loss( + image_features, + f, + logit_scale, + logit_bias, + negative_only=True, + ) + ( + text_features_to_left, + text_features_to_right, + ) = text_features_recv + + if remainder: + text_features_recv = neighbour_exchange_with_grad( + left_rank, right_rank, text_features_to_right + ) + + loss += self._loss( + image_features, + text_features_recv, + logit_scale, + logit_bias, + negative_only=True, + ) + else: + text_features_to_right = text_features + for i in range(self.world_size - 1): + text_features_from_left = neighbour_exchange_with_grad( + left_rank, right_rank, text_features_to_right + ) + + loss += self._loss( + image_features, + text_features_from_left, + logit_scale, + logit_bias, + negative_only=True, + ) + text_features_to_right = text_features_from_left + + return {"contrastive_loss": loss} if output_dict else loss diff --git a/fiftyone/utils/open_clip/model.py b/fiftyone/utils/open_clip/model.py new file mode 100644 index 0000000000..3f03873a1f --- /dev/null +++ b/fiftyone/utils/open_clip/model.py @@ -0,0 +1,761 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import copy +import logging +import math +from dataclasses import dataclass +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.utils.checkpoint import checkpoint +from functools import partial + +from .hf_model import HFTextEncoder +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .transformer import ( + LayerNormFp32, + LayerNorm, + QuickGELU, + Attention, + VisionTransformer, + TextTransformer, + text_global_pool, +) +from .utils import to_2tuple + + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0.0 # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer (overrides pool_type) + attn_pooler_queries: int = 256 # n_queries for attentional pooler + attn_pooler_heads: int = 8 # n heads for attentional_pooling + no_ln_pre: bool = False # disable pre transformer LayerNorm + pos_embed_type: str = "learnable" + final_ln_after_pool: bool = False # apply final LayerNorm after pooling + pool_type: str = "tok" + output_tokens: bool = False + act_kwargs: Optional[dict] = None + norm_kwargs: Optional[dict] = None + + timm_model_name: Optional[ + str + ] = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = ( + False # use (imagenet) pretrained weights for named model + ) + timm_pool: str = "avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = "linear" # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + timm_drop: float = 0.0 # head dropout + timm_drop_path: Optional[float] = None # backbone stochastic depth + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + hf_tokenizer_name: Optional[str] = None + tokenizer_kwargs: Optional[dict] = None + + width: int = 512 + heads: int = 8 + layers: int = 12 + mlp_ratio: float = 4.0 + ls_init_value: Optional[float] = None # layer scale initial value + embed_cls: bool = False + pad_id: int = 0 + no_causal_mask: bool = False # disable causal masking + final_ln_after_pool: bool = False # apply final LayerNorm after pooling + pool_type: str = "argmax" + proj_bias: bool = False + output_tokens: bool = False + act_kwargs: dict = None + norm_kwargs: dict = None + + # HuggingFace specific text tower config + hf_model_name: Optional[str] = None + hf_model_pretrained: bool = True + hf_proj_type: str = "mlp" + hf_pooler_type: str = "mean_pooler" # attentional pooling for HF models + + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == "bf16": + cast_dtype = torch.bfloat16 + elif precision == "fp16": + cast_dtype = torch.float16 + return cast_dtype + + +def get_input_dtype(precision: str): + input_dtype = None + if precision in ("bf16", "pure_bf16"): + input_dtype = torch.bfloat16 + elif precision in ("fp16", "pure_fp16"): + input_dtype = torch.float16 + return input_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + drop=vision_cfg.timm_drop, + drop_path=vision_cfg.timm_drop_path, + patch_drop=vision_cfg.patch_dropout + if vision_cfg.patch_dropout > 0 + else None, + embed_dim=embed_dim, + image_size=vision_cfg.image_size, + ) + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width, + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = ( + LayerNormFp32 + if cast_dtype in (torch.float16, torch.bfloat16) + else LayerNorm + ) + if vision_cfg.norm_kwargs: + norm_layer = partial(norm_layer, **vision_cfg.norm_kwargs) + if vision_cfg.act_kwargs is not None: + act_layer = partial(act_layer, **vision_cfg.act_kwargs) + + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + attentional_pool=vision_cfg.attentional_pool, + attn_pooler_queries=vision_cfg.attn_pooler_queries, + attn_pooler_heads=vision_cfg.attn_pooler_heads, + pos_embed_type=vision_cfg.pos_embed_type, + no_ln_pre=vision_cfg.no_ln_pre, + final_ln_after_pool=vision_cfg.final_ln_after_pool, + pool_type=vision_cfg.pool_type, + output_tokens=vision_cfg.output_tokens, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + proj_type=text_cfg.hf_proj_type, + pooler_type=text_cfg.hf_pooler_type, + pretrained=text_cfg.hf_model_pretrained, + output_tokens=text_cfg.output_tokens, + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = ( + LayerNormFp32 + if cast_dtype in (torch.float16, torch.bfloat16) + else LayerNorm + ) + if text_cfg.norm_kwargs: + norm_layer = partial(norm_layer, **text_cfg.norm_kwargs) + if text_cfg.act_kwargs is not None: + act_layer = partial(act_layer, **text_cfg.act_kwargs) + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + mlp_ratio=text_cfg.mlp_ratio, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + embed_cls=text_cfg.embed_cls, + no_causal_mask=text_cfg.no_causal_mask, + pad_id=text_cfg.pad_id, + pool_type=text_cfg.pool_type, + proj_bias=text_cfg.proj_bias, + output_tokens=text_cfg.output_tokens, + act_layer=act_layer, + norm_layer=norm_layer, + ) + return text + + +class CLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + init_logit_scale: float = np.log(1 / 0.07), + init_logit_bias: Optional[float] = None, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + + self.visual = _build_vision_tower( + embed_dim, vision_cfg, quick_gelu, cast_dtype + ) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.context_length = text.context_length + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.text_pool_type = text.pool_type + self.register_buffer("attn_mask", text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) + if init_logit_bias is not None: + self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) + else: + self.logit_bias = None + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock( + unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats + ) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to( + cast_dtype + ) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + x, _ = text_global_pool(x, text, self.text_pool_type) + if self.text_projection is not None: + if isinstance(self.text_projection, nn.Linear): + x = self.text_projection(x) + else: + x = x @ self.text_projection + + return F.normalize(x, dim=-1) if normalize else x + + def get_logits(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + image_logits = ( + self.logit_scale.exp() * image_features @ text_features.T + ) + if self.logit_bias is not None: + image_logits += self.logit_bias + text_logits = image_logits.T + return image_logits, text_logits + + def forward( + self, + image: Optional[torch.Tensor] = None, + text: Optional[torch.Tensor] = None, + ): + image_features = ( + self.encode_image(image, normalize=True) + if image is not None + else None + ) + text_features = ( + self.encode_text(text, normalize=True) + if text is not None + else None + ) + + if self.output_dict: + out_dict = { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp(), + } + if self.logit_bias is not None: + out_dict["logit_bias"] = self.logit_bias + return out_dict + + if self.logit_bias is not None: + return ( + image_features, + text_features, + self.logit_scale.exp(), + self.logit_bias, + ) + return image_features, text_features, self.logit_scale.exp() + + +class CustomTextCLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + init_logit_scale: float = np.log(1 / 0.07), + init_logit_bias: Optional[float] = None, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + self.visual = _build_vision_tower( + embed_dim, vision_cfg, quick_gelu, cast_dtype + ) + self.text = _build_text_tower( + embed_dim, text_cfg, quick_gelu, cast_dtype + ) + self.context_length = self.text.context_length + self.vocab_size = self.text.vocab_size + self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) + if init_logit_bias is not None: + self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) + else: + self.logit_bias = None + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock( + unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats + ) + + def lock_text_tower( + self, unlocked_layers: int = 0, freeze_layer_norm: bool = True + ): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def get_logits(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + image_logits = ( + self.logit_scale.exp() * image_features @ text_features.T + ) + if self.logit_bias is not None: + image_logits += self.logit_bias + text_logits = image_logits.T + return image_logits, text_logits + + def forward( + self, + image: Optional[torch.Tensor] = None, + text: Optional[torch.Tensor] = None, + ): + image_features = ( + self.encode_image(image, normalize=True) + if image is not None + else None + ) + text_features = ( + self.encode_text(text, normalize=True) + if text is not None + else None + ) + + if self.output_dict: + out_dict = { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp(), + } + if self.logit_bias is not None: + out_dict["logit_bias"] = self.logit_bias + return out_dict + + if self.logit_bias is not None: + return ( + image_features, + text_features, + self.logit_scale.exp(), + self.logit_bias, + ) + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [ + *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], + "in_proj_bias", + "bias_k", + "bias_v", + ]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + if isinstance(l, (CLIP, TextTransformer)): + # convert text nn.Parameter projections + attr = getattr(l, "text_projection", None) + if attr is not None: + attr.data = attr.data.to(dtype) + + if isinstance(l, VisionTransformer): + # convert vision nn.Parameter projections + attr = getattr(l, "proj", None) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if "text_projection" in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any( + k.startswith(p) + for p in ( + "text_projection", + "positional_embedding", + "token_embedding", + "transformer", + "ln_final", + ) + ): + k = "text." + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [ + k + for k in state_dict.keys() + if k.startswith("visual.") + and k.endswith(".attn.in_proj_weight") + ] + ) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round( + (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5 + ) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len( + set( + k.split(".")[2] + for k in state_dict + if k.startswith(f"visual.layer{b}") + ) + ) + for b in [1, 2, 3, 4] + ] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round( + (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) + ** 0.5 + ) + vision_patch_size = None + assert ( + output_width**2 + 1 + == state_dict["visual.attnpool.positional_embedding"].shape[0] + ) + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len( + set( + k.split(".")[2] + for k in state_dict + if k.startswith(f"transformer.resblocks") + ) + ) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers, + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + convert_weights_to_fp16( + model + ) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device("cpu")): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones( + (batch_size, 3, image_size, image_size), device=device + ) + example_text = torch.zeros( + (batch_size, model.context_length), dtype=torch.int, device=device + ) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,), + ), + ) + model.visual.image_size = image_size + return model + + +def resize_pos_embed( + state_dict, model, interpolation: str = "bicubic", antialias: bool = True +): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get("visual.positional_embedding", None) + if old_pos_embed is None or not hasattr(model.visual, "grid_size"): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = ( + 1 # FIXME detect different token configs (ie no class token, or more) + ) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = ( + old_pos_embed[:extra_tokens], + old_pos_embed[extra_tokens:], + ) + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info( + "Resizing position embedding grid-size from %s to %s", + old_grid_size, + grid_size, + ) + pos_emb_img = pos_emb_img.reshape( + 1, old_grid_size[0], old_grid_size[1], -1 + ).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + antialias=antialias, + align_corners=False, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape( + 1, grid_size[0] * grid_size[1], -1 + )[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict["visual.positional_embedding"] = new_pos_embed + + +def resize_text_pos_embed( + state_dict, model, interpolation: str = "linear", antialias: bool = False +): + old_pos_embed = state_dict.get("positional_embedding", None) + if old_pos_embed is None: + return + # FIXME add support for text cls_token + model_pos_embed = getattr(model, "positional_embedding", None) + if model_pos_embed is None: + model_pos_embed = getattr(model.text, "positional_embedding", None) + + old_num_pos = old_pos_embed.shape[0] + old_width = old_pos_embed.shape[1] + num_pos = model_pos_embed.shape[0] + width = model_pos_embed.shape[1] + assert old_width == width, "text pos_embed width changed!" + if old_num_pos == num_pos: + return + + logging.info( + "Resizing text position embedding num_pos from %s to %s", + old_num_pos, + num_pos, + ) + old_pos_embed = old_pos_embed.reshape(1, old_num_pos, old_width).permute( + 0, 2, 1 + ) + old_pos_embed = F.interpolate( + old_pos_embed, + size=num_pos, + mode=interpolation, + antialias=antialias, + align_corners=False, + ) + old_pos_embed = old_pos_embed.permute(0, 2, 1)[0] + new_pos_embed = old_pos_embed + + state_dict["positional_embedding"] = new_pos_embed + + +def get_model_preprocess_cfg(model): + module = getattr(model, "visual", model) + preprocess_cfg = getattr(module, "preprocess_cfg", {}) + if not preprocess_cfg: + # use separate legacy attributes if preprocess_cfg dict not found + size = getattr(module, "image_size") + if size is not None: + preprocess_cfg["size"] = size + mean = getattr(module, "image_mean", None) + if mean is not None: + preprocess_cfg["mean"] = mean + std = getattr(module, "image_std", None) + if std is not None: + preprocess_cfg["std"] = std + return preprocess_cfg + + +def set_model_preprocess_cfg(model, preprocess_cfg: Dict[str, Any]): + module = getattr(model, "visual", model) + module.image_mean = preprocess_cfg[ + "mean" + ] # legacy attribute, keeping for bwd compat + module.image_std = preprocess_cfg[ + "std" + ] # legacy attribute, keeping for bwd compat + module.preprocess_cfg = copy.deepcopy( + preprocess_cfg + ) # new attr, package all pp cfg as dict + + +def get_model_tokenize_cfg(model): + module = getattr(model, "text", model) + cfg = {} + context_length = getattr(module, "context_length", None) + if context_length is not None: + cfg["context_length"] = context_length + vocab_size = getattr(module, "vocab_size", None) + if vocab_size is not None: + cfg["vocab_size"] = vocab_size + return cfg diff --git a/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json b/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json new file mode 100644 index 0000000000..3f3e953321 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva_giant_patch14_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json b/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json new file mode 100644 index 0000000000..9af290be97 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva_giant_patch14_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json b/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json new file mode 100644 index 0000000000..e28b266eb9 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva02_base_patch16_clip_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json b/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json new file mode 100644 index 0000000000..2ff4c4226f --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva02_enormous_patch14_clip_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json b/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json new file mode 100644 index 0000000000..aac0fee80a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva02_enormous_patch14_clip_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json b/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json new file mode 100644 index 0000000000..5d609d744a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "timm_model_name": "eva02_large_patch14_clip_336", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json b/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json new file mode 100644 index 0000000000..2e14428bf7 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "eva02_large_patch14_clip_224", + "timm_model_pretrained": false, + "timm_pool": "token", + "timm_proj": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json b/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json new file mode 100644 index 0000000000..cf7802ee00 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": [3, 4, 23, 3], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN101.json b/fiftyone/utils/open_clip/model_configs/RN101.json new file mode 100644 index 0000000000..55afdcc5b3 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN101.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": [3, 4, 23, 3], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json b/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json new file mode 100644 index 0000000000..05e227d3ef --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 1024, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": [3, 4, 6, 3], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN50.json b/fiftyone/utils/open_clip/model_configs/RN50.json new file mode 100644 index 0000000000..af121b71f0 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN50.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": [3, 4, 6, 3], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x16.json b/fiftyone/utils/open_clip/model_configs/RN50x16.json new file mode 100644 index 0000000000..1189be1463 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN50x16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 384, + "layers": [6, 8, 18, 8], + "width": 96, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x4.json b/fiftyone/utils/open_clip/model_configs/RN50x4.json new file mode 100644 index 0000000000..d63980550d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN50x4.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "image_size": 288, + "layers": [4, 6, 10, 6], + "width": 80, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x64.json b/fiftyone/utils/open_clip/model_configs/RN50x64.json new file mode 100644 index 0000000000..654285a1a9 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/RN50x64.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 448, + "layers": [3, 15, 36, 10], + "width": 128, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json new file mode 100644 index 0000000000..ae15f00dec --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 256, + "timm_model_name": "vit_base_patch16_siglip_256", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 768, + "heads": 12, + "layers": 12, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json new file mode 100644 index 0000000000..6a64104020 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 384, + "timm_model_name": "vit_base_patch16_siglip_384", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 768, + "heads": 12, + "layers": 12, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json new file mode 100644 index 0000000000..a8a963f6e1 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 512, + "timm_model_name": "vit_base_patch16_siglip_512", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 768, + "heads": 12, + "layers": 12, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json new file mode 100644 index 0000000000..7063baf9cf --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 256, + "timm_model_name": "vit_base_patch16_siglip_256", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 250000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP-i18n-256", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 768, + "heads": 12, + "layers": 12, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json new file mode 100644 index 0000000000..ac3a745449 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 768, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "vit_base_patch16_siglip_224", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 768, + "heads": 12, + "layers": 12, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json new file mode 100644 index 0000000000..9347280c60 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "image_size": 240, + "layers": 12, + "width": 896, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json new file mode 100644 index 0000000000..f9cc3e3b00 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 896, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json new file mode 100644 index 0000000000..cb0d662c6c --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16.json new file mode 100644 index 0000000000..9afeef0fbc --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json new file mode 100644 index 0000000000..80a2597d8f --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 256, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json new file mode 100644 index 0000000000..27ae13857a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "image_size": 256, + "layers": 12, + "width": 896, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json new file mode 100644 index 0000000000..f5a063adbf --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32.json new file mode 100644 index 0000000000..abd1f7973d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-B-32.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json new file mode 100644 index 0000000000..826edd480e --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "quick_gelu": true, + "vision_cfg": { + "image_size": 378, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json new file mode 100644 index 0000000000..926d5a3707 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json @@ -0,0 +1,26 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 336, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 1024, + "heads": 16, + "layers": 24, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json new file mode 100644 index 0000000000..c29a7ee4f7 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json @@ -0,0 +1,26 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 1024, + "heads": 16, + "layers": 24, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json new file mode 100644 index 0000000000..825d04a0bf --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14.json new file mode 100644 index 0000000000..d2c01733dc --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-14.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-16.json b/fiftyone/utils/open_clip/model_configs/ViT-H-16.json new file mode 100644 index 0000000000..942ed56bf6 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-H-16.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json new file mode 100644 index 0000000000..c8e5fbac8a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 280, + "layers": 24, + "width": 1024, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json new file mode 100644 index 0000000000..4db3a1e77c --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json new file mode 100644 index 0000000000..027ee6a22d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 336, + "layers": 24, + "width": 1024, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 768, + "heads": 12, + "layers": 12, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json new file mode 100644 index 0000000000..a1ced4148e --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json @@ -0,0 +1,25 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 768, + "heads": 12, + "layers": 12, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json new file mode 100644 index 0000000000..6ecba7d754 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 768, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14.json new file mode 100644 index 0000000000..98951b0cbf --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-14.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json new file mode 100644 index 0000000000..cc09c4877d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 320, + "layers": 24, + "width": 1024, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json new file mode 100644 index 0000000000..8aa6010f2b --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 1024, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 256, + "timm_model_name": "vit_large_patch16_siglip_256", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 1024, + "heads": 16, + "layers": 24, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json new file mode 100644 index 0000000000..6d61af8997 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json @@ -0,0 +1,29 @@ +{ + "embed_dim": 1024, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 384, + "timm_model_name": "vit_large_patch16_siglip_384", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 1024, + "heads": 16, + "layers": 24, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16.json new file mode 100644 index 0000000000..78601e7a68 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-L-16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json new file mode 100644 index 0000000000..f0893f92b4 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 384, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 512, + "patch_size": 16, + "ls_init_value": 1e-4 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 384, + "heads": 6, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-16.json b/fiftyone/utils/open_clip/model_configs/ViT-M-16.json new file mode 100644 index 0000000000..bc71c7c5c8 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-M-16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 512, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json new file mode 100644 index 0000000000..92f57a2c6c --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 384, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 512, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 384, + "heads": 6, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-32.json b/fiftyone/utils/open_clip/model_configs/ViT-M-32.json new file mode 100644 index 0000000000..767c4104e0 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-M-32.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 512, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json new file mode 100644 index 0000000000..2fd9a0913c --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 256, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 384, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 256, + "heads": 4, + "layers": 10 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-16.json b/fiftyone/utils/open_clip/model_configs/ViT-S-16.json new file mode 100644 index 0000000000..4d94a6fe6e --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-S-16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 384, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 384, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 384, + "heads": 6, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json new file mode 100644 index 0000000000..c309e6c193 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 256, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 384, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 256, + "heads": 4, + "layers": 10 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-32.json b/fiftyone/utils/open_clip/model_configs/ViT-S-32.json new file mode 100644 index 0000000000..de254d97b7 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-S-32.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 384, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 384, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 384, + "heads": 6, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json new file mode 100644 index 0000000000..817d20667a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json @@ -0,0 +1,30 @@ +{ + "embed_dim": 1152, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 384, + "timm_model_name": "vit_so400m_patch14_siglip_384", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 64, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 1152, + "heads": 16, + "layers": 27, + "mlp_ratio": 3.7362, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json new file mode 100644 index 0000000000..192e9a6370 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json @@ -0,0 +1,30 @@ +{ + "embed_dim": 1152, + "init_logit_bias": -10, + "custom_text": true, + "vision_cfg": { + "image_size": 224, + "timm_model_name": "vit_so400m_patch14_siglip_224", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "context_length": 16, + "vocab_size": 32000, + "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", + "tokenizer_kwargs": { + "clean": "canonicalize" + }, + "width": 1152, + "heads": 16, + "layers": 27, + "mlp_ratio": 3.7362, + "no_causal_mask": true, + "proj_bias": true, + "pool_type": "last", + "norm_kwargs": { + "eps": 1e-6 + } + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json new file mode 100644 index 0000000000..afe68e974f --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json @@ -0,0 +1,27 @@ +{ + "embed_dim": 1280, + "vision_cfg": { + "image_size": 336, + "layers": 48, + "width": 1664, + "head_width": 104, + "mlp_ratio": 4.9231, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 1280, + "heads": 20, + "layers": 32, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json new file mode 100644 index 0000000000..c6c1c846f9 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json @@ -0,0 +1,27 @@ +{ + "embed_dim": 1280, + "vision_cfg": { + "image_size": 224, + "layers": 48, + "width": 1664, + "head_width": 104, + "mlp_ratio": 4.9231, + "patch_size": 14, + "no_ln_pre": true, + "pool_type": "avg", + "final_ln_after_pool": true + }, + "text_cfg": { + "context_length": 32, + "vocab_size": 32000, + "hf_tokenizer_name": "bert-base-uncased", + "tokenizer_kwargs": { + "strip_sep_token": true + }, + "width": 1280, + "heads": 20, + "layers": 32, + "pool_type": "last", + "no_causal_mask": true + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json new file mode 100644 index 0000000000..c5fd84c073 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1280, + "vision_cfg": { + "image_size": 224, + "layers": 48, + "width": 1664, + "head_width": 104, + "mlp_ratio": 4.9231, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 32 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-e-14.json b/fiftyone/utils/open_clip/model_configs/ViT-e-14.json new file mode 100644 index 0000000000..69d7ef9c24 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-e-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1280, + "vision_cfg": { + "image_size": 224, + "layers": 56, + "width": 1792, + "head_width": 112, + "mlp_ratio": 8.5715, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1280, + "heads": 20, + "layers": 36 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-g-14.json b/fiftyone/utils/open_clip/model_configs/ViT-g-14.json new file mode 100644 index 0000000000..b5c4231a67 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/ViT-g-14.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 40, + "width": 1408, + "head_width": 88, + "mlp_ratio": 4.3637, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json new file mode 100644 index 0000000000..02ea8032e4 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json @@ -0,0 +1,30 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32, + "attentional_pool": true, + "attn_pooler_heads": 8, + "output_tokens": true + }, + "text_cfg": { + "context_length": 76, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "embed_cls": true, + "output_tokens": true + }, + "multimodal_cfg": { + "context_length": 76, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12, + "attn_pooler_heads": 8 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json b/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json new file mode 100644 index 0000000000..3d5ca4ca23 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json @@ -0,0 +1,30 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 14, + "attentional_pool": true, + "attn_pooler_heads": 8, + "output_tokens": true + }, + "text_cfg": { + "context_length": 76, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "embed_cls": true, + "output_tokens": true + }, + "multimodal_cfg": { + "context_length": 76, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12, + "attn_pooler_heads": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/coca_base.json b/fiftyone/utils/open_clip/model_configs/coca_base.json new file mode 100644 index 0000000000..146690d54b --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/coca_base.json @@ -0,0 +1,31 @@ +{ + "embed_dim": 512, + "multimodal_cfg": { + "width": 768, + "context_length": 76, + "vocab_size": 64000, + "mlp_ratio": 4, + "layers": 12, + "dim_head": 64, + "heads": 12, + "n_queries": 256, + "attn_pooler_heads": 8 + }, + "vision_cfg": { + "image_size": 288, + "layers": 12, + "width": 768, + "patch_size": 18, + "output_tokens": true + }, + "text_cfg": { + "context_length": 76, + "vocab_size": 64000, + "layers": 12, + "heads": 12, + "width": 768, + "embed_cls": true, + "output_tokens": true + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json new file mode 100644 index 0000000000..aa9d3f5620 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json @@ -0,0 +1,24 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32, + "output_tokens": true + }, + "text_cfg": { + "hf_model_name": "roberta-base", + "hf_tokenizer_name": "roberta-base", + "hf_proj_type": "linear", + "width": 768, + "output_tokens": true + }, + "multimodal_cfg": { + "context_length": 76, + "width": 768, + "heads": 8, + "layers": 12 + }, + "custom_text": true +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base.json b/fiftyone/utils/open_clip/model_configs/convnext_base.json new file mode 100644 index 0000000000..46718aacc0 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_base.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "timm_model_name": "convnext_base", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base_w.json b/fiftyone/utils/open_clip/model_configs/convnext_base_w.json new file mode 100644 index 0000000000..ed515495c7 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_base_w.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "timm_model_name": "convnext_base", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 256 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json b/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json new file mode 100644 index 0000000000..732ea67b5f --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "timm_model_name": "convnext_base", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 320 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large.json b/fiftyone/utils/open_clip/model_configs/convnext_large.json new file mode 100644 index 0000000000..528a5fd4e8 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_large.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "timm_model_name": "convnext_large", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large_d.json b/fiftyone/utils/open_clip/model_configs/convnext_large_d.json new file mode 100644 index 0000000000..4289d291d1 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_large_d.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "timm_model_name": "convnext_large", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "mlp", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 256 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 16 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json b/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json new file mode 100644 index 0000000000..0097fa4e40 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "timm_model_name": "convnext_large", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "mlp", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 320 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 16 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_small.json b/fiftyone/utils/open_clip/model_configs/convnext_small.json new file mode 100644 index 0000000000..2b070ee3ca --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_small.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "timm_model_name": "convnext_small", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_tiny.json b/fiftyone/utils/open_clip/model_configs/convnext_tiny.json new file mode 100644 index 0000000000..767c389269 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_tiny.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "timm_model_name": "convnext_tiny", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json b/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json new file mode 100644 index 0000000000..71d60fb8c6 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "timm_model_name": "convnext_xlarge", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 256 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 20 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json new file mode 100644 index 0000000000..defe9427df --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "timm_model_name": "convnext_xxlarge", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 256 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json new file mode 100644 index 0000000000..c660b324f7 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json @@ -0,0 +1,19 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "timm_model_name": "convnext_xxlarge", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "timm_drop": 0.0, + "timm_drop_path": 0.1, + "image_size": 320 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json new file mode 100644 index 0000000000..e22366897a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json @@ -0,0 +1,14 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "hf_model_name": "google/mt5-base", + "hf_tokenizer_name": "google/mt5-base", + "hf_pooler_type": "mean_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json new file mode 100644 index 0000000000..f58717cdd5 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json @@ -0,0 +1,15 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "hf_model_name": "google/mt5-xl", + "hf_tokenizer_name": "google/mt5-xl", + "hf_pooler_type": "mean_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json new file mode 100644 index 0000000000..3d2d6fc57a --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 768, + "custom_text": true, + "init_logit_bias": -10, + "vision_cfg": { + "image_size": 384, + "timm_model_name": "vit_base_patch16_siglip_384", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "hf_model_name": "facebook/nllb-200-distilled-600M", + "hf_tokenizer_name": "facebook/nllb-200-distilled-600M", + "hf_proj_type": "linear", + "hf_pooler_type": "cls_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json new file mode 100644 index 0000000000..8cedf3cd7d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json @@ -0,0 +1,15 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "hf_model_name": "facebook/nllb-200-distilled-600M", + "hf_tokenizer_name": "facebook/nllb-200-distilled-600M", + "hf_proj_type": "linear", + "hf_pooler_type": "cls_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json new file mode 100644 index 0000000000..6d9d25fd95 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json @@ -0,0 +1,18 @@ +{ + "embed_dim": 1152, + "custom_text": true, + "init_logit_bias": -10, + "vision_cfg": { + "image_size": 384, + "timm_model_name": "vit_so400m_patch14_siglip_384", + "timm_model_pretrained": false, + "timm_pool": "map", + "timm_proj": "none" + }, + "text_cfg": { + "hf_model_name": "facebook/nllb-200-distilled-1.3B", + "hf_tokenizer_name": "facebook/nllb-200-distilled-1.3B", + "hf_proj_type": "linear", + "hf_pooler_type": "cls_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json new file mode 100644 index 0000000000..a7e25ac900 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "hf_model_name": "facebook/nllb-200-distilled-1.3B", + "hf_tokenizer_name": "facebook/nllb-200-distilled-1.3B", + "hf_proj_type": "linear", + "hf_pooler_type": "cls_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json new file mode 100644 index 0000000000..c0c7a55995 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json @@ -0,0 +1,15 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "hf_model_name": "roberta-base", + "hf_tokenizer_name": "roberta-base", + "hf_pooler_type": "mean_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json b/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json new file mode 100644 index 0000000000..213d34d113 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "timm_model_name": "swin_base_patch4_window7_224", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json b/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json new file mode 100644 index 0000000000..bc7d33df1d --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "timm_model_name": "vit_medium_patch16_gap_256", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "image_size": 256 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json b/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json new file mode 100644 index 0000000000..12e62656f4 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "timm_model_name": "vit_relpos_medium_patch16_cls_224", + "timm_model_pretrained": false, + "timm_pool": "", + "timm_proj": "linear", + "image_size": 224 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json new file mode 100644 index 0000000000..375fa9e12f --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json @@ -0,0 +1,14 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "hf_model_name": "xlm-roberta-base", + "hf_tokenizer_name": "xlm-roberta-base", + "hf_pooler_type": "mean_pooler" + } +} diff --git a/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json new file mode 100644 index 0000000000..c56b4e8988 --- /dev/null +++ b/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json @@ -0,0 +1,15 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "hf_model_name": "xlm-roberta-large", + "hf_tokenizer_name": "xlm-roberta-large", + "hf_pooler_type": "mean_pooler" + } +} diff --git a/fiftyone/utils/open_clip/modified_resnet.py b/fiftyone/utils/open_clip/modified_resnet.py new file mode 100644 index 0000000000..ab6cd94903 --- /dev/null +++ b/fiftyone/utils/open_clip/modified_resnet.py @@ -0,0 +1,225 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential( + OrderedDict( + [ + ("-1", nn.AvgPool2d(stride)), + ( + "0", + nn.Conv2d( + inplanes, + planes * self.expansion, + 1, + stride=1, + bias=False, + ), + ), + ("1", nn.BatchNorm2d(planes * self.expansion)), + ] + ) + ) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter( + torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5 + ) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute( + 2, 0, 1 + ) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, + key=x, + value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat( + [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias] + ), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0.0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False, + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d( + 3, width // 2, kernel_size=3, stride=2, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d( + width // 2, width // 2, kernel_size=3, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d( + width // 2, width, kernel_size=3, padding=1, bias=False + ) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = ( + width # this is a *mutable* variable used during construction + ) + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d( + image_size // 32, embed_dim, heads, output_dim + ) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features**-0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [ + self.layer1, + self.layer2, + self.layer3, + self.layer4, + ]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert ( + unlocked_groups == 0 + ), "partial locking not currently supported for this model" + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/fiftyone/utils/open_clip/openai.py b/fiftyone/utils/open_clip/openai.py new file mode 100644 index 0000000000..447e0e348a --- /dev/null +++ b/fiftyone/utils/open_clip/openai.py @@ -0,0 +1,104 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import ( + build_model_from_openai_state_dict, + convert_weights_to_lp, + get_cast_dtype, +) +from .pretrained import ( + get_pretrained_url, + list_pretrained_models_by_tag, + download_pretrained_from_url, +) + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag("openai") + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = "fp32" if device == "cpu" else "fp16" + + if get_pretrained_url(name, "openai"): + model_path = download_pretrained_from_url( + get_pretrained_url(name, "openai"), cache_dir=cache_dir + ) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError( + f"Model {name} not found; available models = {list_openai_models()}" + ) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + state_dict = torch.load(model_path, map_location="cpu") + + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict( + state_dict or model.state_dict(), cast_dtype=cast_dtype + ) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + # FIXME support pure fp16/bf16 precision modes + if precision != "fp16": + model.float() + if precision == "bf16": + # for bf16, convert back to low-precision + convert_weights_to_lp(model, dtype=torch.bfloat16) + + # add mean / std attributes for consistency with OpenCLIP models + model.visual.image_mean = OPENAI_DATASET_MEAN + model.visual.image_std = OPENAI_DATASET_STD + return model diff --git a/fiftyone/utils/open_clip/pos_embed.py b/fiftyone/utils/open_clip/pos_embed.py new file mode 100644 index 0000000000..f1bbcefb29 --- /dev/null +++ b/fiftyone/utils/open_clip/pos_embed.py @@ -0,0 +1,113 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# Position embedding utils +# -------------------------------------------------------- + +import numpy as np + +import torch + +# -------------------------------------------------------- +# 2D sine-cosine position embedding +# References: +# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py +# MoCo v3: https://github.com/facebookresearch/moco-v3 +# -------------------------------------------------------- +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate( + [np.zeros([1, embed_dim]), pos_embed], axis=0 + ) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid( + embed_dim // 2, grid[0] + ) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid( + embed_dim // 2, grid[1] + ) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=float) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +# -------------------------------------------------------- +# Interpolate position embeddings for high-resolution +# References: +# DeiT: https://github.com/facebookresearch/deit +# -------------------------------------------------------- +def interpolate_pos_embed(model, checkpoint_model): + if "pos_embed" in checkpoint_model: + pos_embed_checkpoint = checkpoint_model["pos_embed"] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.patch_embed.num_patches + num_extra_tokens = model.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int( + (pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5 + ) + # height (== width) for the new position embedding + new_size = int(num_patches**0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print( + "Position interpolate from %dx%d to %dx%d" + % (orig_size, orig_size, new_size, new_size) + ) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape( + -1, orig_size, orig_size, embedding_size + ).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, + size=(new_size, new_size), + mode="bicubic", + align_corners=False, + ) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + checkpoint_model["pos_embed"] = new_pos_embed diff --git a/fiftyone/utils/open_clip/pretrained.py b/fiftyone/utils/open_clip/pretrained.py new file mode 100644 index 0000000000..60f30207db --- /dev/null +++ b/fiftyone/utils/open_clip/pretrained.py @@ -0,0 +1,752 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +from .constants import ( + IMAGENET_MEAN, + IMAGENET_STD, + INCEPTION_MEAN, + INCEPTION_STD, + OPENAI_DATASET_MEAN, + OPENAI_DATASET_STD, +) +from .version import __version__ + +try: + from huggingface_hub import hf_hub_download + + hf_hub_download = partial( + hf_hub_download, library_name="open_clip", library_version=__version__ + ) + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url="", hf_hub="", **kwargs): + # OpenAI / OpenCLIP defaults + return { + "url": url, + "hf_hub": hf_hub, + "mean": OPENAI_DATASET_MEAN, + "std": OPENAI_DATASET_STD, + "interpolation": "bicubic", + "resize_mode": "shortest", + **kwargs, + } + + +def _slpcfg(url="", hf_hub="", **kwargs): + # SiGLIP defaults + return { + "url": url, + "hf_hub": hf_hub, + "mean": INCEPTION_MEAN, + "std": INCEPTION_STD, + "interpolation": "bicubic", + "resize_mode": "squash", + **kwargs, + } + + +def _apcfg(url="", hf_hub="", **kwargs): + # CLIPA defaults + return { + "url": url, + "hf_hub": hf_hub, + "mean": IMAGENET_MEAN, + "std": IMAGENET_STD, + "interpolation": "bilinear", + "resize_mode": "squash", + **kwargs, + } + + +_RN50 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt" + ), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt" + ), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" + ), +) + +_RN50_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt" + ), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt" + ), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" + ), +) + +_RN101 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt" + ), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" + ), +) + +_RN101_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt" + ), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" + ), +) + +_RN50x4 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt" + ), +) + +_RN50x16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt" + ), +) + +_RN50x64 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt" + ), +) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt" + ), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt" + ), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt" + ), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth" + ), + laion2b_s34b_b79k=_pcfg(hf_hub="laion/CLIP-ViT-B-32-laion2B-s34B-b79K/"), + # DataComp-XL models + datacomp_xl_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K/" + ), + # DataComp-M models + datacomp_m_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-DataComp.M-s128M-b4K/" + ), + commonpool_m_clip_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.clip-s128M-b4K/" + ), + commonpool_m_laion_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.laion-s128M-b4K/" + ), + commonpool_m_image_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.image-s128M-b4K/" + ), + commonpool_m_text_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.text-s128M-b4K/" + ), + commonpool_m_basic_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.basic-s128M-b4K/" + ), + commonpool_m_s128m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.M-s128M-b4K/" + ), + # DataComp-S models + datacomp_s_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-DataComp.S-s13M-b4K/" + ), + commonpool_s_clip_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.clip-s13M-b4K/" + ), + commonpool_s_laion_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.laion-s13M-b4K/" + ), + commonpool_s_image_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.image-s13M-b4K/" + ), + commonpool_s_text_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.text-s13M-b4K/" + ), + commonpool_s_basic_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.basic-s13M-b4K/" + ), + commonpool_s_s13m_b4k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-CommonPool.S-s13M-b4K/" + ), +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt" + ), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt" + ), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt" + ), + metaclip_400m=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/b32_400m.pt" + ), + metaclip_fullcc=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/b32_fullcc2.5b.pt" + ), +) + +_VITB32_256 = dict( + datacomp_s34b_b86k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K/" + ), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt" + ), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt" + ), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt" + ), + laion2b_s34b_b88k=_pcfg(hf_hub="laion/CLIP-ViT-B-16-laion2B-s34B-b88K/"), + # DataComp-XL models + datacomp_xl_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K/" + ), + # DataComp-L models + datacomp_l_s1b_b8k=_pcfg(hf_hub="laion/CLIP-ViT-B-16-DataComp.L-s1B-b8K/"), + commonpool_l_clip_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.clip-s1B-b8K/" + ), + commonpool_l_laion_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.laion-s1B-b8K/" + ), + commonpool_l_image_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.image-s1B-b8K/" + ), + commonpool_l_text_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.text-s1B-b8K/" + ), + commonpool_l_basic_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.basic-s1B-b8K/" + ), + commonpool_l_s1b_b8k=_pcfg( + hf_hub="laion/CLIP-ViT-B-16-CommonPool.L-s1B-b8K/" + ), + # DFN + dfn2b=_pcfg(hf_hub="apple/DFN2B-CLIP-ViT-B-16/"), +) + +_VITB16_quickgelu = dict( + metaclip_400m=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/b16_400m.pt" + ), + metaclip_fullcc=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/b16_fullcc2.5b.pt" + ), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt" + ), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt" + ), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt" + ), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt" + ), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt" + ), + laion2b_s32b_b82k=_pcfg( + hf_hub="laion/CLIP-ViT-L-14-laion2B-s32B-b82K/", + mean=INCEPTION_MEAN, + std=INCEPTION_STD, + ), + # DataComp-XL models + datacomp_xl_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K/" + ), + commonpool_xl_clip_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K/" + ), + commonpool_xl_laion_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K/" + ), + commonpool_xl_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K/" + ), +) + +_VITL14_quickgelu = dict( + metaclip_400m=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/l14_400m.pt" + ), + metaclip_fullcc=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/l14_fullcc2.5b.pt" + ), + dfn2b=_pcfg(hf_hub="apple/DFN2B-CLIP-ViT-L-14/"), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt" + ), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub="laion/CLIP-ViT-H-14-laion2B-s32B-b79K/"), +) + +_VITH14_quickgelu = dict( + metaclip_fullcc=_pcfg( + "https://dl.fbaipublicfiles.com/MMPT/metaclip/h14_fullcc2.5b.pt" + ), + dfn5b=_pcfg( + hf_hub="apple/DFN5B-CLIP-ViT-H-14/", + interpolation="bicubic", + resize_mode="squash", + ), +) + +_VITH14_378_quickgelu = dict( + dfn5b=_pcfg( + hf_hub="apple/DFN5B-CLIP-ViT-H-14-378/", + interpolation="bicubic", + resize_mode="squash", + ), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub="laion/CLIP-ViT-g-14-laion2B-s12B-b42K/"), + laion2b_s34b_b88k=_pcfg(hf_hub="laion/CLIP-ViT-g-14-laion2B-s34B-b88K/"), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg( + hf_hub="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/" + ), +) + +_robertaViTB32 = dict( + laion2b_s12b_b32k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/" + ), +) + +_xlmRobertaBaseViTB32 = dict( + laion5b_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/" + ), +) + +_xlmRobertaLargeFrozenViTH14 = dict( + frozen_laion5b_s13b_b90k=_pcfg( + hf_hub="laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/" + ), +) + +_convnext_base = dict( + laion400m_s13b_b51k=_pcfg( + hf_hub="laion/CLIP-convnext_base-laion400M-s13B-b51K/" + ), +) + +_convnext_base_w = dict( + laion2b_s13b_b82k=_pcfg( + hf_hub="laion/CLIP-convnext_base_w-laion2B-s13B-b82K/" + ), + laion2b_s13b_b82k_augreg=_pcfg( + hf_hub="laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/" + ), + laion_aesthetic_s13b_b82k=_pcfg( + hf_hub="laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/" + ), +) + +_convnext_base_w_320 = dict( + laion_aesthetic_s13b_b82k=_pcfg( + hf_hub="laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/" + ), + laion_aesthetic_s13b_b82k_augreg=_pcfg( + hf_hub="laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/" + ), +) + +_convnext_large_d = dict( + laion2b_s26b_b102k_augreg=_pcfg( + hf_hub="laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/" + ), +) + +_convnext_large_d_320 = dict( + laion2b_s29b_b131k_ft=_pcfg( + hf_hub="laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/" + ), + laion2b_s29b_b131k_ft_soup=_pcfg( + hf_hub="laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/" + ), +) + +_convnext_xxlarge = dict( + laion2b_s34b_b82k_augreg=_pcfg( + hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/" + ), + laion2b_s34b_b82k_augreg_rewind=_pcfg( + hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/" + ), + laion2b_s34b_b82k_augreg_soup=_pcfg( + hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/" + ), +) + +_coca_VITB32 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub="laion/CoCa-ViT-B-32-laion2B-s13B-b90k/"), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg( + hf_hub="laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/" + ), +) + +_coca_VITL14 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub="laion/CoCa-ViT-L-14-laion2B-s13B-b90k/"), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg( + hf_hub="laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/" + ), +) + + +_PRETRAINED = { + "RN50": _RN50, + "RN50-quickgelu": _RN50_quickgelu, + "RN101": _RN101, + "RN101-quickgelu": _RN101_quickgelu, + "RN50x4": _RN50x4, + "RN50x16": _RN50x16, + "RN50x64": _RN50x64, + "ViT-B-32": _VITB32, + "ViT-B-32-256": _VITB32_256, + "ViT-B-32-quickgelu": _VITB32_quickgelu, + "ViT-B-16": _VITB16, + "ViT-B-16-quickgelu": _VITB16_quickgelu, + "ViT-B-16-plus-240": _VITB16_PLUS_240, + "ViT-L-14": _VITL14, + "ViT-L-14-quickgelu": _VITL14_quickgelu, + "ViT-L-14-336": _VITL14_336, + "ViT-H-14": _VITH14, + "ViT-H-14-quickgelu": _VITH14_quickgelu, + "ViT-H-14-378-quickgelu": _VITH14_378_quickgelu, + "ViT-g-14": _VITg14, + "ViT-bigG-14": _VITbigG14, + "roberta-ViT-B-32": _robertaViTB32, + "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, + "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, + "convnext_base": _convnext_base, + "convnext_base_w": _convnext_base_w, + "convnext_base_w_320": _convnext_base_w_320, + "convnext_large_d": _convnext_large_d, + "convnext_large_d_320": _convnext_large_d_320, + "convnext_xxlarge": _convnext_xxlarge, + "coca_ViT-B-32": _coca_VITB32, + "coca_ViT-L-14": _coca_VITL14, + "EVA01-g-14": dict( + # from QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt + laion400m_s11b_b41k=_pcfg( + hf_hub="timm/eva_giant_patch14_clip_224.laion400m_s11b_b41k/" + ), + ), + "EVA01-g-14-plus": dict( + # from QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt + merged2b_s11b_b114k=_pcfg( + hf_hub="timm/eva_giant_patch14_plus_clip_224.merged2b_s11b_b114k/" + ), + ), + "EVA02-B-16": dict( + # from QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt + merged2b_s8b_b131k=_pcfg( + hf_hub="timm/eva02_base_patch16_clip_224.merged2b_s8b_b131k/" + ), + ), + "EVA02-L-14": dict( + # from QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt + merged2b_s4b_b131k=_pcfg( + hf_hub="timm/eva02_large_patch14_clip_224.merged2b_s4b_b131k/" + ), + ), + "EVA02-L-14-336": dict( + # from QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt + merged2b_s6b_b61k=_pcfg( + hf_hub="timm/eva02_large_patch14_clip_336.merged2b_s6b_b61k/" + ), + ), + "EVA02-E-14": dict( + # from QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt + laion2b_s4b_b115k=_pcfg( + hf_hub="timm/eva02_enormous_patch14_clip_224.laion2b_s4b_b115k/" + ), + ), + "EVA02-E-14-plus": dict( + # from QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt + laion2b_s9b_b144k=_pcfg( + hf_hub="timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k/" + ), + ), + "ViT-B-16-SigLIP": dict( + webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP/"), + ), + "ViT-B-16-SigLIP-256": dict( + webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-256/"), + ), + "ViT-B-16-SigLIP-i18n-256": dict( + webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-i18n-256/"), + ), + "ViT-B-16-SigLIP-384": dict( + webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-384/"), + ), + "ViT-B-16-SigLIP-512": dict( + webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-512/"), + ), + "ViT-L-16-SigLIP-256": dict( + webli=_slpcfg(hf_hub="timm/ViT-L-16-SigLIP-256/"), + ), + "ViT-L-16-SigLIP-384": dict( + webli=_slpcfg(hf_hub="timm/ViT-L-16-SigLIP-384/"), + ), + "ViT-SO400M-14-SigLIP": dict( + webli=_slpcfg(hf_hub="timm/ViT-SO400M-14-SigLIP/"), + ), + "ViT-SO400M-14-SigLIP-384": dict( + webli=_slpcfg(hf_hub="timm/ViT-SO400M-14-SigLIP-384/"), + ), + "ViT-L-14-CLIPA": dict( + datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-L-14-CLIPA-datacomp1B/"), + ), + "ViT-L-14-CLIPA-336": dict( + datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-L-14-CLIPA-336-datacomp1B/"), + ), + "ViT-H-14-CLIPA": dict( + datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-datacomp1B/"), + ), + "ViT-H-14-CLIPA-336": dict( + laion2b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-336-laion2B/"), + datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-336-datacomp1B/"), + ), + "ViT-bigG-14-CLIPA": dict( + datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-bigG-14-CLIPA-datacomp1B/"), + ), + "ViT-bigG-14-CLIPA-336": dict( + datacomp1b=_apcfg( + hf_hub="UCSC-VLAA/ViT-bigG-14-CLIPA-336-datacomp1B/" + ), + ), + "nllb-clip-base": dict( + v1=_pcfg(hf_hub="visheratin/nllb-clip-base-oc/"), + ), + "nllb-clip-large": dict( + v1=_pcfg(hf_hub="visheratin/nllb-clip-large-oc/"), + ), + "nllb-clip-base-siglip": dict( + v1=_slpcfg(hf_hub="visheratin/nllb-clip-base-siglip/"), + ), + "nllb-clip-large-siglip": dict( + v1=_slpcfg(hf_hub="visheratin/nllb-clip-large-siglip/"), + ), +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace("-", "_") + + +def list_pretrained(as_str: bool = False): + """returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [ + ":".join([k, t]) if as_str else (k, t) + for k in _PRETRAINED.keys() + for t in _PRETRAINED[k].keys() + ] + + +def list_pretrained_models_by_tag(tag: str): + """return all models having the specified pretrain tag""" + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """return all pretrain tags for the specified model architecture""" + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get("url", "") + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if "openaipublic" in url: + expected_sha256 = url.split("/")[-2] + elif "mlfoundations" in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = "" + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError( + f"{download_target} exists and is not a regular file" + ) + + if os.path.isfile(download_target): + if expected_sha256: + if ( + hashlib.sha256(open(download_target, "rb").read()) + .hexdigest() + .startswith(expected_sha256) + ): + return download_target + else: + warnings.warn( + f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" + ) + else: + return download_target + + with urllib.request.urlopen(url) as source, open( + download_target, "wb" + ) as output: + with tqdm( + total=int(source.headers.get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256( + open(download_target, "rb").read() + ).hexdigest().startswith(expected_sha256): + raise RuntimeError( + f"Model has been downloaded but the SHA256 checksum does not not match" + ) + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + "Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`." + ) + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = "open_clip_pytorch_model.bin", + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download( + model_id, filename, revision=revision, cache_dir=cache_dir + ) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = "" + if not cfg: + return target + + download_url = cfg.get("url", "") + download_hf_hub = cfg.get("hf_hub", "") + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = "" + + if download_url: + target = download_pretrained_from_url( + download_url, cache_dir=cache_dir + ) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf( + model_id, filename=filename, cache_dir=cache_dir + ) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/fiftyone/utils/open_clip/push_to_hf_hub.py b/fiftyone/utils/open_clip/push_to_hf_hub.py new file mode 100644 index 0000000000..a0021c7d4c --- /dev/null +++ b/fiftyone/utils/open_clip/push_to_hf_hub.py @@ -0,0 +1,353 @@ +import argparse +import json +import os +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Optional, Tuple, Union + +import torch + +try: + from huggingface_hub import ( + create_repo, + get_hf_file_metadata, + hf_hub_download, + hf_hub_url, + repo_type_and_id_from_hf_id, + upload_folder, + list_repo_files, + ) + from huggingface_hub.utils import EntryNotFoundError + + _has_hf_hub = True +except ImportError: + _has_hf_hub = False + +try: + import safetensors.torch + + _has_safetensors = True +except ImportError: + _has_safetensors = False + +from .factory import ( + create_model_from_pretrained, + get_model_config, + get_tokenizer, +) +from .tokenizer import HFTokenizer + +# Default name for a weights file hosted on the Huggingface Hub. +HF_WEIGHTS_NAME = "open_clip_pytorch_model.bin" # default pytorch pkl +HF_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors" # safetensors version +HF_CONFIG_NAME = "open_clip_config.json" + + +def save_config_for_hf(model, config_path: str, model_config: Optional[dict]): + preprocess_cfg = { + "mean": model.visual.image_mean, + "std": model.visual.image_std, + } + other_pp = getattr(model.visual, "preprocess_cfg", {}) + if "interpolation" in other_pp: + preprocess_cfg["interpolation"] = other_pp["interpolation"] + if "resize_mode" in other_pp: + preprocess_cfg["resize_mode"] = other_pp["resize_mode"] + hf_config = { + "model_cfg": model_config, + "preprocess_cfg": preprocess_cfg, + } + + with config_path.open("w") as f: + json.dump(hf_config, f, indent=2) + + +def save_for_hf( + model, + tokenizer: HFTokenizer, + model_config: dict, + save_directory: str, + safe_serialization: Union[bool, str] = "both", + skip_weights: bool = False, +): + config_filename = HF_CONFIG_NAME + + save_directory = Path(save_directory) + save_directory.mkdir(exist_ok=True, parents=True) + + if not skip_weights: + tensors = model.state_dict() + if safe_serialization is True or safe_serialization == "both": + assert ( + _has_safetensors + ), "`pip install safetensors` to use .safetensors" + safetensors.torch.save_file( + tensors, save_directory / HF_SAFE_WEIGHTS_NAME + ) + if safe_serialization is False or safe_serialization == "both": + torch.save(tensors, save_directory / HF_WEIGHTS_NAME) + + tokenizer.save_pretrained(save_directory) + + config_path = save_directory / config_filename + save_config_for_hf(model, config_path, model_config=model_config) + + +def push_to_hf_hub( + model, + tokenizer, + model_config: Optional[dict], + repo_id: str, + commit_message: str = "Add model", + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, + safe_serialization: Union[bool, str] = False, +): + if not isinstance(tokenizer, HFTokenizer): + # FIXME this makes it awkward to push models with new tokenizers, come up with better soln. + # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14 + tokenizer = HFTokenizer("openai/clip-vit-large-patch14") + + # Create repo if it doesn't exist yet + repo_url = create_repo( + repo_id, token=token, private=private, exist_ok=True + ) + + # Infer complete repo_id from repo_url + # Can be different from the input `repo_id` if repo_owner was implicit + _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) + repo_id = f"{repo_owner}/{repo_name}" + + # Check if repo already exists and determine what needs updating + repo_exists = False + repo_files = {} + try: + repo_files = set(list_repo_files(repo_id)) + repo_exists = True + except Exception as e: + print("Repo does not exist", e) + + try: + get_hf_file_metadata( + hf_hub_url( + repo_id=repo_id, filename="README.md", revision=revision + ) + ) + has_readme = True + except EntryNotFoundError: + has_readme = False + + # Dump model and push to Hub + with TemporaryDirectory() as tmpdir: + # Save model weights and config. + save_for_hf( + model, + tokenizer=tokenizer, + model_config=model_config, + save_directory=tmpdir, + safe_serialization=safe_serialization, + ) + + # Add readme if it does not exist + if not has_readme: + model_card = model_card or {} + model_name = repo_id.split("/")[-1] + readme_path = Path(tmpdir) / "README.md" + readme_text = generate_readme(model_card, model_name) + readme_path.write_text(readme_text) + + # Upload model and return + return upload_folder( + repo_id=repo_id, + folder_path=tmpdir, + revision=revision, + create_pr=create_pr, + commit_message=commit_message, + ) + + +def push_pretrained_to_hf_hub( + model_name, + pretrained: str, + repo_id: str, + precision: str = "fp32", + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + image_interpolation: Optional[str] = None, + image_resize_mode: Optional[str] = None, # only effective for inference + commit_message: str = "Add model", + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, + hf_tokenizer_self: bool = False, +): + model, preprocess_eval = create_model_from_pretrained( + model_name, + pretrained=pretrained, + precision=precision, + image_mean=image_mean, + image_std=image_std, + image_interpolation=image_interpolation, + image_resize_mode=image_resize_mode, + ) + model_config = get_model_config(model_name) + assert model_config + + tokenizer = get_tokenizer(model_name) + if hf_tokenizer_self: + # make hf tokenizer config in the uploaded model point to self instead of original location + model_config["text"]["hf_tokenizer_name"] = repo_id + + push_to_hf_hub( + model=model, + tokenizer=tokenizer, + model_config=model_config, + repo_id=repo_id, + commit_message=commit_message, + token=token, + revision=revision, + private=private, + create_pr=create_pr, + model_card=model_card, + safe_serialization="both", + ) + + +def generate_readme(model_card: dict, model_name: str): + tags = model_card.pop("tags", ("clip",)) + pipeline_tag = model_card.pop( + "pipeline_tag", "zero-shot-image-classification" + ) + readme_text = "---\n" + if tags: + readme_text += "tags:\n" + for t in tags: + readme_text += f"- {t}\n" + readme_text += "library_name: open_clip\n" + readme_text += f"pipeline_tag: {pipeline_tag}\n" + readme_text += f"license: {model_card.get('license', 'mit')}\n" + if "details" in model_card and "Dataset" in model_card["details"]: + readme_text += "datasets:\n" + readme_text += f"- {model_card['details']['Dataset'].lower()}\n" + readme_text += "---\n" + readme_text += f"# Model card for {model_name}\n" + if "description" in model_card: + readme_text += f"\n{model_card['description']}\n" + if "details" in model_card: + readme_text += f"\n## Model Details\n" + for k, v in model_card["details"].items(): + if isinstance(v, (list, tuple)): + readme_text += f"- **{k}:**\n" + for vi in v: + readme_text += f" - {vi}\n" + elif isinstance(v, dict): + readme_text += f"- **{k}:**\n" + for ki, vi in v.items(): + readme_text += f" - {ki}: {vi}\n" + else: + readme_text += f"- **{k}:** {v}\n" + if "usage" in model_card: + readme_text += f"\n## Model Usage\n" + readme_text += model_card["usage"] + readme_text += "\n" + + if "comparison" in model_card: + readme_text += f"\n## Model Comparison\n" + readme_text += model_card["comparison"] + readme_text += "\n" + + if "citation" in model_card: + readme_text += f"\n## Citation\n" + if not isinstance(model_card["citation"], (list, tuple)): + citations = [model_card["citation"]] + else: + citations = model_card["citation"] + for c in citations: + readme_text += f"```bibtex\n{c}\n```\n" + + return readme_text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Push to Hugging Face Hub") + parser.add_argument( + "--model", + type=str, + help="Name of the model to use.", + ) + parser.add_argument( + "--pretrained", + type=str, + help="Use a pretrained CLIP model weights with the specified tag or file path.", + ) + parser.add_argument( + "--repo-id", + type=str, + help="Destination HF Hub repo-id ie 'organization/model_id'.", + ) + parser.add_argument( + "--precision", + type=str, + default="fp32", + ) + parser.add_argument( + "--image-mean", + type=float, + nargs="+", + default=None, + metavar="MEAN", + help="Override default image mean value of dataset", + ) + parser.add_argument( + "--image-std", + type=float, + nargs="+", + default=None, + metavar="STD", + help="Override default image std deviation of of dataset", + ) + parser.add_argument( + "--image-interpolation", + default=None, + type=str, + choices=["bicubic", "bilinear", "random"], + help="image resize interpolation", + ) + parser.add_argument( + "--image-resize-mode", + default=None, + type=str, + choices=["shortest", "longest", "squash"], + help="image resize mode during inference", + ) + parser.add_argument( + "--hf-tokenizer-self", + default=False, + action="store_true", + help="make hf_tokenizer_name point in uploaded config point to itself", + ) + args = parser.parse_args() + + print( + f"Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}" + ) + + # FIXME add support to pass model_card json / template from file via cmd line + + push_pretrained_to_hf_hub( + args.model, + args.pretrained, + args.repo_id, + precision=args.precision, + image_mean=args.image_mean, # override image mean/std if trained w/ non defaults + image_std=args.image_std, + image_interpolation=args.image_interpolation, + image_resize_mode=args.image_resize_mode, + ) + + print(f"{args.model} saved.") diff --git a/fiftyone/utils/open_clip/timm_model.py b/fiftyone/utils/open_clip/timm_model.py new file mode 100644 index 0000000000..e36049ad2c --- /dev/null +++ b/fiftyone/utils/open_clip/timm_model.py @@ -0,0 +1,171 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import ( + AttentionPool2d as AbsAttentionPool2d, + ) + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """timm model adapter""" + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool="avg", + proj="linear", + proj_bias=False, + drop=0.0, + drop_path=None, + patch_drop=None, + pretrained=False, + ): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + self.image_size = to_2tuple(image_size) + + # setup kwargs that may not be common across all models + timm_kwargs = {} + if drop_path is not None: + timm_kwargs["drop_path_rate"] = drop_path + if patch_drop is not None: + timm_kwargs["patch_drop_rate"] = patch_drop + + custom_pool = pool in ("abs_attn", "rot_attn") + if proj: + assert proj in ("linear", "mlp", "none") + extra_proj = proj in ("linear", "mlp") + if not extra_proj and not custom_pool: + # use network classifier head as projection if no proj specified and no custom pooling used + # if projection is explicitly set to "none" will be pass through from network trunk + proj_dim = 0 if proj == "none" else embed_dim + self.trunk = timm.create_model( + model_name, + num_classes=proj_dim, + global_pool=pool, + pretrained=pretrained, + **timm_kwargs, + ) + prev_chs = embed_dim + else: + self.trunk = timm.create_model( + model_name, + pretrained=pretrained, + **timm_kwargs, + ) + feat_size = self.trunk.default_cfg.get("pool_size", None) + feature_ndim = 1 if not feat_size else 2 + if custom_pool: + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool="") + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + + # Add custom pooling to head + if pool == "abs_attn": + head_layers["pool"] = AbsAttentionPool2d( + prev_chs, feat_size=feat_size, out_features=embed_dim + ) + prev_chs = embed_dim + elif pool == "rot_attn": + head_layers["pool"] = RotAttentionPool2d( + prev_chs, out_features=embed_dim + ) + prev_chs = embed_dim + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == "linear": + head_layers["drop"] = nn.Dropout(drop) + head_layers["proj"] = nn.Linear( + prev_chs, embed_dim, bias=proj_bias + ) + elif proj == "mlp": + head_layers["mlp"] = Mlp( + prev_chs, + 2 * embed_dim, + embed_dim, + drop=(drop, 0), + bias=(True, proj_bias), + ) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + "Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`" + ) + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = { + k for k, v in gmodules.items() if v <= max_layer_id + } + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning( + "grad checkpointing not supported for this timm image tower, continuing without..." + ) + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/fiftyone/utils/open_clip/tokenizer.py b/fiftyone/utils/open_clip/tokenizer.py new file mode 100644 index 0000000000..3e6d697bd4 --- /dev/null +++ b/fiftyone/utils/open_clip/tokenizer.py @@ -0,0 +1,573 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +import random +import string +from functools import lru_cache, partial +from typing import Callable, List, Optional, Union +import warnings + +import ftfy +import numpy as np +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +os.environ["TOKENIZERS_PARALLELISM"] = "false" +_nltk_init = False + +DEFAULT_CONTEXT_LENGTH = 77 # default context length for OpenAI CLIP + + +@lru_cache() +def default_bpe(): + return os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "bpe_simple_vocab_16e6.txt.gz", + ) + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r"\s+", " ", text) + text = text.strip() + return text + + +def _clean_canonicalize(x): + # basic, remove whitespace, remove punctuation, lower case + return canonicalize_text(basic_clean(x)) + + +def _clean_lower(x): + # basic, remove whitespace, lower case + return whitespace_clean(basic_clean(x)).lower() + + +def _clean_whitespace(x): + # basic, remove whitespace + return whitespace_clean(basic_clean(x)) + + +def get_clean_fn(type: str): + if type == "canonicalize": + return _clean_canonicalize + elif type == "lower": + return _clean_lower + elif type == "whitespace": + return _clean_whitespace + else: + assert False, f"Invalid clean function ({type})." + + +def canonicalize_text(text, *, keep_punctuation_exact_string=None): + """Returns canonicalized `text` (lowercase and punctuation removed). + + From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94 + + Args: + text: string to be canonicalized. + keep_punctuation_exact_string: If provided, then this exact string kept. + For example providing '{}' will keep any occurrences of '{}' (but will + still remove '{' and '}' that appear separately). + """ + text = text.replace("_", " ") + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + part.translate(str.maketrans("", "", string.punctuation)) + for part in text.split(keep_punctuation_exact_string) + ) + else: + text = text.translate(str.maketrans("", "", string.punctuation)) + text = text.lower() + text = re.sub(r"\s+", " ", text) + return text.strip() + + +class SimpleTokenizer(object): + def __init__( + self, + bpe_path: str = default_bpe(), + additional_special_tokens: Optional[List[str]] = None, + context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH, + clean: str = "lower", + reduction_mask: str = "", + ): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") + merges = merges[1 : 49152 - 256 - 2 + 1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v + "" for v in vocab] + for merge in merges: + vocab.append("".join(merge)) + special_tokens = ["", ""] + if additional_special_tokens: + special_tokens += additional_special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t: t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile( + special + + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""" + ) + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + self.sot_token_id = self.all_special_ids[0] + self.eot_token_id = self.all_special_ids[1] + self.context_length = context_length + self.clean_fn = get_clean_fn(clean) + self.reduction_fn = ( + get_reduction_mask_fn(reduction_mask) if reduction_mask else None + ) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + (token[-1] + "",) + pairs = get_pairs(word) + + if not pairs: + return token + "" + + while True: + bigram = min( + pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")) + ) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if ( + word[i] == first + and i < len(word) - 1 + and word[i + 1] == second + ): + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = " ".join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = self.clean_fn(text) + for token in re.findall(self.pat, text): + token = "".join( + self.byte_encoder[b] for b in token.encode("utf-8") + ) + bpe_tokens.extend( + self.encoder[bpe_token] + for bpe_token in self.bpe(token).split(" ") + ) + return bpe_tokens + + def decode(self, tokens): + text = "".join([self.decoder[token] for token in tokens]) + text = ( + bytearray([self.byte_decoder[c] for c in text]) + .decode("utf-8", errors="replace") + .replace("", " ") + ) + return text + + def __call__( + self, + texts: Union[str, List[str]], + context_length: Optional[int] = None, + ) -> torch.LongTensor: + """Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + context_length = context_length or self.context_length + assert context_length, "Please set a valid context length" + + if self.reduction_fn is not None: + # use reduction strategy for tokenize if set, otherwise default to truncation below + return simple_mask_tokenize( + texts, + context_length=context_length, + sot_token_id=self.sot_token_id, + eot_token_id=self.eot_token_id, + encode_fn=self.encode, + ) + + all_tokens = [ + [self.sot_token_id] + self.encode(text) + [self.eot_token_id] + for text in texts + ] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = self.eot_token_id + result[i, : len(tokens)] = torch.tensor(tokens) + + return result + + +_tokenizer = SimpleTokenizer() + + +def decode(output_ids: torch.Tensor): + output_ids = output_ids.cpu().numpy() + return _tokenizer.decode(output_ids) + + +def tokenize( + texts: Union[str, List[str]], context_length: int = DEFAULT_CONTEXT_LENGTH +) -> torch.LongTensor: + return _tokenizer(texts, context_length=context_length) + + +def random_mask_tokenize( + texts: Union[str, List[str]], + context_length: int, + sot_token_id: int, + eot_token_id: int, + encode_fn: Callable, + shuffle: bool = False, +): + all_tokens = [encode_fn(text) for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + tokens = torch.tensor(tokens) + num_tokens = len(tokens) + if num_tokens > context_length - 2: # 2 for sot and eot token + num_keep = context_length - 2 + indices = torch.randperm(len(tokens)) + indices = indices[:num_keep] + if not shuffle: + indices = indices.msort() + tokens = tokens[indices] + num_tokens = num_keep + result[i, 0] = sot_token_id + result[i, 1 : num_tokens + 1] = tokens + result[i, num_tokens + 1] = eot_token_id + + return result + + +def simple_mask_tokenize( + texts: Union[str, List[str]], + context_length: int, + sot_token_id: int, + eot_token_id: int, + encode_fn: Callable, +): + all_tokens = [encode_fn(text) for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + num_tokens = len(tokens) + if num_tokens > context_length - 2: # 2 for sot and eot token + num_keep = context_length - 2 + start_index = random.randint( + 0, num_tokens - num_keep + ) # high is incl + tokens = tokens[start_index : start_index + num_keep] + tokens = [sot_token_id] + tokens + [eot_token_id] + result[i, : len(tokens)] = torch.tensor(tokens) + + return result + + +def syntax_mask_tokenize( + texts: Union[str, List[str]], + context_length: int, + sot_token_id: int, + eot_token_id: int, + encode_fn: Callable, +) -> torch.LongTensor: + """Returns the tokenized representation of given input string(s). + Apply syntax masking before tokenize. + """ + import nltk + + global _nltk_init + if not _nltk_init: + # run them for the first time + nltk.download("punkt") + nltk.download("averaged_perceptron_tagger") + _nltk_init = True + + def get_order(x): + if x.startswith("NN"): + return 1 + elif x.startswith("JJ"): + return 2 + elif x.startswith("VB"): + return 3 + else: + return 4 + + # syntax masking + new_texts = [] + for text in texts: + list_tokens = nltk.tokenize.word_tokenize(text) + pos_tags = nltk.pos_tag(list_tokens) + # sample the words by get_order method + order_list = [get_order(tag) for _, tag in pos_tags] + sorted_ids = np.argsort(np.array(order_list)) + sampled_ids = sorted( + sorted_ids[: context_length - 2] + ) # need 2 slots for sot and eot tokens + sampled_tokens = np.take( + np.array(list_tokens), sampled_ids, axis=0 + ) # sample the tokens + + new_text = "" + for token in sampled_tokens: + new_text = new_text + str(token) + " " + new_text = new_text.strip() + new_texts.append(new_text) + texts = new_texts + + all_tokens = [ + [sot_token_id] + encode_fn(text) + [eot_token_id] for text in texts + ] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + # still need first truncate because some words produces two tokens + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token_id + result[i, : len(tokens)] = torch.tensor(tokens) + + return result + + +def get_reduction_mask_fn(type: str): + """Choose strategy for dropping (masking) tokens to achieve target context length""" + assert type in ("simple", "random", "shuffle", "syntax") + if type == "simple": + return simple_mask_tokenize # randomly select block [start:end] + elif type == "random": + return random_mask_tokenize # randomly drop tokens (keep order) + elif type == "shuffle": + return partial( + random_mask_tokenize, shuffle=True + ) # randomly drop tokens (shuffle order) + elif type == "syntax": + return syntax_mask_tokenize # randomly drop prioritized by syntax + + +class HFTokenizer: + """HuggingFace tokenizer wrapper""" + + def __init__( + self, + tokenizer_name: str, + context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH, + clean: str = "whitespace", + strip_sep_token: bool = False, + language: Optional[str] = None, + ): + from transformers import AutoTokenizer + + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + try: + set_lang_fn = self.tokenizer.set_src_lang_special_tokens + except AttributeError: + set_lang_fn = None + + if callable(set_lang_fn): + self.set_lang_fn = set_lang_fn + if language is not None: + self.set_language(language) + self.context_length = context_length + self.clean_fn = get_clean_fn(clean) + self.strip_sep_token = strip_sep_token + + def save_pretrained(self, dest): + self.tokenizer.save_pretrained(dest) + + def __call__( + self, + texts: Union[str, List[str]], + context_length: Optional[int] = None, + ) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + + context_length = context_length or self.context_length + assert ( + context_length + ), "Please set a valid context length in class init or call." + + texts = [self.clean_fn(text) for text in texts] + input_ids = self.tokenizer.batch_encode_plus( + texts, + return_tensors="pt", + max_length=context_length, + padding="max_length", + truncation=True, + ).input_ids + + if self.strip_sep_token: + input_ids = torch.where( + input_ids == self.tokenizer.sep_token_id, + torch.zeros_like(input_ids), + input_ids, + ) + + return input_ids + + def set_language(self, src_lang): + if hasattr(self, "set_lang_fn"): + self.set_lang_fn(src_lang) + else: + warnings.warn("Cannot set language for the tokenizer.") + + +class SigLipTokenizer: + """HuggingFace tokenizer wrapper for SigLIP T5 compatible sentencepiece vocabs""" + + VOCAB_FILES = { + # english, vocab_size=32_000 + "c4-en": "http://storage.googleapis.com/t5-data/vocabs/cc_en.32000/sentencepiece.model", + # used in multilingual models (mT5, PaLI), vocab_size=250_000 + "mc4": "http://storage.googleapis.com/t5-data/vocabs/mc4.250000.100extra/sentencepiece.model", + } + + def __init__( + self, + tokenizer_name: str, + context_length: Optional[int] = 64, + ): + from transformers import T5TokenizerFast + + if tokenizer_name in self.VOCAB_FILES: + # FIXME temporary hack? + import tempfile + + import fsspec + + vocab_file = self.VOCAB_FILES[tokenizer_name] + with tempfile.NamedTemporaryFile("wb") as dst: + with fsspec.open(vocab_file, "rb") as src: + dst.write(src.read()) + self.tokenizer = T5TokenizerFast(dst.name, legacy=False) + else: + self.tokenizer = T5TokenizerFast(tokenizer_name, legacy=False) + + self.tokenizer.pad_token_id = 1 + self.tokenizer.eos_token_id = 1 + self.context_length = context_length + + def save_pretrained(self, dest): + self.tokenizer.save_pretrained(dest) + + def __call__( + self, + texts: Union[str, List[str]], + context_length: Optional[int] = None, + ) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + + context_length = context_length or self.context_length + assert ( + context_length + ), "Please set a valid context length in class init or call." + + texts = [canonicalize_text(basic_clean(text)) for text in texts] + output = self.tokenizer( + texts, + return_tensors="pt", + max_length=context_length, + padding="max_length", + truncation=True, + ) + return output.input_ids diff --git a/fiftyone/utils/open_clip/transform.py b/fiftyone/utils/open_clip/transform.py new file mode 100644 index 0000000000..9e6b4830eb --- /dev/null +++ b/fiftyone/utils/open_clip/transform.py @@ -0,0 +1,485 @@ +import numbers +import random +import warnings +from dataclasses import dataclass, asdict +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +import torch +import torchvision.transforms.functional as F +from torchvision.transforms import ( + Normalize, + Compose, + RandomResizedCrop, + InterpolationMode, + ToTensor, + Resize, + CenterCrop, + ColorJitter, + Grayscale, +) + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .utils import to_2tuple + + +@dataclass +class PreprocessCfg: + size: Union[int, Tuple[int, int]] = 224 + mode: str = "RGB" + mean: Tuple[float, ...] = OPENAI_DATASET_MEAN + std: Tuple[float, ...] = OPENAI_DATASET_STD + interpolation: str = "bicubic" + resize_mode: str = "shortest" + fill_color: int = 0 + + def __post_init__(self): + assert self.mode in ("RGB",) + + @property + def num_channels(self): + return 3 + + @property + def input_size(self): + return (self.num_channels,) + to_2tuple(self.size) + + +_PREPROCESS_KEYS = set(asdict(PreprocessCfg()).keys()) + + +def merge_preprocess_dict( + base: Union[PreprocessCfg, Dict], + overlay: Dict, +): + """Merge overlay key-value pairs on top of base preprocess cfg or dict. + Input dicts are filtered based on PreprocessCfg fields. + """ + if isinstance(base, PreprocessCfg): + base_clean = asdict(base) + else: + base_clean = {k: v for k, v in base.items() if k in _PREPROCESS_KEYS} + if overlay: + overlay_clean = { + k: v + for k, v in overlay.items() + if k in _PREPROCESS_KEYS and v is not None + } + base_clean.update(overlay_clean) + return base_clean + + +def merge_preprocess_kwargs(base: PreprocessCfg, **kwargs): + return merge_preprocess_dict(base, kwargs) + + +@dataclass +class AugmentationCfg: + scale: Tuple[float, float] = (0.9, 1.0) + ratio: Optional[Tuple[float, float]] = None + color_jitter: Optional[ + Union[ + float, + Tuple[float, float, float], + Tuple[float, float, float, float], + ] + ] = None + re_prob: Optional[float] = None + re_count: Optional[int] = None + use_timm: bool = False + + # params for simclr_jitter_gray + color_jitter_prob: float = None + gray_scale_prob: float = None + + +def _setup_size(size, error_msg): + if isinstance(size, numbers.Number): + return int(size), int(size) + + if isinstance(size, Sequence) and len(size) == 1: + return size[0], size[0] + + if len(size) != 2: + raise ValueError(error_msg) + + return size + + +class ResizeKeepRatio: + """Resize and Keep Ratio + + Copy & paste from `timm` + """ + + def __init__( + self, + size, + longest=0.0, + interpolation=InterpolationMode.BICUBIC, + random_scale_prob=0.0, + random_scale_range=(0.85, 1.05), + random_aspect_prob=0.0, + random_aspect_range=(0.9, 1.11), + ): + if isinstance(size, (list, tuple)): + self.size = tuple(size) + else: + self.size = (size, size) + self.interpolation = interpolation + self.longest = float( + longest + ) # [0, 1] where 0 == shortest edge, 1 == longest + self.random_scale_prob = random_scale_prob + self.random_scale_range = random_scale_range + self.random_aspect_prob = random_aspect_prob + self.random_aspect_range = random_aspect_range + + @staticmethod + def get_params( + img, + target_size, + longest, + random_scale_prob=0.0, + random_scale_range=(0.85, 1.05), + random_aspect_prob=0.0, + random_aspect_range=(0.9, 1.11), + ): + """Get parameters""" + source_size = img.size[::-1] # h, w + h, w = source_size + target_h, target_w = target_size + ratio_h = h / target_h + ratio_w = w / target_w + ratio = max(ratio_h, ratio_w) * longest + min(ratio_h, ratio_w) * ( + 1.0 - longest + ) + if random_scale_prob > 0 and random.random() < random_scale_prob: + ratio_factor = random.uniform( + random_scale_range[0], random_scale_range[1] + ) + ratio_factor = (ratio_factor, ratio_factor) + else: + ratio_factor = (1.0, 1.0) + if random_aspect_prob > 0 and random.random() < random_aspect_prob: + aspect_factor = random.uniform( + random_aspect_range[0], random_aspect_range[1] + ) + ratio_factor = ( + ratio_factor[0] / aspect_factor, + ratio_factor[1] * aspect_factor, + ) + size = [ + round(x * f / ratio) for x, f in zip(source_size, ratio_factor) + ] + return size + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped and resized. + + Returns: + PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size + """ + size = self.get_params( + img, + self.size, + self.longest, + self.random_scale_prob, + self.random_scale_range, + self.random_aspect_prob, + self.random_aspect_range, + ) + img = F.resize(img, size, self.interpolation) + return img + + def __repr__(self): + format_string = self.__class__.__name__ + "(size={0}".format(self.size) + format_string += f", interpolation={self.interpolation})" + format_string += f", longest={self.longest:.3f})" + return format_string + + +def center_crop_or_pad( + img: torch.Tensor, output_size: List[int], fill=0 +) -> torch.Tensor: + """Center crops and/or pads the given image. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + img (PIL Image or Tensor): Image to be cropped. + output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, + it is used for both directions. + fill (int, Tuple[int]): Padding color + + Returns: + PIL Image or Tensor: Cropped image. + """ + if isinstance(output_size, numbers.Number): + output_size = (int(output_size), int(output_size)) + elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: + output_size = (output_size[0], output_size[0]) + + _, image_height, image_width = F.get_dimensions(img) + crop_height, crop_width = output_size + + if crop_width > image_width or crop_height > image_height: + padding_ltrb = [ + (crop_width - image_width) // 2 if crop_width > image_width else 0, + (crop_height - image_height) // 2 + if crop_height > image_height + else 0, + (crop_width - image_width + 1) // 2 + if crop_width > image_width + else 0, + (crop_height - image_height + 1) // 2 + if crop_height > image_height + else 0, + ] + img = F.pad(img, padding_ltrb, fill=fill) + _, image_height, image_width = F.get_dimensions(img) + if crop_width == image_width and crop_height == image_height: + return img + + crop_top = int(round((image_height - crop_height) / 2.0)) + crop_left = int(round((image_width - crop_width) / 2.0)) + return F.crop(img, crop_top, crop_left, crop_height, crop_width) + + +class CenterCropOrPad(torch.nn.Module): + """Crops the given image at the center. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + """ + + def __init__(self, size, fill=0): + super().__init__() + self.size = _setup_size( + size, + error_msg="Please provide only two dimensions (h, w) for size.", + ) + self.fill = fill + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + return center_crop_or_pad(img, self.size, fill=self.fill) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +def _convert_to_rgb(image): + return image.convert("RGB") + + +class color_jitter(object): + """ + Apply Color Jitter to the PIL image with a specified probability. + """ + + def __init__( + self, brightness=0.0, contrast=0.0, saturation=0.0, hue=0.0, p=0.8 + ): + assert 0.0 <= p <= 1.0 + self.p = p + self.transf = ColorJitter( + brightness=brightness, + contrast=contrast, + saturation=saturation, + hue=hue, + ) + + def __call__(self, img): + if random.random() < self.p: + return self.transf(img) + else: + return img + + +class gray_scale(object): + """ + Apply Gray Scale to the PIL image with a specified probability. + """ + + def __init__(self, p=0.2): + assert 0.0 <= p <= 1.0 + self.p = p + self.transf = Grayscale(num_output_channels=3) + + def __call__(self, img): + if random.random() < self.p: + return self.transf(img) + else: + return img + + +def image_transform( + image_size: Union[int, Tuple[int, int]], + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_mode: Optional[str] = None, + interpolation: Optional[str] = None, + fill_color: int = 0, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + interpolation = interpolation or "bicubic" + assert interpolation in ["bicubic", "bilinear", "random"] + # NOTE random is ignored for interpolation_mode, so defaults to BICUBIC for inference if set + interpolation_mode = ( + InterpolationMode.BILINEAR + if interpolation == "bilinear" + else InterpolationMode.BICUBIC + ) + + resize_mode = resize_mode or "shortest" + assert resize_mode in ("shortest", "longest", "squash") + + if isinstance(aug_cfg, dict): + aug_cfg = AugmentationCfg(**aug_cfg) + else: + aug_cfg = aug_cfg or AugmentationCfg() + + normalize = Normalize(mean=mean, std=std) + + if is_train: + aug_cfg_dict = { + k: v for k, v in asdict(aug_cfg).items() if v is not None + } + use_timm = aug_cfg_dict.pop("use_timm", False) + if use_timm: + from timm.data import ( + create_transform, + ) # timm can still be optional + + if isinstance(image_size, (tuple, list)): + assert len(image_size) >= 2 + input_size = (3,) + image_size[-2:] + else: + input_size = (3, image_size, image_size) + + aug_cfg_dict.setdefault("color_jitter", None) # disable by default + # drop extra non-timm items + aug_cfg_dict.pop("color_jitter_prob", None) + aug_cfg_dict.pop("gray_scale_prob", None) + + train_transform = create_transform( + input_size=input_size, + is_training=True, + hflip=0.0, + mean=mean, + std=std, + re_mode="pixel", + interpolation=interpolation, + **aug_cfg_dict, + ) + else: + train_transform = [ + RandomResizedCrop( + image_size, + scale=aug_cfg_dict.pop("scale"), + interpolation=InterpolationMode.BICUBIC, + ), + _convert_to_rgb, + ] + if aug_cfg.color_jitter_prob: + assert ( + aug_cfg.color_jitter is not None + and len(aug_cfg.color_jitter) == 4 + ) + train_transform.extend( + [ + color_jitter( + *aug_cfg.color_jitter, p=aug_cfg.color_jitter_prob + ) + ] + ) + if aug_cfg.gray_scale_prob: + train_transform.extend([gray_scale(aug_cfg.gray_scale_prob)]) + train_transform.extend( + [ + ToTensor(), + normalize, + ] + ) + train_transform = Compose(train_transform) + if aug_cfg_dict: + warnings.warn( + f"Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())})." + ) + return train_transform + else: + if resize_mode == "longest": + transforms = [ + ResizeKeepRatio( + image_size, interpolation=interpolation_mode, longest=1 + ), + CenterCropOrPad(image_size, fill=fill_color), + ] + elif resize_mode == "squash": + if isinstance(image_size, int): + image_size = (image_size, image_size) + transforms = [ + Resize(image_size, interpolation=interpolation_mode), + ] + else: + assert resize_mode == "shortest" + if not isinstance(image_size, (tuple, list)): + image_size = (image_size, image_size) + if image_size[0] == image_size[1]: + # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg) + transforms = [ + Resize(image_size[0], interpolation=interpolation_mode) + ] + else: + # resize shortest edge to matching target dim for non-square target + transforms = [ResizeKeepRatio(image_size)] + transforms += [CenterCrop(image_size)] + + transforms.extend( + [ + _convert_to_rgb, + ToTensor(), + normalize, + ] + ) + return Compose(transforms) + + +def image_transform_v2( + cfg: PreprocessCfg, + is_train: bool, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +): + return image_transform( + image_size=cfg.size, + is_train=is_train, + mean=cfg.mean, + std=cfg.std, + interpolation=cfg.interpolation, + resize_mode=cfg.resize_mode, + fill_color=cfg.fill_color, + aug_cfg=aug_cfg, + ) diff --git a/fiftyone/utils/open_clip/transformer.py b/fiftyone/utils/open_clip/transformer.py new file mode 100644 index 0000000000..207cc5eede --- /dev/null +++ b/fiftyone/utils/open_clip/transformer.py @@ -0,0 +1,960 @@ +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence, Tuple +from functools import partial + +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint + +from .utils import to_2tuple +from .pos_embed import get_2d_sincos_pos_embed + + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm( + x.to(torch.float32), + self.normalized_shape, + self.weight, + self.bias, + self.eps, + ) + return x.to(orig_type) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm( + x, self.normalized_shape, self.weight, self.bias, self.eps + ) + return x.to(orig_type) + + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1.0 + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + + def forward(self, x): + if not self.training or self.prob == 0.0: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1.0 / 0.01), + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, "dim should be divisible by num_heads" + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim**-0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter( + torch.randn((dim * 3, dim)) * self.scale + ) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))) + ) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk( + 3, dim=-1 + ) + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm( + F.normalize(q, dim=-1), + F.normalize(k, dim=-1).transpose(-1, -2), + ) + logit_scale = torch.clamp( + self.logit_scale, max=self.logit_scale_max + ).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + + +class AttentionalPooler(nn.Module): + def __init__( + self, + d_model: int, + context_dim: int, + n_head: int = 8, + n_queries: int = 256, + norm_layer: Callable = LayerNorm, + ): + super().__init__() + self.query = nn.Parameter(torch.randn(n_queries, d_model)) + self.attn = nn.MultiheadAttention( + d_model, n_head, kdim=context_dim, vdim=context_dim + ) + self.ln_q = norm_layer(d_model) + self.ln_k = norm_layer(context_dim) + + def forward(self, x: torch.Tensor): + x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND + N = x.shape[1] + q = self.ln_q(self.query) + out = self.attn( + q.unsqueeze(1).expand(-1, N, -1), x, x, need_weights=False + )[0] + return out.permute(1, 0, 2) # LND -> NLD + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + is_cross_attention: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = ( + LayerScale(d_model, ls_init_value) + if ls_init_value is not None + else nn.Identity() + ) + if is_cross_attention: + self.ln_1_kv = norm_layer(d_model) + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)), + ] + ) + ) + self.ls_2 = ( + LayerScale(d_model, ls_init_value) + if ls_init_value is not None + else nn.Identity() + ) + + def attention( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = k_x if k_x is not None else q_x + v_x = v_x if v_x is not None else q_x + + attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None + return self.attn( + q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask + )[0] + + def forward( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = ( + self.ln_1_kv(k_x) + if hasattr(self, "ln_1_kv") and k_x is not None + else None + ) + v_x = ( + self.ln_1_kv(v_x) + if hasattr(self, "ln_1_kv") and v_x is not None + else None + ) + + x = q_x + self.ls_1( + self.attention( + q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask + ) + ) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = Attention( + d_model, + n_head, + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + ) + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = ( + LayerScale(d_model, ls_init_value) + if ls_init_value is not None + else nn.Identity() + ) + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ( + "ln", + norm_layer(mlp_width) if scale_fc else nn.Identity(), + ), + ("c_proj", nn.Linear(mlp_width, d_model)), + ] + ) + ) + self.ls_2 = ( + LayerScale(d_model, ls_init_value) + if ls_init_value is not None + else nn.Identity() + ) + + def forward( + self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None + ): + if attn_mask is not None and not isinstance(attn_mask, torch.Tensor): + raise TypeError("attn_mask must be a torch.Tensor or None") + x = x + self.ls_1( + self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)) + ) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +def _expand_token(token, batch_size: int): + return token.view(1, 1, -1).expand(batch_size, -1, -1) + + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList( + [ + ResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + for _ in range(layers) + ] + ) + + def get_cast_dtype(self) -> torch.dtype: + if hasattr(self.resblocks[0].mlp.c_fc, "int8_original_dtype"): + return self.resblocks[0].mlp.c_fc.int8_original_dtype + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward( + self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None + ): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + x = checkpoint(r, x, None, None, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + attentional_pool: bool = False, + attn_pooler_queries: int = 256, + attn_pooler_heads: int = 8, + output_dim: int = 512, + patch_dropout: float = 0.0, + no_ln_pre: bool = False, + pos_embed_type: str = "learnable", + pool_type: str = "tok", + final_ln_after_pool: bool = False, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_tokens: bool = False, + ): + super().__init__() + assert pool_type in ("tok", "avg", "none") + self.output_tokens = output_tokens + image_height, image_width = self.image_size = to_2tuple(image_size) + patch_height, patch_width = self.patch_size = to_2tuple(patch_size) + self.grid_size = ( + image_height // patch_height, + image_width // patch_width, + ) + self.final_ln_after_pool = ( + final_ln_after_pool # currently ignored w/ attn pool enabled + ) + self.output_dim = output_dim + + self.conv1 = nn.Conv2d( + in_channels=3, + out_channels=width, + kernel_size=patch_size, + stride=patch_size, + bias=False, + ) + + # class embeddings and positional embeddings + scale = width**-0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + if pos_embed_type == "learnable": + self.positional_embedding = nn.Parameter( + scale + * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width) + ) + elif pos_embed_type == "sin_cos_2d": + # fixed sin-cos embedding + assert ( + self.grid_size[0] == self.grid_size[1] + ), "currently sin cos 2d pos embedding only supports square input" + self.positional_embedding = nn.Parameter( + torch.zeros(self.grid_size[0] * self.grid_size[1] + 1, width), + requires_grad=False, + ) + pos_embed_type = get_2d_sincos_pos_embed( + width, self.grid_size[0], cls_token=True + ) + self.positional_embedding.data.copy_( + torch.from_numpy(pos_embed_type).float() + ) + else: + raise ValueError + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = ( + PatchDropout(patch_dropout) + if patch_dropout > 0.0 + else nn.Identity() + ) + + self.ln_pre = nn.Identity() if no_ln_pre else norm_layer(width) + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + if attentional_pool: + if isinstance(attentional_pool, str): + self.attn_pool_type = attentional_pool + self.pool_type = "none" + if attentional_pool in ("parallel", "cascade"): + self.attn_pool = AttentionalPooler( + output_dim, + width, + n_head=attn_pooler_heads, + n_queries=attn_pooler_queries, + ) + self.attn_pool_contrastive = AttentionalPooler( + output_dim, + width, + n_head=attn_pooler_heads, + n_queries=1, + ) + else: + assert False + else: + self.attn_pool_type = "" + self.pool_type = pool_type + self.attn_pool = AttentionalPooler( + output_dim, + width, + n_head=attn_pooler_heads, + n_queries=attn_pooler_queries, + ) + self.attn_pool_contrastive = None + pool_dim = output_dim + else: + self.attn_pool = None + pool_dim = width + self.pool_type = pool_type + + self.ln_post = norm_layer(pool_dim) + self.proj = nn.Parameter(scale * torch.randn(pool_dim, output_dim)) + + self.init_parameters() + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def init_parameters(self): + # FIXME OpenAI CLIP did not define an init for the VisualTransformer + # TODO experiment if default PyTorch init, below, or alternate init is best. + + # nn.init.normal_(self.class_embedding, std=self.scale) + # nn.init.normal_(self.positional_embedding, std=self.scale) + # + # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + # attn_std = self.transformer.width ** -0.5 + # fc_std = (2 * self.transformer.width) ** -0.5 + # for block in self.transformer.resblocks: + # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + # + # if self.text_projection is not None: + # nn.init.normal_(self.text_projection, std=self.scale) + pass + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def _global_pool( + self, x: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + if self.pool_type == "avg": + pooled, tokens = x[:, 1:].mean(dim=1), x[:, 1:] + elif self.pool_type == "tok": + pooled, tokens = x[:, 0], x[:, 1:] + else: + pooled = tokens = x + + return pooled, tokens + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape( + x.shape[0], x.shape[1], -1 + ) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], + dim=1, + ) + # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if self.attn_pool is not None: + if self.attn_pool_contrastive is not None: + # This is untested, WIP pooling that should match paper + x = self.ln_post( + x + ) # TBD LN first or separate one after each pool? + tokens = self.attn_pool(x) + if self.attn_pool_type == "parallel": + pooled = self.attn_pool_contrastive(x) + else: + assert self.attn_pool_type == "cascade" + pooled = self.attn_pool_contrastive(tokens) + else: + # this is the original OpenCLIP CoCa setup, does not match paper + x = self.attn_pool(x) + x = self.ln_post(x) + pooled, tokens = self._global_pool(x) + elif self.final_ln_after_pool: + pooled, tokens = self._global_pool(x) + pooled = self.ln_post(pooled) + else: + x = self.ln_post(x) + pooled, tokens = self._global_pool(x) + + if self.proj is not None: + pooled = pooled @ self.proj + + if self.output_tokens: + return pooled, tokens + + return pooled + + +def text_global_pool( + x, text: Optional[torch.Tensor] = None, pool_type: str = "argmax" +): + if pool_type == "first": + pooled, tokens = x[:, 0], x[:, 1:] + elif pool_type == "last": + pooled, tokens = x[:, -1], x[:, :-1] + elif pool_type == "argmax": + # take features from the eot embedding (eot_token is the highest number in each sequence) + assert text is not None + pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x + else: + pooled = tokens = x + + return pooled, tokens + + +class TextTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + output_dim: int = 512, + embed_cls: bool = False, + no_causal_mask: bool = False, + pad_id: int = 0, + pool_type: str = "argmax", + proj_bias: bool = False, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_tokens: bool = False, + ): + super().__init__() + assert pool_type in ("first", "last", "argmax", "none") + self.output_tokens = output_tokens + self.num_pos = self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + self.heads = heads + self.pad_id = pad_id + self.pool_type = pool_type + + self.token_embedding = nn.Embedding(vocab_size, width) + if embed_cls: + self.cls_emb = nn.Parameter(torch.empty(width)) + self.num_pos += 1 + else: + self.cls_emb = None + self.positional_embedding = nn.Parameter( + torch.empty(self.num_pos, width) + ) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + mlp_ratio=mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.ln_final = norm_layer(width) + + if no_causal_mask: + self.attn_mask = None + else: + self.register_buffer( + "attn_mask", self.build_causal_mask(), persistent=False + ) + + if proj_bias: + self.text_projection = nn.Linear(width, output_dim) + else: + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + if self.cls_emb is not None: + nn.init.normal_(self.cls_emb, std=0.01) + + proj_std = (self.transformer.width**-0.5) * ( + (2 * self.transformer.layers) ** -0.5 + ) + attn_std = self.transformer.width**-0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + if isinstance(self.text_projection, nn.Linear): + nn.init.normal_( + self.text_projection.weight, + std=self.transformer.width**-0.5, + ) + if self.text_projection.bias is not None: + nn.init.zeros_(self.text_projection.bias) + else: + nn.init.normal_( + self.text_projection, std=self.transformer.width**-0.5 + ) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def build_causal_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.num_pos, self.num_pos) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def build_cls_mask(self, text, cast_dtype: torch.dtype): + cls_mask = (text != self.pad_id).unsqueeze(1) + cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=True) + additive_mask = torch.empty( + cls_mask.shape, dtype=cast_dtype, device=cls_mask.device + ) + additive_mask.fill_(0) + additive_mask.masked_fill_(~cls_mask, float("-inf")) + additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) + return additive_mask + + def forward(self, text): + cast_dtype = self.transformer.get_cast_dtype() + seq_len = text.shape[1] + + x = self.token_embedding(text).to( + cast_dtype + ) # [batch_size, n_ctx, d_model] + attn_mask = self.attn_mask + if self.cls_emb is not None: + seq_len += 1 + x = torch.cat([x, _expand_token(self.cls_emb, x.shape[0])], dim=1) + cls_mask = self.build_cls_mask(text, cast_dtype) + if attn_mask is not None: + # pylint: disable=unsubscriptable-object + attn_mask = ( + attn_mask[None, :seq_len, :seq_len] + + cls_mask[:, :seq_len, :seq_len] + ) + + x = x + self.positional_embedding[:seq_len].to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + + # x.shape = [batch_size, n_ctx, transformer.width] + if self.cls_emb is not None: + # presence of appended cls embed (CoCa) overrides pool_type, always take last token + pooled, tokens = text_global_pool(x, pool_type="last") + pooled = self.ln_final( + pooled + ) # final LN applied after pooling in this case + else: + x = self.ln_final(x) + pooled, tokens = text_global_pool( + x, text, pool_type=self.pool_type + ) + + if self.text_projection is not None: + if isinstance(self.text_projection, nn.Linear): + pooled = self.text_projection(pooled) + else: + pooled = pooled @ self.text_projection + + if self.output_tokens: + return pooled, tokens + + return pooled + + +class MultimodalTransformer(Transformer): + def __init__( + self, + width: int, + layers: int, + heads: int, + context_length: int = 77, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_dim: int = 512, + ): + + super().__init__( + width=width, + layers=layers, + heads=heads, + mlp_ratio=mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.context_length = context_length + self.cross_attn = nn.ModuleList( + [ + ResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + is_cross_attention=True, + ) + for _ in range(layers) + ] + ) + + self.register_buffer( + "attn_mask", self.build_attention_mask(), persistent=False + ) + + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + def init_parameters(self): + proj_std = (self.transformer.width**-0.5) * ( + (2 * self.transformer.layers) ** -0.5 + ) + attn_std = self.transformer.width**-0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + for block in self.transformer.cross_attn: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_( + self.text_projection, std=self.transformer.width**-0.5 + ) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, image_embs, text_embs): + text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq + image_embs = image_embs.permute(1, 0, 2) # NLD -> LND + seq_len = text_embs.shape[0] + + for resblock, cross_attn in zip(self.resblocks, self.cross_attn): + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + text_embs = checkpoint( + resblock, + text_embs, + None, + None, + self.attn_mask[:seq_len, :seq_len], + ) + text_embs = checkpoint( + cross_attn, text_embs, image_embs, image_embs, None + ) + else: + text_embs = resblock( + text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len] + ) + text_embs = cross_attn( + text_embs, k_x=image_embs, v_x=image_embs + ) + + x = text_embs.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if self.text_projection is not None: + x = x @ self.text_projection + + return x + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable diff --git a/fiftyone/utils/open_clip/utils.py b/fiftyone/utils/open_clip/utils.py new file mode 100644 index 0000000000..7fe2e396d4 --- /dev/null +++ b/fiftyone/utils/open_clip/utils.py @@ -0,0 +1,105 @@ +from itertools import repeat +import collections.abc + +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d + + +def freeze_batch_norm_2d(module, module_match={}, name=""): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance( + module, + (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm), + ): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = ( + ".".join([name, child_name]) if name else child_name + ) + new_child = freeze_batch_norm_2d( + child, module_match, full_child_name + ) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) + +# Replaces all linear layers with linear_replacement +# TODO: add int8 support for other linear layers including attn and convnets +def replace_linear( + model, + linear_replacement, + include_modules=["c_fc", "c_proj"], + copy_weights=True, +): + for name, module in model.named_children(): + if len(list(module.children())) > 0: + replace_linear( + module, linear_replacement, include_modules, copy_weights + ) + + if isinstance(module, torch.nn.Linear) and name in include_modules: + old_module = model._modules[name] + model._modules[name] = linear_replacement( + module.in_features, + module.out_features, + module.bias is not None, + ) + if copy_weights: + model._modules[name].weight.data.copy_(old_module.weight.data) + if model._modules[name].bias is not None: + model._modules[name].bias.data.copy_(old_module.bias) + + return model + + +def convert_int8_model_to_inference_mode(model): + for m in model.modules(): + if hasattr(m, "prepare_for_eval"): + int8_original_dtype = m.weight.dtype + m.prepare_for_eval() + m.int8_original_dtype = int8_original_dtype diff --git a/fiftyone/utils/open_clip/version.py b/fiftyone/utils/open_clip/version.py new file mode 100644 index 0000000000..db969b3af4 --- /dev/null +++ b/fiftyone/utils/open_clip/version.py @@ -0,0 +1 @@ +__version__ = "2.23.0" diff --git a/fiftyone/utils/open_clip/zero_shot_classifier.py b/fiftyone/utils/open_clip/zero_shot_classifier.py new file mode 100644 index 0000000000..408da3561f --- /dev/null +++ b/fiftyone/utils/open_clip/zero_shot_classifier.py @@ -0,0 +1,135 @@ +from functools import partial +from itertools import islice +from typing import Callable, List, Optional, Sequence, Union + +import torch +import torch.nn.functional as F + + +def batched(iterable, n): + """Batch data into lists of length *n*. The last batch may be shorter. + NOTE based on more-itertools impl, to be replaced by python 3.12 itertools.batched impl + """ + it = iter(iterable) + while True: + batch = list(islice(it, n)) + if not batch: + break + yield batch + + +def build_zero_shot_classifier( + model, + tokenizer, + classnames: Sequence[str], + templates: Sequence[Union[Callable, str]], + num_classes_per_batch: Optional[int] = 10, + device: Union[str, torch.device] = "cpu", + use_tqdm: bool = False, +): + """Build zero-shot classifier weights by iterating over class names in batches + Args: + model: CLIP model instance + tokenizer: CLIP tokenizer instance + classnames: A sequence of class (label) names + templates: A sequence of callables or format() friendly strings to produce templates per class name + num_classes_per_batch: The number of classes to batch together in each forward, all if None + device: Device to use. + use_tqdm: Enable TQDM progress bar. + """ + assert isinstance(templates, Sequence) and len(templates) > 0 + assert isinstance(classnames, Sequence) and len(classnames) > 0 + use_format = isinstance(templates[0], str) + num_templates = len(templates) + num_classes = len(classnames) + if use_tqdm: + import tqdm + + num_iter = ( + 1 + if num_classes_per_batch is None + else ((num_classes - 1) // num_classes_per_batch + 1) + ) + iter_wrap = partial( + tqdm.tqdm, total=num_iter, unit_scale=num_classes_per_batch + ) + else: + iter_wrap = iter + + def _process_batch(batch_classnames): + num_batch_classes = len(batch_classnames) + texts = [ + template.format(c) if use_format else template(c) + for c in batch_classnames + for template in templates + ] + texts = tokenizer(texts).to(device) + class_embeddings = model.encode_text(texts, normalize=True) + class_embeddings = class_embeddings.reshape( + num_batch_classes, num_templates, -1 + ).mean(dim=1) + class_embeddings = class_embeddings / class_embeddings.norm( + dim=1, keepdim=True + ) + class_embeddings = class_embeddings.T + return class_embeddings + + with torch.no_grad(): + if num_classes_per_batch: + batched_embeds = [ + _process_batch(batch) + for batch in iter_wrap( + batched(classnames, num_classes_per_batch) + ) + ] + zeroshot_weights = torch.cat(batched_embeds, dim=1) + else: + zeroshot_weights = _process_batch(classnames) + return zeroshot_weights + + +def build_zero_shot_classifier_legacy( + model, + tokenizer, + classnames: Sequence[str], + templates: Sequence[Union[Callable, str]], + device: Union[str, torch.device] = "cpu", + use_tqdm: bool = False, +): + """Build zero-shot classifier weights by iterating over class names 1 by 1 + Args: + model: CLIP model instance + tokenizer: CLIP tokenizer instance + classnames: A sequence of class (label) names + templates: A sequence of callables or format() friendly strings to produce templates per class name + device: Device to use. + use_tqdm: Enable TQDM progress bar. + """ + assert isinstance(templates, Sequence) and len(templates) > 0 + assert isinstance(classnames, Sequence) and len(classnames) > 0 + if use_tqdm: + import tqdm + + iter_wrap = tqdm.tqdm + else: + iter_wrap = iter + + use_format = isinstance(templates[0], str) + + with torch.no_grad(): + zeroshot_weights = [] + for classname in iter_wrap(classnames): + texts = [ + template.format(classname) + if use_format + else template(classname) + for template in templates + ] + texts = tokenizer(texts).to(device) # tokenize + class_embeddings = model.encode_text(texts) + class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0) + class_embedding /= class_embedding.norm() + zeroshot_weights.append(class_embedding) + zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(device) + + return zeroshot_weights diff --git a/fiftyone/utils/open_clip/zero_shot_metadata.py b/fiftyone/utils/open_clip/zero_shot_metadata.py new file mode 100644 index 0000000000..cc8ad5d8a5 --- /dev/null +++ b/fiftyone/utils/open_clip/zero_shot_metadata.py @@ -0,0 +1,1099 @@ +OPENAI_IMAGENET_TEMPLATES = ( + lambda c: f"a bad photo of a {c}.", + lambda c: f"a photo of many {c}.", + lambda c: f"a sculpture of a {c}.", + lambda c: f"a photo of the hard to see {c}.", + lambda c: f"a low resolution photo of the {c}.", + lambda c: f"a rendering of a {c}.", + lambda c: f"graffiti of a {c}.", + lambda c: f"a bad photo of the {c}.", + lambda c: f"a cropped photo of the {c}.", + lambda c: f"a tattoo of a {c}.", + lambda c: f"the embroidered {c}.", + lambda c: f"a photo of a hard to see {c}.", + lambda c: f"a bright photo of a {c}.", + lambda c: f"a photo of a clean {c}.", + lambda c: f"a photo of a dirty {c}.", + lambda c: f"a dark photo of the {c}.", + lambda c: f"a drawing of a {c}.", + lambda c: f"a photo of my {c}.", + lambda c: f"the plastic {c}.", + lambda c: f"a photo of the cool {c}.", + lambda c: f"a close-up photo of a {c}.", + lambda c: f"a black and white photo of the {c}.", + lambda c: f"a painting of the {c}.", + lambda c: f"a painting of a {c}.", + lambda c: f"a pixelated photo of the {c}.", + lambda c: f"a sculpture of the {c}.", + lambda c: f"a bright photo of the {c}.", + lambda c: f"a cropped photo of a {c}.", + lambda c: f"a plastic {c}.", + lambda c: f"a photo of the dirty {c}.", + lambda c: f"a jpeg corrupted photo of a {c}.", + lambda c: f"a blurry photo of the {c}.", + lambda c: f"a photo of the {c}.", + lambda c: f"a good photo of the {c}.", + lambda c: f"a rendering of the {c}.", + lambda c: f"a {c} in a video game.", + lambda c: f"a photo of one {c}.", + lambda c: f"a doodle of a {c}.", + lambda c: f"a close-up photo of the {c}.", + lambda c: f"a photo of a {c}.", + lambda c: f"the origami {c}.", + lambda c: f"the {c} in a video game.", + lambda c: f"a sketch of a {c}.", + lambda c: f"a doodle of the {c}.", + lambda c: f"a origami {c}.", + lambda c: f"a low resolution photo of a {c}.", + lambda c: f"the toy {c}.", + lambda c: f"a rendition of the {c}.", + lambda c: f"a photo of the clean {c}.", + lambda c: f"a photo of a large {c}.", + lambda c: f"a rendition of a {c}.", + lambda c: f"a photo of a nice {c}.", + lambda c: f"a photo of a weird {c}.", + lambda c: f"a blurry photo of a {c}.", + lambda c: f"a cartoon {c}.", + lambda c: f"art of a {c}.", + lambda c: f"a sketch of the {c}.", + lambda c: f"a embroidered {c}.", + lambda c: f"a pixelated photo of a {c}.", + lambda c: f"itap of the {c}.", + lambda c: f"a jpeg corrupted photo of the {c}.", + lambda c: f"a good photo of a {c}.", + lambda c: f"a plushie {c}.", + lambda c: f"a photo of the nice {c}.", + lambda c: f"a photo of the small {c}.", + lambda c: f"a photo of the weird {c}.", + lambda c: f"the cartoon {c}.", + lambda c: f"art of the {c}.", + lambda c: f"a drawing of the {c}.", + lambda c: f"a photo of the large {c}.", + lambda c: f"a black and white photo of a {c}.", + lambda c: f"the plushie {c}.", + lambda c: f"a dark photo of a {c}.", + lambda c: f"itap of a {c}.", + lambda c: f"graffiti of the {c}.", + lambda c: f"a toy {c}.", + lambda c: f"itap of my {c}.", + lambda c: f"a photo of a cool {c}.", + lambda c: f"a photo of a small {c}.", + lambda c: f"a tattoo of the {c}.", +) + + +# a much smaller subset of above prompts +# from https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb +SIMPLE_IMAGENET_TEMPLATES = ( + lambda c: f"itap of a {c}.", + lambda c: f"a bad photo of the {c}.", + lambda c: f"a origami {c}.", + lambda c: f"a photo of the large {c}.", + lambda c: f"a {c} in a video game.", + lambda c: f"art of the {c}.", + lambda c: f"a photo of the small {c}.", +) + + +IMAGENET_CLASSNAMES = ( + "tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead shark", + "electric ray", + "stingray", + "rooster", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "American robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "American dipper", + "kite (bird of prey)", + "bald eagle", + "vulture", + "great grey owl", + "fire salamander", + "smooth newt", + "newt", + "spotted salamander", + "axolotl", + "American bullfrog", + "tree frog", + "tailed frog", + "loggerhead sea turtle", + "leatherback sea turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "green iguana", + "Carolina anole", + "desert grassland whiptail lizard", + "agama", + "frilled-necked lizard", + "alligator lizard", + "Gila monster", + "European green lizard", + "chameleon", + "Komodo dragon", + "Nile crocodile", + "American alligator", + "triceratops", + "worm snake", + "ring-necked snake", + "eastern hog-nosed snake", + "smooth green snake", + "kingsnake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "African rock python", + "Indian cobra", + "green mamba", + "sea snake", + "Saharan horned viper", + "eastern diamondback rattlesnake", + "sidewinder rattlesnake", + "trilobite", + "harvestman", + "scorpion", + "yellow garden spider", + "barn spider", + "European garden spider", + "southern black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie grouse", + "peafowl", + "quail", + "partridge", + "african grey parrot", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "duck", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "red king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "great egret", + "bittern bird", + "crane bird", + "limpkin", + "common gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "dunlin", + "common redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese Chin", + "Maltese", + "Pekingese", + "Shih Tzu", + "King Charles Spaniel", + "Papillon", + "toy terrier", + "Rhodesian Ridgeback", + "Afghan Hound", + "Basset Hound", + "Beagle", + "Bloodhound", + "Bluetick Coonhound", + "Black and Tan Coonhound", + "Treeing Walker Coonhound", + "English foxhound", + "Redbone Coonhound", + "borzoi", + "Irish Wolfhound", + "Italian Greyhound", + "Whippet", + "Ibizan Hound", + "Norwegian Elkhound", + "Otterhound", + "Saluki", + "Scottish Deerhound", + "Weimaraner", + "Staffordshire Bull Terrier", + "American Staffordshire Terrier", + "Bedlington Terrier", + "Border Terrier", + "Kerry Blue Terrier", + "Irish Terrier", + "Norfolk Terrier", + "Norwich Terrier", + "Yorkshire Terrier", + "Wire Fox Terrier", + "Lakeland Terrier", + "Sealyham Terrier", + "Airedale Terrier", + "Cairn Terrier", + "Australian Terrier", + "Dandie Dinmont Terrier", + "Boston Terrier", + "Miniature Schnauzer", + "Giant Schnauzer", + "Standard Schnauzer", + "Scottish Terrier", + "Tibetan Terrier", + "Australian Silky Terrier", + "Soft-coated Wheaten Terrier", + "West Highland White Terrier", + "Lhasa Apso", + "Flat-Coated Retriever", + "Curly-coated Retriever", + "Golden Retriever", + "Labrador Retriever", + "Chesapeake Bay Retriever", + "German Shorthaired Pointer", + "Vizsla", + "English Setter", + "Irish Setter", + "Gordon Setter", + "Brittany dog", + "Clumber Spaniel", + "English Springer Spaniel", + "Welsh Springer Spaniel", + "Cocker Spaniel", + "Sussex Spaniel", + "Irish Water Spaniel", + "Kuvasz", + "Schipperke", + "Groenendael dog", + "Malinois", + "Briard", + "Australian Kelpie", + "Komondor", + "Old English Sheepdog", + "Shetland Sheepdog", + "collie", + "Border Collie", + "Bouvier des Flandres dog", + "Rottweiler", + "German Shepherd Dog", + "Dobermann", + "Miniature Pinscher", + "Greater Swiss Mountain Dog", + "Bernese Mountain Dog", + "Appenzeller Sennenhund", + "Entlebucher Sennenhund", + "Boxer", + "Bullmastiff", + "Tibetan Mastiff", + "French Bulldog", + "Great Dane", + "St. Bernard", + "husky", + "Alaskan Malamute", + "Siberian Husky", + "Dalmatian", + "Affenpinscher", + "Basenji", + "pug", + "Leonberger", + "Newfoundland dog", + "Great Pyrenees dog", + "Samoyed", + "Pomeranian", + "Chow Chow", + "Keeshond", + "brussels griffon", + "Pembroke Welsh Corgi", + "Cardigan Welsh Corgi", + "Toy Poodle", + "Miniature Poodle", + "Standard Poodle", + "Mexican hairless dog (xoloitzcuintli)", + "grey wolf", + "Alaskan tundra wolf", + "red wolf or maned wolf", + "coyote", + "dingo", + "dhole", + "African wild dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby cat", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian Mau", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "polar bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "longhorn beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket insect", + "stick insect", + "cockroach", + "praying mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "red admiral butterfly", + "ringlet butterfly", + "monarch butterfly", + "small white butterfly", + "sulphur butterfly", + "gossamer-winged butterfly", + "starfish", + "sea urchin", + "sea cucumber", + "cottontail rabbit", + "hare", + "Angora rabbit", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "common sorrel horse", + "zebra", + "pig", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram (adult male sheep)", + "bighorn sheep", + "Alpine ibex", + "hartebeest", + "impala (antelope)", + "gazelle", + "arabian camel", + "llama", + "weasel", + "mink", + "European polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas monkey", + "baboon", + "macaque", + "langur", + "black-and-white colobus", + "proboscis monkey", + "marmoset", + "white-headed capuchin", + "howler monkey", + "titi monkey", + "Geoffroy's spider monkey", + "common squirrel monkey", + "ring-tailed lemur", + "indri", + "Asian elephant", + "African bush elephant", + "red panda", + "giant panda", + "snoek fish", + "eel", + "silver salmon", + "rock beauty fish", + "clownfish", + "sturgeon", + "gar fish", + "lionfish", + "pufferfish", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibious vehicle", + "analog clock", + "apiary", + "apron", + "trash can", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint pen", + "Band-Aid", + "banjo", + "baluster / handrail", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "wheelbarrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "swimming cap", + "bath towel", + "bathtub", + "station wagon", + "lighthouse", + "beaker", + "military hat (bearskin or shako)", + "beer bottle", + "beer glass", + "bell tower", + "baby bib", + "tandem bicycle", + "bikini", + "ring binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsleigh", + "bolo tie", + "poke bonnet", + "bookcase", + "bookstore", + "bottle cap", + "hunting bow", + "bow tie", + "brass memorial plaque", + "bra", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "high-speed train", + "butcher shop", + "taxicab", + "cauldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "tool kit", + "cardboard box / carton", + "car wheel", + "automated teller machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "mobile phone", + "chain", + "chain-link fence", + "chain mail", + "chainsaw", + "storage chest", + "chiffonier", + "bell or wind chime", + "china cabinet", + "Christmas stocking", + "church", + "movie theater", + "cleaver", + "cliff dwelling", + "cloak", + "clogs", + "cocktail shaker", + "coffee mug", + "coffeemaker", + "spiral or coil", + "combination lock", + "computer keyboard", + "candy store", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "construction crane", + "crash helmet", + "crate", + "infant bed", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "rotary dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishcloth", + "dishwasher", + "disc brake", + "dock", + "dog sled", + "dome", + "doormat", + "drilling rig", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso machine", + "face powder", + "feather boa", + "filing cabinet", + "fireboat", + "fire truck", + "fire screen", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster bed", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gas mask or respirator", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golf cart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "radiator grille", + "grocery store", + "guillotine", + "hair clip", + "hair spray", + "half-track", + "hammer", + "hamper", + "hair dryer", + "hand-held computer", + "handkerchief", + "hard disk drive", + "harmonica", + "harp", + "combine harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoop skirt", + "gymnastic horizontal bar", + "horse-drawn vehicle", + "hourglass", + "iPod", + "clothes iron", + "carved pumpkin", + "jeans", + "jeep", + "T-shirt", + "jigsaw puzzle", + "rickshaw", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop computer", + "lawn mower", + "lens cap", + "letter opener", + "library", + "lifeboat", + "lighter", + "limousine", + "ocean liner", + "lipstick", + "slip-on shoe", + "lotion", + "music speaker", + "loupe magnifying glass", + "sawmill", + "magnetic compass", + "messenger bag", + "mailbox", + "tights", + "one-piece bathing suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "matchstick", + "maypole", + "maze", + "measuring cup", + "medicine cabinet", + "megalith", + "microphone", + "microwave oven", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "ford model t", + "modem", + "monastery", + "monitor", + "moped", + "mortar and pestle", + "graduation cap", + "mosque", + "mosquito net", + "vespa", + "mountain bike", + "tent", + "computer mouse", + "mousetrap", + "moving van", + "muzzle", + "metal nail", + "neck brace", + "necklace", + "baby pacifier", + "notebook computer", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "pipe organ", + "oscilloscope", + "overskirt", + "bullock cart", + "oxygen mask", + "product packet / packaging", + "paddle", + "paddle wheel", + "padlock", + "paintbrush", + "pajamas", + "palace", + "pan flute", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "railroad car", + "patio", + "payphone", + "pedestal", + "pencil case", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "plectrum", + "Pickelhaube", + "picket fence", + "pickup truck", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate ship", + "drink pitcher", + "block plane", + "planetarium", + "plastic bag", + "plate rack", + "farm plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "pool table", + "soda bottle", + "plant pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "missile", + "projector", + "hockey puck", + "punching bag", + "purse", + "quill", + "quilt", + "race car", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "fishing casting reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "eraser", + "rugby ball", + "ruler measuring stick", + "sneaker", + "safe", + "safety pin", + "salt shaker", + "sandal", + "sarong", + "saxophone", + "scabbard", + "weighing scale", + "school bus", + "schooner", + "scoreboard", + "CRT monitor", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe store", + "shoji screen / room divider", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "balaclava ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot machine", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar thermal collector", + "sombrero", + "soup bowl", + "keyboard space bar", + "space heater", + "space shuttle", + "spatula", + "motorboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "through arch bridge", + "steel drum", + "stethoscope", + "scarf", + "stone wall", + "stopwatch", + "stove", + "strainer", + "tram", + "stretcher", + "couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglasses", + "sunglasses", + "sunscreen", + "suspension bridge", + "mop", + "sweatshirt", + "swim trunks / shorts", + "swing", + "electrical switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy bear", + "television", + "tennis ball", + "thatched roof", + "front curtain", + "thimble", + "threshing machine", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toy store", + "tractor", + "semi-trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "hot tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright piano", + "vacuum cleaner", + "vase", + "vaulted or arched ceiling", + "velvet fabric", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "military aircraft", + "sink", + "washing machine", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "hair wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "airplane wing", + "wok", + "wooden spoon", + "wool", + "split-rail fence", + "shipwreck", + "sailboat", + "yurt", + "website", + "comic book", + "crossword", + "traffic or street sign", + "traffic light", + "dust jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "popsicle", + "baguette", + "bagel", + "pretzel", + "cheeseburger", + "hot dog", + "mashed potatoes", + "cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith apple", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "cherimoya (custard apple)", + "pomegranate", + "hay", + "carbonara", + "chocolate syrup", + "dough", + "meatloaf", + "pizza", + "pot pie", + "burrito", + "red wine", + "espresso", + "tea cup", + "eggnog", + "mountain", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeshore", + "promontory", + "sandbar", + "beach", + "valley", + "volcano", + "baseball player", + "bridegroom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "rose hip", + "horse chestnut seed", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn mushroom", + "earth star fungus", + "hen of the woods mushroom", + "bolete", + "corn cob", + "toilet paper", +) diff --git a/fiftyone/utils/open_clip/zoo.py b/fiftyone/utils/open_clip/zoo.py new file mode 100644 index 0000000000..47c7099f33 --- /dev/null +++ b/fiftyone/utils/open_clip/zoo.py @@ -0,0 +1,233 @@ +""" +CLIP model wrapper for the FiftyOne Model Zoo. + +| Copyright 2017-2023, Voxel51, Inc. +| `voxel51.com `_ +| +""" +import logging +import os +from packaging.version import Version +import warnings + +import eta.core.web as etaw + +import fiftyone as fo +import fiftyone.core.models as fom +import fiftyone.core.utils as fou +import fiftyone.utils.torch as fout +import fiftyone.zoo.models as fozm + +fou.ensure_torch() +import torch + +from .factory import create_model_and_transforms, get_tokenizer + +logger = logging.getLogger(__name__) + + +class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): + """Configuration for running a :class:`TorchCLIPModel`. + + See :class:`fiftyone.utils.torch.TorchImageModelConfig` for additional + arguments. + + Args: + tokenizer_base_filename: the filename in ``fo.config.model_zoo_dir`` in + which to store the model's tokenizer + tokenizer_base_url: a URL from which the tokenizer can be downloaded, + if necessary + context_length: the model's context length + text_prompt: the text prompt to use, e.g., ``"A photo of"`` + classes (None): a list of custom classes for zero-shot prediction + """ + + def __init__(self, d): + + d = self.init(d) + super().__init__(d) + + self.tokenizer_base_filename = self.parse_string( + d, "tokenizer_base_filename" + ) + self.tokenizer_base_url = self.parse_string(d, "tokenizer_base_url") + self.context_length = self.parse_int(d, "context_length") + self.text_prompt = self.parse_string(d, "text_prompt") + + self.model_name_clip = self.parse_string(d, "model_name_clip") + self.pretrained = self.parse_string(d, "pretrained") + + self._tokenizer_path = os.path.join( + fo.config.model_zoo_dir, self.tokenizer_base_filename + ) + + @property + def tokenizer_path(self): + return self._tokenizer_path + + def download_tokenizer_if_necessary(self): + if not os.path.isfile(self._tokenizer_path): + logger.info("Downloading CLIP tokenizer...") + etaw.download_file( + self.tokenizer_base_url, path=self._tokenizer_path + ) + + +class TorchOpenClipModel(fout.TorchImageModel, fom.PromptMixin): + """Torch implementation of CLIP from https://github.com/mlfoundations/open_clip. + + Args: + config: a :class:`TorchOpenClipPModelConfig` + """ + + def __init__(self, config): + super().__init__(config) + + self._text_features = None + + @property + def can_embed_prompts(self): + return True + + def embed_prompt(self, prompt): + """Generates an embedding for the given text prompt. + + Args: + prompt: a text string + + Returns: + a numpy vector + """ + return self.embed_prompts([prompt])[0] + + def embed_prompts(self, prompts): + """Generates an embedding for the given text prompts. + + Args: + prompts: an iterable of text strings + + Returns: + a ``num_prompts x num_dims`` array of prompt embeddings + """ + return self._embed_prompts(prompts).detach().cpu().numpy() + + def _download_model(self, config): + config.download_model_if_necessary() + config.download_tokenizer_if_necessary() + + def _load_model(self, config): + self._model, _, self.preprocess = create_model_and_transforms( + config.model_name_clip, pretrained=config.pretrained + ) + self._tokenizer = get_tokenizer(config.model_name_clip) + return self._model + + def _embed_prompts(self, prompts): + # source: https://github.com/openai/CLIP/blob/main/clip/clip.py + sot_token = self._tokenizer.encoder["<|startoftext|>"] + eot_token = self._tokenizer.encoder["<|endoftext|>"] + all_tokens = [ + [sot_token] + self._tokenizer.encode(p) + [eot_token] + for p in prompts + ] + + if Version(torch.__version__) < Version("1.8.0"): + dtype = torch.long + else: + dtype = torch.int + + text_features = torch.zeros( + len(all_tokens), + self.config.context_length, + dtype=dtype, + device=self.device, + ) + + for i, (prompt, tokens) in enumerate(zip(prompts, all_tokens)): + if len(tokens) > self.config.context_length: + tokens = tokens[: self.config.context_length] + tokens[-1] = eot_token + msg = ( + "Truncating prompt '%s'; too long for context length '%d'" + % (prompt, self.config.context_length) + ) + warnings.warn(msg) + + text_features[i, : len(tokens)] = torch.tensor(tokens) + + with torch.no_grad(): + return self._model.encode_text(text_features) + + def _get_text_features(self): + if self._text_features is None: + prompts = [ + "%s %s" % (self.config.text_prompt, c) for c in self.classes + ] + # Tokenize text + text = self._tokenizer(prompts) + self._text_features = self._model.encode_text(text) + + return self._text_features + + def _get_class_logits(self, text_features, image_features): + # source: https://github.com/openai/CLIP/blob/main/README.md + image_features = image_features / image_features.norm( + dim=1, keepdim=True + ) + text_features = text_features / text_features.norm(dim=1, keepdim=True) + logit_scale = self._model.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logits_per_image.t() + return logits_per_image, logits_per_text + + def _predict_all(self, imgs): + if self._preprocess: + imgs = [self._preprocess(img).unsqueeze(0) for img in imgs] + + if isinstance(imgs, (list, tuple)): + imgs = torch.stack(imgs) + + height, width = imgs.size()[-2:] + frame_size = (width, height) + + if self._using_gpu: + imgs = imgs.cuda() + + with torch.no_grad(), torch.cuda.amp.autocast(): + # Encode images and texts + image_features = self._model.encode_image(imgs) + text_features = self._get_text_features() + + output, _ = self._get_class_logits(text_features, image_features) + + if self.has_logits: + self._output_processor.store_logits = self.store_logits + + return self._output_processor( + output, frame_size, confidence_thresh=self.config.confidence_thresh + ) + + def _predict_all_incase(self, imgs): + print("PREDICT ALL") + if self._preprocess: + imgs = [self._transforms(img) for img in imgs] + + if isinstance(imgs, (list, tuple)): + imgs = torch.stack(imgs) + + height, width = imgs.size()[-2:] + frame_size = (width, height) + + if self._using_gpu: + imgs = imgs.cuda() + + text_features = self._get_text_features() + image_features = self._model.encode_image(imgs) + output, _ = self._get_class_logits(text_features, image_features) + + if self.has_logits: + self._output_processor.store_logits = self.store_logits + + return self._output_processor( + output, frame_size, confidence_thresh=self.config.confidence_thresh + ) diff --git a/fiftyone/zoo/models/manifest-torch.json b/fiftyone/zoo/models/manifest-torch.json index 9f296217c2..16035e9cc1 100644 --- a/fiftyone/zoo/models/manifest-torch.json +++ b/fiftyone/zoo/models/manifest-torch.json @@ -1862,6 +1862,58 @@ ], "date_added": "2020-12-11 13:45:51" }, + { + "base_name": "open-clip", + "base_filename": "", + "version": null, + "description": "OPENN CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs", + "source": "https://github.com/mlfoundations/open_clip", + "size_bytes": 353976522, + "manager": { + "type": "fiftyone.core.models.ModelManager", + "config": {} + }, + "default_deployment_config_dict": { + "type": "fiftyone.utils.open_clip.TorchOpenClipModel", + "config": { + "entrypoint_fcn": "", + "labels_path": "{{eta-resources}}/voc-labels.txt", + "output_processor_cls": "fiftyone.utils.torch.ClassifierOutputProcessor", + "image_size": [224, 224], + "image_mean": [0.48145466, 0.4578275, 0.40821073], + "image_std": [0.26862954, 0.26130258, 0.27577711], + "embeddings_layer": "visual", + "tokenizer_base_filename": "clip_bpe_simple_vocab_16e6.txt.gz", + "tokenizer_base_url": "https://github.com/openai/CLIP/raw/main/clip/bpe_simple_vocab_16e6.txt.gz", + "context_length": 77, + "text_prompt": "A photo of" + } + }, + "requirements": { + "packages": [ + "torch", + "torchvision", + "huggingface_hub", + "timm", + "transformers" + ], + "cpu": { + "support": true + }, + "gpu": { + "support": true + } + }, + "tags": [ + "classification", + "logits", + "embeddings", + "torch", + "clip", + "zero-shot" + ], + "date_added": "2023-12-13 14:25:51" + }, { "base_name": "clip-vit-base32-torch", "base_filename": "CLIP-ViT-B-32.pt", diff --git a/test.py b/test.py new file mode 100644 index 0000000000..b97b954662 --- /dev/null +++ b/test.py @@ -0,0 +1,56 @@ +import fiftyone as fo +import fiftyone.zoo as foz +import time + +# List available zoo models +model_names = foz.list_zoo_models() +print(model_names) + +# +# Load zoo model +# +# This will download the model from the web, if necessary, and ensure +# that any required packages are installed +# +framework = "open-clip" +model_name = "coca_ViT-B-32" +pretrained = "laion2b_s13b_b90k" +model = foz.load_zoo_model( + name=framework, + # install_requirements=True, # If its the first time, yoo need to install the requirements. + model_name_clip=model_name, + pretrained=pretrained, +) + +# +# Load some samples from the COCO-2017 validation split +# +# This will download the dataset from the web, if necessary +# +dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name="coco-2017-validation-sample", + max_samples=50, + shuffle=True, +) + +# +# Choose some samples to process. This can be the entire dataset, or a +# subset of the dataset. In this case, we'll choose some samples at +# random +# +samples = dataset.take(25) + +# +# Generate predictions for each sample and store the results in the +# `faster_rcnn` field of the dataset, discarding all predictions with +# confidence below 0.5 +# +samples.apply_model(model, label_field="faster_rcnn", confidence_thresh=0.5) +print(samples) + +# Visualize predictions in the App +session = fo.launch_app(view=samples) + +time.sleep(10000) From 7b62453adced0464bf362bc39dbef0a91d1cfb06 Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Thu, 14 Dec 2023 13:24:48 -0600 Subject: [PATCH 43/59] remove test --- test.py | 56 -------------------------------------------------------- 1 file changed, 56 deletions(-) delete mode 100644 test.py diff --git a/test.py b/test.py deleted file mode 100644 index b97b954662..0000000000 --- a/test.py +++ /dev/null @@ -1,56 +0,0 @@ -import fiftyone as fo -import fiftyone.zoo as foz -import time - -# List available zoo models -model_names = foz.list_zoo_models() -print(model_names) - -# -# Load zoo model -# -# This will download the model from the web, if necessary, and ensure -# that any required packages are installed -# -framework = "open-clip" -model_name = "coca_ViT-B-32" -pretrained = "laion2b_s13b_b90k" -model = foz.load_zoo_model( - name=framework, - # install_requirements=True, # If its the first time, yoo need to install the requirements. - model_name_clip=model_name, - pretrained=pretrained, -) - -# -# Load some samples from the COCO-2017 validation split -# -# This will download the dataset from the web, if necessary -# -dataset = foz.load_zoo_dataset( - "coco-2017", - split="validation", - dataset_name="coco-2017-validation-sample", - max_samples=50, - shuffle=True, -) - -# -# Choose some samples to process. This can be the entire dataset, or a -# subset of the dataset. In this case, we'll choose some samples at -# random -# -samples = dataset.take(25) - -# -# Generate predictions for each sample and store the results in the -# `faster_rcnn` field of the dataset, discarding all predictions with -# confidence below 0.5 -# -samples.apply_model(model, label_field="faster_rcnn", confidence_thresh=0.5) -print(samples) - -# Visualize predictions in the App -session = fo.launch_app(view=samples) - -time.sleep(10000) From ac1a3afd6561c20599140c99023275b7babc4135 Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Thu, 14 Dec 2023 15:21:16 -0600 Subject: [PATCH 44/59] solved issue size input --- fiftyone/utils/open_clip/tokenizer.py | 14 +++---- fiftyone/utils/open_clip/transformer.py | 2 - test2.py | 56 +++++++++++++++++++++++++ 3 files changed, 63 insertions(+), 9 deletions(-) create mode 100644 test2.py diff --git a/fiftyone/utils/open_clip/tokenizer.py b/fiftyone/utils/open_clip/tokenizer.py index 3e6d697bd4..09de1da3a8 100644 --- a/fiftyone/utils/open_clip/tokenizer.py +++ b/fiftyone/utils/open_clip/tokenizer.py @@ -161,7 +161,7 @@ def __init__( special = "|".join(special_tokens) self.pat = re.compile( special - + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""" + + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", ) self.vocab_size = len(self.encoder) self.all_special_ids = [self.encoder[t] for t in special_tokens] @@ -268,7 +268,8 @@ def __call__( if self.reduction_fn is not None: # use reduction strategy for tokenize if set, otherwise default to truncation below - return simple_mask_tokenize( + # pylint: disable=not-callable + return self.reduction_fn( texts, context_length=context_length, sot_token_id=self.sot_token_id, @@ -453,11 +454,9 @@ def __init__( from transformers import AutoTokenizer self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) - try: - set_lang_fn = self.tokenizer.set_src_lang_special_tokens - except AttributeError: - set_lang_fn = None - + set_lang_fn = getattr( + self.tokenizer, "set_src_lang_special_tokens", None + ) if callable(set_lang_fn): self.set_lang_fn = set_lang_fn if language is not None: @@ -504,6 +503,7 @@ def __call__( def set_language(self, src_lang): if hasattr(self, "set_lang_fn"): + # pylint: disable=not-callable self.set_lang_fn(src_lang) else: warnings.warn("Cannot set language for the tokenizer.") diff --git a/fiftyone/utils/open_clip/transformer.py b/fiftyone/utils/open_clip/transformer.py index 207cc5eede..7585e9a0ff 100644 --- a/fiftyone/utils/open_clip/transformer.py +++ b/fiftyone/utils/open_clip/transformer.py @@ -344,8 +344,6 @@ def __init__( def forward( self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None ): - if attn_mask is not None and not isinstance(attn_mask, torch.Tensor): - raise TypeError("attn_mask must be a torch.Tensor or None") x = x + self.ls_1( self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)) ) diff --git a/test2.py b/test2.py new file mode 100644 index 0000000000..b97b954662 --- /dev/null +++ b/test2.py @@ -0,0 +1,56 @@ +import fiftyone as fo +import fiftyone.zoo as foz +import time + +# List available zoo models +model_names = foz.list_zoo_models() +print(model_names) + +# +# Load zoo model +# +# This will download the model from the web, if necessary, and ensure +# that any required packages are installed +# +framework = "open-clip" +model_name = "coca_ViT-B-32" +pretrained = "laion2b_s13b_b90k" +model = foz.load_zoo_model( + name=framework, + # install_requirements=True, # If its the first time, yoo need to install the requirements. + model_name_clip=model_name, + pretrained=pretrained, +) + +# +# Load some samples from the COCO-2017 validation split +# +# This will download the dataset from the web, if necessary +# +dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name="coco-2017-validation-sample", + max_samples=50, + shuffle=True, +) + +# +# Choose some samples to process. This can be the entire dataset, or a +# subset of the dataset. In this case, we'll choose some samples at +# random +# +samples = dataset.take(25) + +# +# Generate predictions for each sample and store the results in the +# `faster_rcnn` field of the dataset, discarding all predictions with +# confidence below 0.5 +# +samples.apply_model(model, label_field="faster_rcnn", confidence_thresh=0.5) +print(samples) + +# Visualize predictions in the App +session = fo.launch_app(view=samples) + +time.sleep(10000) From 1494dce3135cc7e81311b8cd82ecb9c7fe43e04d Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Thu, 14 Dec 2023 15:24:21 -0600 Subject: [PATCH 45/59] remove the issue with the entry --- test2.py | 56 -------------------------------------------------------- 1 file changed, 56 deletions(-) delete mode 100644 test2.py diff --git a/test2.py b/test2.py deleted file mode 100644 index b97b954662..0000000000 --- a/test2.py +++ /dev/null @@ -1,56 +0,0 @@ -import fiftyone as fo -import fiftyone.zoo as foz -import time - -# List available zoo models -model_names = foz.list_zoo_models() -print(model_names) - -# -# Load zoo model -# -# This will download the model from the web, if necessary, and ensure -# that any required packages are installed -# -framework = "open-clip" -model_name = "coca_ViT-B-32" -pretrained = "laion2b_s13b_b90k" -model = foz.load_zoo_model( - name=framework, - # install_requirements=True, # If its the first time, yoo need to install the requirements. - model_name_clip=model_name, - pretrained=pretrained, -) - -# -# Load some samples from the COCO-2017 validation split -# -# This will download the dataset from the web, if necessary -# -dataset = foz.load_zoo_dataset( - "coco-2017", - split="validation", - dataset_name="coco-2017-validation-sample", - max_samples=50, - shuffle=True, -) - -# -# Choose some samples to process. This can be the entire dataset, or a -# subset of the dataset. In this case, we'll choose some samples at -# random -# -samples = dataset.take(25) - -# -# Generate predictions for each sample and store the results in the -# `faster_rcnn` field of the dataset, discarding all predictions with -# confidence below 0.5 -# -samples.apply_model(model, label_field="faster_rcnn", confidence_thresh=0.5) -print(samples) - -# Visualize predictions in the App -session = fo.launch_app(view=samples) - -time.sleep(10000) From 8ed72604718efa690b4cf4a10a540007fb82b28f Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Thu, 14 Dec 2023 21:53:13 -0600 Subject: [PATCH 46/59] Remove the source code and only use pip intall configuration --- fiftyone/utils/open_clip.py | 122 ++ fiftyone/utils/open_clip/__init__.py | 70 -- fiftyone/utils/open_clip/big_vision.py | 239 ---- .../open_clip/bpe_simple_vocab_16e6.txt.gz | Bin 1356917 -> 0 bytes fiftyone/utils/open_clip/coca_model.py | 593 --------- 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2017-2023, Voxel51, Inc. +| `voxel51.com `_ +| +""" +import logging +import os +from packaging.version import Version +import warnings + +import eta.core.web as etaw + +import fiftyone as fo +import fiftyone.core.models as fom +import fiftyone.core.utils as fou +import fiftyone.utils.torch as fout +import fiftyone.zoo.models as fozm + +fou.ensure_torch() +import torch + +import open_clip + +logger = logging.getLogger(__name__) + + +class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): + """Configuration for running a :class:`TorchCLIPModel`. + + See :class:`fiftyone.utils.torch.TorchImageModelConfig` for additional + arguments. + + Args: + context_length: the model's context length + text_prompt: the text prompt to use, e.g., ``"A photo of"`` + classes (None): a list of custom classes for zero-shot prediction + model_name_clip (None): the model to use + pretrained (None): the pretrained version to use + """ + + def __init__(self, d): + d = self.init(d) + super().__init__(d) + + self.context_length = self.parse_int(d, "context_length") + self.text_prompt = self.parse_string(d, "text_prompt") + + self.model_name_clip = self.parse_string(d, "model_name_clip") + self.pretrained = self.parse_string(d, "pretrained") + + +class TorchOpenClipModel(fout.TorchImageModel, fom.PromptMixin): + """Torch implementation of CLIP from https://github.com/mlfoundations/open_clip. + + Args: + config: a :class:`TorchOpenClipPModelConfig` + """ + + def __init__(self, config): + super().__init__(config) + self._text_features = None + + def _load_model(self, config): + ( + self._model, + _, + self.preprocess, + ) = open_clip.create_model_and_transforms( + config.model_name_clip, pretrained=config.pretrained + ) + self._tokenizer = open_clip.get_tokenizer(config.model_name_clip) + return self._model + + def _get_text_features(self): + if self._text_features is None: + prompts = [ + "%s %s" % (self.config.text_prompt, c) for c in self.classes + ] + # Tokenize text + text = self._tokenizer(prompts) + self._text_features = self._model.encode_text(text) + + return self._text_features + + def _get_class_logits(self, text_features, image_features): + # source: https://github.com/openai/CLIP/blob/main/README.md + image_features = image_features / image_features.norm( + dim=1, keepdim=True + ) + text_features = text_features / text_features.norm(dim=1, keepdim=True) + logit_scale = self._model.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logits_per_image.t() + return logits_per_image, logits_per_text + + def _predict_all(self, imgs): + if self._preprocess: + imgs = [self._preprocess(img).unsqueeze(0) for img in imgs] + + if isinstance(imgs, (list, tuple)): + imgs = torch.stack(imgs) + + height, width = imgs.size()[-2:] + frame_size = (width, height) + + if self._using_gpu: + imgs = imgs.cuda() + + with torch.no_grad(), torch.cuda.amp.autocast(): + image_features = self._model.encode_image(imgs) + text_features = self._get_text_features() + + output, _ = self._get_class_logits(text_features, image_features) + + if self.has_logits: + self._output_processor.store_logits = self.store_logits + + return self._output_processor( + output, frame_size, confidence_thresh=self.config.confidence_thresh + ) diff --git a/fiftyone/utils/open_clip/__init__.py b/fiftyone/utils/open_clip/__init__.py deleted file mode 100644 index 911d8509ea..0000000000 --- a/fiftyone/utils/open_clip/__init__.py +++ /dev/null @@ -1,70 +0,0 @@ -"""CLIP model utils. - -| Copyright 2017-2023, Voxel51, Inc. -| `voxel51.com `_ -| -""" -import types -from .coca_model import CoCa -from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD -from .factory import ( - create_model, - create_model_and_transforms, - create_model_from_pretrained, - get_tokenizer, - create_loss, -) -from .factory import ( - list_models, - add_model_config, - get_model_config, - load_checkpoint, -) -from .loss import ClipLoss, DistillClipLoss, CoCaLoss -from .model import ( - CLIP, - CustomTextCLIP, - CLIPTextCfg, - CLIPVisionCfg, - convert_weights_to_lp, - convert_weights_to_fp16, - trace_model, - get_cast_dtype, - get_input_dtype, - get_model_tokenize_cfg, - get_model_preprocess_cfg, - set_model_preprocess_cfg, -) -from .openai import load_openai_model, list_openai_models -from .pretrained import ( - list_pretrained, - list_pretrained_models_by_tag, - list_pretrained_tags_by_model, - get_pretrained_url, - download_pretrained_from_url, - is_pretrained_cfg, - get_pretrained_cfg, - download_pretrained, -) -from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub -from .tokenizer import SimpleTokenizer, tokenize, decode -from .transform import image_transform, AugmentationCfg -from .zero_shot_classifier import ( - build_zero_shot_classifier, - build_zero_shot_classifier_legacy, -) -from .zero_shot_metadata import ( - OPENAI_IMAGENET_TEMPLATES, - SIMPLE_IMAGENET_TEMPLATES, - IMAGENET_CLASSNAMES, -) - - -from .zoo import TorchOpenClipModelConfig, TorchOpenClipModel - -# This enables Sphinx refs to directly use paths imported here -__all__ = [ - k - for k, v in globals().items() - if not k.startswith("_") and not isinstance(v, types.ModuleType) -] diff --git a/fiftyone/utils/open_clip/big_vision.py b/fiftyone/utils/open_clip/big_vision.py deleted file mode 100644 index a169d9568a..0000000000 --- a/fiftyone/utils/open_clip/big_vision.py +++ /dev/null @@ -1,239 +0,0 @@ -import torch -import numpy as np - -from .model import CustomTextCLIP -from .transformer import TextTransformer, Transformer - - -@torch.no_grad() -def load_big_vision_weights(model: CustomTextCLIP, checkpoint_path: str): - """Load weights from .npz checkpoints for official Google big_vision image-text models - - Currently the SigLIP source models are supported and a CustomTextCLIP destination model - w/ timm image encoder. - """ - from timm.layers import resample_patch_embed, resample_abs_pos_embed - - def _n2p(w, t=True): - if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: - w = w.flatten() - if t: - if w.ndim == 4: - w = w.transpose([3, 2, 0, 1]) - elif w.ndim == 3: - w = w.transpose([2, 0, 1]) - elif w.ndim == 2: - w = w.transpose([1, 0]) - return torch.from_numpy(w) - - w = np.load(checkpoint_path) - interpolation = "bilinear" - antialias = False - - def _convert_timm_img(module, prefix): - embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"]) - if ( - embed_conv_w.shape[-2:] - != module.patch_embed.proj.weight.shape[-2:] - ): - embed_conv_w = resample_patch_embed( - embed_conv_w, - module.patch_embed.proj.weight.shape[-2:], - interpolation=interpolation, - antialias=antialias, - verbose=True, - ) - module.patch_embed.proj.weight.copy_(embed_conv_w) - module.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"])) - - if module.cls_token is not None: - module.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False)) - - pos_embed_w = _n2p(w[f"{prefix}pos_embedding"], t=False) - if pos_embed_w.shape != module.pos_embed.shape: - assert False, f"{pos_embed_w.shape}, {module.pos_embed.shape}" - num_prefix_tokens = ( - 0 - if getattr(module, "no_embed_class", False) - else getattr(module, "num_prefix_tokens", 1) - ) - pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights - pos_embed_w, - new_size=module.patch_embed.grid_size, - num_prefix_tokens=num_prefix_tokens, - interpolation=interpolation, - antialias=antialias, - verbose=True, - ) - module.pos_embed.copy_(pos_embed_w) - - mha_sub, b_sub, ln1_sub = (0, 0, 1) - for i, block in enumerate(module.blocks.children()): - block_prefix = f"{prefix}Transformer/encoderblock_{i}/" - mha_prefix = ( - block_prefix + f"MultiHeadDotProductAttention_{mha_sub}/" - ) - block.norm1.weight.copy_( - _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) - ) - block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"])) - block.attn.qkv.weight.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) - .flatten(1) - .T - for n in ("query", "key", "value") - ] - ) - ) - block.attn.qkv.bias.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) - for n in ("query", "key", "value") - ] - ) - ) - block.attn.proj.weight.copy_( - _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) - ) - block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) - for r in range(2): - getattr(block.mlp, f"fc{r + 1}").weight.copy_( - _n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel"]) - ) - getattr(block.mlp, f"fc{r + 1}").bias.copy_( - _n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias"]) - ) - block.norm2.weight.copy_( - _n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/scale"]) - ) - block.norm2.bias.copy_( - _n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/bias"]) - ) - - module.norm.weight.copy_( - _n2p(w[f"{prefix}Transformer/encoder_norm/scale"]) - ) - module.norm.bias.copy_( - _n2p(w[f"{prefix}Transformer/encoder_norm/bias"]) - ) - - if module.attn_pool is not None: - block_prefix = f"{prefix}MAPHead_0/" - mha_prefix = block_prefix + f"MultiHeadDotProductAttention_0/" - module.attn_pool.latent.copy_( - _n2p(w[f"{block_prefix}probe"], t=False) - ) - module.attn_pool.q.weight.copy_( - _n2p(w[f"{mha_prefix}query/kernel"], t=False).flatten(1).T - ) - module.attn_pool.q.bias.copy_( - _n2p(w[f"{mha_prefix}query/bias"], t=False).reshape(-1) - ) - module.attn_pool.kv.weight.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) - .flatten(1) - .T - for n in ("key", "value") - ] - ) - ) - module.attn_pool.kv.bias.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) - for n in ("key", "value") - ] - ) - ) - module.attn_pool.proj.weight.copy_( - _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) - ) - module.attn_pool.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) - module.attn_pool.norm.weight.copy_( - _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) - ) - module.attn_pool.norm.bias.copy_( - _n2p(w[f"{block_prefix}LayerNorm_0/bias"]) - ) - for r in range(2): - getattr(module.attn_pool.mlp, f"fc{r + 1}").weight.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/kernel"]) - ) - getattr(module.attn_pool.mlp, f"fc{r + 1}").bias.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/bias"]) - ) - - def _convert_openclip_transformer(module: Transformer, prefix): - for i, block in enumerate(module.resblocks.children()): - block_prefix = f"{prefix}encoderblock_{i}/" - mha_prefix = block_prefix + f"MultiHeadDotProductAttention_0/" - block.ln_1.weight.copy_( - _n2p(w[f"{block_prefix}LayerNorm_0/scale"]) - ) - block.ln_1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"])) - block.attn.in_proj_weight.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/kernel"], t=False) - .flatten(1) - .T - for n in ("query", "key", "value") - ] - ) - ) - block.attn.in_proj_bias.copy_( - torch.cat( - [ - _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) - for n in ("query", "key", "value") - ] - ) - ) - block.attn.out_proj.weight.copy_( - _n2p(w[f"{mha_prefix}out/kernel"]).flatten(1) - ) - block.attn.out_proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"])) - block.ln_2.weight.copy_( - _n2p(w[f"{block_prefix}LayerNorm_1/scale"]) - ) - block.ln_2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_1/bias"])) - block.mlp.c_fc.weight.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_0/kernel"]) - ) - block.mlp.c_fc.bias.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_0/bias"]) - ) - block.mlp.c_proj.weight.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_1/kernel"]) - ) - block.mlp.c_proj.bias.copy_( - _n2p(w[f"{block_prefix}MlpBlock_0/Dense_1/bias"]) - ) - - def _convert_openclip_txt(module: TextTransformer, prefix): - module.token_embedding.weight.copy_( - _n2p(w[f"{prefix}Embed_0/embedding"], t=False) - ) - pos_embed_w = _n2p(w[f"{prefix}pos_embedding"], t=False).squeeze(0) - module.positional_embedding.copy_(pos_embed_w) - _convert_openclip_transformer( - module.transformer, prefix=prefix + "Encoder_0/" - ) - module.ln_final.weight.copy_( - _n2p(w[f"{prefix}Encoder_0/encoder_norm/scale"]) - ) - module.ln_final.bias.copy_( - _n2p(w[f"{prefix}Encoder_0/encoder_norm/bias"]) - ) - module.text_projection.weight.copy_(_n2p(w[f"{prefix}head/kernel"])) - module.text_projection.bias.copy_(_n2p(w[f"{prefix}head/bias"])) - - _convert_timm_img(model.visual.trunk, "params/img/") - _convert_openclip_txt(model.text, "params/txt/") - model.logit_bias.copy_(_n2p(w["params/b"])[0]) - model.logit_scale.copy_(_n2p(w["params/t"])[0]) diff --git a/fiftyone/utils/open_clip/bpe_simple_vocab_16e6.txt.gz b/fiftyone/utils/open_clip/bpe_simple_vocab_16e6.txt.gz deleted file mode 100644 index 7b5088a527f720063f044eb928eee315f63b2fc0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1356917 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ztxONk0vixS8S>(hzAbw(#0>;C#ks|_ha+((8DqrW_Ad=hHlbPG7JCqoQYc=?dg}X> z=@SQQ4dhuRpq4|p(g#`?$z1JCMe_-s9ZCq=j%b8zcK3ZHG1D=BRYyW{d_f?@sE3m= z&|HmgkeI;Mhm!J^35$8OxjG^dfASN<0q3^NpF2JSh2Ov5RXTanenuCT&t8oT^u^mh zg}2{*uTwPnR^NXf-ae+l{Il@(HKu7_1qjU1RQlcj|G(kSeL{bx0Jc0m-n()VY;oHw zIzVB?O@_32+^NgTKrF&CdZi=_#;fQd&@66bKGZ$rBa(EUG?_*5Y5sg0q!@<>3)C3x zDHjQ08Yct5byJU)RZx}jpJ%P1PloKt@FZl9Bj8yPOMy-m){oxJYiEr>nJ)qwWT{Qp zj_mW)R04a=pMh1%Dv(aj1Q1nfowhih;WqMv}`9*1Gi{T$c5f9??B^HHhv{z(}TVD%q4)no;HK6^>A)q~X hSpdDrox+^4cgglrz7rRzDPZO0{{oPT@#kp3FaTBF64n3! diff --git a/fiftyone/utils/open_clip/coca_model.py b/fiftyone/utils/open_clip/coca_model.py deleted file mode 100644 index 84b4781d67..0000000000 --- a/fiftyone/utils/open_clip/coca_model.py +++ /dev/null @@ -1,593 +0,0 @@ -from typing import Optional - -import torch -from torch import nn -from torch.nn import functional as F -import numpy as np -from dataclasses import dataclass - -from .transformer import ( - LayerNormFp32, - LayerNorm, - QuickGELU, - MultimodalTransformer, -) -from .model import ( - CLIPTextCfg, - CLIPVisionCfg, - _build_vision_tower, - _build_text_tower, -) - -try: - from transformers import ( - BeamSearchScorer, - LogitsProcessorList, - TopPLogitsWarper, - TopKLogitsWarper, - RepetitionPenaltyLogitsProcessor, - MinLengthLogitsProcessor, - MaxLengthCriteria, - StoppingCriteriaList, - ) - - GENERATION_TYPES = { - "top_k": TopKLogitsWarper, - "top_p": TopPLogitsWarper, - "beam_search": "beam_search", - } - _has_transformers = True -except ImportError as e: - GENERATION_TYPES = { - "top_k": None, - "top_p": None, - "beam_search": "beam_search", - } - _has_transformers = False - - -@dataclass -class MultimodalCfg(CLIPTextCfg): - mlp_ratio: int = 4 - dim_head: int = 64 - heads: int = 8 - n_queries: int = 256 - attn_pooler_heads: int = 8 - - -def _build_text_decoder_tower( - embed_dim, - multimodal_cfg, - quick_gelu: bool = False, - cast_dtype: Optional[torch.dtype] = None, -): - multimodal_cfg = ( - MultimodalCfg(**multimodal_cfg) - if isinstance(multimodal_cfg, dict) - else multimodal_cfg - ) - act_layer = QuickGELU if quick_gelu else nn.GELU - norm_layer = ( - LayerNormFp32 - if cast_dtype in (torch.float16, torch.bfloat16) - else LayerNorm - ) - - decoder = MultimodalTransformer( - context_length=multimodal_cfg.context_length, - width=multimodal_cfg.width, - heads=multimodal_cfg.heads, - layers=multimodal_cfg.layers, - ls_init_value=multimodal_cfg.ls_init_value, - output_dim=embed_dim, - act_layer=act_layer, - norm_layer=norm_layer, - ) - - return decoder - - -class CoCa(nn.Module): - def __init__( - self, - embed_dim, - multimodal_cfg: MultimodalCfg, - text_cfg: CLIPTextCfg, - vision_cfg: CLIPVisionCfg, - quick_gelu: bool = False, - init_logit_scale: float = np.log(1 / 0.07), - init_logit_bias: Optional[float] = None, - cast_dtype: Optional[torch.dtype] = None, - pad_id: int = 0, - ): - super().__init__() - multimodal_cfg = ( - MultimodalCfg(**multimodal_cfg) - if isinstance(multimodal_cfg, dict) - else multimodal_cfg - ) - text_cfg = ( - CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg - ) - vision_cfg = ( - CLIPVisionCfg(**vision_cfg) - if isinstance(vision_cfg, dict) - else vision_cfg - ) - - self.text = _build_text_tower( - embed_dim=embed_dim, - text_cfg=text_cfg, - quick_gelu=quick_gelu, - cast_dtype=cast_dtype, - ) - - vocab_size = ( - text_cfg.vocab_size # for hf models - if hasattr(text_cfg, "hf_model_name") - and text_cfg.hf_model_name is not None - else text_cfg.vocab_size - ) - - self.visual = _build_vision_tower( - embed_dim=embed_dim, - vision_cfg=vision_cfg, - quick_gelu=quick_gelu, - cast_dtype=cast_dtype, - ) - - self.text_decoder = _build_text_decoder_tower( - vocab_size, - multimodal_cfg=multimodal_cfg, - quick_gelu=quick_gelu, - cast_dtype=cast_dtype, - ) - - self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) - if init_logit_bias is not None: - self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) - else: - self.logit_bias = None - self.pad_id = pad_id - - self.context_length = multimodal_cfg.context_length - - @torch.jit.ignore - def set_grad_checkpointing(self, enable: bool = True): - self.visual.set_grad_checkpointing(enable) - self.text.set_grad_checkpointing(enable) - self.text_decoder.set_grad_checkpointing(enable) - - def _encode_image(self, images, normalize: bool = True): - image_latent, tokens_embs = self.visual(images) - image_latent = ( - F.normalize(image_latent, dim=-1) if normalize else image_latent - ) - return image_latent, tokens_embs - - def _encode_text(self, text, normalize: bool = True): - text_latent, token_emb = self.text(text) - text_latent = ( - F.normalize(text_latent, dim=-1) if normalize else text_latent - ) - return text_latent, token_emb - - def encode_image(self, images, normalize: bool = True): - image_latent, _ = self._encode_image(images, normalize=normalize) - return image_latent - - def encode_text(self, text, normalize: bool = True): - text_latent, _ = self._encode_text(text, normalize=normalize) - return text_latent - - def forward( - self, - image, - text: Optional[torch.Tensor] = None, - image_latent: Optional[torch.Tensor] = None, - image_embs: Optional[torch.Tensor] = None, - ): - if image_latent is None or image_embs is None: - image_latent, image_embs = self._encode_image(image) - - if text is None: - return {"image_features": image_latent, "image_embs": image_embs} - - text_latent, token_embs = self._encode_text(text) - - # TODO: add assertion to avoid bugs? - labels = text[:, -token_embs.shape[1] :] - - logits = self.text_decoder(image_embs, token_embs) - out_dict = { - "image_features": image_latent, - "text_features": text_latent, - "logits": logits, - "labels": labels, - "logit_scale": self.logit_scale.exp(), - } - if self.logit_bias is not None: - out_dict["logit_bias"] = self.logit_bias - return out_dict - - def generate( - self, - image, - text=None, - seq_len=30, - max_seq_len=77, - temperature=1.0, - generation_type="beam_search", - top_p=0.1, # keep tokens in the 1 - top_p quantile - top_k=1, # keeps the top_k most probable tokens - pad_token_id=None, - eos_token_id=None, - sot_token_id=None, - num_beams=6, - num_beam_groups=3, - min_seq_len=5, - stopping_criteria=None, - repetition_penalty=1.0, - fixed_output_length=False, # if True output.shape == (batch_size, seq_len) - ): - # taking many ideas and components from HuggingFace GenerationMixin - # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation - assert ( - _has_transformers - ), "Please install transformers for generate functionality. `pip install transformers`." - assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len" - - with torch.no_grad(): - sot_token_id = 49406 if sot_token_id is None else sot_token_id - eos_token_id = 49407 if eos_token_id is None else eos_token_id - pad_token_id = ( - self.pad_id if pad_token_id is None else pad_token_id - ) - logit_processor = LogitsProcessorList( - [ - MinLengthLogitsProcessor(min_seq_len, eos_token_id), - RepetitionPenaltyLogitsProcessor(repetition_penalty), - ] - ) - - if stopping_criteria is None: - stopping_criteria = [MaxLengthCriteria(max_length=seq_len)] - - stopping_criteria = StoppingCriteriaList(stopping_criteria) - - device = image.device - - if generation_type == "beam_search": - output = self._generate_beamsearch( - image_inputs=image, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - sot_token_id=sot_token_id, - num_beams=num_beams, - num_beam_groups=num_beam_groups, - min_seq_len=min_seq_len, - stopping_criteria=stopping_criteria, - logit_processor=logit_processor, - ) - if fixed_output_length and output.shape[1] < seq_len: - return torch.cat( - ( - output, - torch.ones( - output.shape[0], - seq_len - output.shape[1], - device=device, - dtype=output.dtype, - ) - * self.pad_id, - ), - dim=1, - ) - return output - - elif generation_type == "top_p": - logit_warper = GENERATION_TYPES[generation_type](top_p) - elif generation_type == "top_k": - logit_warper = GENERATION_TYPES[generation_type](top_k) - else: - raise ValueError( - f"generation_type has to be one of " - f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}." - ) - - image_latent, image_embs = self._encode_image(image) - - if text is None: - text = ( - torch.ones( - (image.shape[0], 1), device=device, dtype=torch.long - ) - * sot_token_id - ) - - was_training = self.training - num_dims = len(text.shape) - - if num_dims == 1: - text = text[None, :] - - cur_len = text.shape[1] - self.eval() - out = text - - while True: - x = out[:, -max_seq_len:] - cur_len = x.shape[1] - logits = self( - image, x, image_latent=image_latent, image_embs=image_embs - )["logits"][:, -1] - mask = (out[:, -1] == eos_token_id) | ( - out[:, -1] == pad_token_id - ) - sample = ( - torch.ones( - (out.shape[0], 1), device=device, dtype=torch.long - ) - * pad_token_id - ) - - if mask.all(): - if not fixed_output_length: - break - else: - logits = logits[~mask, :] - filtered_logits = logit_processor(x[~mask, :], logits) - filtered_logits = logit_warper( - x[~mask, :], filtered_logits - ) - probs = F.softmax(filtered_logits / temperature, dim=-1) - - if cur_len + 1 == seq_len: - sample[~mask, :] = ( - torch.ones( - (sum(~mask), 1), - device=device, - dtype=torch.long, - ) - * eos_token_id - ) - else: - sample[~mask, :] = torch.multinomial(probs, 1) - - out = torch.cat((out, sample), dim=-1) - - cur_len += 1 - - if stopping_criteria(out, None): - break - - if num_dims == 1: - out = out.squeeze(0) - - self.train(was_training) - return out - - def _generate_beamsearch( - self, - image_inputs, - pad_token_id=None, - eos_token_id=None, - sot_token_id=None, - num_beams=6, - num_beam_groups=3, - min_seq_len=5, - stopping_criteria=None, - logit_processor=None, - logit_warper=None, - ): - device = image_inputs.device - batch_size = image_inputs.shape[0] - image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0) - image_latent, image_embs = self._encode_image(image_inputs) - - input_ids = torch.ones( - (batch_size * num_beams, 1), device=device, dtype=torch.long - ) - input_ids = input_ids * sot_token_id - beam_scorer = BeamSearchScorer( - batch_size=batch_size, - num_beams=num_beams, - device=device, - num_beam_groups=num_beam_groups, - ) - # instantiate logits processors - logits_processor = ( - LogitsProcessorList( - [ - MinLengthLogitsProcessor( - min_seq_len, eos_token_id=eos_token_id - ) - ] - ) - if logit_processor is None - else logit_processor - ) - - num_beams = beam_scorer.num_beams - num_beam_groups = beam_scorer.num_beam_groups - num_sub_beams = num_beams // num_beam_groups - batch_size = len(beam_scorer._beam_hyps) // num_beam_groups - batch_beam_size, cur_len = input_ids.shape - beam_indices = None - - if num_beams * batch_size != batch_beam_size: - raise ValueError( - f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." - ) - - beam_scores = torch.full( - (batch_size, num_beams), -1e9, dtype=torch.float, device=device - ) - # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in - # the same group don't produce same tokens everytime. - beam_scores[:, ::num_sub_beams] = 0 - beam_scores = beam_scores.view((batch_size * num_beams,)) - - while True: - - # predicted tokens in cur_len step - current_tokens = torch.zeros( - batch_size * num_beams, dtype=input_ids.dtype, device=device - ) - - # indices which will form the beams in the next time step - reordering_indices = torch.zeros( - batch_size * num_beams, dtype=torch.long, device=device - ) - - # do one decoder step on all beams of all sentences in batch - model_inputs = prepare_inputs_for_generation( - input_ids=input_ids, image_inputs=image_inputs - ) - outputs = self( - model_inputs["images"], - model_inputs["text"], - image_latent=image_latent, - image_embs=image_embs, - ) - - for beam_group_idx in range(num_beam_groups): - group_start_idx = beam_group_idx * num_sub_beams - group_end_idx = min(group_start_idx + num_sub_beams, num_beams) - group_size = group_end_idx - group_start_idx - - # indices of beams of current group among all sentences in batch - batch_group_indices = [] - - for batch_idx in range(batch_size): - batch_group_indices.extend( - [ - batch_idx * num_beams + idx - for idx in range(group_start_idx, group_end_idx) - ] - ) - group_input_ids = input_ids[batch_group_indices] - - # select outputs of beams of currentg group only - next_token_logits = outputs["logits"][ - batch_group_indices, -1, : - ] - vocab_size = next_token_logits.shape[-1] - - next_token_scores_processed = logits_processor( - group_input_ids, - next_token_logits, - current_tokens=current_tokens, - beam_group_idx=beam_group_idx, - ) - next_token_scores = next_token_scores_processed + beam_scores[ - batch_group_indices - ].unsqueeze(-1) - next_token_scores = next_token_scores.expand_as( - next_token_scores_processed - ) - - # reshape for beam search - next_token_scores = next_token_scores.view( - batch_size, group_size * vocab_size - ) - - next_token_scores, next_tokens = torch.topk( - next_token_scores, - 2 * group_size, - dim=1, - largest=True, - sorted=True, - ) - - next_indices = torch.div( - next_tokens, vocab_size, rounding_mode="floor" - ) - next_tokens = next_tokens % vocab_size - - # stateless - process_beam_indices = ( - sum(beam_indices, ()) if beam_indices is not None else None - ) - beam_outputs = beam_scorer.process( - group_input_ids, - next_token_scores, - next_tokens, - next_indices, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - beam_indices=process_beam_indices, - group_index=beam_group_idx, - ) - beam_scores[batch_group_indices] = beam_outputs[ - "next_beam_scores" - ] - beam_next_tokens = beam_outputs["next_beam_tokens"] - beam_idx = beam_outputs["next_beam_indices"] - - input_ids[batch_group_indices] = group_input_ids[beam_idx] - group_input_ids = torch.cat( - [ - group_input_ids[beam_idx, :], - beam_next_tokens.unsqueeze(-1), - ], - dim=-1, - ) - current_tokens[batch_group_indices] = group_input_ids[:, -1] - - # (beam_idx // group_size) -> batch_idx - # (beam_idx % group_size) -> offset of idx inside the group - reordering_indices[batch_group_indices] = ( - num_beams - * torch.div(beam_idx, group_size, rounding_mode="floor") - + group_start_idx - + (beam_idx % group_size) - ) - - input_ids = torch.cat( - [input_ids, current_tokens.unsqueeze(-1)], dim=-1 - ) - - # increase cur_len - cur_len = cur_len + 1 - if beam_scorer.is_done or stopping_criteria(input_ids, None): - break - - final_beam_indices = ( - sum(beam_indices, ()) if beam_indices is not None else None - ) - sequence_outputs = beam_scorer.finalize( - input_ids, - beam_scores, - next_tokens, - next_indices, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - max_length=stopping_criteria.max_length, - beam_indices=final_beam_indices, - ) - return sequence_outputs["sequences"] - - -def prepare_inputs_for_generation( - input_ids, image_inputs, past=None, **kwargs -): - if past: - input_ids = input_ids[:, -1].unsqueeze(-1) - - attention_mask = kwargs.get("attention_mask", None) - position_ids = kwargs.get("position_ids", None) - - if attention_mask is not None and position_ids is None: - # create position_ids on the fly for batch generation - position_ids = attention_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attention_mask == 0, 1) - else: - position_ids = None - return { - "text": input_ids, - "images": image_inputs, - "past_key_values": past, - "position_ids": position_ids, - "attention_mask": attention_mask, - } diff --git a/fiftyone/utils/open_clip/constants.py b/fiftyone/utils/open_clip/constants.py deleted file mode 100644 index 599c48c03f..0000000000 --- a/fiftyone/utils/open_clip/constants.py +++ /dev/null @@ -1,6 +0,0 @@ -OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) -OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) -IMAGENET_MEAN = (0.485, 0.456, 0.406) -IMAGENET_STD = (0.229, 0.224, 0.225) -INCEPTION_MEAN = (0.5, 0.5, 0.5) -INCEPTION_STD = (0.5, 0.5, 0.5) diff --git a/fiftyone/utils/open_clip/factory.py b/fiftyone/utils/open_clip/factory.py deleted file mode 100644 index 92816a9670..0000000000 --- a/fiftyone/utils/open_clip/factory.py +++ /dev/null @@ -1,543 +0,0 @@ -import json -import logging -import os -import re -from copy import deepcopy -from dataclasses import asdict -from pathlib import Path -from typing import Any, Dict, Optional, Tuple, Union - -import torch - -from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD -from .model import ( - CLIP, - CustomTextCLIP, - convert_weights_to_lp, - convert_to_custom_text_state_dict, - resize_pos_embed, - get_cast_dtype, - resize_text_pos_embed, - set_model_preprocess_cfg, -) -from .coca_model import CoCa -from .loss import ClipLoss, DistillClipLoss, CoCaLoss, SigLipLoss -from .openai import load_openai_model -from .pretrained import ( - is_pretrained_cfg, - get_pretrained_cfg, - download_pretrained, - list_pretrained_tags_by_model, - download_pretrained_from_hf, -) -from .transform import ( - image_transform_v2, - AugmentationCfg, - PreprocessCfg, - merge_preprocess_dict, - merge_preprocess_kwargs, -) -from .tokenizer import HFTokenizer, SimpleTokenizer, DEFAULT_CONTEXT_LENGTH - -HF_HUB_PREFIX = "hf-hub:" -_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] -_MODEL_CONFIGS = ( - {} -) # directory (model_name: config) of model architecture configs - - -def _natural_key(string_): - return [ - int(s) if s.isdigit() else s - for s in re.split(r"(\d+)", string_.lower()) - ] - - -def _rescan_model_configs(): - global _MODEL_CONFIGS - - config_ext = (".json",) - config_files = [] - for config_path in _MODEL_CONFIG_PATHS: - if config_path.is_file() and config_path.suffix in config_ext: - config_files.append(config_path) - elif config_path.is_dir(): - for ext in config_ext: - config_files.extend(config_path.glob(f"*{ext}")) - - for cf in config_files: - with open(cf, "r") as f: - model_cfg = json.load(f) - if all( - a in model_cfg for a in ("embed_dim", "vision_cfg", "text_cfg") - ): - _MODEL_CONFIGS[cf.stem] = model_cfg - - _MODEL_CONFIGS = { - k: v - for k, v in sorted( - _MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]) - ) - } - - -_rescan_model_configs() # initial populate of model config registry - - -def list_models(): - """enumerate available model architectures based on config files""" - return list(_MODEL_CONFIGS.keys()) - - -def add_model_config(path): - """add model config path or file and update registry""" - if not isinstance(path, Path): - path = Path(path) - _MODEL_CONFIG_PATHS.append(path) - _rescan_model_configs() - - -def get_model_config(model_name): - if model_name in _MODEL_CONFIGS: - return deepcopy(_MODEL_CONFIGS[model_name]) - else: - return None - - -def _get_hf_config(model_id, cache_dir=None): - config_path = download_pretrained_from_hf( - model_id, filename="open_clip_config.json", cache_dir=cache_dir - ) - with open(config_path, "r", encoding="utf-8") as f: - config = json.load(f) - return config - - -def get_tokenizer( - model_name: str = "", - context_length: Optional[int] = None, - **kwargs, -): - if model_name.startswith(HF_HUB_PREFIX): - model_name = model_name[len(HF_HUB_PREFIX) :] - try: - config = _get_hf_config(model_name)["model_cfg"] - except Exception: - tokenizer = HFTokenizer( - model_name, - context_length=context_length or DEFAULT_CONTEXT_LENGTH, - **kwargs, - ) - return tokenizer - else: - config = get_model_config(model_name) - assert ( - config is not None - ), f"No valid model config found for {model_name}." - - text_config = config.get("text_cfg", {}) - if "tokenizer_kwargs" in text_config: - tokenizer_kwargs = dict(text_config["tokenizer_kwargs"], **kwargs) - else: - tokenizer_kwargs = kwargs - - if context_length is None: - context_length = text_config.get( - "context_length", DEFAULT_CONTEXT_LENGTH - ) - - if "hf_tokenizer_name" in text_config: - tokenizer = HFTokenizer( - text_config["hf_tokenizer_name"], - context_length=context_length, - **tokenizer_kwargs, - ) - else: - tokenizer = SimpleTokenizer( - context_length=context_length, - **tokenizer_kwargs, - ) - - return tokenizer - - -def load_state_dict(checkpoint_path: str, map_location="cpu"): - checkpoint = torch.load(checkpoint_path, map_location=map_location) - if isinstance(checkpoint, dict) and "state_dict" in checkpoint: - state_dict = checkpoint["state_dict"] - elif isinstance(checkpoint, torch.jit.ScriptModule): - state_dict = checkpoint.state_dict() - for key in ["input_resolution", "context_length", "vocab_size"]: - state_dict.pop(key, None) - else: - state_dict = checkpoint - if next(iter(state_dict.items()))[0].startswith("module"): - state_dict = {k[7:]: v for k, v in state_dict.items()} - return state_dict - - -def load_checkpoint(model, checkpoint_path, strict=True): - if Path(checkpoint_path).suffix in (".npz", ".npy"): - from .big_vision import load_big_vision_weights - - load_big_vision_weights(model, checkpoint_path) - return {} - - state_dict = load_state_dict(checkpoint_path) - # detect old format and make compatible with new format - if "positional_embedding" in state_dict and not hasattr( - model, "positional_embedding" - ): - state_dict = convert_to_custom_text_state_dict(state_dict) - # If loading a non-SigLIP model for SigLIP training. See https://github.com/mlfoundations/open_clip/issues/712 - if "logit_bias" not in state_dict and model.logit_bias is not None: - state_dict["logit_bias"] = torch.zeros_like(state_dict["logit_scale"]) - # Certain text transformers no longer expect position_ids after transformers==4.31 - position_id_key = "text.transformer.embeddings.position_ids" - if position_id_key in state_dict and not hasattr(model, position_id_key): - del state_dict[position_id_key] - resize_pos_embed(state_dict, model) - resize_text_pos_embed(state_dict, model) - incompatible_keys = model.load_state_dict(state_dict, strict=strict) - return incompatible_keys - - -def create_model( - model_name: str, - pretrained: Optional[str] = None, - precision: str = "fp32", - device: Union[str, torch.device] = "cpu", - jit: bool = False, - force_quick_gelu: bool = False, - force_custom_text: bool = False, - force_patch_dropout: Optional[float] = None, - force_image_size: Optional[Union[int, Tuple[int, int]]] = None, - force_preprocess_cfg: Optional[Dict[str, Any]] = None, - pretrained_image: bool = False, - pretrained_hf: bool = True, - cache_dir: Optional[str] = None, - output_dict: Optional[bool] = None, - require_pretrained: bool = False, - **model_kwargs, -): - force_preprocess_cfg = force_preprocess_cfg or {} - preprocess_cfg = asdict(PreprocessCfg()) - has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX) - if has_hf_hub_prefix: - model_id = model_name[len(HF_HUB_PREFIX) :] - checkpoint_path = download_pretrained_from_hf( - model_id, cache_dir=cache_dir - ) - config = _get_hf_config(model_id, cache_dir) - preprocess_cfg = merge_preprocess_dict( - preprocess_cfg, config["preprocess_cfg"] - ) - model_cfg = config["model_cfg"] - pretrained_hf = ( - False # override, no need to load original HF text weights - ) - else: - model_name = model_name.replace( - "/", "-" - ) # for callers using old naming with / in ViT names - checkpoint_path = None - model_cfg = None - - if isinstance(device, str): - device = torch.device(device) - - if pretrained and pretrained.lower() == "openai": - logging.info(f"Loading pretrained {model_name} from OpenAI.") - model = load_openai_model( - model_name, - precision=precision, - device=device, - cache_dir=cache_dir, - ) - else: - model_cfg = model_cfg or get_model_config(model_name) - if model_cfg is not None: - logging.info(f"Loaded {model_name} model config.") - else: - logging.error( - f"Model config for {model_name} not found; available models {list_models()}." - ) - raise RuntimeError(f"Model config for {model_name} not found.") - - if force_quick_gelu: - # override for use of QuickGELU on non-OpenAI transformer models - model_cfg["quick_gelu"] = True - - if force_patch_dropout is not None: - # override the default patch dropout value - model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout - - if force_image_size is not None: - # override model config's image size - model_cfg["vision_cfg"]["image_size"] = force_image_size - - is_timm_model = "timm_model_name" in model_cfg.get("vision_cfg", {}) - if pretrained_image: - if is_timm_model: - # pretrained weight loading for timm models set via vision_cfg - model_cfg["vision_cfg"]["timm_model_pretrained"] = True - else: - assert ( - False - ), "pretrained image towers currently only supported for timm models" - - # cast_dtype set for fp16 and bf16 (manual mixed-precision), not set for 'amp' or 'pure' modes - cast_dtype = get_cast_dtype(precision) - is_hf_model = "hf_model_name" in model_cfg.get("text_cfg", {}) - if is_hf_model: - # load pretrained weights for HF text model IFF no CLIP weights being loaded - model_cfg["text_cfg"]["hf_model_pretrained"] = ( - pretrained_hf and not pretrained - ) - custom_text = ( - model_cfg.pop("custom_text", False) - or force_custom_text - or is_hf_model - ) - - model_cfg = dict( - model_cfg, **model_kwargs - ) # merge cfg dict w/ kwargs (kwargs overrides cfg) - if custom_text: - if "multimodal_cfg" in model_cfg: - model = CoCa(**model_cfg, cast_dtype=cast_dtype) - else: - model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype) - else: - model = CLIP(**model_cfg, cast_dtype=cast_dtype) - - if precision in ("fp16", "bf16"): - dtype = torch.float16 if "fp16" in precision else torch.bfloat16 - # manual mixed precision that matches original OpenAI behaviour - if is_timm_model: - # FIXME this is a bit janky, create timm based model in low-precision and - # then cast only LayerNormFp32 instances back to float32 so they don't break. - # Why? The convert_weights_to_lp fn only works with native models. - model.to(device=device, dtype=dtype) - from .transformer import LayerNormFp32 - - def _convert_ln(m): - if isinstance(m, LayerNormFp32): - m.weight.data = m.weight.data.to(torch.float32) - m.bias.data = m.bias.data.to(torch.float32) - - model.apply(_convert_ln) - else: - model.to(device=device) - convert_weights_to_lp(model, dtype=dtype) - elif precision in ("pure_fp16", "pure_bf16"): - dtype = torch.float16 if "fp16" in precision else torch.bfloat16 - model.to(device=device, dtype=dtype) - else: - model.to(device=device) - - pretrained_loaded = False - if pretrained: - checkpoint_path = "" - pretrained_cfg = get_pretrained_cfg(model_name, pretrained) - if pretrained_cfg: - checkpoint_path = download_pretrained( - pretrained_cfg, cache_dir=cache_dir - ) - preprocess_cfg = merge_preprocess_dict( - preprocess_cfg, pretrained_cfg - ) - elif os.path.exists(pretrained): - checkpoint_path = pretrained - - if checkpoint_path: - logging.info( - f"Loading pretrained {model_name} weights ({pretrained})." - ) - load_checkpoint(model, checkpoint_path) - else: - error_str = ( - f"Pretrained weights ({pretrained}) not found for model {model_name}." - f" Available pretrained tags ({list_pretrained_tags_by_model(model_name)}." - ) - logging.warning(error_str) - raise RuntimeError(error_str) - pretrained_loaded = True - elif has_hf_hub_prefix: - logging.info( - f"Loading pretrained {model_name} weights ({checkpoint_path})." - ) - load_checkpoint(model, checkpoint_path) - pretrained_loaded = True - - if require_pretrained and not pretrained_loaded: - # callers of create_model_from_pretrained always expect pretrained weights - raise RuntimeError( - f"Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded." - ) - - if output_dict and hasattr(model, "output_dict"): - model.output_dict = True - - if jit: - model = torch.jit.script(model) - - # set image preprocessing configuration in model attributes for convenience - if getattr(model.visual, "image_size", None) is not None: - # use image_size set on model creation (via config or force_image_size arg) - force_preprocess_cfg["size"] = model.visual.image_size - set_model_preprocess_cfg( - model, merge_preprocess_dict(preprocess_cfg, force_preprocess_cfg) - ) - - return model - - -def create_loss(args): - if args.distill: - return DistillClipLoss( - local_loss=args.local_loss, - gather_with_grad=args.gather_with_grad, - cache_labels=True, - rank=args.rank, - world_size=args.world_size, - use_horovod=args.horovod, - ) - elif "coca" in args.model.lower(): - return CoCaLoss( - caption_loss_weight=args.coca_caption_loss_weight, - clip_loss_weight=args.coca_contrastive_loss_weight, - local_loss=args.local_loss, - gather_with_grad=args.gather_with_grad, - cache_labels=True, - rank=args.rank, - world_size=args.world_size, - use_horovod=args.horovod, - ) - elif args.siglip: - assert not args.horovod, "Horovod not currently supported for SigLip" - return SigLipLoss( - rank=args.rank, - world_size=args.world_size, - ) - return ClipLoss( - local_loss=args.local_loss, - gather_with_grad=args.gather_with_grad, - cache_labels=True, - rank=args.rank, - world_size=args.world_size, - use_horovod=args.horovod, - ) - - -def create_model_and_transforms( - model_name: str, - pretrained: Optional[str] = None, - precision: str = "fp32", - device: Union[str, torch.device] = "cpu", - jit: bool = False, - force_quick_gelu: bool = False, - force_custom_text: bool = False, - force_patch_dropout: Optional[float] = None, - force_image_size: Optional[Union[int, Tuple[int, int]]] = None, - image_mean: Optional[Tuple[float, ...]] = None, - image_std: Optional[Tuple[float, ...]] = None, - image_interpolation: Optional[str] = None, - image_resize_mode: Optional[str] = None, # only effective for inference - aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, - pretrained_image: bool = False, - pretrained_hf: bool = True, - cache_dir: Optional[str] = None, - output_dict: Optional[bool] = None, - **model_kwargs, -): - force_preprocess_cfg = merge_preprocess_kwargs( - {}, - mean=image_mean, - std=image_std, - interpolation=image_interpolation, - resize_mode=image_resize_mode, - ) - - model = create_model( - model_name, - pretrained, - precision=precision, - device=device, - jit=jit, - force_quick_gelu=force_quick_gelu, - force_custom_text=force_custom_text, - force_patch_dropout=force_patch_dropout, - force_image_size=force_image_size, - force_preprocess_cfg=force_preprocess_cfg, - pretrained_image=pretrained_image, - pretrained_hf=pretrained_hf, - cache_dir=cache_dir, - output_dict=output_dict, - **model_kwargs, - ) - - pp_cfg = PreprocessCfg(**model.visual.preprocess_cfg) - - preprocess_train = image_transform_v2( - pp_cfg, - is_train=True, - aug_cfg=aug_cfg, - ) - preprocess_val = image_transform_v2( - pp_cfg, - is_train=False, - ) - - return model, preprocess_train, preprocess_val - - -def create_model_from_pretrained( - model_name: str, - pretrained: Optional[str] = None, - precision: str = "fp32", - device: Union[str, torch.device] = "cpu", - jit: bool = False, - force_quick_gelu: bool = False, - force_custom_text: bool = False, - force_image_size: Optional[Union[int, Tuple[int, int]]] = None, - image_mean: Optional[Tuple[float, ...]] = None, - image_std: Optional[Tuple[float, ...]] = None, - image_interpolation: Optional[str] = None, - image_resize_mode: Optional[str] = None, # only effective for inference - return_transform: bool = True, - cache_dir: Optional[str] = None, - **model_kwargs, -): - force_preprocess_cfg = merge_preprocess_kwargs( - {}, - mean=image_mean, - std=image_std, - interpolation=image_interpolation, - resize_mode=image_resize_mode, - ) - - model = create_model( - model_name, - pretrained, - precision=precision, - device=device, - jit=jit, - force_quick_gelu=force_quick_gelu, - force_custom_text=force_custom_text, - force_image_size=force_image_size, - force_preprocess_cfg=force_preprocess_cfg, - cache_dir=cache_dir, - require_pretrained=True, - **model_kwargs, - ) - - if not return_transform: - return model - - preprocess = image_transform_v2( - PreprocessCfg(**model.visual.preprocess_cfg), - is_train=False, - ) - - return model, preprocess diff --git a/fiftyone/utils/open_clip/generation_utils.py b/fiftyone/utils/open_clip/generation_utils.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/fiftyone/utils/open_clip/hf_configs.py b/fiftyone/utils/open_clip/hf_configs.py deleted file mode 100644 index c012d1a795..0000000000 --- a/fiftyone/utils/open_clip/hf_configs.py +++ /dev/null @@ -1,67 +0,0 @@ -# HF architecture dict: -arch_dict = { - # https://huggingface.co/docs/transformers/model_doc/roberta#roberta - "roberta": { - "config_names": { - "context_length": "max_position_embeddings", - "vocab_size": "vocab_size", - "width": "hidden_size", - "heads": "num_attention_heads", - "layers": "num_hidden_layers", - "layer_attr": "layer", - "token_embeddings_attr": "embeddings", - }, - "pooler": "mean_pooler", - }, - # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig - "xlm-roberta": { - "config_names": { - "context_length": "max_position_embeddings", - "vocab_size": "vocab_size", - "width": "hidden_size", - "heads": "num_attention_heads", - "layers": "num_hidden_layers", - "layer_attr": "layer", - "token_embeddings_attr": "embeddings", - }, - "pooler": "mean_pooler", - }, - # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 - "mt5": { - "config_names": { - # unlimited seqlen - # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 - # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 - "context_length": "", - "vocab_size": "vocab_size", - "width": "d_model", - "heads": "num_heads", - "layers": "num_layers", - "layer_attr": "block", - "token_embeddings_attr": "embed_tokens", - }, - "pooler": "mean_pooler", - }, - # https://huggingface.co/docs/transformers/model_doc/bert - "bert": { - "config_names": { - "context_length": "max_position_embeddings", - "vocab_size": "vocab_size", - "width": "hidden_size", - "heads": "num_attention_heads", - "layers": "num_hidden_layers", - }, - "pooler": "cls_pooler", - }, - # https://huggingface.co/docs/transformers/model_doc/m2m_100 - "m2m_100": { - "config_names": { - "context_length": "max_position_embeddings", - "vocab_size": "vocab_size", - "width": "d_model", - "heads": "encoder_attention_heads", - "layers": "encoder_layers", - }, - "pooler": "cls_pooler", - }, -} diff --git a/fiftyone/utils/open_clip/hf_model.py b/fiftyone/utils/open_clip/hf_model.py deleted file mode 100644 index e192e9a713..0000000000 --- a/fiftyone/utils/open_clip/hf_model.py +++ /dev/null @@ -1,249 +0,0 @@ -""" huggingface model adapter - -Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. -""" -import re - -import torch -import torch.nn as nn - -try: - import transformers - from transformers import ( - AutoModel, - AutoTokenizer, - AutoConfig, - PretrainedConfig, - ) - from transformers.modeling_outputs import ( - BaseModelOutput, - BaseModelOutputWithPooling, - BaseModelOutputWithPoolingAndCrossAttentions, - ) -except ImportError as e: - transformers = None - - class BaseModelOutput: - pass - - class PretrainedConfig: - pass - - -from .hf_configs import arch_dict - - -# utils -def _camel2snake(s): - return re.sub(r"(? torch.Tensor: - # calculated ground-truth and cache if enabled - if self.prev_num_logits != num_logits or device not in self.labels: - labels = torch.arange(num_logits, device=device, dtype=torch.long) - if self.world_size > 1 and self.local_loss: - labels = labels + num_logits * self.rank - if self.cache_labels: - self.labels[device] = labels - self.prev_num_logits = num_logits - else: - labels = self.labels[device] - return labels - - def get_logits(self, image_features, text_features, logit_scale): - if self.world_size > 1: - all_image_features, all_text_features = gather_features( - image_features, - text_features, - self.local_loss, - self.gather_with_grad, - self.rank, - self.world_size, - self.use_horovod, - ) - - if self.local_loss: - logits_per_image = ( - logit_scale * image_features @ all_text_features.T - ) - logits_per_text = ( - logit_scale * text_features @ all_image_features.T - ) - else: - logits_per_image = ( - logit_scale * all_image_features @ all_text_features.T - ) - logits_per_text = logits_per_image.T - else: - logits_per_image = logit_scale * image_features @ text_features.T - logits_per_text = logit_scale * text_features @ image_features.T - - return logits_per_image, logits_per_text - - def forward( - self, image_features, text_features, logit_scale, output_dict=False - ): - device = image_features.device - logits_per_image, logits_per_text = self.get_logits( - image_features, text_features, logit_scale - ) - - labels = self.get_ground_truth(device, logits_per_image.shape[0]) - - total_loss = ( - F.cross_entropy(logits_per_image, labels) - + F.cross_entropy(logits_per_text, labels) - ) / 2 - - return {"contrastive_loss": total_loss} if output_dict else total_loss - - -class CoCaLoss(ClipLoss): - def __init__( - self, - caption_loss_weight, - clip_loss_weight, - pad_id=0, # pad_token for open_clip custom tokenizer - local_loss=False, - gather_with_grad=False, - cache_labels=False, - rank=0, - world_size=1, - use_horovod=False, - ): - super().__init__( - local_loss=local_loss, - gather_with_grad=gather_with_grad, - cache_labels=cache_labels, - rank=rank, - world_size=world_size, - use_horovod=use_horovod, - ) - - self.clip_loss_weight = clip_loss_weight - self.caption_loss_weight = caption_loss_weight - self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) - - def forward( - self, - image_features, - text_features, - logits, - labels, - logit_scale, - output_dict=False, - ): - - clip_loss = torch.tensor(0) - - if self.clip_loss_weight: - clip_loss = super().forward( - image_features, text_features, logit_scale - ) - clip_loss = self.clip_loss_weight * clip_loss - - caption_loss = self.caption_loss( - logits.permute(0, 2, 1), - labels, - ) - caption_loss = caption_loss * self.caption_loss_weight - - if output_dict: - return { - "contrastive_loss": clip_loss, - "caption_loss": caption_loss, - } - - return clip_loss, caption_loss - - -class DistillClipLoss(ClipLoss): - def dist_loss(self, teacher_logits, student_logits): - return ( - -( - teacher_logits.softmax(dim=1) - * student_logits.log_softmax(dim=1) - ) - .sum(dim=1) - .mean(dim=0) - ) - - def forward( - self, - image_features, - text_features, - logit_scale, - dist_image_features, - dist_text_features, - dist_logit_scale, - output_dict=False, - ): - logits_per_image, logits_per_text = self.get_logits( - image_features, text_features, logit_scale - ) - - dist_logits_per_image, dist_logits_per_text = self.get_logits( - dist_image_features, dist_text_features, dist_logit_scale - ) - - labels = self.get_ground_truth( - image_features.device, logits_per_image.shape[0] - ) - - contrastive_loss = ( - F.cross_entropy(logits_per_image, labels) - + F.cross_entropy(logits_per_text, labels) - ) / 2 - - distill_loss = ( - self.dist_loss(dist_logits_per_image, logits_per_image) - + self.dist_loss(dist_logits_per_text, logits_per_text) - ) / 2 - - if output_dict: - return { - "contrastive_loss": contrastive_loss, - "distill_loss": distill_loss, - } - - return contrastive_loss, distill_loss - - -def neighbour_exchange(from_rank, to_rank, tensor, group=None): - tensor_recv = torch.zeros_like(tensor) - send_op = torch.distributed.P2POp( - torch.distributed.isend, - tensor, - to_rank, - group=group, - ) - recv_op = torch.distributed.P2POp( - torch.distributed.irecv, - tensor_recv, - from_rank, - group=group, - ) - reqs = torch.distributed.batch_isend_irecv([send_op, recv_op]) - for req in reqs: - req.wait() - return tensor_recv - - -def neighbour_exchange_bidir( - left_rank, right_rank, tensor_to_left, tensor_to_right, group=None -): - tensor_from_left = torch.zeros_like(tensor_to_right) - tensor_from_right = torch.zeros_like(tensor_to_left) - send_op_left = torch.distributed.P2POp( - torch.distributed.isend, - tensor_to_left, - left_rank, - group=group, - ) - send_op_right = torch.distributed.P2POp( - torch.distributed.isend, - tensor_to_right, - right_rank, - group=group, - ) - recv_op_left = torch.distributed.P2POp( - torch.distributed.irecv, - tensor_from_left, - left_rank, - group=group, - ) - recv_op_right = torch.distributed.P2POp( - torch.distributed.irecv, - tensor_from_right, - right_rank, - group=group, - ) - reqs = torch.distributed.batch_isend_irecv( - [send_op_right, send_op_left, recv_op_right, recv_op_left] - ) - for req in reqs: - req.wait() - return tensor_from_right, tensor_from_left - - -class NeighbourExchange(torch.autograd.Function): - @staticmethod - def forward(ctx, from_rank, to_rank, group, tensor): - ctx.group = group - ctx.from_rank = from_rank - ctx.to_rank = to_rank - return neighbour_exchange(from_rank, to_rank, tensor, group=group) - - @staticmethod - def backward(ctx, grad_output): - return (None, None, None) + ( - NeighbourExchange.apply( - ctx.to_rank, ctx.from_rank, ctx.group, grad_output - ), - ) - - -def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None): - return NeighbourExchange.apply(from_rank, to_rank, group, tensor) - - -class NeighbourExchangeBidir(torch.autograd.Function): - @staticmethod - def forward( - ctx, left_rank, right_rank, group, tensor_to_left, tensor_to_right - ): - ctx.group = group - ctx.left_rank = left_rank - ctx.right_rank = right_rank - return neighbour_exchange_bidir( - left_rank, right_rank, tensor_to_left, tensor_to_right, group=group - ) - - @staticmethod - def backward(ctx, *grad_outputs): - return (None, None, None) + NeighbourExchangeBidir.apply( - ctx.right_rank, ctx.left_rank, ctx.group, *grad_outputs - ) - - -def neighbour_exchange_bidir_with_grad( - left_rank, right_rank, tensor_to_left, tensor_to_right, group=None -): - return NeighbourExchangeBidir.apply( - left_rank, right_rank, group, tensor_to_left, tensor_to_right - ) - - -class SigLipLoss(nn.Module): - """Sigmoid Loss for Language Image Pre-Training (SigLIP) - https://arxiv.org/abs/2303.15343 - - @article{zhai2023sigmoid, - title={Sigmoid loss for language image pre-training}, - author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, - journal={arXiv preprint arXiv:2303.15343}, - year={2023} - } - """ - - def __init__( - self, - cache_labels=False, - rank=0, - world_size=1, - bidir=True, - use_horovod=False, - ): - super().__init__() - self.cache_labels = cache_labels - self.rank = rank - self.world_size = world_size - assert ( - not use_horovod - ) # FIXME need to look at hvd ops for ring transfers - self.use_horovod = use_horovod - self.bidir = bidir - - # cache state FIXME cache not currently used, worthwhile? - self.prev_num_logits = 0 - self.labels = {} - - def get_ground_truth( - self, device, dtype, num_logits, negative_only=False - ) -> torch.Tensor: - labels = -torch.ones( - (num_logits, num_logits), device=device, dtype=dtype - ) - if not negative_only: - labels = ( - 2 * torch.eye(num_logits, device=device, dtype=dtype) + labels - ) - return labels - - def get_logits( - self, image_features, text_features, logit_scale, logit_bias=None - ): - logits = logit_scale * image_features @ text_features.T - if logit_bias is not None: - logits += logit_bias - return logits - - def _loss( - self, - image_features, - text_features, - logit_scale, - logit_bias=None, - negative_only=False, - ): - logits = self.get_logits( - image_features, text_features, logit_scale, logit_bias - ) - labels = self.get_ground_truth( - image_features.device, - image_features.dtype, - image_features.shape[0], - negative_only=negative_only, - ) - loss = -F.logsigmoid(labels * logits).sum() / image_features.shape[0] - return loss - - def forward( - self, - image_features, - text_features, - logit_scale, - logit_bias, - output_dict=False, - ): - loss = self._loss( - image_features, text_features, logit_scale, logit_bias - ) - - if self.world_size > 1: - # exchange text features w/ neighbour world_size - 1 times - right_rank = (self.rank + 1) % self.world_size - left_rank = (self.rank - 1 + self.world_size) % self.world_size - if self.bidir: - text_features_to_right = text_features_to_left = text_features - num_bidir, remainder = divmod(self.world_size - 1, 2) - for i in range(num_bidir): - text_features_recv = neighbour_exchange_bidir_with_grad( - left_rank, - right_rank, - text_features_to_left, - text_features_to_right, - ) - - for f in text_features_recv: - loss += self._loss( - image_features, - f, - logit_scale, - logit_bias, - negative_only=True, - ) - ( - text_features_to_left, - text_features_to_right, - ) = text_features_recv - - if remainder: - text_features_recv = neighbour_exchange_with_grad( - left_rank, right_rank, text_features_to_right - ) - - loss += self._loss( - image_features, - text_features_recv, - logit_scale, - logit_bias, - negative_only=True, - ) - else: - text_features_to_right = text_features - for i in range(self.world_size - 1): - text_features_from_left = neighbour_exchange_with_grad( - left_rank, right_rank, text_features_to_right - ) - - loss += self._loss( - image_features, - text_features_from_left, - logit_scale, - logit_bias, - negative_only=True, - ) - text_features_to_right = text_features_from_left - - return {"contrastive_loss": loss} if output_dict else loss diff --git a/fiftyone/utils/open_clip/model.py b/fiftyone/utils/open_clip/model.py deleted file mode 100644 index 3f03873a1f..0000000000 --- a/fiftyone/utils/open_clip/model.py +++ /dev/null @@ -1,761 +0,0 @@ -""" CLIP Model - -Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. -""" -import copy -import logging -import math -from dataclasses import dataclass -from typing import Any, Dict, Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from torch.utils.checkpoint import checkpoint -from functools import partial - -from .hf_model import HFTextEncoder -from .modified_resnet import ModifiedResNet -from .timm_model import TimmModel -from .transformer import ( - LayerNormFp32, - LayerNorm, - QuickGELU, - Attention, - VisionTransformer, - TextTransformer, - text_global_pool, -) -from .utils import to_2tuple - - -@dataclass -class CLIPVisionCfg: - layers: Union[Tuple[int, int, int, int], int] = 12 - width: int = 768 - head_width: int = 64 - mlp_ratio: float = 4.0 - patch_size: int = 16 - image_size: Union[Tuple[int, int], int] = 224 - - ls_init_value: Optional[float] = None # layer scale initial value - patch_dropout: float = 0.0 # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results - attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer (overrides pool_type) - attn_pooler_queries: int = 256 # n_queries for attentional pooler - attn_pooler_heads: int = 8 # n heads for attentional_pooling - no_ln_pre: bool = False # disable pre transformer LayerNorm - pos_embed_type: str = "learnable" - final_ln_after_pool: bool = False # apply final LayerNorm after pooling - pool_type: str = "tok" - output_tokens: bool = False - act_kwargs: Optional[dict] = None - norm_kwargs: Optional[dict] = None - - timm_model_name: Optional[ - str - ] = None # a valid model name overrides layers, width, patch_size - timm_model_pretrained: bool = ( - False # use (imagenet) pretrained weights for named model - ) - timm_pool: str = "avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') - timm_proj: str = "linear" # linear projection for timm model output ('linear', 'mlp', '') - timm_proj_bias: bool = False # enable bias final projection - timm_drop: float = 0.0 # head dropout - timm_drop_path: Optional[float] = None # backbone stochastic depth - - -@dataclass -class CLIPTextCfg: - context_length: int = 77 - vocab_size: int = 49408 - hf_tokenizer_name: Optional[str] = None - tokenizer_kwargs: Optional[dict] = None - - width: int = 512 - heads: int = 8 - layers: int = 12 - mlp_ratio: float = 4.0 - ls_init_value: Optional[float] = None # layer scale initial value - embed_cls: bool = False - pad_id: int = 0 - no_causal_mask: bool = False # disable causal masking - final_ln_after_pool: bool = False # apply final LayerNorm after pooling - pool_type: str = "argmax" - proj_bias: bool = False - output_tokens: bool = False - act_kwargs: dict = None - norm_kwargs: dict = None - - # HuggingFace specific text tower config - hf_model_name: Optional[str] = None - hf_model_pretrained: bool = True - hf_proj_type: str = "mlp" - hf_pooler_type: str = "mean_pooler" # attentional pooling for HF models - - -def get_cast_dtype(precision: str): - cast_dtype = None - if precision == "bf16": - cast_dtype = torch.bfloat16 - elif precision == "fp16": - cast_dtype = torch.float16 - return cast_dtype - - -def get_input_dtype(precision: str): - input_dtype = None - if precision in ("bf16", "pure_bf16"): - input_dtype = torch.bfloat16 - elif precision in ("fp16", "pure_fp16"): - input_dtype = torch.float16 - return input_dtype - - -def _build_vision_tower( - embed_dim: int, - vision_cfg: CLIPVisionCfg, - quick_gelu: bool = False, - cast_dtype: Optional[torch.dtype] = None, -): - if isinstance(vision_cfg, dict): - vision_cfg = CLIPVisionCfg(**vision_cfg) - - # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more - # memory efficient in recent PyTorch releases (>= 1.10). - # NOTE: timm models always use native GELU regardless of quick_gelu flag. - act_layer = QuickGELU if quick_gelu else nn.GELU - - if vision_cfg.timm_model_name: - visual = TimmModel( - vision_cfg.timm_model_name, - pretrained=vision_cfg.timm_model_pretrained, - pool=vision_cfg.timm_pool, - proj=vision_cfg.timm_proj, - proj_bias=vision_cfg.timm_proj_bias, - drop=vision_cfg.timm_drop, - drop_path=vision_cfg.timm_drop_path, - patch_drop=vision_cfg.patch_dropout - if vision_cfg.patch_dropout > 0 - else None, - embed_dim=embed_dim, - image_size=vision_cfg.image_size, - ) - elif isinstance(vision_cfg.layers, (tuple, list)): - vision_heads = vision_cfg.width * 32 // vision_cfg.head_width - visual = ModifiedResNet( - layers=vision_cfg.layers, - output_dim=embed_dim, - heads=vision_heads, - image_size=vision_cfg.image_size, - width=vision_cfg.width, - ) - else: - vision_heads = vision_cfg.width // vision_cfg.head_width - norm_layer = ( - LayerNormFp32 - if cast_dtype in (torch.float16, torch.bfloat16) - else LayerNorm - ) - if vision_cfg.norm_kwargs: - norm_layer = partial(norm_layer, **vision_cfg.norm_kwargs) - if vision_cfg.act_kwargs is not None: - act_layer = partial(act_layer, **vision_cfg.act_kwargs) - - visual = VisionTransformer( - image_size=vision_cfg.image_size, - patch_size=vision_cfg.patch_size, - width=vision_cfg.width, - layers=vision_cfg.layers, - heads=vision_heads, - mlp_ratio=vision_cfg.mlp_ratio, - ls_init_value=vision_cfg.ls_init_value, - patch_dropout=vision_cfg.patch_dropout, - attentional_pool=vision_cfg.attentional_pool, - attn_pooler_queries=vision_cfg.attn_pooler_queries, - attn_pooler_heads=vision_cfg.attn_pooler_heads, - pos_embed_type=vision_cfg.pos_embed_type, - no_ln_pre=vision_cfg.no_ln_pre, - final_ln_after_pool=vision_cfg.final_ln_after_pool, - pool_type=vision_cfg.pool_type, - output_tokens=vision_cfg.output_tokens, - output_dim=embed_dim, - act_layer=act_layer, - norm_layer=norm_layer, - ) - - return visual - - -def _build_text_tower( - embed_dim: int, - text_cfg: CLIPTextCfg, - quick_gelu: bool = False, - cast_dtype: Optional[torch.dtype] = None, -): - if isinstance(text_cfg, dict): - text_cfg = CLIPTextCfg(**text_cfg) - - if text_cfg.hf_model_name: - text = HFTextEncoder( - text_cfg.hf_model_name, - output_dim=embed_dim, - proj_type=text_cfg.hf_proj_type, - pooler_type=text_cfg.hf_pooler_type, - pretrained=text_cfg.hf_model_pretrained, - output_tokens=text_cfg.output_tokens, - ) - else: - act_layer = QuickGELU if quick_gelu else nn.GELU - norm_layer = ( - LayerNormFp32 - if cast_dtype in (torch.float16, torch.bfloat16) - else LayerNorm - ) - if text_cfg.norm_kwargs: - norm_layer = partial(norm_layer, **text_cfg.norm_kwargs) - if text_cfg.act_kwargs is not None: - act_layer = partial(act_layer, **text_cfg.act_kwargs) - - text = TextTransformer( - context_length=text_cfg.context_length, - vocab_size=text_cfg.vocab_size, - width=text_cfg.width, - heads=text_cfg.heads, - layers=text_cfg.layers, - mlp_ratio=text_cfg.mlp_ratio, - ls_init_value=text_cfg.ls_init_value, - output_dim=embed_dim, - embed_cls=text_cfg.embed_cls, - no_causal_mask=text_cfg.no_causal_mask, - pad_id=text_cfg.pad_id, - pool_type=text_cfg.pool_type, - proj_bias=text_cfg.proj_bias, - output_tokens=text_cfg.output_tokens, - act_layer=act_layer, - norm_layer=norm_layer, - ) - return text - - -class CLIP(nn.Module): - output_dict: torch.jit.Final[bool] - - def __init__( - self, - embed_dim: int, - vision_cfg: CLIPVisionCfg, - text_cfg: CLIPTextCfg, - quick_gelu: bool = False, - init_logit_scale: float = np.log(1 / 0.07), - init_logit_bias: Optional[float] = None, - cast_dtype: Optional[torch.dtype] = None, - output_dict: bool = False, - ): - super().__init__() - self.output_dict = output_dict - - self.visual = _build_vision_tower( - embed_dim, vision_cfg, quick_gelu, cast_dtype - ) - - text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) - self.transformer = text.transformer - self.context_length = text.context_length - self.vocab_size = text.vocab_size - self.token_embedding = text.token_embedding - self.positional_embedding = text.positional_embedding - self.ln_final = text.ln_final - self.text_projection = text.text_projection - self.text_pool_type = text.pool_type - self.register_buffer("attn_mask", text.attn_mask, persistent=False) - - self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) - if init_logit_bias is not None: - self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) - else: - self.logit_bias = None - - def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): - # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 - self.visual.lock( - unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats - ) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - self.visual.set_grad_checkpointing(enable) - self.transformer.grad_checkpointing = enable - - def encode_image(self, image, normalize: bool = False): - features = self.visual(image) - return F.normalize(features, dim=-1) if normalize else features - - def encode_text(self, text, normalize: bool = False): - cast_dtype = self.transformer.get_cast_dtype() - - x = self.token_embedding(text).to( - cast_dtype - ) # [batch_size, n_ctx, d_model] - - x = x + self.positional_embedding.to(cast_dtype) - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x, attn_mask=self.attn_mask) - x = x.permute(1, 0, 2) # LND -> NLD - x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] - x, _ = text_global_pool(x, text, self.text_pool_type) - if self.text_projection is not None: - if isinstance(self.text_projection, nn.Linear): - x = self.text_projection(x) - else: - x = x @ self.text_projection - - return F.normalize(x, dim=-1) if normalize else x - - def get_logits(self, image, text): - image_features = self.encode_image(image, normalize=True) - text_features = self.encode_text(text, normalize=True) - image_logits = ( - self.logit_scale.exp() * image_features @ text_features.T - ) - if self.logit_bias is not None: - image_logits += self.logit_bias - text_logits = image_logits.T - return image_logits, text_logits - - def forward( - self, - image: Optional[torch.Tensor] = None, - text: Optional[torch.Tensor] = None, - ): - image_features = ( - self.encode_image(image, normalize=True) - if image is not None - else None - ) - text_features = ( - self.encode_text(text, normalize=True) - if text is not None - else None - ) - - if self.output_dict: - out_dict = { - "image_features": image_features, - "text_features": text_features, - "logit_scale": self.logit_scale.exp(), - } - if self.logit_bias is not None: - out_dict["logit_bias"] = self.logit_bias - return out_dict - - if self.logit_bias is not None: - return ( - image_features, - text_features, - self.logit_scale.exp(), - self.logit_bias, - ) - return image_features, text_features, self.logit_scale.exp() - - -class CustomTextCLIP(nn.Module): - output_dict: torch.jit.Final[bool] - - def __init__( - self, - embed_dim: int, - vision_cfg: CLIPVisionCfg, - text_cfg: CLIPTextCfg, - quick_gelu: bool = False, - init_logit_scale: float = np.log(1 / 0.07), - init_logit_bias: Optional[float] = None, - cast_dtype: Optional[torch.dtype] = None, - output_dict: bool = False, - ): - super().__init__() - self.output_dict = output_dict - self.visual = _build_vision_tower( - embed_dim, vision_cfg, quick_gelu, cast_dtype - ) - self.text = _build_text_tower( - embed_dim, text_cfg, quick_gelu, cast_dtype - ) - self.context_length = self.text.context_length - self.vocab_size = self.text.vocab_size - self.logit_scale = nn.Parameter(torch.ones([]) * init_logit_scale) - if init_logit_bias is not None: - self.logit_bias = nn.Parameter(torch.ones([]) * init_logit_bias) - else: - self.logit_bias = None - - def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): - # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 - self.visual.lock( - unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats - ) - - def lock_text_tower( - self, unlocked_layers: int = 0, freeze_layer_norm: bool = True - ): - self.text.lock(unlocked_layers, freeze_layer_norm) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - self.visual.set_grad_checkpointing(enable) - self.text.set_grad_checkpointing(enable) - - def encode_image(self, image, normalize: bool = False): - features = self.visual(image) - return F.normalize(features, dim=-1) if normalize else features - - def encode_text(self, text, normalize: bool = False): - features = self.text(text) - return F.normalize(features, dim=-1) if normalize else features - - def get_logits(self, image, text): - image_features = self.encode_image(image, normalize=True) - text_features = self.encode_text(text, normalize=True) - image_logits = ( - self.logit_scale.exp() * image_features @ text_features.T - ) - if self.logit_bias is not None: - image_logits += self.logit_bias - text_logits = image_logits.T - return image_logits, text_logits - - def forward( - self, - image: Optional[torch.Tensor] = None, - text: Optional[torch.Tensor] = None, - ): - image_features = ( - self.encode_image(image, normalize=True) - if image is not None - else None - ) - text_features = ( - self.encode_text(text, normalize=True) - if text is not None - else None - ) - - if self.output_dict: - out_dict = { - "image_features": image_features, - "text_features": text_features, - "logit_scale": self.logit_scale.exp(), - } - if self.logit_bias is not None: - out_dict["logit_bias"] = self.logit_bias - return out_dict - - if self.logit_bias is not None: - return ( - image_features, - text_features, - self.logit_scale.exp(), - self.logit_bias, - ) - return image_features, text_features, self.logit_scale.exp() - - -def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): - """Convert applicable model parameters to low-precision (bf16 or fp16)""" - - def _convert_weights(l): - if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): - l.weight.data = l.weight.data.to(dtype) - if l.bias is not None: - l.bias.data = l.bias.data.to(dtype) - - if isinstance(l, (nn.MultiheadAttention, Attention)): - for attr in [ - *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], - "in_proj_bias", - "bias_k", - "bias_v", - ]: - tensor = getattr(l, attr) - if tensor is not None: - tensor.data = tensor.data.to(dtype) - - if isinstance(l, (CLIP, TextTransformer)): - # convert text nn.Parameter projections - attr = getattr(l, "text_projection", None) - if attr is not None: - attr.data = attr.data.to(dtype) - - if isinstance(l, VisionTransformer): - # convert vision nn.Parameter projections - attr = getattr(l, "proj", None) - if attr is not None: - attr.data = attr.data.to(dtype) - - model.apply(_convert_weights) - - -convert_weights_to_fp16 = convert_weights_to_lp # backwards compat - - -# used to maintain checkpoint compatibility -def convert_to_custom_text_state_dict(state_dict: dict): - if "text_projection" in state_dict: - # old format state_dict, move text tower -> .text - new_state_dict = {} - for k, v in state_dict.items(): - if any( - k.startswith(p) - for p in ( - "text_projection", - "positional_embedding", - "token_embedding", - "transformer", - "ln_final", - ) - ): - k = "text." + k - new_state_dict[k] = v - return new_state_dict - return state_dict - - -def build_model_from_openai_state_dict( - state_dict: dict, - quick_gelu=True, - cast_dtype=torch.float16, -): - vit = "visual.proj" in state_dict - - if vit: - vision_width = state_dict["visual.conv1.weight"].shape[0] - vision_layers = len( - [ - k - for k in state_dict.keys() - if k.startswith("visual.") - and k.endswith(".attn.in_proj_weight") - ] - ) - vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] - grid_size = round( - (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5 - ) - image_size = vision_patch_size * grid_size - else: - counts: list = [ - len( - set( - k.split(".")[2] - for k in state_dict - if k.startswith(f"visual.layer{b}") - ) - ) - for b in [1, 2, 3, 4] - ] - vision_layers = tuple(counts) - vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] - output_width = round( - (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) - ** 0.5 - ) - vision_patch_size = None - assert ( - output_width**2 + 1 - == state_dict["visual.attnpool.positional_embedding"].shape[0] - ) - image_size = output_width * 32 - - embed_dim = state_dict["text_projection"].shape[1] - context_length = state_dict["positional_embedding"].shape[0] - vocab_size = state_dict["token_embedding.weight"].shape[0] - transformer_width = state_dict["ln_final.weight"].shape[0] - transformer_heads = transformer_width // 64 - transformer_layers = len( - set( - k.split(".")[2] - for k in state_dict - if k.startswith(f"transformer.resblocks") - ) - ) - - vision_cfg = CLIPVisionCfg( - layers=vision_layers, - width=vision_width, - patch_size=vision_patch_size, - image_size=image_size, - ) - text_cfg = CLIPTextCfg( - context_length=context_length, - vocab_size=vocab_size, - width=transformer_width, - heads=transformer_heads, - layers=transformer_layers, - ) - model = CLIP( - embed_dim, - vision_cfg=vision_cfg, - text_cfg=text_cfg, - quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU - cast_dtype=cast_dtype, - ) - - for key in ["input_resolution", "context_length", "vocab_size"]: - state_dict.pop(key, None) - convert_weights_to_fp16( - model - ) # OpenAI state dicts are partially converted to float16 - model.load_state_dict(state_dict) - return model.eval() - - -def trace_model(model, batch_size=256, device=torch.device("cpu")): - model.eval() - image_size = model.visual.image_size - example_images = torch.ones( - (batch_size, 3, image_size, image_size), device=device - ) - example_text = torch.zeros( - (batch_size, model.context_length), dtype=torch.int, device=device - ) - model = torch.jit.trace_module( - model, - inputs=dict( - forward=(example_images, example_text), - encode_text=(example_text,), - encode_image=(example_images,), - ), - ) - model.visual.image_size = image_size - return model - - -def resize_pos_embed( - state_dict, model, interpolation: str = "bicubic", antialias: bool = True -): - # Rescale the grid of position embeddings when loading from state_dict - old_pos_embed = state_dict.get("visual.positional_embedding", None) - if old_pos_embed is None or not hasattr(model.visual, "grid_size"): - return - grid_size = to_2tuple(model.visual.grid_size) - extra_tokens = ( - 1 # FIXME detect different token configs (ie no class token, or more) - ) - new_seq_len = grid_size[0] * grid_size[1] + extra_tokens - if new_seq_len == old_pos_embed.shape[0]: - return - - if extra_tokens: - pos_emb_tok, pos_emb_img = ( - old_pos_embed[:extra_tokens], - old_pos_embed[extra_tokens:], - ) - else: - pos_emb_tok, pos_emb_img = None, old_pos_embed - old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) - - logging.info( - "Resizing position embedding grid-size from %s to %s", - old_grid_size, - grid_size, - ) - pos_emb_img = pos_emb_img.reshape( - 1, old_grid_size[0], old_grid_size[1], -1 - ).permute(0, 3, 1, 2) - pos_emb_img = F.interpolate( - pos_emb_img, - size=grid_size, - mode=interpolation, - antialias=antialias, - align_corners=False, - ) - pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape( - 1, grid_size[0] * grid_size[1], -1 - )[0] - if pos_emb_tok is not None: - new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) - else: - new_pos_embed = pos_emb_img - state_dict["visual.positional_embedding"] = new_pos_embed - - -def resize_text_pos_embed( - state_dict, model, interpolation: str = "linear", antialias: bool = False -): - old_pos_embed = state_dict.get("positional_embedding", None) - if old_pos_embed is None: - return - # FIXME add support for text cls_token - model_pos_embed = getattr(model, "positional_embedding", None) - if model_pos_embed is None: - model_pos_embed = getattr(model.text, "positional_embedding", None) - - old_num_pos = old_pos_embed.shape[0] - old_width = old_pos_embed.shape[1] - num_pos = model_pos_embed.shape[0] - width = model_pos_embed.shape[1] - assert old_width == width, "text pos_embed width changed!" - if old_num_pos == num_pos: - return - - logging.info( - "Resizing text position embedding num_pos from %s to %s", - old_num_pos, - num_pos, - ) - old_pos_embed = old_pos_embed.reshape(1, old_num_pos, old_width).permute( - 0, 2, 1 - ) - old_pos_embed = F.interpolate( - old_pos_embed, - size=num_pos, - mode=interpolation, - antialias=antialias, - align_corners=False, - ) - old_pos_embed = old_pos_embed.permute(0, 2, 1)[0] - new_pos_embed = old_pos_embed - - state_dict["positional_embedding"] = new_pos_embed - - -def get_model_preprocess_cfg(model): - module = getattr(model, "visual", model) - preprocess_cfg = getattr(module, "preprocess_cfg", {}) - if not preprocess_cfg: - # use separate legacy attributes if preprocess_cfg dict not found - size = getattr(module, "image_size") - if size is not None: - preprocess_cfg["size"] = size - mean = getattr(module, "image_mean", None) - if mean is not None: - preprocess_cfg["mean"] = mean - std = getattr(module, "image_std", None) - if std is not None: - preprocess_cfg["std"] = std - return preprocess_cfg - - -def set_model_preprocess_cfg(model, preprocess_cfg: Dict[str, Any]): - module = getattr(model, "visual", model) - module.image_mean = preprocess_cfg[ - "mean" - ] # legacy attribute, keeping for bwd compat - module.image_std = preprocess_cfg[ - "std" - ] # legacy attribute, keeping for bwd compat - module.preprocess_cfg = copy.deepcopy( - preprocess_cfg - ) # new attr, package all pp cfg as dict - - -def get_model_tokenize_cfg(model): - module = getattr(model, "text", model) - cfg = {} - context_length = getattr(module, "context_length", None) - if context_length is not None: - cfg["context_length"] = context_length - vocab_size = getattr(module, "vocab_size", None) - if vocab_size is not None: - cfg["vocab_size"] = vocab_size - return cfg diff --git a/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json b/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json deleted file mode 100644 index 3f3e953321..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA01-g-14-plus.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva_giant_patch14_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json b/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json deleted file mode 100644 index 9af290be97..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA01-g-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva_giant_patch14_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json b/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json deleted file mode 100644 index e28b266eb9..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA02-B-16.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva02_base_patch16_clip_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json b/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json deleted file mode 100644 index 2ff4c4226f..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA02-E-14-plus.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva02_enormous_patch14_clip_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1280, - "heads": 20, - "layers": 32 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json b/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json deleted file mode 100644 index aac0fee80a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA02-E-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva02_enormous_patch14_clip_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json b/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json deleted file mode 100644 index 5d609d744a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA02-L-14-336.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 336, - "timm_model_name": "eva02_large_patch14_clip_336", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json b/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json deleted file mode 100644 index 2e14428bf7..0000000000 --- a/fiftyone/utils/open_clip/model_configs/EVA02-L-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "eva02_large_patch14_clip_224", - "timm_model_pretrained": false, - "timm_pool": "token", - "timm_proj": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json b/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json deleted file mode 100644 index cf7802ee00..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN101-quickgelu.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 512, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": [3, 4, 23, 3], - "width": 64, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN101.json b/fiftyone/utils/open_clip/model_configs/RN101.json deleted file mode 100644 index 55afdcc5b3..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN101.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": [3, 4, 23, 3], - "width": 64, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json b/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json deleted file mode 100644 index 05e227d3ef..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN50-quickgelu.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 1024, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": [3, 4, 6, 3], - "width": 64, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN50.json b/fiftyone/utils/open_clip/model_configs/RN50.json deleted file mode 100644 index af121b71f0..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN50.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": [3, 4, 6, 3], - "width": 64, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x16.json b/fiftyone/utils/open_clip/model_configs/RN50x16.json deleted file mode 100644 index 1189be1463..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN50x16.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 384, - "layers": [6, 8, 18, 8], - "width": 96, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x4.json b/fiftyone/utils/open_clip/model_configs/RN50x4.json deleted file mode 100644 index d63980550d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN50x4.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "image_size": 288, - "layers": [4, 6, 10, 6], - "width": 80, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/RN50x64.json b/fiftyone/utils/open_clip/model_configs/RN50x64.json deleted file mode 100644 index 654285a1a9..0000000000 --- a/fiftyone/utils/open_clip/model_configs/RN50x64.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 448, - "layers": [3, 15, 36, 10], - "width": 128, - "patch_size": null - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json deleted file mode 100644 index ae15f00dec..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-256.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 768, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 256, - "timm_model_name": "vit_base_patch16_siglip_256", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 768, - "heads": 12, - "layers": 12, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json deleted file mode 100644 index 6a64104020..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-384.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 768, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 384, - "timm_model_name": "vit_base_patch16_siglip_384", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 768, - "heads": 12, - "layers": 12, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json deleted file mode 100644 index a8a963f6e1..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-512.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 768, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 512, - "timm_model_name": "vit_base_patch16_siglip_512", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 768, - "heads": 12, - "layers": 12, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json deleted file mode 100644 index 7063baf9cf..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP-i18n-256.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 768, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 256, - "timm_model_name": "vit_base_patch16_siglip_256", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 250000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP-i18n-256", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 768, - "heads": 12, - "layers": 12, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json deleted file mode 100644 index ac3a745449..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-SigLIP.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 768, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "vit_base_patch16_siglip_224", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 768, - "heads": 12, - "layers": 12, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json deleted file mode 100644 index 9347280c60..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus-240.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "image_size": 240, - "layers": 12, - "width": 896, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json deleted file mode 100644 index f9cc3e3b00..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-plus.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 896, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json deleted file mode 100644 index cb0d662c6c..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16-quickgelu.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 512, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-16.json b/fiftyone/utils/open_clip/model_configs/ViT-B-16.json deleted file mode 100644 index 9afeef0fbc..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-16.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json deleted file mode 100644 index 80a2597d8f..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-32-256.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 256, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json deleted file mode 100644 index 27ae13857a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-32-plus-256.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "image_size": 256, - "layers": 12, - "width": 896, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json deleted file mode 100644 index f5a063adbf..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-32-quickgelu.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 512, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/ViT-B-32.json deleted file mode 100644 index abd1f7973d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-B-32.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json deleted file mode 100644 index 826edd480e..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-14-378-quickgelu.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "quick_gelu": true, - "vision_cfg": { - "image_size": 378, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json deleted file mode 100644 index 926d5a3707..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA-336.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 336, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 1024, - "heads": 16, - "layers": 24, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json deleted file mode 100644 index c29a7ee4f7..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-14-CLIPA.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 1024, - "heads": 16, - "layers": 24, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json deleted file mode 100644 index 825d04a0bf..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-14-quickgelu.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/ViT-H-14.json deleted file mode 100644 index d2c01733dc..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-14.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-H-16.json b/fiftyone/utils/open_clip/model_configs/ViT-H-16.json deleted file mode 100644 index 942ed56bf6..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-H-16.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json deleted file mode 100644 index c8e5fbac8a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14-280.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 280, - "layers": 24, - "width": 1024, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json deleted file mode 100644 index 4db3a1e77c..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14-336.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 336, - "layers": 24, - "width": 1024, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json deleted file mode 100644 index 027ee6a22d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA-336.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 336, - "layers": 24, - "width": 1024, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 768, - "heads": 12, - "layers": 12, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json deleted file mode 100644 index a1ced4148e..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14-CLIPA.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 224, - "layers": 24, - "width": 1024, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 768, - "heads": 12, - "layers": 12, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json deleted file mode 100644 index 6ecba7d754..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14-quickgelu.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 768, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": 24, - "width": 1024, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-14.json b/fiftyone/utils/open_clip/model_configs/ViT-L-14.json deleted file mode 100644 index 98951b0cbf..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-14.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 224, - "layers": 24, - "width": 1024, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json deleted file mode 100644 index cc09c4877d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-16-320.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 320, - "layers": 24, - "width": 1024, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json deleted file mode 100644 index 8aa6010f2b..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-256.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 1024, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 256, - "timm_model_name": "vit_large_patch16_siglip_256", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 1024, - "heads": 16, - "layers": 24, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json deleted file mode 100644 index 6d61af8997..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-16-SigLIP-384.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "embed_dim": 1024, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 384, - "timm_model_name": "vit_large_patch16_siglip_384", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 1024, - "heads": 16, - "layers": 24, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-L-16.json b/fiftyone/utils/open_clip/model_configs/ViT-L-16.json deleted file mode 100644 index 78601e7a68..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-L-16.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 224, - "layers": 24, - "width": 1024, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json deleted file mode 100644 index f0893f92b4..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-M-16-alt.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 384, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 512, - "patch_size": 16, - "ls_init_value": 1e-4 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 384, - "heads": 6, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-16.json b/fiftyone/utils/open_clip/model_configs/ViT-M-16.json deleted file mode 100644 index bc71c7c5c8..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-M-16.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 512, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json deleted file mode 100644 index 92f57a2c6c..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-M-32-alt.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 384, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 512, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 384, - "heads": 6, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-M-32.json b/fiftyone/utils/open_clip/model_configs/ViT-M-32.json deleted file mode 100644 index 767c4104e0..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-M-32.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 512, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json deleted file mode 100644 index 2fd9a0913c..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-S-16-alt.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 256, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 384, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 256, - "heads": 4, - "layers": 10 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-16.json b/fiftyone/utils/open_clip/model_configs/ViT-S-16.json deleted file mode 100644 index 4d94a6fe6e..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-S-16.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 384, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 384, - "patch_size": 16 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 384, - "heads": 6, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json b/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json deleted file mode 100644 index c309e6c193..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-S-32-alt.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 256, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 384, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 256, - "heads": 4, - "layers": 10 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-S-32.json b/fiftyone/utils/open_clip/model_configs/ViT-S-32.json deleted file mode 100644 index de254d97b7..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-S-32.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 384, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 384, - "patch_size": 32 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 384, - "heads": 6, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json deleted file mode 100644 index 817d20667a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP-384.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "embed_dim": 1152, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 384, - "timm_model_name": "vit_so400m_patch14_siglip_384", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 64, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 1152, - "heads": 16, - "layers": 27, - "mlp_ratio": 3.7362, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json b/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json deleted file mode 100644 index 192e9a6370..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-SO400M-14-SigLIP.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "embed_dim": 1152, - "init_logit_bias": -10, - "custom_text": true, - "vision_cfg": { - "image_size": 224, - "timm_model_name": "vit_so400m_patch14_siglip_224", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "context_length": 16, - "vocab_size": 32000, - "hf_tokenizer_name": "timm/ViT-B-16-SigLIP", - "tokenizer_kwargs": { - "clean": "canonicalize" - }, - "width": 1152, - "heads": 16, - "layers": 27, - "mlp_ratio": 3.7362, - "no_causal_mask": true, - "proj_bias": true, - "pool_type": "last", - "norm_kwargs": { - "eps": 1e-6 - } - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json deleted file mode 100644 index afe68e974f..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA-336.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "embed_dim": 1280, - "vision_cfg": { - "image_size": 336, - "layers": 48, - "width": 1664, - "head_width": 104, - "mlp_ratio": 4.9231, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 1280, - "heads": 20, - "layers": 32, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json deleted file mode 100644 index c6c1c846f9..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14-CLIPA.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "embed_dim": 1280, - "vision_cfg": { - "image_size": 224, - "layers": 48, - "width": 1664, - "head_width": 104, - "mlp_ratio": 4.9231, - "patch_size": 14, - "no_ln_pre": true, - "pool_type": "avg", - "final_ln_after_pool": true - }, - "text_cfg": { - "context_length": 32, - "vocab_size": 32000, - "hf_tokenizer_name": "bert-base-uncased", - "tokenizer_kwargs": { - "strip_sep_token": true - }, - "width": 1280, - "heads": 20, - "layers": 32, - "pool_type": "last", - "no_causal_mask": true - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json b/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json deleted file mode 100644 index c5fd84c073..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-bigG-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1280, - "vision_cfg": { - "image_size": 224, - "layers": 48, - "width": 1664, - "head_width": 104, - "mlp_ratio": 4.9231, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1280, - "heads": 20, - "layers": 32 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-e-14.json b/fiftyone/utils/open_clip/model_configs/ViT-e-14.json deleted file mode 100644 index 69d7ef9c24..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-e-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1280, - "vision_cfg": { - "image_size": 224, - "layers": 56, - "width": 1792, - "head_width": 112, - "mlp_ratio": 8.5715, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1280, - "heads": 20, - "layers": 36 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/ViT-g-14.json b/fiftyone/utils/open_clip/model_configs/ViT-g-14.json deleted file mode 100644 index b5c4231a67..0000000000 --- a/fiftyone/utils/open_clip/model_configs/ViT-g-14.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 40, - "width": 1408, - "head_width": 88, - "mlp_ratio": 4.3637, - "patch_size": 14 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json deleted file mode 100644 index 02ea8032e4..0000000000 --- a/fiftyone/utils/open_clip/model_configs/coca_ViT-B-32.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32, - "attentional_pool": true, - "attn_pooler_heads": 8, - "output_tokens": true - }, - "text_cfg": { - "context_length": 76, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12, - "embed_cls": true, - "output_tokens": true - }, - "multimodal_cfg": { - "context_length": 76, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12, - "attn_pooler_heads": 8 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json b/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json deleted file mode 100644 index 3d5ca4ca23..0000000000 --- a/fiftyone/utils/open_clip/model_configs/coca_ViT-L-14.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "image_size": 224, - "layers": 24, - "width": 1024, - "patch_size": 14, - "attentional_pool": true, - "attn_pooler_heads": 8, - "output_tokens": true - }, - "text_cfg": { - "context_length": 76, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12, - "embed_cls": true, - "output_tokens": true - }, - "multimodal_cfg": { - "context_length": 76, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12, - "attn_pooler_heads": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/coca_base.json b/fiftyone/utils/open_clip/model_configs/coca_base.json deleted file mode 100644 index 146690d54b..0000000000 --- a/fiftyone/utils/open_clip/model_configs/coca_base.json +++ /dev/null @@ -1,31 +0,0 @@ -{ - "embed_dim": 512, - "multimodal_cfg": { - "width": 768, - "context_length": 76, - "vocab_size": 64000, - "mlp_ratio": 4, - "layers": 12, - "dim_head": 64, - "heads": 12, - "n_queries": 256, - "attn_pooler_heads": 8 - }, - "vision_cfg": { - "image_size": 288, - "layers": 12, - "width": 768, - "patch_size": 18, - "output_tokens": true - }, - "text_cfg": { - "context_length": 76, - "vocab_size": 64000, - "layers": 12, - "heads": 12, - "width": 768, - "embed_cls": true, - "output_tokens": true - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json deleted file mode 100644 index aa9d3f5620..0000000000 --- a/fiftyone/utils/open_clip/model_configs/coca_roberta-ViT-B-32.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32, - "output_tokens": true - }, - "text_cfg": { - "hf_model_name": "roberta-base", - "hf_tokenizer_name": "roberta-base", - "hf_proj_type": "linear", - "width": 768, - "output_tokens": true - }, - "multimodal_cfg": { - "context_length": 76, - "width": 768, - "heads": 8, - "layers": 12 - }, - "custom_text": true -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base.json b/fiftyone/utils/open_clip/model_configs/convnext_base.json deleted file mode 100644 index 46718aacc0..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_base.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "timm_model_name": "convnext_base", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base_w.json b/fiftyone/utils/open_clip/model_configs/convnext_base_w.json deleted file mode 100644 index ed515495c7..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_base_w.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "timm_model_name": "convnext_base", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 256 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json b/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json deleted file mode 100644 index 732ea67b5f..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_base_w_320.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "timm_model_name": "convnext_base", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 320 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large.json b/fiftyone/utils/open_clip/model_configs/convnext_large.json deleted file mode 100644 index 528a5fd4e8..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_large.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "timm_model_name": "convnext_large", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large_d.json b/fiftyone/utils/open_clip/model_configs/convnext_large_d.json deleted file mode 100644 index 4289d291d1..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_large_d.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "timm_model_name": "convnext_large", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "mlp", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 256 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 16 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json b/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json deleted file mode 100644 index 0097fa4e40..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_large_d_320.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 768, - "vision_cfg": { - "timm_model_name": "convnext_large", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "mlp", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 320 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 768, - "heads": 12, - "layers": 16 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_small.json b/fiftyone/utils/open_clip/model_configs/convnext_small.json deleted file mode 100644 index 2b070ee3ca..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_small.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "timm_model_name": "convnext_small", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_tiny.json b/fiftyone/utils/open_clip/model_configs/convnext_tiny.json deleted file mode 100644 index 767c389269..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_tiny.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "timm_model_name": "convnext_tiny", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json b/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json deleted file mode 100644 index 71d60fb8c6..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_xlarge.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "timm_model_name": "convnext_xlarge", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 256 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 20 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json deleted file mode 100644 index defe9427df..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "timm_model_name": "convnext_xxlarge", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 256 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json b/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json deleted file mode 100644 index c660b324f7..0000000000 --- a/fiftyone/utils/open_clip/model_configs/convnext_xxlarge_320.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "timm_model_name": "convnext_xxlarge", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "timm_drop": 0.0, - "timm_drop_path": 0.1, - "image_size": 320 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 1024, - "heads": 16, - "layers": 24 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json deleted file mode 100644 index e22366897a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/mt5-base-ViT-B-32.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "hf_model_name": "google/mt5-base", - "hf_tokenizer_name": "google/mt5-base", - "hf_pooler_type": "mean_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json deleted file mode 100644 index f58717cdd5..0000000000 --- a/fiftyone/utils/open_clip/model_configs/mt5-xl-ViT-H-14.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "hf_model_name": "google/mt5-xl", - "hf_tokenizer_name": "google/mt5-xl", - "hf_pooler_type": "mean_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json deleted file mode 100644 index 3d2d6fc57a..0000000000 --- a/fiftyone/utils/open_clip/model_configs/nllb-clip-base-siglip.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 768, - "custom_text": true, - "init_logit_bias": -10, - "vision_cfg": { - "image_size": 384, - "timm_model_name": "vit_base_patch16_siglip_384", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "hf_model_name": "facebook/nllb-200-distilled-600M", - "hf_tokenizer_name": "facebook/nllb-200-distilled-600M", - "hf_proj_type": "linear", - "hf_pooler_type": "cls_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json deleted file mode 100644 index 8cedf3cd7d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/nllb-clip-base.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "hf_model_name": "facebook/nllb-200-distilled-600M", - "hf_tokenizer_name": "facebook/nllb-200-distilled-600M", - "hf_proj_type": "linear", - "hf_pooler_type": "cls_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json deleted file mode 100644 index 6d9d25fd95..0000000000 --- a/fiftyone/utils/open_clip/model_configs/nllb-clip-large-siglip.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "embed_dim": 1152, - "custom_text": true, - "init_logit_bias": -10, - "vision_cfg": { - "image_size": 384, - "timm_model_name": "vit_so400m_patch14_siglip_384", - "timm_model_pretrained": false, - "timm_pool": "map", - "timm_proj": "none" - }, - "text_cfg": { - "hf_model_name": "facebook/nllb-200-distilled-1.3B", - "hf_tokenizer_name": "facebook/nllb-200-distilled-1.3B", - "hf_proj_type": "linear", - "hf_pooler_type": "cls_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json b/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json deleted file mode 100644 index a7e25ac900..0000000000 --- a/fiftyone/utils/open_clip/model_configs/nllb-clip-large.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "hf_model_name": "facebook/nllb-200-distilled-1.3B", - "hf_tokenizer_name": "facebook/nllb-200-distilled-1.3B", - "hf_proj_type": "linear", - "hf_pooler_type": "cls_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json deleted file mode 100644 index c0c7a55995..0000000000 --- a/fiftyone/utils/open_clip/model_configs/roberta-ViT-B-32.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "embed_dim": 512, - "quick_gelu": true, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "hf_model_name": "roberta-base", - "hf_tokenizer_name": "roberta-base", - "hf_pooler_type": "mean_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json b/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json deleted file mode 100644 index 213d34d113..0000000000 --- a/fiftyone/utils/open_clip/model_configs/swin_base_patch4_window7_224.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 640, - "vision_cfg": { - "timm_model_name": "swin_base_patch4_window7_224", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 640, - "heads": 10, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json b/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json deleted file mode 100644 index bc7d33df1d..0000000000 --- a/fiftyone/utils/open_clip/model_configs/vit_medium_patch16_gap_256.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "timm_model_name": "vit_medium_patch16_gap_256", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "image_size": 256 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json b/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json deleted file mode 100644 index 12e62656f4..0000000000 --- a/fiftyone/utils/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "timm_model_name": "vit_relpos_medium_patch16_cls_224", - "timm_model_pretrained": false, - "timm_pool": "", - "timm_proj": "linear", - "image_size": 224 - }, - "text_cfg": { - "context_length": 77, - "vocab_size": 49408, - "width": 512, - "heads": 8, - "layers": 12 - } -} diff --git a/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json b/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json deleted file mode 100644 index 375fa9e12f..0000000000 --- a/fiftyone/utils/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "embed_dim": 512, - "vision_cfg": { - "image_size": 224, - "layers": 12, - "width": 768, - "patch_size": 32 - }, - "text_cfg": { - "hf_model_name": "xlm-roberta-base", - "hf_tokenizer_name": "xlm-roberta-base", - "hf_pooler_type": "mean_pooler" - } -} diff --git a/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json b/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json deleted file mode 100644 index c56b4e8988..0000000000 --- a/fiftyone/utils/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "embed_dim": 1024, - "vision_cfg": { - "image_size": 224, - "layers": 32, - "width": 1280, - "head_width": 80, - "patch_size": 14 - }, - "text_cfg": { - "hf_model_name": "xlm-roberta-large", - "hf_tokenizer_name": "xlm-roberta-large", - "hf_pooler_type": "mean_pooler" - } -} diff --git a/fiftyone/utils/open_clip/modified_resnet.py b/fiftyone/utils/open_clip/modified_resnet.py deleted file mode 100644 index ab6cd94903..0000000000 --- a/fiftyone/utils/open_clip/modified_resnet.py +++ /dev/null @@ -1,225 +0,0 @@ -from collections import OrderedDict - -import torch -from torch import nn -from torch.nn import functional as F - -from .utils import freeze_batch_norm_2d - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1): - super().__init__() - - # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 - self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.act1 = nn.ReLU(inplace=True) - - self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.act2 = nn.ReLU(inplace=True) - - self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() - - self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.act3 = nn.ReLU(inplace=True) - - self.downsample = None - self.stride = stride - - if stride > 1 or inplanes != planes * Bottleneck.expansion: - # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 - self.downsample = nn.Sequential( - OrderedDict( - [ - ("-1", nn.AvgPool2d(stride)), - ( - "0", - nn.Conv2d( - inplanes, - planes * self.expansion, - 1, - stride=1, - bias=False, - ), - ), - ("1", nn.BatchNorm2d(planes * self.expansion)), - ] - ) - ) - - def forward(self, x: torch.Tensor): - identity = x - - out = self.act1(self.bn1(self.conv1(x))) - out = self.act2(self.bn2(self.conv2(out))) - out = self.avgpool(out) - out = self.bn3(self.conv3(out)) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.act3(out) - return out - - -class AttentionPool2d(nn.Module): - def __init__( - self, - spacial_dim: int, - embed_dim: int, - num_heads: int, - output_dim: int = None, - ): - super().__init__() - self.positional_embedding = nn.Parameter( - torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5 - ) - self.k_proj = nn.Linear(embed_dim, embed_dim) - self.q_proj = nn.Linear(embed_dim, embed_dim) - self.v_proj = nn.Linear(embed_dim, embed_dim) - self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) - self.num_heads = num_heads - - def forward(self, x): - x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute( - 2, 0, 1 - ) # NCHW -> (HW)NC - x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC - x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC - x, _ = F.multi_head_attention_forward( - query=x, - key=x, - value=x, - embed_dim_to_check=x.shape[-1], - num_heads=self.num_heads, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - in_proj_weight=None, - in_proj_bias=torch.cat( - [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias] - ), - bias_k=None, - bias_v=None, - add_zero_attn=False, - dropout_p=0.0, - out_proj_weight=self.c_proj.weight, - out_proj_bias=self.c_proj.bias, - use_separate_proj_weight=True, - training=self.training, - need_weights=False, - ) - - return x[0] - - -class ModifiedResNet(nn.Module): - """ - A ResNet class that is similar to torchvision's but contains the following changes: - - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. - - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 - - The final pooling layer is a QKV attention instead of an average pool - """ - - def __init__(self, layers, output_dim, heads, image_size=224, width=64): - super().__init__() - self.output_dim = output_dim - self.image_size = image_size - - # the 3-layer stem - self.conv1 = nn.Conv2d( - 3, width // 2, kernel_size=3, stride=2, padding=1, bias=False - ) - self.bn1 = nn.BatchNorm2d(width // 2) - self.act1 = nn.ReLU(inplace=True) - self.conv2 = nn.Conv2d( - width // 2, width // 2, kernel_size=3, padding=1, bias=False - ) - self.bn2 = nn.BatchNorm2d(width // 2) - self.act2 = nn.ReLU(inplace=True) - self.conv3 = nn.Conv2d( - width // 2, width, kernel_size=3, padding=1, bias=False - ) - self.bn3 = nn.BatchNorm2d(width) - self.act3 = nn.ReLU(inplace=True) - self.avgpool = nn.AvgPool2d(2) - - # residual layers - self._inplanes = ( - width # this is a *mutable* variable used during construction - ) - self.layer1 = self._make_layer(width, layers[0]) - self.layer2 = self._make_layer(width * 2, layers[1], stride=2) - self.layer3 = self._make_layer(width * 4, layers[2], stride=2) - self.layer4 = self._make_layer(width * 8, layers[3], stride=2) - - embed_dim = width * 32 # the ResNet feature dimension - self.attnpool = AttentionPool2d( - image_size // 32, embed_dim, heads, output_dim - ) - - self.init_parameters() - - def _make_layer(self, planes, blocks, stride=1): - layers = [Bottleneck(self._inplanes, planes, stride)] - - self._inplanes = planes * Bottleneck.expansion - for _ in range(1, blocks): - layers.append(Bottleneck(self._inplanes, planes)) - - return nn.Sequential(*layers) - - def init_parameters(self): - if self.attnpool is not None: - std = self.attnpool.c_proj.in_features**-0.5 - nn.init.normal_(self.attnpool.q_proj.weight, std=std) - nn.init.normal_(self.attnpool.k_proj.weight, std=std) - nn.init.normal_(self.attnpool.v_proj.weight, std=std) - nn.init.normal_(self.attnpool.c_proj.weight, std=std) - - for resnet_block in [ - self.layer1, - self.layer2, - self.layer3, - self.layer4, - ]: - for name, param in resnet_block.named_parameters(): - if name.endswith("bn3.weight"): - nn.init.zeros_(param) - - def lock(self, unlocked_groups=0, freeze_bn_stats=False): - assert ( - unlocked_groups == 0 - ), "partial locking not currently supported for this model" - for param in self.parameters(): - param.requires_grad = False - if freeze_bn_stats: - freeze_batch_norm_2d(self) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - # FIXME support for non-transformer - pass - - def stem(self, x): - x = self.act1(self.bn1(self.conv1(x))) - x = self.act2(self.bn2(self.conv2(x))) - x = self.act3(self.bn3(self.conv3(x))) - x = self.avgpool(x) - return x - - def forward(self, x): - x = self.stem(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.attnpool(x) - - return x diff --git a/fiftyone/utils/open_clip/openai.py b/fiftyone/utils/open_clip/openai.py deleted file mode 100644 index 447e0e348a..0000000000 --- a/fiftyone/utils/open_clip/openai.py +++ /dev/null @@ -1,104 +0,0 @@ -""" OpenAI pretrained model functions - -Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. -""" - -import os -import warnings -from typing import List, Optional, Union - -import torch - -from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD -from .model import ( - build_model_from_openai_state_dict, - convert_weights_to_lp, - get_cast_dtype, -) -from .pretrained import ( - get_pretrained_url, - list_pretrained_models_by_tag, - download_pretrained_from_url, -) - -__all__ = ["list_openai_models", "load_openai_model"] - - -def list_openai_models() -> List[str]: - """Returns the names of available CLIP models""" - return list_pretrained_models_by_tag("openai") - - -def load_openai_model( - name: str, - precision: Optional[str] = None, - device: Optional[Union[str, torch.device]] = None, - cache_dir: Optional[str] = None, -): - """Load a CLIP model - - Parameters - ---------- - name : str - A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict - precision: str - Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. - device : Union[str, torch.device] - The device to put the loaded model - cache_dir : Optional[str] - The directory to cache the downloaded model weights - - Returns - ------- - model : torch.nn.Module - The CLIP model - preprocess : Callable[[PIL.Image], torch.Tensor] - A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input - """ - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - if precision is None: - precision = "fp32" if device == "cpu" else "fp16" - - if get_pretrained_url(name, "openai"): - model_path = download_pretrained_from_url( - get_pretrained_url(name, "openai"), cache_dir=cache_dir - ) - elif os.path.isfile(name): - model_path = name - else: - raise RuntimeError( - f"Model {name} not found; available models = {list_openai_models()}" - ) - - try: - # loading JIT archive - model = torch.jit.load(model_path, map_location="cpu").eval() - state_dict = None - except RuntimeError: - # loading saved state dict - state_dict = torch.load(model_path, map_location="cpu") - - # Build a non-jit model from the OpenAI jitted model state dict - cast_dtype = get_cast_dtype(precision) - try: - model = build_model_from_openai_state_dict( - state_dict or model.state_dict(), cast_dtype=cast_dtype - ) - except KeyError: - sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} - model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) - - # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use - model = model.to(device) - # FIXME support pure fp16/bf16 precision modes - if precision != "fp16": - model.float() - if precision == "bf16": - # for bf16, convert back to low-precision - convert_weights_to_lp(model, dtype=torch.bfloat16) - - # add mean / std attributes for consistency with OpenCLIP models - model.visual.image_mean = OPENAI_DATASET_MEAN - model.visual.image_std = OPENAI_DATASET_STD - return model diff --git a/fiftyone/utils/open_clip/pos_embed.py b/fiftyone/utils/open_clip/pos_embed.py deleted file mode 100644 index f1bbcefb29..0000000000 --- a/fiftyone/utils/open_clip/pos_embed.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# Position embedding utils -# -------------------------------------------------------- - -import numpy as np - -import torch - -# -------------------------------------------------------- -# 2D sine-cosine position embedding -# References: -# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py -# MoCo v3: https://github.com/facebookresearch/moco-v3 -# -------------------------------------------------------- -def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): - """ - grid_size: int of the grid height and width - return: - pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) - """ - grid_h = np.arange(grid_size, dtype=np.float32) - grid_w = np.arange(grid_size, dtype=np.float32) - grid = np.meshgrid(grid_w, grid_h) # here w goes first - grid = np.stack(grid, axis=0) - - grid = grid.reshape([2, 1, grid_size, grid_size]) - pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) - if cls_token: - pos_embed = np.concatenate( - [np.zeros([1, embed_dim]), pos_embed], axis=0 - ) - return pos_embed - - -def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): - assert embed_dim % 2 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_sincos_pos_embed_from_grid( - embed_dim // 2, grid[0] - ) # (H*W, D/2) - emb_w = get_1d_sincos_pos_embed_from_grid( - embed_dim // 2, grid[1] - ) # (H*W, D/2) - - emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) - return emb - - -def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): - """ - embed_dim: output dimension for each position - pos: a list of positions to be encoded: size (M,) - out: (M, D) - """ - assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=float) - omega /= embed_dim / 2.0 - omega = 1.0 / 10000**omega # (D/2,) - - pos = pos.reshape(-1) # (M,) - out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product - - emb_sin = np.sin(out) # (M, D/2) - emb_cos = np.cos(out) # (M, D/2) - - emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) - return emb - - -# -------------------------------------------------------- -# Interpolate position embeddings for high-resolution -# References: -# DeiT: https://github.com/facebookresearch/deit -# -------------------------------------------------------- -def interpolate_pos_embed(model, checkpoint_model): - if "pos_embed" in checkpoint_model: - pos_embed_checkpoint = checkpoint_model["pos_embed"] - embedding_size = pos_embed_checkpoint.shape[-1] - num_patches = model.patch_embed.num_patches - num_extra_tokens = model.pos_embed.shape[-2] - num_patches - # height (== width) for the checkpoint position embedding - orig_size = int( - (pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5 - ) - # height (== width) for the new position embedding - new_size = int(num_patches**0.5) - # class_token and dist_token are kept unchanged - if orig_size != new_size: - print( - "Position interpolate from %dx%d to %dx%d" - % (orig_size, orig_size, new_size, new_size) - ) - extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] - # only the position tokens are interpolated - pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] - pos_tokens = pos_tokens.reshape( - -1, orig_size, orig_size, embedding_size - ).permute(0, 3, 1, 2) - pos_tokens = torch.nn.functional.interpolate( - pos_tokens, - size=(new_size, new_size), - mode="bicubic", - align_corners=False, - ) - pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) - new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) - checkpoint_model["pos_embed"] = new_pos_embed diff --git a/fiftyone/utils/open_clip/pretrained.py b/fiftyone/utils/open_clip/pretrained.py deleted file mode 100644 index 60f30207db..0000000000 --- a/fiftyone/utils/open_clip/pretrained.py +++ /dev/null @@ -1,752 +0,0 @@ -import hashlib -import os -import urllib -import warnings -from functools import partial -from typing import Dict, Union - -from tqdm import tqdm - -from .constants import ( - IMAGENET_MEAN, - IMAGENET_STD, - INCEPTION_MEAN, - INCEPTION_STD, - OPENAI_DATASET_MEAN, - OPENAI_DATASET_STD, -) -from .version import __version__ - -try: - from huggingface_hub import hf_hub_download - - hf_hub_download = partial( - hf_hub_download, library_name="open_clip", library_version=__version__ - ) - _has_hf_hub = True -except ImportError: - hf_hub_download = None - _has_hf_hub = False - - -def _pcfg(url="", hf_hub="", **kwargs): - # OpenAI / OpenCLIP defaults - return { - "url": url, - "hf_hub": hf_hub, - "mean": OPENAI_DATASET_MEAN, - "std": OPENAI_DATASET_STD, - "interpolation": "bicubic", - "resize_mode": "shortest", - **kwargs, - } - - -def _slpcfg(url="", hf_hub="", **kwargs): - # SiGLIP defaults - return { - "url": url, - "hf_hub": hf_hub, - "mean": INCEPTION_MEAN, - "std": INCEPTION_STD, - "interpolation": "bicubic", - "resize_mode": "squash", - **kwargs, - } - - -def _apcfg(url="", hf_hub="", **kwargs): - # CLIPA defaults - return { - "url": url, - "hf_hub": hf_hub, - "mean": IMAGENET_MEAN, - "std": IMAGENET_STD, - "interpolation": "bilinear", - "resize_mode": "squash", - **kwargs, - } - - -_RN50 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt" - ), - yfcc15m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt" - ), - cc12m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" - ), -) - -_RN50_quickgelu = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt" - ), - yfcc15m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt" - ), - cc12m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" - ), -) - -_RN101 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt" - ), - yfcc15m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" - ), -) - -_RN101_quickgelu = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt" - ), - yfcc15m=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" - ), -) - -_RN50x4 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt" - ), -) - -_RN50x16 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt" - ), -) - -_RN50x64 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt" - ), -) - -_VITB32 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt" - ), - laion400m_e31=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt" - ), - laion400m_e32=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt" - ), - laion2b_e16=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth" - ), - laion2b_s34b_b79k=_pcfg(hf_hub="laion/CLIP-ViT-B-32-laion2B-s34B-b79K/"), - # DataComp-XL models - datacomp_xl_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K/" - ), - # DataComp-M models - datacomp_m_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-DataComp.M-s128M-b4K/" - ), - commonpool_m_clip_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.clip-s128M-b4K/" - ), - commonpool_m_laion_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.laion-s128M-b4K/" - ), - commonpool_m_image_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.image-s128M-b4K/" - ), - commonpool_m_text_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.text-s128M-b4K/" - ), - commonpool_m_basic_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M.basic-s128M-b4K/" - ), - commonpool_m_s128m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.M-s128M-b4K/" - ), - # DataComp-S models - datacomp_s_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-DataComp.S-s13M-b4K/" - ), - commonpool_s_clip_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.clip-s13M-b4K/" - ), - commonpool_s_laion_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.laion-s13M-b4K/" - ), - commonpool_s_image_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.image-s13M-b4K/" - ), - commonpool_s_text_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.text-s13M-b4K/" - ), - commonpool_s_basic_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S.basic-s13M-b4K/" - ), - commonpool_s_s13m_b4k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-CommonPool.S-s13M-b4K/" - ), -) - -_VITB32_quickgelu = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt" - ), - laion400m_e31=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt" - ), - laion400m_e32=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt" - ), - metaclip_400m=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/b32_400m.pt" - ), - metaclip_fullcc=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/b32_fullcc2.5b.pt" - ), -) - -_VITB32_256 = dict( - datacomp_s34b_b86k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-256x256-DataComp-s34B-b86K/" - ), -) - -_VITB16 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt" - ), - laion400m_e31=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt" - ), - laion400m_e32=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt" - ), - laion2b_s34b_b88k=_pcfg(hf_hub="laion/CLIP-ViT-B-16-laion2B-s34B-b88K/"), - # DataComp-XL models - datacomp_xl_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K/" - ), - # DataComp-L models - datacomp_l_s1b_b8k=_pcfg(hf_hub="laion/CLIP-ViT-B-16-DataComp.L-s1B-b8K/"), - commonpool_l_clip_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.clip-s1B-b8K/" - ), - commonpool_l_laion_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.laion-s1B-b8K/" - ), - commonpool_l_image_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.image-s1B-b8K/" - ), - commonpool_l_text_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.text-s1B-b8K/" - ), - commonpool_l_basic_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L.basic-s1B-b8K/" - ), - commonpool_l_s1b_b8k=_pcfg( - hf_hub="laion/CLIP-ViT-B-16-CommonPool.L-s1B-b8K/" - ), - # DFN - dfn2b=_pcfg(hf_hub="apple/DFN2B-CLIP-ViT-B-16/"), -) - -_VITB16_quickgelu = dict( - metaclip_400m=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/b16_400m.pt" - ), - metaclip_fullcc=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/b16_fullcc2.5b.pt" - ), -) - -_VITB16_PLUS_240 = dict( - laion400m_e31=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt" - ), - laion400m_e32=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt" - ), -) - -_VITL14 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt" - ), - laion400m_e31=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt" - ), - laion400m_e32=_pcfg( - "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt" - ), - laion2b_s32b_b82k=_pcfg( - hf_hub="laion/CLIP-ViT-L-14-laion2B-s32B-b82K/", - mean=INCEPTION_MEAN, - std=INCEPTION_STD, - ), - # DataComp-XL models - datacomp_xl_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K/" - ), - commonpool_xl_clip_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K/" - ), - commonpool_xl_laion_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL.laion-s13B-b90K/" - ), - commonpool_xl_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-L-14-CommonPool.XL-s13B-b90K/" - ), -) - -_VITL14_quickgelu = dict( - metaclip_400m=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/l14_400m.pt" - ), - metaclip_fullcc=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/l14_fullcc2.5b.pt" - ), - dfn2b=_pcfg(hf_hub="apple/DFN2B-CLIP-ViT-L-14/"), -) - -_VITL14_336 = dict( - openai=_pcfg( - "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt" - ), -) - -_VITH14 = dict( - laion2b_s32b_b79k=_pcfg(hf_hub="laion/CLIP-ViT-H-14-laion2B-s32B-b79K/"), -) - -_VITH14_quickgelu = dict( - metaclip_fullcc=_pcfg( - "https://dl.fbaipublicfiles.com/MMPT/metaclip/h14_fullcc2.5b.pt" - ), - dfn5b=_pcfg( - hf_hub="apple/DFN5B-CLIP-ViT-H-14/", - interpolation="bicubic", - resize_mode="squash", - ), -) - -_VITH14_378_quickgelu = dict( - dfn5b=_pcfg( - hf_hub="apple/DFN5B-CLIP-ViT-H-14-378/", - interpolation="bicubic", - resize_mode="squash", - ), -) - -_VITg14 = dict( - laion2b_s12b_b42k=_pcfg(hf_hub="laion/CLIP-ViT-g-14-laion2B-s12B-b42K/"), - laion2b_s34b_b88k=_pcfg(hf_hub="laion/CLIP-ViT-g-14-laion2B-s34B-b88K/"), -) - -_VITbigG14 = dict( - laion2b_s39b_b160k=_pcfg( - hf_hub="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/" - ), -) - -_robertaViTB32 = dict( - laion2b_s12b_b32k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/" - ), -) - -_xlmRobertaBaseViTB32 = dict( - laion5b_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/" - ), -) - -_xlmRobertaLargeFrozenViTH14 = dict( - frozen_laion5b_s13b_b90k=_pcfg( - hf_hub="laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/" - ), -) - -_convnext_base = dict( - laion400m_s13b_b51k=_pcfg( - hf_hub="laion/CLIP-convnext_base-laion400M-s13B-b51K/" - ), -) - -_convnext_base_w = dict( - laion2b_s13b_b82k=_pcfg( - hf_hub="laion/CLIP-convnext_base_w-laion2B-s13B-b82K/" - ), - laion2b_s13b_b82k_augreg=_pcfg( - hf_hub="laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/" - ), - laion_aesthetic_s13b_b82k=_pcfg( - hf_hub="laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/" - ), -) - -_convnext_base_w_320 = dict( - laion_aesthetic_s13b_b82k=_pcfg( - hf_hub="laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/" - ), - laion_aesthetic_s13b_b82k_augreg=_pcfg( - hf_hub="laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/" - ), -) - -_convnext_large_d = dict( - laion2b_s26b_b102k_augreg=_pcfg( - hf_hub="laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/" - ), -) - -_convnext_large_d_320 = dict( - laion2b_s29b_b131k_ft=_pcfg( - hf_hub="laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/" - ), - laion2b_s29b_b131k_ft_soup=_pcfg( - hf_hub="laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/" - ), -) - -_convnext_xxlarge = dict( - laion2b_s34b_b82k_augreg=_pcfg( - hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/" - ), - laion2b_s34b_b82k_augreg_rewind=_pcfg( - hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/" - ), - laion2b_s34b_b82k_augreg_soup=_pcfg( - hf_hub="laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/" - ), -) - -_coca_VITB32 = dict( - laion2b_s13b_b90k=_pcfg(hf_hub="laion/CoCa-ViT-B-32-laion2B-s13B-b90k/"), - mscoco_finetuned_laion2b_s13b_b90k=_pcfg( - hf_hub="laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/" - ), -) - -_coca_VITL14 = dict( - laion2b_s13b_b90k=_pcfg(hf_hub="laion/CoCa-ViT-L-14-laion2B-s13B-b90k/"), - mscoco_finetuned_laion2b_s13b_b90k=_pcfg( - hf_hub="laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/" - ), -) - - -_PRETRAINED = { - "RN50": _RN50, - "RN50-quickgelu": _RN50_quickgelu, - "RN101": _RN101, - "RN101-quickgelu": _RN101_quickgelu, - "RN50x4": _RN50x4, - "RN50x16": _RN50x16, - "RN50x64": _RN50x64, - "ViT-B-32": _VITB32, - "ViT-B-32-256": _VITB32_256, - "ViT-B-32-quickgelu": _VITB32_quickgelu, - "ViT-B-16": _VITB16, - "ViT-B-16-quickgelu": _VITB16_quickgelu, - "ViT-B-16-plus-240": _VITB16_PLUS_240, - "ViT-L-14": _VITL14, - "ViT-L-14-quickgelu": _VITL14_quickgelu, - "ViT-L-14-336": _VITL14_336, - "ViT-H-14": _VITH14, - "ViT-H-14-quickgelu": _VITH14_quickgelu, - "ViT-H-14-378-quickgelu": _VITH14_378_quickgelu, - "ViT-g-14": _VITg14, - "ViT-bigG-14": _VITbigG14, - "roberta-ViT-B-32": _robertaViTB32, - "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, - "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, - "convnext_base": _convnext_base, - "convnext_base_w": _convnext_base_w, - "convnext_base_w_320": _convnext_base_w_320, - "convnext_large_d": _convnext_large_d, - "convnext_large_d_320": _convnext_large_d_320, - "convnext_xxlarge": _convnext_xxlarge, - "coca_ViT-B-32": _coca_VITB32, - "coca_ViT-L-14": _coca_VITL14, - "EVA01-g-14": dict( - # from QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt - laion400m_s11b_b41k=_pcfg( - hf_hub="timm/eva_giant_patch14_clip_224.laion400m_s11b_b41k/" - ), - ), - "EVA01-g-14-plus": dict( - # from QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt - merged2b_s11b_b114k=_pcfg( - hf_hub="timm/eva_giant_patch14_plus_clip_224.merged2b_s11b_b114k/" - ), - ), - "EVA02-B-16": dict( - # from QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt - merged2b_s8b_b131k=_pcfg( - hf_hub="timm/eva02_base_patch16_clip_224.merged2b_s8b_b131k/" - ), - ), - "EVA02-L-14": dict( - # from QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt - merged2b_s4b_b131k=_pcfg( - hf_hub="timm/eva02_large_patch14_clip_224.merged2b_s4b_b131k/" - ), - ), - "EVA02-L-14-336": dict( - # from QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt - merged2b_s6b_b61k=_pcfg( - hf_hub="timm/eva02_large_patch14_clip_336.merged2b_s6b_b61k/" - ), - ), - "EVA02-E-14": dict( - # from QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt - laion2b_s4b_b115k=_pcfg( - hf_hub="timm/eva02_enormous_patch14_clip_224.laion2b_s4b_b115k/" - ), - ), - "EVA02-E-14-plus": dict( - # from QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt - laion2b_s9b_b144k=_pcfg( - hf_hub="timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k/" - ), - ), - "ViT-B-16-SigLIP": dict( - webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP/"), - ), - "ViT-B-16-SigLIP-256": dict( - webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-256/"), - ), - "ViT-B-16-SigLIP-i18n-256": dict( - webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-i18n-256/"), - ), - "ViT-B-16-SigLIP-384": dict( - webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-384/"), - ), - "ViT-B-16-SigLIP-512": dict( - webli=_slpcfg(hf_hub="timm/ViT-B-16-SigLIP-512/"), - ), - "ViT-L-16-SigLIP-256": dict( - webli=_slpcfg(hf_hub="timm/ViT-L-16-SigLIP-256/"), - ), - "ViT-L-16-SigLIP-384": dict( - webli=_slpcfg(hf_hub="timm/ViT-L-16-SigLIP-384/"), - ), - "ViT-SO400M-14-SigLIP": dict( - webli=_slpcfg(hf_hub="timm/ViT-SO400M-14-SigLIP/"), - ), - "ViT-SO400M-14-SigLIP-384": dict( - webli=_slpcfg(hf_hub="timm/ViT-SO400M-14-SigLIP-384/"), - ), - "ViT-L-14-CLIPA": dict( - datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-L-14-CLIPA-datacomp1B/"), - ), - "ViT-L-14-CLIPA-336": dict( - datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-L-14-CLIPA-336-datacomp1B/"), - ), - "ViT-H-14-CLIPA": dict( - datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-datacomp1B/"), - ), - "ViT-H-14-CLIPA-336": dict( - laion2b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-336-laion2B/"), - datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-H-14-CLIPA-336-datacomp1B/"), - ), - "ViT-bigG-14-CLIPA": dict( - datacomp1b=_apcfg(hf_hub="UCSC-VLAA/ViT-bigG-14-CLIPA-datacomp1B/"), - ), - "ViT-bigG-14-CLIPA-336": dict( - datacomp1b=_apcfg( - hf_hub="UCSC-VLAA/ViT-bigG-14-CLIPA-336-datacomp1B/" - ), - ), - "nllb-clip-base": dict( - v1=_pcfg(hf_hub="visheratin/nllb-clip-base-oc/"), - ), - "nllb-clip-large": dict( - v1=_pcfg(hf_hub="visheratin/nllb-clip-large-oc/"), - ), - "nllb-clip-base-siglip": dict( - v1=_slpcfg(hf_hub="visheratin/nllb-clip-base-siglip/"), - ), - "nllb-clip-large-siglip": dict( - v1=_slpcfg(hf_hub="visheratin/nllb-clip-large-siglip/"), - ), -} - - -def _clean_tag(tag: str): - # normalize pretrained tags - return tag.lower().replace("-", "_") - - -def list_pretrained(as_str: bool = False): - """returns list of pretrained models - Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True - """ - return [ - ":".join([k, t]) if as_str else (k, t) - for k in _PRETRAINED.keys() - for t in _PRETRAINED[k].keys() - ] - - -def list_pretrained_models_by_tag(tag: str): - """return all models having the specified pretrain tag""" - models = [] - tag = _clean_tag(tag) - for k in _PRETRAINED.keys(): - if tag in _PRETRAINED[k]: - models.append(k) - return models - - -def list_pretrained_tags_by_model(model: str): - """return all pretrain tags for the specified model architecture""" - tags = [] - if model in _PRETRAINED: - tags.extend(_PRETRAINED[model].keys()) - return tags - - -def is_pretrained_cfg(model: str, tag: str): - if model not in _PRETRAINED: - return False - return _clean_tag(tag) in _PRETRAINED[model] - - -def get_pretrained_cfg(model: str, tag: str): - if model not in _PRETRAINED: - return {} - model_pretrained = _PRETRAINED[model] - return model_pretrained.get(_clean_tag(tag), {}) - - -def get_pretrained_url(model: str, tag: str): - cfg = get_pretrained_cfg(model, _clean_tag(tag)) - return cfg.get("url", "") - - -def download_pretrained_from_url( - url: str, - cache_dir: Union[str, None] = None, -): - if not cache_dir: - cache_dir = os.path.expanduser("~/.cache/clip") - os.makedirs(cache_dir, exist_ok=True) - filename = os.path.basename(url) - - if "openaipublic" in url: - expected_sha256 = url.split("/")[-2] - elif "mlfoundations" in url: - expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] - else: - expected_sha256 = "" - - download_target = os.path.join(cache_dir, filename) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError( - f"{download_target} exists and is not a regular file" - ) - - if os.path.isfile(download_target): - if expected_sha256: - if ( - hashlib.sha256(open(download_target, "rb").read()) - .hexdigest() - .startswith(expected_sha256) - ): - return download_target - else: - warnings.warn( - f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" - ) - else: - return download_target - - with urllib.request.urlopen(url) as source, open( - download_target, "wb" - ) as output: - with tqdm( - total=int(source.headers.get("Content-Length")), - ncols=80, - unit="iB", - unit_scale=True, - ) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - if expected_sha256 and not hashlib.sha256( - open(download_target, "rb").read() - ).hexdigest().startswith(expected_sha256): - raise RuntimeError( - f"Model has been downloaded but the SHA256 checksum does not not match" - ) - - return download_target - - -def has_hf_hub(necessary=False): - if not _has_hf_hub and necessary: - # if no HF Hub module installed, and it is necessary to continue, raise error - raise RuntimeError( - "Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`." - ) - return _has_hf_hub - - -def download_pretrained_from_hf( - model_id: str, - filename: str = "open_clip_pytorch_model.bin", - revision=None, - cache_dir: Union[str, None] = None, -): - has_hf_hub(True) - cached_file = hf_hub_download( - model_id, filename, revision=revision, cache_dir=cache_dir - ) - return cached_file - - -def download_pretrained( - cfg: Dict, - force_hf_hub: bool = False, - cache_dir: Union[str, None] = None, -): - target = "" - if not cfg: - return target - - download_url = cfg.get("url", "") - download_hf_hub = cfg.get("hf_hub", "") - if download_hf_hub and force_hf_hub: - # use HF hub even if url exists - download_url = "" - - if download_url: - target = download_pretrained_from_url( - download_url, cache_dir=cache_dir - ) - elif download_hf_hub: - has_hf_hub(True) - # we assume the hf_hub entries in pretrained config combine model_id + filename in - # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and - # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. - model_id, filename = os.path.split(download_hf_hub) - if filename: - target = download_pretrained_from_hf( - model_id, filename=filename, cache_dir=cache_dir - ) - else: - target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) - - return target diff --git a/fiftyone/utils/open_clip/push_to_hf_hub.py b/fiftyone/utils/open_clip/push_to_hf_hub.py deleted file mode 100644 index a0021c7d4c..0000000000 --- a/fiftyone/utils/open_clip/push_to_hf_hub.py +++ /dev/null @@ -1,353 +0,0 @@ -import argparse -import json -import os -from pathlib import Path -from tempfile import TemporaryDirectory -from typing import Optional, Tuple, Union - -import torch - -try: - from huggingface_hub import ( - create_repo, - get_hf_file_metadata, - hf_hub_download, - hf_hub_url, - repo_type_and_id_from_hf_id, - upload_folder, - list_repo_files, - ) - from huggingface_hub.utils import EntryNotFoundError - - _has_hf_hub = True -except ImportError: - _has_hf_hub = False - -try: - import safetensors.torch - - _has_safetensors = True -except ImportError: - _has_safetensors = False - -from .factory import ( - create_model_from_pretrained, - get_model_config, - get_tokenizer, -) -from .tokenizer import HFTokenizer - -# Default name for a weights file hosted on the Huggingface Hub. -HF_WEIGHTS_NAME = "open_clip_pytorch_model.bin" # default pytorch pkl -HF_SAFE_WEIGHTS_NAME = "open_clip_model.safetensors" # safetensors version -HF_CONFIG_NAME = "open_clip_config.json" - - -def save_config_for_hf(model, config_path: str, model_config: Optional[dict]): - preprocess_cfg = { - "mean": model.visual.image_mean, - "std": model.visual.image_std, - } - other_pp = getattr(model.visual, "preprocess_cfg", {}) - if "interpolation" in other_pp: - preprocess_cfg["interpolation"] = other_pp["interpolation"] - if "resize_mode" in other_pp: - preprocess_cfg["resize_mode"] = other_pp["resize_mode"] - hf_config = { - "model_cfg": model_config, - "preprocess_cfg": preprocess_cfg, - } - - with config_path.open("w") as f: - json.dump(hf_config, f, indent=2) - - -def save_for_hf( - model, - tokenizer: HFTokenizer, - model_config: dict, - save_directory: str, - safe_serialization: Union[bool, str] = "both", - skip_weights: bool = False, -): - config_filename = HF_CONFIG_NAME - - save_directory = Path(save_directory) - save_directory.mkdir(exist_ok=True, parents=True) - - if not skip_weights: - tensors = model.state_dict() - if safe_serialization is True or safe_serialization == "both": - assert ( - _has_safetensors - ), "`pip install safetensors` to use .safetensors" - safetensors.torch.save_file( - tensors, save_directory / HF_SAFE_WEIGHTS_NAME - ) - if safe_serialization is False or safe_serialization == "both": - torch.save(tensors, save_directory / HF_WEIGHTS_NAME) - - tokenizer.save_pretrained(save_directory) - - config_path = save_directory / config_filename - save_config_for_hf(model, config_path, model_config=model_config) - - -def push_to_hf_hub( - model, - tokenizer, - model_config: Optional[dict], - repo_id: str, - commit_message: str = "Add model", - token: Optional[str] = None, - revision: Optional[str] = None, - private: bool = False, - create_pr: bool = False, - model_card: Optional[dict] = None, - safe_serialization: Union[bool, str] = False, -): - if not isinstance(tokenizer, HFTokenizer): - # FIXME this makes it awkward to push models with new tokenizers, come up with better soln. - # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14 - tokenizer = HFTokenizer("openai/clip-vit-large-patch14") - - # Create repo if it doesn't exist yet - repo_url = create_repo( - repo_id, token=token, private=private, exist_ok=True - ) - - # Infer complete repo_id from repo_url - # Can be different from the input `repo_id` if repo_owner was implicit - _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) - repo_id = f"{repo_owner}/{repo_name}" - - # Check if repo already exists and determine what needs updating - repo_exists = False - repo_files = {} - try: - repo_files = set(list_repo_files(repo_id)) - repo_exists = True - except Exception as e: - print("Repo does not exist", e) - - try: - get_hf_file_metadata( - hf_hub_url( - repo_id=repo_id, filename="README.md", revision=revision - ) - ) - has_readme = True - except EntryNotFoundError: - has_readme = False - - # Dump model and push to Hub - with TemporaryDirectory() as tmpdir: - # Save model weights and config. - save_for_hf( - model, - tokenizer=tokenizer, - model_config=model_config, - save_directory=tmpdir, - safe_serialization=safe_serialization, - ) - - # Add readme if it does not exist - if not has_readme: - model_card = model_card or {} - model_name = repo_id.split("/")[-1] - readme_path = Path(tmpdir) / "README.md" - readme_text = generate_readme(model_card, model_name) - readme_path.write_text(readme_text) - - # Upload model and return - return upload_folder( - repo_id=repo_id, - folder_path=tmpdir, - revision=revision, - create_pr=create_pr, - commit_message=commit_message, - ) - - -def push_pretrained_to_hf_hub( - model_name, - pretrained: str, - repo_id: str, - precision: str = "fp32", - image_mean: Optional[Tuple[float, ...]] = None, - image_std: Optional[Tuple[float, ...]] = None, - image_interpolation: Optional[str] = None, - image_resize_mode: Optional[str] = None, # only effective for inference - commit_message: str = "Add model", - token: Optional[str] = None, - revision: Optional[str] = None, - private: bool = False, - create_pr: bool = False, - model_card: Optional[dict] = None, - hf_tokenizer_self: bool = False, -): - model, preprocess_eval = create_model_from_pretrained( - model_name, - pretrained=pretrained, - precision=precision, - image_mean=image_mean, - image_std=image_std, - image_interpolation=image_interpolation, - image_resize_mode=image_resize_mode, - ) - model_config = get_model_config(model_name) - assert model_config - - tokenizer = get_tokenizer(model_name) - if hf_tokenizer_self: - # make hf tokenizer config in the uploaded model point to self instead of original location - model_config["text"]["hf_tokenizer_name"] = repo_id - - push_to_hf_hub( - model=model, - tokenizer=tokenizer, - model_config=model_config, - repo_id=repo_id, - commit_message=commit_message, - token=token, - revision=revision, - private=private, - create_pr=create_pr, - model_card=model_card, - safe_serialization="both", - ) - - -def generate_readme(model_card: dict, model_name: str): - tags = model_card.pop("tags", ("clip",)) - pipeline_tag = model_card.pop( - "pipeline_tag", "zero-shot-image-classification" - ) - readme_text = "---\n" - if tags: - readme_text += "tags:\n" - for t in tags: - readme_text += f"- {t}\n" - readme_text += "library_name: open_clip\n" - readme_text += f"pipeline_tag: {pipeline_tag}\n" - readme_text += f"license: {model_card.get('license', 'mit')}\n" - if "details" in model_card and "Dataset" in model_card["details"]: - readme_text += "datasets:\n" - readme_text += f"- {model_card['details']['Dataset'].lower()}\n" - readme_text += "---\n" - readme_text += f"# Model card for {model_name}\n" - if "description" in model_card: - readme_text += f"\n{model_card['description']}\n" - if "details" in model_card: - readme_text += f"\n## Model Details\n" - for k, v in model_card["details"].items(): - if isinstance(v, (list, tuple)): - readme_text += f"- **{k}:**\n" - for vi in v: - readme_text += f" - {vi}\n" - elif isinstance(v, dict): - readme_text += f"- **{k}:**\n" - for ki, vi in v.items(): - readme_text += f" - {ki}: {vi}\n" - else: - readme_text += f"- **{k}:** {v}\n" - if "usage" in model_card: - readme_text += f"\n## Model Usage\n" - readme_text += model_card["usage"] - readme_text += "\n" - - if "comparison" in model_card: - readme_text += f"\n## Model Comparison\n" - readme_text += model_card["comparison"] - readme_text += "\n" - - if "citation" in model_card: - readme_text += f"\n## Citation\n" - if not isinstance(model_card["citation"], (list, tuple)): - citations = [model_card["citation"]] - else: - citations = model_card["citation"] - for c in citations: - readme_text += f"```bibtex\n{c}\n```\n" - - return readme_text - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Push to Hugging Face Hub") - parser.add_argument( - "--model", - type=str, - help="Name of the model to use.", - ) - parser.add_argument( - "--pretrained", - type=str, - help="Use a pretrained CLIP model weights with the specified tag or file path.", - ) - parser.add_argument( - "--repo-id", - type=str, - help="Destination HF Hub repo-id ie 'organization/model_id'.", - ) - parser.add_argument( - "--precision", - type=str, - default="fp32", - ) - parser.add_argument( - "--image-mean", - type=float, - nargs="+", - default=None, - metavar="MEAN", - help="Override default image mean value of dataset", - ) - parser.add_argument( - "--image-std", - type=float, - nargs="+", - default=None, - metavar="STD", - help="Override default image std deviation of of dataset", - ) - parser.add_argument( - "--image-interpolation", - default=None, - type=str, - choices=["bicubic", "bilinear", "random"], - help="image resize interpolation", - ) - parser.add_argument( - "--image-resize-mode", - default=None, - type=str, - choices=["shortest", "longest", "squash"], - help="image resize mode during inference", - ) - parser.add_argument( - "--hf-tokenizer-self", - default=False, - action="store_true", - help="make hf_tokenizer_name point in uploaded config point to itself", - ) - args = parser.parse_args() - - print( - f"Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}" - ) - - # FIXME add support to pass model_card json / template from file via cmd line - - push_pretrained_to_hf_hub( - args.model, - args.pretrained, - args.repo_id, - precision=args.precision, - image_mean=args.image_mean, # override image mean/std if trained w/ non defaults - image_std=args.image_std, - image_interpolation=args.image_interpolation, - image_resize_mode=args.image_resize_mode, - ) - - print(f"{args.model} saved.") diff --git a/fiftyone/utils/open_clip/timm_model.py b/fiftyone/utils/open_clip/timm_model.py deleted file mode 100644 index e36049ad2c..0000000000 --- a/fiftyone/utils/open_clip/timm_model.py +++ /dev/null @@ -1,171 +0,0 @@ -""" timm model adapter - -Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. -""" -import logging -from collections import OrderedDict - -import torch -import torch.nn as nn - -try: - import timm - from timm.models.layers import Mlp, to_2tuple - - try: - # old timm imports < 0.8.1 - from timm.models.layers.attention_pool2d import RotAttentionPool2d - from timm.models.layers.attention_pool2d import ( - AttentionPool2d as AbsAttentionPool2d, - ) - except ImportError: - # new timm imports >= 0.8.1 - from timm.layers import RotAttentionPool2d - from timm.layers import AttentionPool2d as AbsAttentionPool2d -except ImportError: - timm = None - -from .utils import freeze_batch_norm_2d - - -class TimmModel(nn.Module): - """timm model adapter""" - - def __init__( - self, - model_name, - embed_dim, - image_size=224, - pool="avg", - proj="linear", - proj_bias=False, - drop=0.0, - drop_path=None, - patch_drop=None, - pretrained=False, - ): - super().__init__() - if timm is None: - raise RuntimeError("Please `pip install timm` to use timm models.") - self.image_size = to_2tuple(image_size) - - # setup kwargs that may not be common across all models - timm_kwargs = {} - if drop_path is not None: - timm_kwargs["drop_path_rate"] = drop_path - if patch_drop is not None: - timm_kwargs["patch_drop_rate"] = patch_drop - - custom_pool = pool in ("abs_attn", "rot_attn") - if proj: - assert proj in ("linear", "mlp", "none") - extra_proj = proj in ("linear", "mlp") - if not extra_proj and not custom_pool: - # use network classifier head as projection if no proj specified and no custom pooling used - # if projection is explicitly set to "none" will be pass through from network trunk - proj_dim = 0 if proj == "none" else embed_dim - self.trunk = timm.create_model( - model_name, - num_classes=proj_dim, - global_pool=pool, - pretrained=pretrained, - **timm_kwargs, - ) - prev_chs = embed_dim - else: - self.trunk = timm.create_model( - model_name, - pretrained=pretrained, - **timm_kwargs, - ) - feat_size = self.trunk.default_cfg.get("pool_size", None) - feature_ndim = 1 if not feat_size else 2 - if custom_pool: - assert feature_ndim == 2 - # if attn pooling used, remove both classifier and default pool - self.trunk.reset_classifier(0, global_pool="") - else: - # reset global pool if pool config set, otherwise leave as network default - reset_kwargs = dict(global_pool=pool) if pool else {} - self.trunk.reset_classifier(0, **reset_kwargs) - prev_chs = self.trunk.num_features - - head_layers = OrderedDict() - - # Add custom pooling to head - if pool == "abs_attn": - head_layers["pool"] = AbsAttentionPool2d( - prev_chs, feat_size=feat_size, out_features=embed_dim - ) - prev_chs = embed_dim - elif pool == "rot_attn": - head_layers["pool"] = RotAttentionPool2d( - prev_chs, out_features=embed_dim - ) - prev_chs = embed_dim - - # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used - if proj == "linear": - head_layers["drop"] = nn.Dropout(drop) - head_layers["proj"] = nn.Linear( - prev_chs, embed_dim, bias=proj_bias - ) - elif proj == "mlp": - head_layers["mlp"] = Mlp( - prev_chs, - 2 * embed_dim, - embed_dim, - drop=(drop, 0), - bias=(True, proj_bias), - ) - - self.head = nn.Sequential(head_layers) - - def lock(self, unlocked_groups=0, freeze_bn_stats=False): - """lock modules - Args: - unlocked_groups (int): leave last n layer groups unlocked (default: 0) - """ - if not unlocked_groups: - # lock full model - for param in self.trunk.parameters(): - param.requires_grad = False - if freeze_bn_stats: - freeze_batch_norm_2d(self.trunk) - else: - # NOTE: partial freeze requires latest timm (master) branch and is subject to change - try: - # FIXME import here until API stable and in an official release - from timm.models.helpers import group_parameters, group_modules - except ImportError: - raise RuntimeError( - "Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`" - ) - matcher = self.trunk.group_matcher() - gparams = group_parameters(self.trunk, matcher) - max_layer_id = max(gparams.keys()) - max_layer_id = max_layer_id - unlocked_groups - for group_idx in range(max_layer_id + 1): - group = gparams[group_idx] - for param in group: - self.trunk.get_parameter(param).requires_grad = False - if freeze_bn_stats: - gmodules = group_modules(self.trunk, matcher, reverse=True) - gmodules = { - k for k, v in gmodules.items() if v <= max_layer_id - } - freeze_batch_norm_2d(self.trunk, gmodules) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - try: - self.trunk.set_grad_checkpointing(enable) - except Exception as e: - logging.warning( - "grad checkpointing not supported for this timm image tower, continuing without..." - ) - - def forward(self, x): - x = self.trunk(x) - x = self.head(x) - return x diff --git a/fiftyone/utils/open_clip/tokenizer.py b/fiftyone/utils/open_clip/tokenizer.py deleted file mode 100644 index 09de1da3a8..0000000000 --- a/fiftyone/utils/open_clip/tokenizer.py +++ /dev/null @@ -1,573 +0,0 @@ -""" CLIP tokenizer - -Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. -""" -import gzip -import html -import os -import random -import string -from functools import lru_cache, partial -from typing import Callable, List, Optional, Union -import warnings - -import ftfy -import numpy as np -import regex as re -import torch - -# https://stackoverflow.com/q/62691279 -os.environ["TOKENIZERS_PARALLELISM"] = "false" -_nltk_init = False - -DEFAULT_CONTEXT_LENGTH = 77 # default context length for OpenAI CLIP - - -@lru_cache() -def default_bpe(): - return os.path.join( - os.path.dirname(os.path.abspath(__file__)), - "bpe_simple_vocab_16e6.txt.gz", - ) - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a significant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = ( - list(range(ord("!"), ord("~") + 1)) - + list(range(ord("¡"), ord("¬") + 1)) - + list(range(ord("®"), ord("ÿ") + 1)) - ) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8 + n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """Return set of symbol pairs in a word. - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -def basic_clean(text): - text = ftfy.fix_text(text) - text = html.unescape(html.unescape(text)) - return text.strip() - - -def whitespace_clean(text): - text = re.sub(r"\s+", " ", text) - text = text.strip() - return text - - -def _clean_canonicalize(x): - # basic, remove whitespace, remove punctuation, lower case - return canonicalize_text(basic_clean(x)) - - -def _clean_lower(x): - # basic, remove whitespace, lower case - return whitespace_clean(basic_clean(x)).lower() - - -def _clean_whitespace(x): - # basic, remove whitespace - return whitespace_clean(basic_clean(x)) - - -def get_clean_fn(type: str): - if type == "canonicalize": - return _clean_canonicalize - elif type == "lower": - return _clean_lower - elif type == "whitespace": - return _clean_whitespace - else: - assert False, f"Invalid clean function ({type})." - - -def canonicalize_text(text, *, keep_punctuation_exact_string=None): - """Returns canonicalized `text` (lowercase and punctuation removed). - - From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94 - - Args: - text: string to be canonicalized. - keep_punctuation_exact_string: If provided, then this exact string kept. - For example providing '{}' will keep any occurrences of '{}' (but will - still remove '{' and '}' that appear separately). - """ - text = text.replace("_", " ") - if keep_punctuation_exact_string: - text = keep_punctuation_exact_string.join( - part.translate(str.maketrans("", "", string.punctuation)) - for part in text.split(keep_punctuation_exact_string) - ) - else: - text = text.translate(str.maketrans("", "", string.punctuation)) - text = text.lower() - text = re.sub(r"\s+", " ", text) - return text.strip() - - -class SimpleTokenizer(object): - def __init__( - self, - bpe_path: str = default_bpe(), - additional_special_tokens: Optional[List[str]] = None, - context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH, - clean: str = "lower", - reduction_mask: str = "", - ): - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") - merges = merges[1 : 49152 - 256 - 2 + 1] - merges = [tuple(merge.split()) for merge in merges] - vocab = list(bytes_to_unicode().values()) - vocab = vocab + [v + "" for v in vocab] - for merge in merges: - vocab.append("".join(merge)) - special_tokens = ["", ""] - if additional_special_tokens: - special_tokens += additional_special_tokens - vocab.extend(special_tokens) - self.encoder = dict(zip(vocab, range(len(vocab)))) - self.decoder = {v: k for k, v in self.encoder.items()} - self.bpe_ranks = dict(zip(merges, range(len(merges)))) - self.cache = {t: t for t in special_tokens} - special = "|".join(special_tokens) - self.pat = re.compile( - special - + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", - ) - self.vocab_size = len(self.encoder) - self.all_special_ids = [self.encoder[t] for t in special_tokens] - self.sot_token_id = self.all_special_ids[0] - self.eot_token_id = self.all_special_ids[1] - self.context_length = context_length - self.clean_fn = get_clean_fn(clean) - self.reduction_fn = ( - get_reduction_mask_fn(reduction_mask) if reduction_mask else None - ) - - def bpe(self, token): - if token in self.cache: - return self.cache[token] - word = tuple(token[:-1]) + (token[-1] + "",) - pairs = get_pairs(word) - - if not pairs: - return token + "" - - while True: - bigram = min( - pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")) - ) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - new_word.extend(word[i:j]) - i = j - except: - new_word.extend(word[i:]) - break - - if ( - word[i] == first - and i < len(word) - 1 - and word[i + 1] == second - ): - new_word.append(first + second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = " ".join(word) - self.cache[token] = word - return word - - def encode(self, text): - bpe_tokens = [] - text = self.clean_fn(text) - for token in re.findall(self.pat, text): - token = "".join( - self.byte_encoder[b] for b in token.encode("utf-8") - ) - bpe_tokens.extend( - self.encoder[bpe_token] - for bpe_token in self.bpe(token).split(" ") - ) - return bpe_tokens - - def decode(self, tokens): - text = "".join([self.decoder[token] for token in tokens]) - text = ( - bytearray([self.byte_decoder[c] for c in text]) - .decode("utf-8", errors="replace") - .replace("", " ") - ) - return text - - def __call__( - self, - texts: Union[str, List[str]], - context_length: Optional[int] = None, - ) -> torch.LongTensor: - """Returns the tokenized representation of given input string(s) - - Parameters - ---------- - texts : Union[str, List[str]] - An input string or a list of input strings to tokenize - context_length : int - The context length to use; all CLIP models use 77 as the context length - - Returns - ------- - A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] - """ - if isinstance(texts, str): - texts = [texts] - - context_length = context_length or self.context_length - assert context_length, "Please set a valid context length" - - if self.reduction_fn is not None: - # use reduction strategy for tokenize if set, otherwise default to truncation below - # pylint: disable=not-callable - return self.reduction_fn( - texts, - context_length=context_length, - sot_token_id=self.sot_token_id, - eot_token_id=self.eot_token_id, - encode_fn=self.encode, - ) - - all_tokens = [ - [self.sot_token_id] + self.encode(text) + [self.eot_token_id] - for text in texts - ] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - if len(tokens) > context_length: - tokens = tokens[:context_length] # Truncate - tokens[-1] = self.eot_token_id - result[i, : len(tokens)] = torch.tensor(tokens) - - return result - - -_tokenizer = SimpleTokenizer() - - -def decode(output_ids: torch.Tensor): - output_ids = output_ids.cpu().numpy() - return _tokenizer.decode(output_ids) - - -def tokenize( - texts: Union[str, List[str]], context_length: int = DEFAULT_CONTEXT_LENGTH -) -> torch.LongTensor: - return _tokenizer(texts, context_length=context_length) - - -def random_mask_tokenize( - texts: Union[str, List[str]], - context_length: int, - sot_token_id: int, - eot_token_id: int, - encode_fn: Callable, - shuffle: bool = False, -): - all_tokens = [encode_fn(text) for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - tokens = torch.tensor(tokens) - num_tokens = len(tokens) - if num_tokens > context_length - 2: # 2 for sot and eot token - num_keep = context_length - 2 - indices = torch.randperm(len(tokens)) - indices = indices[:num_keep] - if not shuffle: - indices = indices.msort() - tokens = tokens[indices] - num_tokens = num_keep - result[i, 0] = sot_token_id - result[i, 1 : num_tokens + 1] = tokens - result[i, num_tokens + 1] = eot_token_id - - return result - - -def simple_mask_tokenize( - texts: Union[str, List[str]], - context_length: int, - sot_token_id: int, - eot_token_id: int, - encode_fn: Callable, -): - all_tokens = [encode_fn(text) for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - num_tokens = len(tokens) - if num_tokens > context_length - 2: # 2 for sot and eot token - num_keep = context_length - 2 - start_index = random.randint( - 0, num_tokens - num_keep - ) # high is incl - tokens = tokens[start_index : start_index + num_keep] - tokens = [sot_token_id] + tokens + [eot_token_id] - result[i, : len(tokens)] = torch.tensor(tokens) - - return result - - -def syntax_mask_tokenize( - texts: Union[str, List[str]], - context_length: int, - sot_token_id: int, - eot_token_id: int, - encode_fn: Callable, -) -> torch.LongTensor: - """Returns the tokenized representation of given input string(s). - Apply syntax masking before tokenize. - """ - import nltk - - global _nltk_init - if not _nltk_init: - # run them for the first time - nltk.download("punkt") - nltk.download("averaged_perceptron_tagger") - _nltk_init = True - - def get_order(x): - if x.startswith("NN"): - return 1 - elif x.startswith("JJ"): - return 2 - elif x.startswith("VB"): - return 3 - else: - return 4 - - # syntax masking - new_texts = [] - for text in texts: - list_tokens = nltk.tokenize.word_tokenize(text) - pos_tags = nltk.pos_tag(list_tokens) - # sample the words by get_order method - order_list = [get_order(tag) for _, tag in pos_tags] - sorted_ids = np.argsort(np.array(order_list)) - sampled_ids = sorted( - sorted_ids[: context_length - 2] - ) # need 2 slots for sot and eot tokens - sampled_tokens = np.take( - np.array(list_tokens), sampled_ids, axis=0 - ) # sample the tokens - - new_text = "" - for token in sampled_tokens: - new_text = new_text + str(token) + " " - new_text = new_text.strip() - new_texts.append(new_text) - texts = new_texts - - all_tokens = [ - [sot_token_id] + encode_fn(text) + [eot_token_id] for text in texts - ] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - # still need first truncate because some words produces two tokens - if len(tokens) > context_length: - tokens = tokens[:context_length] # Truncate - tokens[-1] = eot_token_id - result[i, : len(tokens)] = torch.tensor(tokens) - - return result - - -def get_reduction_mask_fn(type: str): - """Choose strategy for dropping (masking) tokens to achieve target context length""" - assert type in ("simple", "random", "shuffle", "syntax") - if type == "simple": - return simple_mask_tokenize # randomly select block [start:end] - elif type == "random": - return random_mask_tokenize # randomly drop tokens (keep order) - elif type == "shuffle": - return partial( - random_mask_tokenize, shuffle=True - ) # randomly drop tokens (shuffle order) - elif type == "syntax": - return syntax_mask_tokenize # randomly drop prioritized by syntax - - -class HFTokenizer: - """HuggingFace tokenizer wrapper""" - - def __init__( - self, - tokenizer_name: str, - context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH, - clean: str = "whitespace", - strip_sep_token: bool = False, - language: Optional[str] = None, - ): - from transformers import AutoTokenizer - - self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) - set_lang_fn = getattr( - self.tokenizer, "set_src_lang_special_tokens", None - ) - if callable(set_lang_fn): - self.set_lang_fn = set_lang_fn - if language is not None: - self.set_language(language) - self.context_length = context_length - self.clean_fn = get_clean_fn(clean) - self.strip_sep_token = strip_sep_token - - def save_pretrained(self, dest): - self.tokenizer.save_pretrained(dest) - - def __call__( - self, - texts: Union[str, List[str]], - context_length: Optional[int] = None, - ) -> torch.Tensor: - # same cleaning as for default tokenizer, except lowercasing - # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance - if isinstance(texts, str): - texts = [texts] - - context_length = context_length or self.context_length - assert ( - context_length - ), "Please set a valid context length in class init or call." - - texts = [self.clean_fn(text) for text in texts] - input_ids = self.tokenizer.batch_encode_plus( - texts, - return_tensors="pt", - max_length=context_length, - padding="max_length", - truncation=True, - ).input_ids - - if self.strip_sep_token: - input_ids = torch.where( - input_ids == self.tokenizer.sep_token_id, - torch.zeros_like(input_ids), - input_ids, - ) - - return input_ids - - def set_language(self, src_lang): - if hasattr(self, "set_lang_fn"): - # pylint: disable=not-callable - self.set_lang_fn(src_lang) - else: - warnings.warn("Cannot set language for the tokenizer.") - - -class SigLipTokenizer: - """HuggingFace tokenizer wrapper for SigLIP T5 compatible sentencepiece vocabs""" - - VOCAB_FILES = { - # english, vocab_size=32_000 - "c4-en": "http://storage.googleapis.com/t5-data/vocabs/cc_en.32000/sentencepiece.model", - # used in multilingual models (mT5, PaLI), vocab_size=250_000 - "mc4": "http://storage.googleapis.com/t5-data/vocabs/mc4.250000.100extra/sentencepiece.model", - } - - def __init__( - self, - tokenizer_name: str, - context_length: Optional[int] = 64, - ): - from transformers import T5TokenizerFast - - if tokenizer_name in self.VOCAB_FILES: - # FIXME temporary hack? - import tempfile - - import fsspec - - vocab_file = self.VOCAB_FILES[tokenizer_name] - with tempfile.NamedTemporaryFile("wb") as dst: - with fsspec.open(vocab_file, "rb") as src: - dst.write(src.read()) - self.tokenizer = T5TokenizerFast(dst.name, legacy=False) - else: - self.tokenizer = T5TokenizerFast(tokenizer_name, legacy=False) - - self.tokenizer.pad_token_id = 1 - self.tokenizer.eos_token_id = 1 - self.context_length = context_length - - def save_pretrained(self, dest): - self.tokenizer.save_pretrained(dest) - - def __call__( - self, - texts: Union[str, List[str]], - context_length: Optional[int] = None, - ) -> torch.Tensor: - # same cleaning as for default tokenizer, except lowercasing - # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance - if isinstance(texts, str): - texts = [texts] - - context_length = context_length or self.context_length - assert ( - context_length - ), "Please set a valid context length in class init or call." - - texts = [canonicalize_text(basic_clean(text)) for text in texts] - output = self.tokenizer( - texts, - return_tensors="pt", - max_length=context_length, - padding="max_length", - truncation=True, - ) - return output.input_ids diff --git a/fiftyone/utils/open_clip/transform.py b/fiftyone/utils/open_clip/transform.py deleted file mode 100644 index 9e6b4830eb..0000000000 --- a/fiftyone/utils/open_clip/transform.py +++ /dev/null @@ -1,485 +0,0 @@ -import numbers -import random -import warnings -from dataclasses import dataclass, asdict -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union - -import torch -import torchvision.transforms.functional as F -from torchvision.transforms import ( - Normalize, - Compose, - RandomResizedCrop, - InterpolationMode, - ToTensor, - Resize, - CenterCrop, - ColorJitter, - Grayscale, -) - -from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD -from .utils import to_2tuple - - -@dataclass -class PreprocessCfg: - size: Union[int, Tuple[int, int]] = 224 - mode: str = "RGB" - mean: Tuple[float, ...] = OPENAI_DATASET_MEAN - std: Tuple[float, ...] = OPENAI_DATASET_STD - interpolation: str = "bicubic" - resize_mode: str = "shortest" - fill_color: int = 0 - - def __post_init__(self): - assert self.mode in ("RGB",) - - @property - def num_channels(self): - return 3 - - @property - def input_size(self): - return (self.num_channels,) + to_2tuple(self.size) - - -_PREPROCESS_KEYS = set(asdict(PreprocessCfg()).keys()) - - -def merge_preprocess_dict( - base: Union[PreprocessCfg, Dict], - overlay: Dict, -): - """Merge overlay key-value pairs on top of base preprocess cfg or dict. - Input dicts are filtered based on PreprocessCfg fields. - """ - if isinstance(base, PreprocessCfg): - base_clean = asdict(base) - else: - base_clean = {k: v for k, v in base.items() if k in _PREPROCESS_KEYS} - if overlay: - overlay_clean = { - k: v - for k, v in overlay.items() - if k in _PREPROCESS_KEYS and v is not None - } - base_clean.update(overlay_clean) - return base_clean - - -def merge_preprocess_kwargs(base: PreprocessCfg, **kwargs): - return merge_preprocess_dict(base, kwargs) - - -@dataclass -class AugmentationCfg: - scale: Tuple[float, float] = (0.9, 1.0) - ratio: Optional[Tuple[float, float]] = None - color_jitter: Optional[ - Union[ - float, - Tuple[float, float, float], - Tuple[float, float, float, float], - ] - ] = None - re_prob: Optional[float] = None - re_count: Optional[int] = None - use_timm: bool = False - - # params for simclr_jitter_gray - color_jitter_prob: float = None - gray_scale_prob: float = None - - -def _setup_size(size, error_msg): - if isinstance(size, numbers.Number): - return int(size), int(size) - - if isinstance(size, Sequence) and len(size) == 1: - return size[0], size[0] - - if len(size) != 2: - raise ValueError(error_msg) - - return size - - -class ResizeKeepRatio: - """Resize and Keep Ratio - - Copy & paste from `timm` - """ - - def __init__( - self, - size, - longest=0.0, - interpolation=InterpolationMode.BICUBIC, - random_scale_prob=0.0, - random_scale_range=(0.85, 1.05), - random_aspect_prob=0.0, - random_aspect_range=(0.9, 1.11), - ): - if isinstance(size, (list, tuple)): - self.size = tuple(size) - else: - self.size = (size, size) - self.interpolation = interpolation - self.longest = float( - longest - ) # [0, 1] where 0 == shortest edge, 1 == longest - self.random_scale_prob = random_scale_prob - self.random_scale_range = random_scale_range - self.random_aspect_prob = random_aspect_prob - self.random_aspect_range = random_aspect_range - - @staticmethod - def get_params( - img, - target_size, - longest, - random_scale_prob=0.0, - random_scale_range=(0.85, 1.05), - random_aspect_prob=0.0, - random_aspect_range=(0.9, 1.11), - ): - """Get parameters""" - source_size = img.size[::-1] # h, w - h, w = source_size - target_h, target_w = target_size - ratio_h = h / target_h - ratio_w = w / target_w - ratio = max(ratio_h, ratio_w) * longest + min(ratio_h, ratio_w) * ( - 1.0 - longest - ) - if random_scale_prob > 0 and random.random() < random_scale_prob: - ratio_factor = random.uniform( - random_scale_range[0], random_scale_range[1] - ) - ratio_factor = (ratio_factor, ratio_factor) - else: - ratio_factor = (1.0, 1.0) - if random_aspect_prob > 0 and random.random() < random_aspect_prob: - aspect_factor = random.uniform( - random_aspect_range[0], random_aspect_range[1] - ) - ratio_factor = ( - ratio_factor[0] / aspect_factor, - ratio_factor[1] * aspect_factor, - ) - size = [ - round(x * f / ratio) for x, f in zip(source_size, ratio_factor) - ] - return size - - def __call__(self, img): - """ - Args: - img (PIL Image): Image to be cropped and resized. - - Returns: - PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size - """ - size = self.get_params( - img, - self.size, - self.longest, - self.random_scale_prob, - self.random_scale_range, - self.random_aspect_prob, - self.random_aspect_range, - ) - img = F.resize(img, size, self.interpolation) - return img - - def __repr__(self): - format_string = self.__class__.__name__ + "(size={0}".format(self.size) - format_string += f", interpolation={self.interpolation})" - format_string += f", longest={self.longest:.3f})" - return format_string - - -def center_crop_or_pad( - img: torch.Tensor, output_size: List[int], fill=0 -) -> torch.Tensor: - """Center crops and/or pads the given image. - If the image is torch Tensor, it is expected - to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. - If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. - - Args: - img (PIL Image or Tensor): Image to be cropped. - output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, - it is used for both directions. - fill (int, Tuple[int]): Padding color - - Returns: - PIL Image or Tensor: Cropped image. - """ - if isinstance(output_size, numbers.Number): - output_size = (int(output_size), int(output_size)) - elif isinstance(output_size, (tuple, list)) and len(output_size) == 1: - output_size = (output_size[0], output_size[0]) - - _, image_height, image_width = F.get_dimensions(img) - crop_height, crop_width = output_size - - if crop_width > image_width or crop_height > image_height: - padding_ltrb = [ - (crop_width - image_width) // 2 if crop_width > image_width else 0, - (crop_height - image_height) // 2 - if crop_height > image_height - else 0, - (crop_width - image_width + 1) // 2 - if crop_width > image_width - else 0, - (crop_height - image_height + 1) // 2 - if crop_height > image_height - else 0, - ] - img = F.pad(img, padding_ltrb, fill=fill) - _, image_height, image_width = F.get_dimensions(img) - if crop_width == image_width and crop_height == image_height: - return img - - crop_top = int(round((image_height - crop_height) / 2.0)) - crop_left = int(round((image_width - crop_width) / 2.0)) - return F.crop(img, crop_top, crop_left, crop_height, crop_width) - - -class CenterCropOrPad(torch.nn.Module): - """Crops the given image at the center. - If the image is torch Tensor, it is expected - to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. - If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. - - Args: - size (sequence or int): Desired output size of the crop. If size is an - int instead of sequence like (h, w), a square crop (size, size) is - made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). - """ - - def __init__(self, size, fill=0): - super().__init__() - self.size = _setup_size( - size, - error_msg="Please provide only two dimensions (h, w) for size.", - ) - self.fill = fill - - def forward(self, img): - """ - Args: - img (PIL Image or Tensor): Image to be cropped. - - Returns: - PIL Image or Tensor: Cropped image. - """ - return center_crop_or_pad(img, self.size, fill=self.fill) - - def __repr__(self) -> str: - return f"{self.__class__.__name__}(size={self.size})" - - -def _convert_to_rgb(image): - return image.convert("RGB") - - -class color_jitter(object): - """ - Apply Color Jitter to the PIL image with a specified probability. - """ - - def __init__( - self, brightness=0.0, contrast=0.0, saturation=0.0, hue=0.0, p=0.8 - ): - assert 0.0 <= p <= 1.0 - self.p = p - self.transf = ColorJitter( - brightness=brightness, - contrast=contrast, - saturation=saturation, - hue=hue, - ) - - def __call__(self, img): - if random.random() < self.p: - return self.transf(img) - else: - return img - - -class gray_scale(object): - """ - Apply Gray Scale to the PIL image with a specified probability. - """ - - def __init__(self, p=0.2): - assert 0.0 <= p <= 1.0 - self.p = p - self.transf = Grayscale(num_output_channels=3) - - def __call__(self, img): - if random.random() < self.p: - return self.transf(img) - else: - return img - - -def image_transform( - image_size: Union[int, Tuple[int, int]], - is_train: bool, - mean: Optional[Tuple[float, ...]] = None, - std: Optional[Tuple[float, ...]] = None, - resize_mode: Optional[str] = None, - interpolation: Optional[str] = None, - fill_color: int = 0, - aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, -): - mean = mean or OPENAI_DATASET_MEAN - if not isinstance(mean, (list, tuple)): - mean = (mean,) * 3 - - std = std or OPENAI_DATASET_STD - if not isinstance(std, (list, tuple)): - std = (std,) * 3 - - interpolation = interpolation or "bicubic" - assert interpolation in ["bicubic", "bilinear", "random"] - # NOTE random is ignored for interpolation_mode, so defaults to BICUBIC for inference if set - interpolation_mode = ( - InterpolationMode.BILINEAR - if interpolation == "bilinear" - else InterpolationMode.BICUBIC - ) - - resize_mode = resize_mode or "shortest" - assert resize_mode in ("shortest", "longest", "squash") - - if isinstance(aug_cfg, dict): - aug_cfg = AugmentationCfg(**aug_cfg) - else: - aug_cfg = aug_cfg or AugmentationCfg() - - normalize = Normalize(mean=mean, std=std) - - if is_train: - aug_cfg_dict = { - k: v for k, v in asdict(aug_cfg).items() if v is not None - } - use_timm = aug_cfg_dict.pop("use_timm", False) - if use_timm: - from timm.data import ( - create_transform, - ) # timm can still be optional - - if isinstance(image_size, (tuple, list)): - assert len(image_size) >= 2 - input_size = (3,) + image_size[-2:] - else: - input_size = (3, image_size, image_size) - - aug_cfg_dict.setdefault("color_jitter", None) # disable by default - # drop extra non-timm items - aug_cfg_dict.pop("color_jitter_prob", None) - aug_cfg_dict.pop("gray_scale_prob", None) - - train_transform = create_transform( - input_size=input_size, - is_training=True, - hflip=0.0, - mean=mean, - std=std, - re_mode="pixel", - interpolation=interpolation, - **aug_cfg_dict, - ) - else: - train_transform = [ - RandomResizedCrop( - image_size, - scale=aug_cfg_dict.pop("scale"), - interpolation=InterpolationMode.BICUBIC, - ), - _convert_to_rgb, - ] - if aug_cfg.color_jitter_prob: - assert ( - aug_cfg.color_jitter is not None - and len(aug_cfg.color_jitter) == 4 - ) - train_transform.extend( - [ - color_jitter( - *aug_cfg.color_jitter, p=aug_cfg.color_jitter_prob - ) - ] - ) - if aug_cfg.gray_scale_prob: - train_transform.extend([gray_scale(aug_cfg.gray_scale_prob)]) - train_transform.extend( - [ - ToTensor(), - normalize, - ] - ) - train_transform = Compose(train_transform) - if aug_cfg_dict: - warnings.warn( - f"Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())})." - ) - return train_transform - else: - if resize_mode == "longest": - transforms = [ - ResizeKeepRatio( - image_size, interpolation=interpolation_mode, longest=1 - ), - CenterCropOrPad(image_size, fill=fill_color), - ] - elif resize_mode == "squash": - if isinstance(image_size, int): - image_size = (image_size, image_size) - transforms = [ - Resize(image_size, interpolation=interpolation_mode), - ] - else: - assert resize_mode == "shortest" - if not isinstance(image_size, (tuple, list)): - image_size = (image_size, image_size) - if image_size[0] == image_size[1]: - # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg) - transforms = [ - Resize(image_size[0], interpolation=interpolation_mode) - ] - else: - # resize shortest edge to matching target dim for non-square target - transforms = [ResizeKeepRatio(image_size)] - transforms += [CenterCrop(image_size)] - - transforms.extend( - [ - _convert_to_rgb, - ToTensor(), - normalize, - ] - ) - return Compose(transforms) - - -def image_transform_v2( - cfg: PreprocessCfg, - is_train: bool, - aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, -): - return image_transform( - image_size=cfg.size, - is_train=is_train, - mean=cfg.mean, - std=cfg.std, - interpolation=cfg.interpolation, - resize_mode=cfg.resize_mode, - fill_color=cfg.fill_color, - aug_cfg=aug_cfg, - ) diff --git a/fiftyone/utils/open_clip/transformer.py b/fiftyone/utils/open_clip/transformer.py deleted file mode 100644 index 7585e9a0ff..0000000000 --- a/fiftyone/utils/open_clip/transformer.py +++ /dev/null @@ -1,958 +0,0 @@ -from collections import OrderedDict -import math -from typing import Callable, Optional, Sequence, Tuple -from functools import partial - -import torch -from torch import nn -from torch.nn import functional as F -from torch.utils.checkpoint import checkpoint - -from .utils import to_2tuple -from .pos_embed import get_2d_sincos_pos_embed - - -class LayerNormFp32(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - x = F.layer_norm( - x.to(torch.float32), - self.normalized_shape, - self.weight, - self.bias, - self.eps, - ) - return x.to(orig_type) - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm (with cast back to input dtype).""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - x = F.layer_norm( - x, self.normalized_shape, self.weight, self.bias, self.eps - ) - return x.to(orig_type) - - -class QuickGELU(nn.Module): - # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class LayerScale(nn.Module): - def __init__(self, dim, init_values=1e-5, inplace=False): - super().__init__() - self.inplace = inplace - self.gamma = nn.Parameter(init_values * torch.ones(dim)) - - def forward(self, x): - return x.mul_(self.gamma) if self.inplace else x * self.gamma - - -class PatchDropout(nn.Module): - """ - https://arxiv.org/abs/2212.00794 - """ - - def __init__(self, prob, exclude_first_token=True): - super().__init__() - assert 0 <= prob < 1.0 - self.prob = prob - self.exclude_first_token = exclude_first_token # exclude CLS token - - def forward(self, x): - if not self.training or self.prob == 0.0: - return x - - if self.exclude_first_token: - cls_tokens, x = x[:, :1], x[:, 1:] - else: - cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) - - batch = x.size()[0] - num_tokens = x.size()[1] - - batch_indices = torch.arange(batch) - batch_indices = batch_indices[..., None] - - keep_prob = 1 - self.prob - num_patches_keep = max(1, int(num_tokens * keep_prob)) - - rand = torch.randn(batch, num_tokens) - patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices - - x = x[batch_indices, patch_indices_keep] - - if self.exclude_first_token: - x = torch.cat((cls_tokens, x), dim=1) - - return x - - -class Attention(nn.Module): - def __init__( - self, - dim, - num_heads=8, - qkv_bias=True, - scaled_cosine=False, - scale_heads=False, - logit_scale_max=math.log(1.0 / 0.01), - attn_drop=0.0, - proj_drop=0.0, - ): - super().__init__() - self.scaled_cosine = scaled_cosine - self.scale_heads = scale_heads - assert dim % num_heads == 0, "dim should be divisible by num_heads" - self.num_heads = num_heads - self.head_dim = dim // num_heads - self.scale = self.head_dim**-0.5 - self.logit_scale_max = logit_scale_max - - # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original - self.in_proj_weight = nn.Parameter( - torch.randn((dim * 3, dim)) * self.scale - ) - if qkv_bias: - self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) - else: - self.in_proj_bias = None - - if self.scaled_cosine: - self.logit_scale = nn.Parameter( - torch.log(10 * torch.ones((num_heads, 1, 1))) - ) - else: - self.logit_scale = None - self.attn_drop = nn.Dropout(attn_drop) - if self.scale_heads: - self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) - else: - self.head_scale = None - self.out_proj = nn.Linear(dim, dim) - self.out_drop = nn.Dropout(proj_drop) - - def forward(self, x, attn_mask: Optional[torch.Tensor] = None): - L, N, C = x.shape - q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk( - 3, dim=-1 - ) - q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) - k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) - v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) - - if self.logit_scale is not None: - attn = torch.bmm( - F.normalize(q, dim=-1), - F.normalize(k, dim=-1).transpose(-1, -2), - ) - logit_scale = torch.clamp( - self.logit_scale, max=self.logit_scale_max - ).exp() - attn = attn.view(N, self.num_heads, L, L) * logit_scale - attn = attn.view(-1, L, L) - else: - q = q * self.scale - attn = torch.bmm(q, k.transpose(-1, -2)) - - if attn_mask is not None: - if attn_mask.dtype == torch.bool: - new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) - new_attn_mask.masked_fill_(attn_mask, float("-inf")) - attn_mask = new_attn_mask - attn += attn_mask - - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = torch.bmm(attn, v) - if self.head_scale is not None: - x = x.view(N, self.num_heads, L, C) * self.head_scale - x = x.view(-1, L, C) - x = x.transpose(0, 1).reshape(L, N, C) - x = self.out_proj(x) - x = self.out_drop(x) - return x - - -class AttentionalPooler(nn.Module): - def __init__( - self, - d_model: int, - context_dim: int, - n_head: int = 8, - n_queries: int = 256, - norm_layer: Callable = LayerNorm, - ): - super().__init__() - self.query = nn.Parameter(torch.randn(n_queries, d_model)) - self.attn = nn.MultiheadAttention( - d_model, n_head, kdim=context_dim, vdim=context_dim - ) - self.ln_q = norm_layer(d_model) - self.ln_k = norm_layer(context_dim) - - def forward(self, x: torch.Tensor): - x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND - N = x.shape[1] - q = self.ln_q(self.query) - out = self.attn( - q.unsqueeze(1).expand(-1, N, -1), x, x, need_weights=False - )[0] - return out.permute(1, 0, 2) # LND -> NLD - - -class ResidualAttentionBlock(nn.Module): - def __init__( - self, - d_model: int, - n_head: int, - mlp_ratio: float = 4.0, - ls_init_value: float = None, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - is_cross_attention: bool = False, - ): - super().__init__() - - self.ln_1 = norm_layer(d_model) - self.attn = nn.MultiheadAttention(d_model, n_head) - self.ls_1 = ( - LayerScale(d_model, ls_init_value) - if ls_init_value is not None - else nn.Identity() - ) - if is_cross_attention: - self.ln_1_kv = norm_layer(d_model) - - self.ln_2 = norm_layer(d_model) - mlp_width = int(d_model * mlp_ratio) - self.mlp = nn.Sequential( - OrderedDict( - [ - ("c_fc", nn.Linear(d_model, mlp_width)), - ("gelu", act_layer()), - ("c_proj", nn.Linear(mlp_width, d_model)), - ] - ) - ) - self.ls_2 = ( - LayerScale(d_model, ls_init_value) - if ls_init_value is not None - else nn.Identity() - ) - - def attention( - self, - q_x: torch.Tensor, - k_x: Optional[torch.Tensor] = None, - v_x: Optional[torch.Tensor] = None, - attn_mask: Optional[torch.Tensor] = None, - ): - k_x = k_x if k_x is not None else q_x - v_x = v_x if v_x is not None else q_x - - attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None - return self.attn( - q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask - )[0] - - def forward( - self, - q_x: torch.Tensor, - k_x: Optional[torch.Tensor] = None, - v_x: Optional[torch.Tensor] = None, - attn_mask: Optional[torch.Tensor] = None, - ): - k_x = ( - self.ln_1_kv(k_x) - if hasattr(self, "ln_1_kv") and k_x is not None - else None - ) - v_x = ( - self.ln_1_kv(v_x) - if hasattr(self, "ln_1_kv") and v_x is not None - else None - ) - - x = q_x + self.ls_1( - self.attention( - q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask - ) - ) - x = x + self.ls_2(self.mlp(self.ln_2(x))) - return x - - -class CustomResidualAttentionBlock(nn.Module): - def __init__( - self, - d_model: int, - n_head: int, - mlp_ratio: float = 4.0, - ls_init_value: float = None, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - scale_cosine_attn: bool = False, - scale_heads: bool = False, - scale_attn: bool = False, - scale_fc: bool = False, - ): - super().__init__() - - self.ln_1 = norm_layer(d_model) - self.attn = Attention( - d_model, - n_head, - scaled_cosine=scale_cosine_attn, - scale_heads=scale_heads, - ) - self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() - self.ls_1 = ( - LayerScale(d_model, ls_init_value) - if ls_init_value is not None - else nn.Identity() - ) - - self.ln_2 = norm_layer(d_model) - mlp_width = int(d_model * mlp_ratio) - self.mlp = nn.Sequential( - OrderedDict( - [ - ("c_fc", nn.Linear(d_model, mlp_width)), - ("gelu", act_layer()), - ( - "ln", - norm_layer(mlp_width) if scale_fc else nn.Identity(), - ), - ("c_proj", nn.Linear(mlp_width, d_model)), - ] - ) - ) - self.ls_2 = ( - LayerScale(d_model, ls_init_value) - if ls_init_value is not None - else nn.Identity() - ) - - def forward( - self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None - ): - x = x + self.ls_1( - self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)) - ) - x = x + self.ls_2(self.mlp(self.ln_2(x))) - return x - - -def _expand_token(token, batch_size: int): - return token.view(1, 1, -1).expand(batch_size, -1, -1) - - -class Transformer(nn.Module): - def __init__( - self, - width: int, - layers: int, - heads: int, - mlp_ratio: float = 4.0, - ls_init_value: float = None, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - ): - super().__init__() - self.width = width - self.layers = layers - self.grad_checkpointing = False - - self.resblocks = nn.ModuleList( - [ - ResidualAttentionBlock( - width, - heads, - mlp_ratio, - ls_init_value=ls_init_value, - act_layer=act_layer, - norm_layer=norm_layer, - ) - for _ in range(layers) - ] - ) - - def get_cast_dtype(self) -> torch.dtype: - if hasattr(self.resblocks[0].mlp.c_fc, "int8_original_dtype"): - return self.resblocks[0].mlp.c_fc.int8_original_dtype - return self.resblocks[0].mlp.c_fc.weight.dtype - - def forward( - self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None - ): - for r in self.resblocks: - if self.grad_checkpointing and not torch.jit.is_scripting(): - # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 - x = checkpoint(r, x, None, None, attn_mask) - else: - x = r(x, attn_mask=attn_mask) - return x - - -class VisionTransformer(nn.Module): - output_tokens: torch.jit.Final[bool] - - def __init__( - self, - image_size: int, - patch_size: int, - width: int, - layers: int, - heads: int, - mlp_ratio: float, - ls_init_value: float = None, - attentional_pool: bool = False, - attn_pooler_queries: int = 256, - attn_pooler_heads: int = 8, - output_dim: int = 512, - patch_dropout: float = 0.0, - no_ln_pre: bool = False, - pos_embed_type: str = "learnable", - pool_type: str = "tok", - final_ln_after_pool: bool = False, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - output_tokens: bool = False, - ): - super().__init__() - assert pool_type in ("tok", "avg", "none") - self.output_tokens = output_tokens - image_height, image_width = self.image_size = to_2tuple(image_size) - patch_height, patch_width = self.patch_size = to_2tuple(patch_size) - self.grid_size = ( - image_height // patch_height, - image_width // patch_width, - ) - self.final_ln_after_pool = ( - final_ln_after_pool # currently ignored w/ attn pool enabled - ) - self.output_dim = output_dim - - self.conv1 = nn.Conv2d( - in_channels=3, - out_channels=width, - kernel_size=patch_size, - stride=patch_size, - bias=False, - ) - - # class embeddings and positional embeddings - scale = width**-0.5 - self.class_embedding = nn.Parameter(scale * torch.randn(width)) - if pos_embed_type == "learnable": - self.positional_embedding = nn.Parameter( - scale - * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width) - ) - elif pos_embed_type == "sin_cos_2d": - # fixed sin-cos embedding - assert ( - self.grid_size[0] == self.grid_size[1] - ), "currently sin cos 2d pos embedding only supports square input" - self.positional_embedding = nn.Parameter( - torch.zeros(self.grid_size[0] * self.grid_size[1] + 1, width), - requires_grad=False, - ) - pos_embed_type = get_2d_sincos_pos_embed( - width, self.grid_size[0], cls_token=True - ) - self.positional_embedding.data.copy_( - torch.from_numpy(pos_embed_type).float() - ) - else: - raise ValueError - - # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn - self.patch_dropout = ( - PatchDropout(patch_dropout) - if patch_dropout > 0.0 - else nn.Identity() - ) - - self.ln_pre = nn.Identity() if no_ln_pre else norm_layer(width) - self.transformer = Transformer( - width, - layers, - heads, - mlp_ratio, - ls_init_value=ls_init_value, - act_layer=act_layer, - norm_layer=norm_layer, - ) - - if attentional_pool: - if isinstance(attentional_pool, str): - self.attn_pool_type = attentional_pool - self.pool_type = "none" - if attentional_pool in ("parallel", "cascade"): - self.attn_pool = AttentionalPooler( - output_dim, - width, - n_head=attn_pooler_heads, - n_queries=attn_pooler_queries, - ) - self.attn_pool_contrastive = AttentionalPooler( - output_dim, - width, - n_head=attn_pooler_heads, - n_queries=1, - ) - else: - assert False - else: - self.attn_pool_type = "" - self.pool_type = pool_type - self.attn_pool = AttentionalPooler( - output_dim, - width, - n_head=attn_pooler_heads, - n_queries=attn_pooler_queries, - ) - self.attn_pool_contrastive = None - pool_dim = output_dim - else: - self.attn_pool = None - pool_dim = width - self.pool_type = pool_type - - self.ln_post = norm_layer(pool_dim) - self.proj = nn.Parameter(scale * torch.randn(pool_dim, output_dim)) - - self.init_parameters() - - def lock(self, unlocked_groups=0, freeze_bn_stats=False): - for param in self.parameters(): - param.requires_grad = False - - if unlocked_groups != 0: - groups = [ - [ - self.conv1, - self.class_embedding, - self.positional_embedding, - self.ln_pre, - ], - *self.transformer.resblocks[:-1], - [ - self.transformer.resblocks[-1], - self.ln_post, - ], - self.proj, - ] - - def _unlock(x): - if isinstance(x, Sequence): - for g in x: - _unlock(g) - else: - if isinstance(x, torch.nn.Parameter): - x.requires_grad = True - else: - for p in x.parameters(): - p.requires_grad = True - - _unlock(groups[-unlocked_groups:]) - - def init_parameters(self): - # FIXME OpenAI CLIP did not define an init for the VisualTransformer - # TODO experiment if default PyTorch init, below, or alternate init is best. - - # nn.init.normal_(self.class_embedding, std=self.scale) - # nn.init.normal_(self.positional_embedding, std=self.scale) - # - # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) - # attn_std = self.transformer.width ** -0.5 - # fc_std = (2 * self.transformer.width) ** -0.5 - # for block in self.transformer.resblocks: - # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - # - # if self.text_projection is not None: - # nn.init.normal_(self.text_projection, std=self.scale) - pass - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - self.transformer.grad_checkpointing = enable - - def _global_pool( - self, x: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - if self.pool_type == "avg": - pooled, tokens = x[:, 1:].mean(dim=1), x[:, 1:] - elif self.pool_type == "tok": - pooled, tokens = x[:, 0], x[:, 1:] - else: - pooled = tokens = x - - return pooled, tokens - - def forward(self, x: torch.Tensor): - x = self.conv1(x) # shape = [*, width, grid, grid] - x = x.reshape( - x.shape[0], x.shape[1], -1 - ) # shape = [*, width, grid ** 2] - x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] - - # class embeddings and positional embeddings - x = torch.cat( - [_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], - dim=1, - ) - # shape = [*, grid ** 2 + 1, width] - x = x + self.positional_embedding.to(x.dtype) - - x = self.patch_dropout(x) - x = self.ln_pre(x) - - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x) - x = x.permute(1, 0, 2) # LND -> NLD - - if self.attn_pool is not None: - if self.attn_pool_contrastive is not None: - # This is untested, WIP pooling that should match paper - x = self.ln_post( - x - ) # TBD LN first or separate one after each pool? - tokens = self.attn_pool(x) - if self.attn_pool_type == "parallel": - pooled = self.attn_pool_contrastive(x) - else: - assert self.attn_pool_type == "cascade" - pooled = self.attn_pool_contrastive(tokens) - else: - # this is the original OpenCLIP CoCa setup, does not match paper - x = self.attn_pool(x) - x = self.ln_post(x) - pooled, tokens = self._global_pool(x) - elif self.final_ln_after_pool: - pooled, tokens = self._global_pool(x) - pooled = self.ln_post(pooled) - else: - x = self.ln_post(x) - pooled, tokens = self._global_pool(x) - - if self.proj is not None: - pooled = pooled @ self.proj - - if self.output_tokens: - return pooled, tokens - - return pooled - - -def text_global_pool( - x, text: Optional[torch.Tensor] = None, pool_type: str = "argmax" -): - if pool_type == "first": - pooled, tokens = x[:, 0], x[:, 1:] - elif pool_type == "last": - pooled, tokens = x[:, -1], x[:, :-1] - elif pool_type == "argmax": - # take features from the eot embedding (eot_token is the highest number in each sequence) - assert text is not None - pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x - else: - pooled = tokens = x - - return pooled, tokens - - -class TextTransformer(nn.Module): - output_tokens: torch.jit.Final[bool] - - def __init__( - self, - context_length: int = 77, - vocab_size: int = 49408, - width: int = 512, - heads: int = 8, - layers: int = 12, - mlp_ratio: float = 4.0, - ls_init_value: float = None, - output_dim: int = 512, - embed_cls: bool = False, - no_causal_mask: bool = False, - pad_id: int = 0, - pool_type: str = "argmax", - proj_bias: bool = False, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - output_tokens: bool = False, - ): - super().__init__() - assert pool_type in ("first", "last", "argmax", "none") - self.output_tokens = output_tokens - self.num_pos = self.context_length = context_length - self.vocab_size = vocab_size - self.width = width - self.output_dim = output_dim - self.heads = heads - self.pad_id = pad_id - self.pool_type = pool_type - - self.token_embedding = nn.Embedding(vocab_size, width) - if embed_cls: - self.cls_emb = nn.Parameter(torch.empty(width)) - self.num_pos += 1 - else: - self.cls_emb = None - self.positional_embedding = nn.Parameter( - torch.empty(self.num_pos, width) - ) - self.transformer = Transformer( - width=width, - layers=layers, - heads=heads, - mlp_ratio=mlp_ratio, - ls_init_value=ls_init_value, - act_layer=act_layer, - norm_layer=norm_layer, - ) - self.ln_final = norm_layer(width) - - if no_causal_mask: - self.attn_mask = None - else: - self.register_buffer( - "attn_mask", self.build_causal_mask(), persistent=False - ) - - if proj_bias: - self.text_projection = nn.Linear(width, output_dim) - else: - self.text_projection = nn.Parameter(torch.empty(width, output_dim)) - - self.init_parameters() - - def init_parameters(self): - nn.init.normal_(self.token_embedding.weight, std=0.02) - nn.init.normal_(self.positional_embedding, std=0.01) - if self.cls_emb is not None: - nn.init.normal_(self.cls_emb, std=0.01) - - proj_std = (self.transformer.width**-0.5) * ( - (2 * self.transformer.layers) ** -0.5 - ) - attn_std = self.transformer.width**-0.5 - fc_std = (2 * self.transformer.width) ** -0.5 - for block in self.transformer.resblocks: - nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - - if self.text_projection is not None: - if isinstance(self.text_projection, nn.Linear): - nn.init.normal_( - self.text_projection.weight, - std=self.transformer.width**-0.5, - ) - if self.text_projection.bias is not None: - nn.init.zeros_(self.text_projection.bias) - else: - nn.init.normal_( - self.text_projection, std=self.transformer.width**-0.5 - ) - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - self.transformer.grad_checkpointing = enable - - def build_causal_mask(self): - # lazily create causal attention mask, with full attention between the tokens - # pytorch uses additive attention mask; fill with -inf - mask = torch.empty(self.num_pos, self.num_pos) - mask.fill_(float("-inf")) - mask.triu_(1) # zero out the lower diagonal - return mask - - def build_cls_mask(self, text, cast_dtype: torch.dtype): - cls_mask = (text != self.pad_id).unsqueeze(1) - cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=True) - additive_mask = torch.empty( - cls_mask.shape, dtype=cast_dtype, device=cls_mask.device - ) - additive_mask.fill_(0) - additive_mask.masked_fill_(~cls_mask, float("-inf")) - additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) - return additive_mask - - def forward(self, text): - cast_dtype = self.transformer.get_cast_dtype() - seq_len = text.shape[1] - - x = self.token_embedding(text).to( - cast_dtype - ) # [batch_size, n_ctx, d_model] - attn_mask = self.attn_mask - if self.cls_emb is not None: - seq_len += 1 - x = torch.cat([x, _expand_token(self.cls_emb, x.shape[0])], dim=1) - cls_mask = self.build_cls_mask(text, cast_dtype) - if attn_mask is not None: - # pylint: disable=unsubscriptable-object - attn_mask = ( - attn_mask[None, :seq_len, :seq_len] - + cls_mask[:, :seq_len, :seq_len] - ) - - x = x + self.positional_embedding[:seq_len].to(cast_dtype) - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer(x, attn_mask=attn_mask) - x = x.permute(1, 0, 2) # LND -> NLD - - # x.shape = [batch_size, n_ctx, transformer.width] - if self.cls_emb is not None: - # presence of appended cls embed (CoCa) overrides pool_type, always take last token - pooled, tokens = text_global_pool(x, pool_type="last") - pooled = self.ln_final( - pooled - ) # final LN applied after pooling in this case - else: - x = self.ln_final(x) - pooled, tokens = text_global_pool( - x, text, pool_type=self.pool_type - ) - - if self.text_projection is not None: - if isinstance(self.text_projection, nn.Linear): - pooled = self.text_projection(pooled) - else: - pooled = pooled @ self.text_projection - - if self.output_tokens: - return pooled, tokens - - return pooled - - -class MultimodalTransformer(Transformer): - def __init__( - self, - width: int, - layers: int, - heads: int, - context_length: int = 77, - mlp_ratio: float = 4.0, - ls_init_value: float = None, - act_layer: Callable = nn.GELU, - norm_layer: Callable = LayerNorm, - output_dim: int = 512, - ): - - super().__init__( - width=width, - layers=layers, - heads=heads, - mlp_ratio=mlp_ratio, - ls_init_value=ls_init_value, - act_layer=act_layer, - norm_layer=norm_layer, - ) - self.context_length = context_length - self.cross_attn = nn.ModuleList( - [ - ResidualAttentionBlock( - width, - heads, - mlp_ratio, - ls_init_value=ls_init_value, - act_layer=act_layer, - norm_layer=norm_layer, - is_cross_attention=True, - ) - for _ in range(layers) - ] - ) - - self.register_buffer( - "attn_mask", self.build_attention_mask(), persistent=False - ) - - self.ln_final = norm_layer(width) - self.text_projection = nn.Parameter(torch.empty(width, output_dim)) - - def init_parameters(self): - proj_std = (self.transformer.width**-0.5) * ( - (2 * self.transformer.layers) ** -0.5 - ) - attn_std = self.transformer.width**-0.5 - fc_std = (2 * self.transformer.width) ** -0.5 - for block in self.transformer.resblocks: - nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - for block in self.transformer.cross_attn: - nn.init.normal_(block.attn.in_proj_weight, std=attn_std) - nn.init.normal_(block.attn.out_proj.weight, std=proj_std) - nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) - nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) - - if self.text_projection is not None: - nn.init.normal_( - self.text_projection, std=self.transformer.width**-0.5 - ) - - def build_attention_mask(self): - # lazily create causal attention mask, with full attention between the tokens - # pytorch uses additive attention mask; fill with -inf - mask = torch.empty(self.context_length, self.context_length) - mask.fill_(float("-inf")) - mask.triu_(1) # zero out the lower diagonal - return mask - - def forward(self, image_embs, text_embs): - text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq - image_embs = image_embs.permute(1, 0, 2) # NLD -> LND - seq_len = text_embs.shape[0] - - for resblock, cross_attn in zip(self.resblocks, self.cross_attn): - if self.grad_checkpointing and not torch.jit.is_scripting(): - # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 - text_embs = checkpoint( - resblock, - text_embs, - None, - None, - self.attn_mask[:seq_len, :seq_len], - ) - text_embs = checkpoint( - cross_attn, text_embs, image_embs, image_embs, None - ) - else: - text_embs = resblock( - text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len] - ) - text_embs = cross_attn( - text_embs, k_x=image_embs, v_x=image_embs - ) - - x = text_embs.permute(1, 0, 2) # LND -> NLD - x = self.ln_final(x) - - if self.text_projection is not None: - x = x @ self.text_projection - - return x - - @torch.jit.ignore - def set_grad_checkpointing(self, enable=True): - self.grad_checkpointing = enable diff --git a/fiftyone/utils/open_clip/utils.py b/fiftyone/utils/open_clip/utils.py deleted file mode 100644 index 7fe2e396d4..0000000000 --- a/fiftyone/utils/open_clip/utils.py +++ /dev/null @@ -1,105 +0,0 @@ -from itertools import repeat -import collections.abc - -import torch -from torch import nn as nn -from torchvision.ops.misc import FrozenBatchNorm2d - - -def freeze_batch_norm_2d(module, module_match={}, name=""): - """ - Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is - itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and - returned. Otherwise, the module is walked recursively and submodules are converted in place. - - Args: - module (torch.nn.Module): Any PyTorch module. - module_match (dict): Dictionary of full module names to freeze (all if empty) - name (str): Full module name (prefix) - - Returns: - torch.nn.Module: Resulting module - - Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 - """ - res = module - is_match = True - if module_match: - is_match = name in module_match - if is_match and isinstance( - module, - (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm), - ): - res = FrozenBatchNorm2d(module.num_features) - res.num_features = module.num_features - res.affine = module.affine - if module.affine: - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data - res.running_var.data = module.running_var.data - res.eps = module.eps - else: - for child_name, child in module.named_children(): - full_child_name = ( - ".".join([name, child_name]) if name else child_name - ) - new_child = freeze_batch_norm_2d( - child, module_match, full_child_name - ) - if new_child is not child: - res.add_module(child_name, new_child) - return res - - -# From PyTorch internals -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = lambda n, x: _ntuple(n)(x) - -# Replaces all linear layers with linear_replacement -# TODO: add int8 support for other linear layers including attn and convnets -def replace_linear( - model, - linear_replacement, - include_modules=["c_fc", "c_proj"], - copy_weights=True, -): - for name, module in model.named_children(): - if len(list(module.children())) > 0: - replace_linear( - module, linear_replacement, include_modules, copy_weights - ) - - if isinstance(module, torch.nn.Linear) and name in include_modules: - old_module = model._modules[name] - model._modules[name] = linear_replacement( - module.in_features, - module.out_features, - module.bias is not None, - ) - if copy_weights: - model._modules[name].weight.data.copy_(old_module.weight.data) - if model._modules[name].bias is not None: - model._modules[name].bias.data.copy_(old_module.bias) - - return model - - -def convert_int8_model_to_inference_mode(model): - for m in model.modules(): - if hasattr(m, "prepare_for_eval"): - int8_original_dtype = m.weight.dtype - m.prepare_for_eval() - m.int8_original_dtype = int8_original_dtype diff --git a/fiftyone/utils/open_clip/version.py b/fiftyone/utils/open_clip/version.py deleted file mode 100644 index db969b3af4..0000000000 --- a/fiftyone/utils/open_clip/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "2.23.0" diff --git a/fiftyone/utils/open_clip/zero_shot_classifier.py b/fiftyone/utils/open_clip/zero_shot_classifier.py deleted file mode 100644 index 408da3561f..0000000000 --- a/fiftyone/utils/open_clip/zero_shot_classifier.py +++ /dev/null @@ -1,135 +0,0 @@ -from functools import partial -from itertools import islice -from typing import Callable, List, Optional, Sequence, Union - -import torch -import torch.nn.functional as F - - -def batched(iterable, n): - """Batch data into lists of length *n*. The last batch may be shorter. - NOTE based on more-itertools impl, to be replaced by python 3.12 itertools.batched impl - """ - it = iter(iterable) - while True: - batch = list(islice(it, n)) - if not batch: - break - yield batch - - -def build_zero_shot_classifier( - model, - tokenizer, - classnames: Sequence[str], - templates: Sequence[Union[Callable, str]], - num_classes_per_batch: Optional[int] = 10, - device: Union[str, torch.device] = "cpu", - use_tqdm: bool = False, -): - """Build zero-shot classifier weights by iterating over class names in batches - Args: - model: CLIP model instance - tokenizer: CLIP tokenizer instance - classnames: A sequence of class (label) names - templates: A sequence of callables or format() friendly strings to produce templates per class name - num_classes_per_batch: The number of classes to batch together in each forward, all if None - device: Device to use. - use_tqdm: Enable TQDM progress bar. - """ - assert isinstance(templates, Sequence) and len(templates) > 0 - assert isinstance(classnames, Sequence) and len(classnames) > 0 - use_format = isinstance(templates[0], str) - num_templates = len(templates) - num_classes = len(classnames) - if use_tqdm: - import tqdm - - num_iter = ( - 1 - if num_classes_per_batch is None - else ((num_classes - 1) // num_classes_per_batch + 1) - ) - iter_wrap = partial( - tqdm.tqdm, total=num_iter, unit_scale=num_classes_per_batch - ) - else: - iter_wrap = iter - - def _process_batch(batch_classnames): - num_batch_classes = len(batch_classnames) - texts = [ - template.format(c) if use_format else template(c) - for c in batch_classnames - for template in templates - ] - texts = tokenizer(texts).to(device) - class_embeddings = model.encode_text(texts, normalize=True) - class_embeddings = class_embeddings.reshape( - num_batch_classes, num_templates, -1 - ).mean(dim=1) - class_embeddings = class_embeddings / class_embeddings.norm( - dim=1, keepdim=True - ) - class_embeddings = class_embeddings.T - return class_embeddings - - with torch.no_grad(): - if num_classes_per_batch: - batched_embeds = [ - _process_batch(batch) - for batch in iter_wrap( - batched(classnames, num_classes_per_batch) - ) - ] - zeroshot_weights = torch.cat(batched_embeds, dim=1) - else: - zeroshot_weights = _process_batch(classnames) - return zeroshot_weights - - -def build_zero_shot_classifier_legacy( - model, - tokenizer, - classnames: Sequence[str], - templates: Sequence[Union[Callable, str]], - device: Union[str, torch.device] = "cpu", - use_tqdm: bool = False, -): - """Build zero-shot classifier weights by iterating over class names 1 by 1 - Args: - model: CLIP model instance - tokenizer: CLIP tokenizer instance - classnames: A sequence of class (label) names - templates: A sequence of callables or format() friendly strings to produce templates per class name - device: Device to use. - use_tqdm: Enable TQDM progress bar. - """ - assert isinstance(templates, Sequence) and len(templates) > 0 - assert isinstance(classnames, Sequence) and len(classnames) > 0 - if use_tqdm: - import tqdm - - iter_wrap = tqdm.tqdm - else: - iter_wrap = iter - - use_format = isinstance(templates[0], str) - - with torch.no_grad(): - zeroshot_weights = [] - for classname in iter_wrap(classnames): - texts = [ - template.format(classname) - if use_format - else template(classname) - for template in templates - ] - texts = tokenizer(texts).to(device) # tokenize - class_embeddings = model.encode_text(texts) - class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0) - class_embedding /= class_embedding.norm() - zeroshot_weights.append(class_embedding) - zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(device) - - return zeroshot_weights diff --git a/fiftyone/utils/open_clip/zero_shot_metadata.py b/fiftyone/utils/open_clip/zero_shot_metadata.py deleted file mode 100644 index cc8ad5d8a5..0000000000 --- a/fiftyone/utils/open_clip/zero_shot_metadata.py +++ /dev/null @@ -1,1099 +0,0 @@ -OPENAI_IMAGENET_TEMPLATES = ( - lambda c: f"a bad photo of a {c}.", - lambda c: f"a photo of many {c}.", - lambda c: f"a sculpture of a {c}.", - lambda c: f"a photo of the hard to see {c}.", - lambda c: f"a low resolution photo of the {c}.", - lambda c: f"a rendering of a {c}.", - lambda c: f"graffiti of a {c}.", - lambda c: f"a bad photo of the {c}.", - lambda c: f"a cropped photo of the {c}.", - lambda c: f"a tattoo of a {c}.", - lambda c: f"the embroidered {c}.", - lambda c: f"a photo of a hard to see {c}.", - lambda c: f"a bright photo of a {c}.", - lambda c: f"a photo of a clean {c}.", - lambda c: f"a photo of a dirty {c}.", - lambda c: f"a dark photo of the {c}.", - lambda c: f"a drawing of a {c}.", - lambda c: f"a photo of my {c}.", - lambda c: f"the plastic {c}.", - lambda c: f"a photo of the cool {c}.", - lambda c: f"a close-up photo of a {c}.", - lambda c: f"a black and white photo of the {c}.", - lambda c: f"a painting of the {c}.", - lambda c: f"a painting of a {c}.", - lambda c: f"a pixelated photo of the {c}.", - lambda c: f"a sculpture of the {c}.", - lambda c: f"a bright photo of the {c}.", - lambda c: f"a cropped photo of a {c}.", - lambda c: f"a plastic {c}.", - lambda c: f"a photo of the dirty {c}.", - lambda c: f"a jpeg corrupted photo of a {c}.", - lambda c: f"a blurry photo of the {c}.", - lambda c: f"a photo of the {c}.", - lambda c: f"a good photo of the {c}.", - lambda c: f"a rendering of the {c}.", - lambda c: f"a {c} in a video game.", - lambda c: f"a photo of one {c}.", - lambda c: f"a doodle of a {c}.", - lambda c: f"a close-up photo of the {c}.", - lambda c: f"a photo of a {c}.", - lambda c: f"the origami {c}.", - lambda c: f"the {c} in a video game.", - lambda c: f"a sketch of a {c}.", - lambda c: f"a doodle of the {c}.", - lambda c: f"a origami {c}.", - lambda c: f"a low resolution photo of a {c}.", - lambda c: f"the toy {c}.", - lambda c: f"a rendition of the {c}.", - lambda c: f"a photo of the clean {c}.", - lambda c: f"a photo of a large {c}.", - lambda c: f"a rendition of a {c}.", - lambda c: f"a photo of a nice {c}.", - lambda c: f"a photo of a weird {c}.", - lambda c: f"a blurry photo of a {c}.", - lambda c: f"a cartoon {c}.", - lambda c: f"art of a {c}.", - lambda c: f"a sketch of the {c}.", - lambda c: f"a embroidered {c}.", - lambda c: f"a pixelated photo of a {c}.", - lambda c: f"itap of the {c}.", - lambda c: f"a jpeg corrupted photo of the {c}.", - lambda c: f"a good photo of a {c}.", - lambda c: f"a plushie {c}.", - lambda c: f"a photo of the nice {c}.", - lambda c: f"a photo of the small {c}.", - lambda c: f"a photo of the weird {c}.", - lambda c: f"the cartoon {c}.", - lambda c: f"art of the {c}.", - lambda c: f"a drawing of the {c}.", - lambda c: f"a photo of the large {c}.", - lambda c: f"a black and white photo of a {c}.", - lambda c: f"the plushie {c}.", - lambda c: f"a dark photo of a {c}.", - lambda c: f"itap of a {c}.", - lambda c: f"graffiti of the {c}.", - lambda c: f"a toy {c}.", - lambda c: f"itap of my {c}.", - lambda c: f"a photo of a cool {c}.", - lambda c: f"a photo of a small {c}.", - lambda c: f"a tattoo of the {c}.", -) - - -# a much smaller subset of above prompts -# from https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb -SIMPLE_IMAGENET_TEMPLATES = ( - lambda c: f"itap of a {c}.", - lambda c: f"a bad photo of the {c}.", - lambda c: f"a origami {c}.", - lambda c: f"a photo of the large {c}.", - lambda c: f"a {c} in a video game.", - lambda c: f"art of the {c}.", - lambda c: f"a photo of the small {c}.", -) - - -IMAGENET_CLASSNAMES = ( - "tench", - "goldfish", - "great white shark", - "tiger shark", - "hammerhead shark", - "electric ray", - "stingray", - "rooster", - "hen", - "ostrich", - "brambling", - "goldfinch", - "house finch", - "junco", - "indigo bunting", - "American robin", - "bulbul", - "jay", - "magpie", - "chickadee", - "American dipper", - "kite (bird of prey)", - "bald eagle", - "vulture", - "great grey owl", - "fire salamander", - "smooth newt", - "newt", - "spotted salamander", - "axolotl", - "American bullfrog", - "tree frog", - "tailed frog", - "loggerhead sea turtle", - "leatherback sea turtle", - "mud turtle", - "terrapin", - "box turtle", - "banded gecko", - "green iguana", - "Carolina anole", - "desert grassland whiptail lizard", - "agama", - "frilled-necked lizard", - "alligator lizard", - "Gila monster", - "European green lizard", - "chameleon", - "Komodo dragon", - "Nile crocodile", - "American alligator", - "triceratops", - "worm snake", - "ring-necked snake", - "eastern hog-nosed snake", - "smooth green snake", - "kingsnake", - "garter snake", - "water snake", - "vine snake", - "night snake", - "boa constrictor", - "African rock python", - "Indian cobra", - "green mamba", - "sea snake", - "Saharan horned viper", - "eastern diamondback rattlesnake", - "sidewinder rattlesnake", - "trilobite", - "harvestman", - "scorpion", - "yellow garden spider", - "barn spider", - "European garden spider", - "southern black widow", - "tarantula", - "wolf spider", - "tick", - "centipede", - "black grouse", - "ptarmigan", - "ruffed grouse", - "prairie grouse", - "peafowl", - "quail", - "partridge", - "african grey parrot", - "macaw", - "sulphur-crested cockatoo", - "lorikeet", - "coucal", - "bee eater", - "hornbill", - "hummingbird", - "jacamar", - "toucan", - "duck", - "red-breasted merganser", - "goose", - "black swan", - "tusker", - "echidna", - "platypus", - "wallaby", - "koala", - "wombat", - "jellyfish", - "sea anemone", - "brain coral", - "flatworm", - "nematode", - "conch", - "snail", - "slug", - "sea slug", - "chiton", - "chambered nautilus", - "Dungeness crab", - "rock crab", - "fiddler crab", - "red king crab", - "American lobster", - "spiny lobster", - "crayfish", - "hermit crab", - "isopod", - "white stork", - "black stork", - "spoonbill", - "flamingo", - "little blue heron", - "great egret", - "bittern bird", - "crane bird", - "limpkin", - "common gallinule", - "American coot", - "bustard", - "ruddy turnstone", - "dunlin", - "common redshank", - "dowitcher", - "oystercatcher", - "pelican", - "king penguin", - "albatross", - "grey whale", - "killer whale", - "dugong", - "sea lion", - "Chihuahua", - "Japanese Chin", - "Maltese", - "Pekingese", - "Shih Tzu", - "King Charles Spaniel", - "Papillon", - "toy terrier", - "Rhodesian Ridgeback", - "Afghan Hound", - "Basset Hound", - "Beagle", - "Bloodhound", - "Bluetick Coonhound", - "Black and Tan Coonhound", - "Treeing Walker Coonhound", - "English foxhound", - "Redbone Coonhound", - "borzoi", - "Irish Wolfhound", - "Italian Greyhound", - "Whippet", - "Ibizan Hound", - "Norwegian Elkhound", - "Otterhound", - "Saluki", - "Scottish Deerhound", - "Weimaraner", - "Staffordshire Bull Terrier", - "American Staffordshire Terrier", - "Bedlington Terrier", - "Border Terrier", - "Kerry Blue Terrier", - "Irish Terrier", - "Norfolk Terrier", - "Norwich Terrier", - "Yorkshire Terrier", - "Wire Fox Terrier", - "Lakeland Terrier", - "Sealyham Terrier", - "Airedale Terrier", - "Cairn Terrier", - "Australian Terrier", - "Dandie Dinmont Terrier", - "Boston Terrier", - "Miniature Schnauzer", - "Giant Schnauzer", - "Standard Schnauzer", - "Scottish Terrier", - "Tibetan Terrier", - "Australian Silky Terrier", - "Soft-coated Wheaten Terrier", - "West Highland White Terrier", - "Lhasa Apso", - "Flat-Coated Retriever", - "Curly-coated Retriever", - "Golden Retriever", - "Labrador Retriever", - "Chesapeake Bay Retriever", - "German Shorthaired Pointer", - "Vizsla", - "English Setter", - "Irish Setter", - "Gordon Setter", - "Brittany dog", - "Clumber Spaniel", - "English Springer Spaniel", - "Welsh Springer Spaniel", - "Cocker Spaniel", - "Sussex Spaniel", - "Irish Water Spaniel", - "Kuvasz", - "Schipperke", - "Groenendael dog", - "Malinois", - "Briard", - "Australian Kelpie", - "Komondor", - "Old English Sheepdog", - "Shetland Sheepdog", - "collie", - "Border Collie", - "Bouvier des Flandres dog", - "Rottweiler", - "German Shepherd Dog", - "Dobermann", - "Miniature Pinscher", - "Greater Swiss Mountain Dog", - "Bernese Mountain Dog", - "Appenzeller Sennenhund", - "Entlebucher Sennenhund", - "Boxer", - "Bullmastiff", - "Tibetan Mastiff", - "French Bulldog", - "Great Dane", - "St. Bernard", - "husky", - "Alaskan Malamute", - "Siberian Husky", - "Dalmatian", - "Affenpinscher", - "Basenji", - "pug", - "Leonberger", - "Newfoundland dog", - "Great Pyrenees dog", - "Samoyed", - "Pomeranian", - "Chow Chow", - "Keeshond", - "brussels griffon", - "Pembroke Welsh Corgi", - "Cardigan Welsh Corgi", - "Toy Poodle", - "Miniature Poodle", - "Standard Poodle", - "Mexican hairless dog (xoloitzcuintli)", - "grey wolf", - "Alaskan tundra wolf", - "red wolf or maned wolf", - "coyote", - "dingo", - "dhole", - "African wild dog", - "hyena", - "red fox", - "kit fox", - "Arctic fox", - "grey fox", - "tabby cat", - "tiger cat", - "Persian cat", - "Siamese cat", - "Egyptian Mau", - "cougar", - "lynx", - "leopard", - "snow leopard", - "jaguar", - "lion", - "tiger", - "cheetah", - "brown bear", - "American black bear", - "polar bear", - "sloth bear", - "mongoose", - "meerkat", - "tiger beetle", - "ladybug", - "ground beetle", - "longhorn beetle", - "leaf beetle", - "dung beetle", - "rhinoceros beetle", - "weevil", - "fly", - "bee", - "ant", - "grasshopper", - "cricket insect", - "stick insect", - "cockroach", - "praying mantis", - "cicada", - "leafhopper", - "lacewing", - "dragonfly", - "damselfly", - "red admiral butterfly", - "ringlet butterfly", - "monarch butterfly", - "small white butterfly", - "sulphur butterfly", - "gossamer-winged butterfly", - "starfish", - "sea urchin", - "sea cucumber", - "cottontail rabbit", - "hare", - "Angora rabbit", - "hamster", - "porcupine", - "fox squirrel", - "marmot", - "beaver", - "guinea pig", - "common sorrel horse", - "zebra", - "pig", - "wild boar", - "warthog", - "hippopotamus", - "ox", - "water buffalo", - "bison", - "ram (adult male sheep)", - "bighorn sheep", - "Alpine ibex", - "hartebeest", - "impala (antelope)", - "gazelle", - "arabian camel", - "llama", - "weasel", - "mink", - "European polecat", - "black-footed ferret", - "otter", - "skunk", - "badger", - "armadillo", - "three-toed sloth", - "orangutan", - "gorilla", - "chimpanzee", - "gibbon", - "siamang", - "guenon", - "patas monkey", - "baboon", - "macaque", - "langur", - "black-and-white colobus", - "proboscis monkey", - "marmoset", - "white-headed capuchin", - "howler monkey", - "titi monkey", - "Geoffroy's spider monkey", - "common squirrel monkey", - "ring-tailed lemur", - "indri", - "Asian elephant", - "African bush elephant", - "red panda", - "giant panda", - "snoek fish", - "eel", - "silver salmon", - "rock beauty fish", - "clownfish", - "sturgeon", - "gar fish", - "lionfish", - "pufferfish", - "abacus", - "abaya", - "academic gown", - "accordion", - "acoustic guitar", - "aircraft carrier", - "airliner", - "airship", - "altar", - "ambulance", - "amphibious vehicle", - "analog clock", - "apiary", - "apron", - "trash can", - "assault rifle", - "backpack", - "bakery", - "balance beam", - "balloon", - "ballpoint pen", - "Band-Aid", - "banjo", - "baluster / handrail", - "barbell", - "barber chair", - "barbershop", - "barn", - "barometer", - "barrel", - "wheelbarrow", - "baseball", - "basketball", - "bassinet", - "bassoon", - "swimming cap", - "bath towel", - "bathtub", - "station wagon", - "lighthouse", - "beaker", - "military hat (bearskin or shako)", - "beer bottle", - "beer glass", - "bell tower", - "baby bib", - "tandem bicycle", - "bikini", - "ring binder", - "binoculars", - "birdhouse", - "boathouse", - "bobsleigh", - "bolo tie", - "poke bonnet", - "bookcase", - "bookstore", - "bottle cap", - "hunting bow", - "bow tie", - "brass memorial plaque", - "bra", - "breakwater", - "breastplate", - "broom", - "bucket", - "buckle", - "bulletproof vest", - "high-speed train", - "butcher shop", - "taxicab", - "cauldron", - "candle", - "cannon", - "canoe", - "can opener", - "cardigan", - "car mirror", - "carousel", - "tool kit", - "cardboard box / carton", - "car wheel", - "automated teller machine", - "cassette", - "cassette player", - "castle", - "catamaran", - "CD player", - "cello", - "mobile phone", - "chain", - "chain-link fence", - "chain mail", - "chainsaw", - "storage chest", - "chiffonier", - "bell or wind chime", - "china cabinet", - "Christmas stocking", - "church", - "movie theater", - "cleaver", - "cliff dwelling", - "cloak", - "clogs", - "cocktail shaker", - "coffee mug", - "coffeemaker", - "spiral or coil", - "combination lock", - "computer keyboard", - "candy store", - "container ship", - "convertible", - "corkscrew", - "cornet", - "cowboy boot", - "cowboy hat", - "cradle", - "construction crane", - "crash helmet", - "crate", - "infant bed", - "Crock Pot", - "croquet ball", - "crutch", - "cuirass", - "dam", - "desk", - "desktop computer", - "rotary dial telephone", - "diaper", - "digital clock", - "digital watch", - "dining table", - "dishcloth", - "dishwasher", - "disc brake", - "dock", - "dog sled", - "dome", - "doormat", - "drilling rig", - "drum", - "drumstick", - "dumbbell", - "Dutch oven", - "electric fan", - "electric guitar", - "electric locomotive", - "entertainment center", - "envelope", - "espresso machine", - "face powder", - "feather boa", - "filing cabinet", - "fireboat", - "fire truck", - "fire screen", - "flagpole", - "flute", - "folding chair", - "football helmet", - "forklift", - "fountain", - "fountain pen", - "four-poster bed", - "freight car", - "French horn", - "frying pan", - "fur coat", - "garbage truck", - "gas mask or respirator", - "gas pump", - "goblet", - "go-kart", - "golf ball", - "golf cart", - "gondola", - "gong", - "gown", - "grand piano", - "greenhouse", - "radiator grille", - "grocery store", - "guillotine", - "hair clip", - "hair spray", - "half-track", - "hammer", - "hamper", - "hair dryer", - "hand-held computer", - "handkerchief", - "hard disk drive", - "harmonica", - "harp", - "combine harvester", - "hatchet", - "holster", - "home theater", - "honeycomb", - "hook", - "hoop skirt", - "gymnastic horizontal bar", - "horse-drawn vehicle", - "hourglass", - "iPod", - "clothes iron", - "carved pumpkin", - "jeans", - "jeep", - "T-shirt", - "jigsaw puzzle", - "rickshaw", - "joystick", - "kimono", - "knee pad", - "knot", - "lab coat", - "ladle", - "lampshade", - "laptop computer", - "lawn mower", - "lens cap", - "letter opener", - "library", - "lifeboat", - "lighter", - "limousine", - "ocean liner", - "lipstick", - "slip-on shoe", - "lotion", - "music speaker", - "loupe magnifying glass", - "sawmill", - "magnetic compass", - "messenger bag", - "mailbox", - "tights", - "one-piece bathing suit", - "manhole cover", - "maraca", - "marimba", - "mask", - "matchstick", - "maypole", - "maze", - "measuring cup", - "medicine cabinet", - "megalith", - "microphone", - "microwave oven", - "military uniform", - "milk can", - "minibus", - "miniskirt", - "minivan", - "missile", - "mitten", - "mixing bowl", - "mobile home", - "ford model t", - "modem", - "monastery", - "monitor", - "moped", - "mortar and pestle", - "graduation cap", - "mosque", - "mosquito net", - "vespa", - "mountain bike", - "tent", - "computer mouse", - "mousetrap", - "moving van", - "muzzle", - "metal nail", - "neck brace", - "necklace", - "baby pacifier", - "notebook computer", - "obelisk", - "oboe", - "ocarina", - "odometer", - "oil filter", - "pipe organ", - "oscilloscope", - "overskirt", - "bullock cart", - "oxygen mask", - "product packet / packaging", - "paddle", - "paddle wheel", - "padlock", - "paintbrush", - "pajamas", - "palace", - "pan flute", - "paper towel", - "parachute", - "parallel bars", - "park bench", - "parking meter", - "railroad car", - "patio", - "payphone", - "pedestal", - "pencil case", - "pencil sharpener", - "perfume", - "Petri dish", - "photocopier", - "plectrum", - "Pickelhaube", - "picket fence", - "pickup truck", - "pier", - "piggy bank", - "pill bottle", - "pillow", - "ping-pong ball", - "pinwheel", - "pirate ship", - "drink pitcher", - "block plane", - "planetarium", - "plastic bag", - "plate rack", - "farm plow", - "plunger", - "Polaroid camera", - "pole", - "police van", - "poncho", - "pool table", - "soda bottle", - "plant pot", - "potter's wheel", - "power drill", - "prayer rug", - "printer", - "prison", - "missile", - "projector", - "hockey puck", - "punching bag", - "purse", - "quill", - "quilt", - "race car", - "racket", - "radiator", - "radio", - "radio telescope", - "rain barrel", - "recreational vehicle", - "fishing casting reel", - "reflex camera", - "refrigerator", - "remote control", - "restaurant", - "revolver", - "rifle", - "rocking chair", - "rotisserie", - "eraser", - "rugby ball", - "ruler measuring stick", - "sneaker", - "safe", - "safety pin", - "salt shaker", - "sandal", - "sarong", - "saxophone", - "scabbard", - "weighing scale", - "school bus", - "schooner", - "scoreboard", - "CRT monitor", - "screw", - "screwdriver", - "seat belt", - "sewing machine", - "shield", - "shoe store", - "shoji screen / room divider", - "shopping basket", - "shopping cart", - "shovel", - "shower cap", - "shower curtain", - "ski", - "balaclava ski mask", - "sleeping bag", - "slide rule", - "sliding door", - "slot machine", - "snorkel", - "snowmobile", - "snowplow", - "soap dispenser", - "soccer ball", - "sock", - "solar thermal collector", - "sombrero", - "soup bowl", - "keyboard space bar", - "space heater", - "space shuttle", - "spatula", - "motorboat", - "spider web", - "spindle", - "sports car", - "spotlight", - "stage", - "steam locomotive", - "through arch bridge", - "steel drum", - "stethoscope", - "scarf", - "stone wall", - "stopwatch", - "stove", - "strainer", - "tram", - "stretcher", - "couch", - "stupa", - "submarine", - "suit", - "sundial", - "sunglasses", - "sunglasses", - "sunscreen", - "suspension bridge", - "mop", - "sweatshirt", - "swim trunks / shorts", - "swing", - "electrical switch", - "syringe", - "table lamp", - "tank", - "tape player", - "teapot", - "teddy bear", - "television", - "tennis ball", - "thatched roof", - "front curtain", - "thimble", - "threshing machine", - "throne", - "tile roof", - "toaster", - "tobacco shop", - "toilet seat", - "torch", - "totem pole", - "tow truck", - "toy store", - "tractor", - "semi-trailer truck", - "tray", - "trench coat", - "tricycle", - "trimaran", - "tripod", - "triumphal arch", - "trolleybus", - "trombone", - "hot tub", - "turnstile", - "typewriter keyboard", - "umbrella", - "unicycle", - "upright piano", - "vacuum cleaner", - "vase", - "vaulted or arched ceiling", - "velvet fabric", - "vending machine", - "vestment", - "viaduct", - "violin", - "volleyball", - "waffle iron", - "wall clock", - "wallet", - "wardrobe", - "military aircraft", - "sink", - "washing machine", - "water bottle", - "water jug", - "water tower", - "whiskey jug", - "whistle", - "hair wig", - "window screen", - "window shade", - "Windsor tie", - "wine bottle", - "airplane wing", - "wok", - "wooden spoon", - "wool", - "split-rail fence", - "shipwreck", - "sailboat", - "yurt", - "website", - "comic book", - "crossword", - "traffic or street sign", - "traffic light", - "dust jacket", - "menu", - "plate", - "guacamole", - "consomme", - "hot pot", - "trifle", - "ice cream", - "popsicle", - "baguette", - "bagel", - "pretzel", - "cheeseburger", - "hot dog", - "mashed potatoes", - "cabbage", - "broccoli", - "cauliflower", - "zucchini", - "spaghetti squash", - "acorn squash", - "butternut squash", - "cucumber", - "artichoke", - "bell pepper", - "cardoon", - "mushroom", - "Granny Smith apple", - "strawberry", - "orange", - "lemon", - "fig", - "pineapple", - "banana", - "jackfruit", - "cherimoya (custard apple)", - "pomegranate", - "hay", - "carbonara", - "chocolate syrup", - "dough", - "meatloaf", - "pizza", - "pot pie", - "burrito", - "red wine", - "espresso", - "tea cup", - "eggnog", - "mountain", - "bubble", - "cliff", - "coral reef", - "geyser", - "lakeshore", - "promontory", - "sandbar", - "beach", - "valley", - "volcano", - "baseball player", - "bridegroom", - "scuba diver", - "rapeseed", - "daisy", - "yellow lady's slipper", - "corn", - "acorn", - "rose hip", - "horse chestnut seed", - "coral fungus", - "agaric", - "gyromitra", - "stinkhorn mushroom", - "earth star fungus", - "hen of the woods mushroom", - "bolete", - "corn cob", - "toilet paper", -) diff --git a/fiftyone/utils/open_clip/zoo.py b/fiftyone/utils/open_clip/zoo.py deleted file mode 100644 index 47c7099f33..0000000000 --- a/fiftyone/utils/open_clip/zoo.py +++ /dev/null @@ -1,233 +0,0 @@ -""" -CLIP model wrapper for the FiftyOne Model Zoo. - -| Copyright 2017-2023, Voxel51, Inc. -| `voxel51.com `_ -| -""" -import logging -import os -from packaging.version import Version -import warnings - -import eta.core.web as etaw - -import fiftyone as fo -import fiftyone.core.models as fom -import fiftyone.core.utils as fou -import fiftyone.utils.torch as fout -import fiftyone.zoo.models as fozm - -fou.ensure_torch() -import torch - -from .factory import create_model_and_transforms, get_tokenizer - -logger = logging.getLogger(__name__) - - -class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): - """Configuration for running a :class:`TorchCLIPModel`. - - See :class:`fiftyone.utils.torch.TorchImageModelConfig` for additional - arguments. - - Args: - tokenizer_base_filename: the filename in ``fo.config.model_zoo_dir`` in - which to store the model's tokenizer - tokenizer_base_url: a URL from which the tokenizer can be downloaded, - if necessary - context_length: the model's context length - text_prompt: the text prompt to use, e.g., ``"A photo of"`` - classes (None): a list of custom classes for zero-shot prediction - """ - - def __init__(self, d): - - d = self.init(d) - super().__init__(d) - - self.tokenizer_base_filename = self.parse_string( - d, "tokenizer_base_filename" - ) - self.tokenizer_base_url = self.parse_string(d, "tokenizer_base_url") - self.context_length = self.parse_int(d, "context_length") - self.text_prompt = self.parse_string(d, "text_prompt") - - self.model_name_clip = self.parse_string(d, "model_name_clip") - self.pretrained = self.parse_string(d, "pretrained") - - self._tokenizer_path = os.path.join( - fo.config.model_zoo_dir, self.tokenizer_base_filename - ) - - @property - def tokenizer_path(self): - return self._tokenizer_path - - def download_tokenizer_if_necessary(self): - if not os.path.isfile(self._tokenizer_path): - logger.info("Downloading CLIP tokenizer...") - etaw.download_file( - self.tokenizer_base_url, path=self._tokenizer_path - ) - - -class TorchOpenClipModel(fout.TorchImageModel, fom.PromptMixin): - """Torch implementation of CLIP from https://github.com/mlfoundations/open_clip. - - Args: - config: a :class:`TorchOpenClipPModelConfig` - """ - - def __init__(self, config): - super().__init__(config) - - self._text_features = None - - @property - def can_embed_prompts(self): - return True - - def embed_prompt(self, prompt): - """Generates an embedding for the given text prompt. - - Args: - prompt: a text string - - Returns: - a numpy vector - """ - return self.embed_prompts([prompt])[0] - - def embed_prompts(self, prompts): - """Generates an embedding for the given text prompts. - - Args: - prompts: an iterable of text strings - - Returns: - a ``num_prompts x num_dims`` array of prompt embeddings - """ - return self._embed_prompts(prompts).detach().cpu().numpy() - - def _download_model(self, config): - config.download_model_if_necessary() - config.download_tokenizer_if_necessary() - - def _load_model(self, config): - self._model, _, self.preprocess = create_model_and_transforms( - config.model_name_clip, pretrained=config.pretrained - ) - self._tokenizer = get_tokenizer(config.model_name_clip) - return self._model - - def _embed_prompts(self, prompts): - # source: https://github.com/openai/CLIP/blob/main/clip/clip.py - sot_token = self._tokenizer.encoder["<|startoftext|>"] - eot_token = self._tokenizer.encoder["<|endoftext|>"] - all_tokens = [ - [sot_token] + self._tokenizer.encode(p) + [eot_token] - for p in prompts - ] - - if Version(torch.__version__) < Version("1.8.0"): - dtype = torch.long - else: - dtype = torch.int - - text_features = torch.zeros( - len(all_tokens), - self.config.context_length, - dtype=dtype, - device=self.device, - ) - - for i, (prompt, tokens) in enumerate(zip(prompts, all_tokens)): - if len(tokens) > self.config.context_length: - tokens = tokens[: self.config.context_length] - tokens[-1] = eot_token - msg = ( - "Truncating prompt '%s'; too long for context length '%d'" - % (prompt, self.config.context_length) - ) - warnings.warn(msg) - - text_features[i, : len(tokens)] = torch.tensor(tokens) - - with torch.no_grad(): - return self._model.encode_text(text_features) - - def _get_text_features(self): - if self._text_features is None: - prompts = [ - "%s %s" % (self.config.text_prompt, c) for c in self.classes - ] - # Tokenize text - text = self._tokenizer(prompts) - self._text_features = self._model.encode_text(text) - - return self._text_features - - def _get_class_logits(self, text_features, image_features): - # source: https://github.com/openai/CLIP/blob/main/README.md - image_features = image_features / image_features.norm( - dim=1, keepdim=True - ) - text_features = text_features / text_features.norm(dim=1, keepdim=True) - logit_scale = self._model.logit_scale.exp() - logits_per_image = logit_scale * image_features @ text_features.t() - logits_per_text = logits_per_image.t() - return logits_per_image, logits_per_text - - def _predict_all(self, imgs): - if self._preprocess: - imgs = [self._preprocess(img).unsqueeze(0) for img in imgs] - - if isinstance(imgs, (list, tuple)): - imgs = torch.stack(imgs) - - height, width = imgs.size()[-2:] - frame_size = (width, height) - - if self._using_gpu: - imgs = imgs.cuda() - - with torch.no_grad(), torch.cuda.amp.autocast(): - # Encode images and texts - image_features = self._model.encode_image(imgs) - text_features = self._get_text_features() - - output, _ = self._get_class_logits(text_features, image_features) - - if self.has_logits: - self._output_processor.store_logits = self.store_logits - - return self._output_processor( - output, frame_size, confidence_thresh=self.config.confidence_thresh - ) - - def _predict_all_incase(self, imgs): - print("PREDICT ALL") - if self._preprocess: - imgs = [self._transforms(img) for img in imgs] - - if isinstance(imgs, (list, tuple)): - imgs = torch.stack(imgs) - - height, width = imgs.size()[-2:] - frame_size = (width, height) - - if self._using_gpu: - imgs = imgs.cuda() - - text_features = self._get_text_features() - image_features = self._model.encode_image(imgs) - output, _ = self._get_class_logits(text_features, image_features) - - if self.has_logits: - self._output_processor.store_logits = self.store_logits - - return self._output_processor( - output, frame_size, confidence_thresh=self.config.confidence_thresh - ) diff --git a/fiftyone/zoo/models/manifest-torch.json b/fiftyone/zoo/models/manifest-torch.json index 16035e9cc1..27bf03a142 100644 --- a/fiftyone/zoo/models/manifest-torch.json +++ b/fiftyone/zoo/models/manifest-torch.json @@ -1866,7 +1866,7 @@ "base_name": "open-clip", "base_filename": "", "version": null, - "description": "OPENN CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs", + "description": "OPEN CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs", "source": "https://github.com/mlfoundations/open_clip", "size_bytes": 353976522, "manager": { @@ -1890,13 +1890,7 @@ } }, "requirements": { - "packages": [ - "torch", - "torchvision", - "huggingface_hub", - "timm", - "transformers" - ], + "packages": ["torch", "torchvision", "open_clip_torch"], "cpu": { "support": true }, From 66c12ab17b8c016d4807ededad4b5ac4cdd615a1 Mon Sep 17 00:00:00 2001 From: Adonai Vera <45982251+AdonaiVera@users.noreply.github.com> Date: Fri, 15 Dec 2023 20:38:03 -0600 Subject: [PATCH 47/59] Fixed typos, streamlined code by removing unnecessary package imports, and adjusted optional variable handling. These changes enhance code clarity and efficiency. Related to PR #3925 --- fiftyone/utils/open_clip.py | 22 ++++++++++------------ fiftyone/zoo/models/manifest-torch.json | 2 +- 2 files changed, 11 insertions(+), 13 deletions(-) diff --git a/fiftyone/utils/open_clip.py b/fiftyone/utils/open_clip.py index 96536f42fe..031e23b500 100644 --- a/fiftyone/utils/open_clip.py +++ b/fiftyone/utils/open_clip.py @@ -6,13 +6,7 @@ | """ import logging -import os -from packaging.version import Version -import warnings -import eta.core.web as etaw - -import fiftyone as fo import fiftyone.core.models as fom import fiftyone.core.utils as fou import fiftyone.utils.torch as fout @@ -21,13 +15,15 @@ fou.ensure_torch() import torch -import open_clip +open_clip = fou.lazy_import( + "open_clip", callback=lambda: fou.ensure_package("open_clip_torch") +) logger = logging.getLogger(__name__) class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): - """Configuration for running a :class:`TorchCLIPModel`. + """Configuration for running a :class:`TorchOpenClipModel`. See :class:`fiftyone.utils.torch.TorchImageModelConfig` for additional arguments. @@ -36,7 +32,7 @@ class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): context_length: the model's context length text_prompt: the text prompt to use, e.g., ``"A photo of"`` classes (None): a list of custom classes for zero-shot prediction - model_name_clip (None): the model to use + model_name (None): the model to use pretrained (None): the pretrained version to use """ @@ -47,15 +43,17 @@ def __init__(self, d): self.context_length = self.parse_int(d, "context_length") self.text_prompt = self.parse_string(d, "text_prompt") - self.model_name_clip = self.parse_string(d, "model_name_clip") - self.pretrained = self.parse_string(d, "pretrained") + self.model_name_clip = self.parse_string( + d, "model_name_clip", default="ViT-B-32" + ) + self.pretrained = self.parse_string(d, "pretrained", default=None) class TorchOpenClipModel(fout.TorchImageModel, fom.PromptMixin): """Torch implementation of CLIP from https://github.com/mlfoundations/open_clip. Args: - config: a :class:`TorchOpenClipPModelConfig` + config: a :class:`TorchOpenClipModelConfig` """ def __init__(self, config): diff --git a/fiftyone/zoo/models/manifest-torch.json b/fiftyone/zoo/models/manifest-torch.json index 27bf03a142..3704d39017 100644 --- a/fiftyone/zoo/models/manifest-torch.json +++ b/fiftyone/zoo/models/manifest-torch.json @@ -1863,7 +1863,7 @@ "date_added": "2020-12-11 13:45:51" }, { - "base_name": "open-clip", + "base_name": "open-clip-torch", "base_filename": "", "version": null, "description": "OPEN CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs", From e5319c8a37ffde64cca18b54edc31c1ef3137620 Mon Sep 17 00:00:00 2001 From: Stuart Wheaton Date: Wed, 20 Dec 2023 15:04:35 -0500 Subject: [PATCH 48/59] add 3923 --- docs/source/release-notes.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 6fea4580b4..5d09bdfa04 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -5,7 +5,7 @@ FiftyOne Release Notes .. _release-notes-teams-v1.5.2: -FiftyOne Teams 1.5.2 +FiftyOne Teams 1.5.3 -------------------- *Released December 20, 2023* @@ -16,6 +16,9 @@ General - Added 'label' as searchable field for delegated operations - Fixed issue where invalid tokens were not causing redirects - Re-running a delegated operation now uses dataset id instead of name +- Improved performance of ``add_samples()``, ``set_values()``, + ``compute_metadata()``, and other large batched computations, through + API-connected SDK. .. _release-notes-v0.23.2: @@ -37,6 +40,8 @@ Core `#3942 `_ - Added configurable batch sizes to `bulk_write()` `#3944 `_ +- Added configurable batcher choices used in some operations like ``add_samples()`` + `#3923 `_ - Added built-in Ubuntu 23 support `#3936 `_ - Fixed evaluation of unclosed polylines From 83b80df6ebecd31617997987507692578790c1af Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 15:06:02 -0500 Subject: [PATCH 49/59] use clip_model_name --- fiftyone/utils/open_clip.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/fiftyone/utils/open_clip.py b/fiftyone/utils/open_clip.py index 031e23b500..88545e2d9a 100644 --- a/fiftyone/utils/open_clip.py +++ b/fiftyone/utils/open_clip.py @@ -32,7 +32,7 @@ class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): context_length: the model's context length text_prompt: the text prompt to use, e.g., ``"A photo of"`` classes (None): a list of custom classes for zero-shot prediction - model_name (None): the model to use + clip_model_name ("ViT-B-32"): the Open CLIP model to use pretrained (None): the pretrained version to use """ @@ -43,8 +43,8 @@ def __init__(self, d): self.context_length = self.parse_int(d, "context_length") self.text_prompt = self.parse_string(d, "text_prompt") - self.model_name_clip = self.parse_string( - d, "model_name_clip", default="ViT-B-32" + self.clip_model_name = self.parse_string( + d, "clip_model_name", default="ViT-B-32" ) self.pretrained = self.parse_string(d, "pretrained", default=None) @@ -66,9 +66,9 @@ def _load_model(self, config): _, self.preprocess, ) = open_clip.create_model_and_transforms( - config.model_name_clip, pretrained=config.pretrained + config.clip_model_name, pretrained=config.pretrained ) - self._tokenizer = open_clip.get_tokenizer(config.model_name_clip) + self._tokenizer = open_clip.get_tokenizer(config.clip_model_name) return self._model def _get_text_features(self): From a912a718867632bb73f672a01db922d94f3ffdf6 Mon Sep 17 00:00:00 2001 From: Ritchie Martori Date: Wed, 20 Dec 2023 12:07:28 -0800 Subject: [PATCH 50/59] merge 1.5.2 release notes --- docs/source/release-notes.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 5d09bdfa04..7dec60c313 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -75,6 +75,17 @@ Plugins - Python Operators now load datasets by ID instead of name `#3920 `_ +.. _release-notes-teams-v1.5.2: + +FiftyOne Teams 1.5.2 +-------------------- +*Released December 11, 2023* + +Bugs + +- Avoid creating non-existent database indexes on API startup +- Avoid errors when archiving snapshots with corrupted run results + .. _release-notes-teams-v1.5.1: FiftyOne Teams 1.5.1 From ea2055217800ecfad401587672e925f6aca0b6da Mon Sep 17 00:00:00 2001 From: Stuart Wheaton Date: Wed, 20 Dec 2023 15:39:52 -0500 Subject: [PATCH 51/59] add note on trimming api logging --- docs/source/release-notes.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 7dec60c313..a1e833b827 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -19,6 +19,7 @@ General - Improved performance of ``add_samples()``, ``set_values()``, ``compute_metadata()``, and other large batched computations, through API-connected SDK. +- Trim API logging of large batch SDK operations. .. _release-notes-v0.23.2: From 69c28ba8c3c9e84c00562c4ab6b64d20b1c54dc3 Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 15:56:10 -0500 Subject: [PATCH 52/59] fixes --- docs/scripts/make_model_zoo_docs.py | 1 + fiftyone/utils/open_clip.py | 20 +++++++++----------- fiftyone/zoo/models/manifest-torch.json | 1 - 3 files changed, 10 insertions(+), 12 deletions(-) diff --git a/docs/scripts/make_model_zoo_docs.py b/docs/scripts/make_model_zoo_docs.py index e315097e18..4ab0316496 100644 --- a/docs/scripts/make_model_zoo_docs.py +++ b/docs/scripts/make_model_zoo_docs.py @@ -143,6 +143,7 @@ "{{ name }}", text_prompt="A photo of a", classes=["person", "dog", "cat", "bird", "car", "tree", "chair"], + cache=False, ) dataset.apply_model(model, label_field="predictions") diff --git a/fiftyone/utils/open_clip.py b/fiftyone/utils/open_clip.py index 88545e2d9a..15d4cab964 100644 --- a/fiftyone/utils/open_clip.py +++ b/fiftyone/utils/open_clip.py @@ -29,28 +29,26 @@ class TorchOpenClipModelConfig(fout.TorchImageModelConfig, fozm.HasZooModel): arguments. Args: - context_length: the model's context length text_prompt: the text prompt to use, e.g., ``"A photo of"`` + clip_model ("ViT-B-32"): the Open CLIP model to use + pretrained ("openai"): the pretrained version to use classes (None): a list of custom classes for zero-shot prediction - clip_model_name ("ViT-B-32"): the Open CLIP model to use - pretrained (None): the pretrained version to use """ def __init__(self, d): d = self.init(d) super().__init__(d) - self.context_length = self.parse_int(d, "context_length") self.text_prompt = self.parse_string(d, "text_prompt") - - self.clip_model_name = self.parse_string( - d, "clip_model_name", default="ViT-B-32" + self.clip_model = self.parse_string( + d, "clip_model", default="ViT-B-32" ) - self.pretrained = self.parse_string(d, "pretrained", default=None) + self.pretrained = self.parse_string(d, "pretrained", default="openai") class TorchOpenClipModel(fout.TorchImageModel, fom.PromptMixin): - """Torch implementation of CLIP from https://github.com/mlfoundations/open_clip. + """Torch implementation of CLIP from + https://github.com/mlfoundations/open_clip. Args: config: a :class:`TorchOpenClipModelConfig` @@ -66,9 +64,9 @@ def _load_model(self, config): _, self.preprocess, ) = open_clip.create_model_and_transforms( - config.clip_model_name, pretrained=config.pretrained + config.clip_model, pretrained=config.pretrained ) - self._tokenizer = open_clip.get_tokenizer(config.clip_model_name) + self._tokenizer = open_clip.get_tokenizer(config.clip_model) return self._model def _get_text_features(self): diff --git a/fiftyone/zoo/models/manifest-torch.json b/fiftyone/zoo/models/manifest-torch.json index 3704d39017..0bb1459f73 100644 --- a/fiftyone/zoo/models/manifest-torch.json +++ b/fiftyone/zoo/models/manifest-torch.json @@ -1885,7 +1885,6 @@ "embeddings_layer": "visual", "tokenizer_base_filename": "clip_bpe_simple_vocab_16e6.txt.gz", "tokenizer_base_url": "https://github.com/openai/CLIP/raw/main/clip/bpe_simple_vocab_16e6.txt.gz", - "context_length": 77, "text_prompt": "A photo of" } }, From da4895dcfc0897ab78bc8148d3d98d9a7faf9525 Mon Sep 17 00:00:00 2001 From: bonlime Date: Thu, 7 Dec 2023 12:28:23 +0000 Subject: [PATCH 53/59] support multiple fields in Label-Studio --- fiftyone/utils/labelstudio.py | 145 +++++++------ tests/intensive/labelstudio_tests.py | 307 +++++++++++++++++++++++---- 2 files changed, 346 insertions(+), 106 deletions(-) diff --git a/fiftyone/utils/labelstudio.py b/fiftyone/utils/labelstudio.py index 0d377b5787..162ec95f4f 100644 --- a/fiftyone/utils/labelstudio.py +++ b/fiftyone/utils/labelstudio.py @@ -8,6 +8,7 @@ """ from copy import deepcopy from datetime import datetime +from collections import defaultdict import itertools import json import logging @@ -223,12 +224,8 @@ def _init_project(self, config, samples): break # generate label config - assert len(label_schema) == 1 - _, label_info = label_schema.popitem() label_config = generate_labeling_config( - media=samples.media_type, - label_type=label_info["type"], - labels=label_info["classes"], + label_schema, samples.media_type ) project = self._client.start_project( @@ -264,7 +261,7 @@ def _prepare_tasks(self, samples, label_schema, media_field): smp[label_field], label_type=label_type, full_result={ - "from_name": "label", + "from_name": label_field, "to_name": "image", "original_width": smp.metadata["width"], "original_height": smp.metadata["height"], @@ -294,11 +291,7 @@ def _upload_tasks(self, project, tasks, predictions=None): files = [ ( one["source_id"], - ( - one["source_id"], - open(one[one["media_type"]], "rb"), - one["mime_type"], - ), + (open(one[one["media_type"]], "rb")), ) for one in tasks ] @@ -353,7 +346,7 @@ def task_filter(x): return list(filter(task_filter, matched_tasks)) - def _import_annotations(self, tasks, task_map, label_type): + def _import_annotations(self, tasks, task_map): results = {} for t in tasks: # convert latest annotation results @@ -367,25 +360,27 @@ def _import_annotations(self, tasks, task_map, label_type): if len(annotations) == 0 else sorted(annotations, key=lambda x: x["updated_at"])[-1] ) - if label_type == "keypoints": - labels = import_label_studio_annotation( - latest_annotation["result"] - ) - else: - labels = [ - import_label_studio_annotation(r, label_type=label_type) - for r in latest_annotation.get("result", []) - ] + latest_result = latest_annotation.get("result", []) + # we need logic below to group keypoints with same label together + result_kps = [ + r for r in latest_result if _result_type(r) == "keypointlabels" + ] + result_not_kps = [ + r for r in latest_result if _result_type(r) != "keypointlabels" + ] + labels = [ + import_label_studio_annotation(r) for r in result_not_kps + ] + if result_kps: + labels += [import_label_studio_annotation(result_kps)] + + if not labels: + continue # add to dict - if labels: - label_ids = ( - {l.id: l for l in labels} - if not isinstance(labels[0], fol.Regression) - else labels[0] - ) - sample_id = task_map[t["id"]] - results[sample_id] = label_ids + sample_id = task_map[t["id"]] + # we save and pass both id and the name of the label field + results[sample_id] = {l.id: (ln, l) for (ln, l) in labels} return results @@ -449,14 +444,24 @@ def download_annotations(self, results): project, list(results.uploaded_tasks.keys()) ) annotations = {} + all_labels = self._import_annotations( + labeled_tasks, results.uploaded_tasks + ) + annotations = defaultdict(lambda: defaultdict(dict)) + # TODO: where to fix label type for "instances" to convert segm -> detection? + for task_id, task_labels in all_labels.items(): + for label_id, (label_field, label) in task_labels.items(): + annotations[label_field][task_id][label_id] = label + + # convert to expected format + final_annotations = {} for label_field, label_info in results.config.label_schema.items(): return_type = foua._RETURN_TYPES_MAP[label_info["type"]] - labels = self._import_annotations( - labeled_tasks, results.uploaded_tasks, return_type - ) - annotations.update({label_field: {return_type: labels}}) + final_annotations[label_field] = { + return_type: annotations[label_field] + } - return annotations + return final_annotations def upload_predictions(self, project, tasks, sample_labels, label_type): """Uploads the given predictions to an existing Label Studio project. @@ -540,63 +545,67 @@ def _from_dict(cls, d, samples, config, anno_key): ) -def generate_labeling_config(media, label_type, labels=None): +def generate_labeling_config(label_schema, media): """Generates a labeling config for a Label Studio project. Args: + label_shema: dict defining each label type media: The media type to label - label_type: The type of labels to use - labels (None): the labels to use Returns: a labeling config """ assert media in ["image", "video"] - assert ( - label_type in _LABEL_TYPES.keys() - or label_type in foua._RETURN_TYPES_MAP.keys() + possible_types = list(_LABEL_TYPES.keys()) + list( + foua._RETURN_TYPES_MAP.keys() ) # root view and media view root = etree.Element("View") etree.SubElement(root, media.capitalize(), name=media, value=f"${media}") - # labels view - parent_tag, child_tag, tag_kwargs = _ls_tags_from_type(label_type) - # parent_name = child_tag.lower() if child_tag else parent_tag.lower() - label_view = etree.SubElement( - root, parent_tag, name="label", toName=media, **tag_kwargs - ) - if labels: - for one in labels: - etree.SubElement(label_view, child_tag, value=one) + for label_field, label_info in label_schema.items(): + assert label_info["type"] in possible_types + parent_tag, child_tag, tag_kwargs = _ls_tags_from_type( + label_info["type"] + ) + label_view = etree.SubElement( + root, parent_tag, name=label_field, toName=media, **tag_kwargs + ) + if "classes" in label_info and label_info["classes"]: + for one in label_info["classes"]: + etree.SubElement(label_view, child_tag, value=one) config_str = etree.tostring(root, pretty_print=True).decode() return config_str -def import_label_studio_annotation(result, label_type=None): +def _result_type(result): + if isinstance(result, dict): + return result["type"] + elif isinstance(result, list): + return result[0]["type"] + else: + raise TypeError("Result type %s is not understood" % type(result)) + + +def import_label_studio_annotation(result): """Imports an annotation from Label Studio. Args: result: the annotation result from Label Studio - label_type (None): the label type to use when importing the annotation. - This argument is only used when importing brush labels. By default, - these labels are imported as semantic segmentations, but you can - pass ``label_type="instances"`` to import them as instance - segmentations instead Returns: a :class:`fiftyone.core.labels.Label` """ # TODO link keypoints by parent id # TODO handle multiple classes for segmentation + ls_type = _result_type(result) + # we return this to match with label field later if isinstance(result, dict): - ls_type = result["type"] + from_name = result.get("from_name", "from_name") elif isinstance(result, list): - ls_type = result[0]["type"] - else: - raise TypeError("Result type %s is not understood" % type(result)) + from_name = result[0].get("from_name", "from_name") if ls_type == "choices": label = _from_choices(result) @@ -607,7 +616,7 @@ def import_label_studio_annotation(result, label_type=None): elif ls_type == "keypointlabels": label = _from_keypointlabels(result) elif ls_type == "brushlabels": - label = _from_brushlabels(result, label_type=label_type) + label = _from_brushlabels(result) elif ls_type == "number": label = _from_number(result) else: @@ -619,8 +628,7 @@ def import_label_studio_annotation(result, label_type=None): label.id = label_id except: pass - - return label + return from_name, label def _update_dict(src_dict, update_dict): @@ -867,10 +875,13 @@ def _from_keypointlabels(result): points = _normalize_values(points) keypoints.append(fol.Keypoint(label=key, points=points)) - return keypoints + if len(keypoints) == 1: + return keypoints[0] + else: + return fol.Keypoints(keypoints=keypoints) -def _from_brushlabels(result, label_type=None): +def _from_brushlabels(result): label_values = result["value"]["brushlabels"] img = brush.decode_rle(result["value"]["rle"]) shape = (result["original_height"], result["original_width"], 4) @@ -879,9 +890,9 @@ def _from_brushlabels(result, label_type=None): label = label_values[0] segmentation = fol.Segmentation(label=label, mask=mask) - if label_type == "instances": - detections = segmentation.to_detections({255: label}).detections - return detections[0] if detections else None + # if label_type == "instances": + # detections = segmentation.to_detections({255: label}).detections + # return detections[0] if detections else None return segmentation diff --git a/tests/intensive/labelstudio_tests.py b/tests/intensive/labelstudio_tests.py index 6f4a34077e..8562ab2a9e 100644 --- a/tests/intensive/labelstudio_tests.py +++ b/tests/intensive/labelstudio_tests.py @@ -38,12 +38,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "classification", - "labels": ["Adult content", "Weapons", "Violence"], + "label_schema": { + "some_random_field": { + "type": "classification", + "classes": ["Adult content", "Weapons", "Violence"], + } + }, }, "output": """ - + @@ -53,12 +57,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "classifications", - "labels": ["Boeing", "Airbus"], + "label_schema": { + "some_random_field": { + "type": "classifications", + "classes": ["Boeing", "Airbus"], + } + }, }, "output": """ - + @@ -67,12 +75,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "detections", - "labels": ["Airplane", "Car"], + "label_schema": { + "some_random_field": { + "type": "detections", + "classes": ["Airplane", "Car"], + } + }, }, "output": """ - + @@ -81,26 +93,34 @@ def label_configs(): { "input": { "media": "image", - "label_type": "instances", - "labels": ["Airplane", "Car"], + "label_schema": { + "some_random_field": { + "type": "instances", + "classes": ["Airplane", "Car"], + } + }, }, "output": """ - + + """, }, { "input": { "media": "image", - "label_type": "polylines", - "labels": ["Airplane", "Car"], + "label_schema": { + "some_random_field": { + "type": "polylines", + "classes": ["Airplane", "Car"], + } + }, }, "output": """ - + @@ -109,12 +129,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "polygons", - "labels": ["Airplane", "Car"], + "label_schema": { + "some_random_field": { + "type": "polygons", + "classes": ["Airplane", "Car"], + } + }, }, "output": """ - + @@ -123,12 +147,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "keypoints", - "labels": ["Engine", "Tail"], + "label_schema": { + "some_random_field": { + "type": "keypoints", + "classes": ["Engine", "Tail"], + } + }, }, "output": """ - + @@ -137,12 +165,16 @@ def label_configs(): { "input": { "media": "image", - "label_type": "segmentation", - "labels": ["Planet", "Moonwalker"], + "label_schema": { + "some_random_field": { + "type": "segmentation", + "classes": ["Planet", "Moonwalker"], + } + }, }, "output": """ - + @@ -151,11 +183,58 @@ def label_configs(): { "input": { "media": "image", - "label_type": "scalar", + "label_schema": { + "some_random_field": { + "type": "scalar", + } + }, + }, + "output": """ + + +""", + }, + # Multiple fields example. Also check that can have multiple of the same type + { + "input": { + "media": "image", + "label_schema": { + "some_random_field": { + "type": "segmentation", + "classes": ["Planet", "Moonwalker"], + }, + "some_random_field2": { + "type": "classification", + "classes": ["PlanetClass", "MoonwalkerClass"], + }, + "some_random_field3": { + "type": "classification", + "classes": ["PlanetClass2", "MoonwalkerClass2"], + }, + "some_random_field4": { + "type": "detection", + "classes": ["PlanetDet", "MoonwalkerDet"], + }, + }, }, "output": """ - + + + + + + + + + + + + """, }, ] @@ -298,17 +377,89 @@ def label_mappings(): "type": "keypointlabels", }, ], - "fiftyone": [ - fol.Keypoint( - label="Tail", - points=[ - (0.39, 0.81), - (0.333, 0.771), - (0.424, 0.894), - (0.424, 0.745), - ], - ), + "fiftyone": fol.Keypoint( + label="Tail", + points=[ + (0.39, 0.81), + (0.333, 0.771), + (0.424, 0.894), + (0.424, 0.745), + ], + ), + }, + { + "labelstudio": [ + { + "original_width": 600, + "original_height": 403, + "image_rotation": 0, + "value": { + "x": 39.0, + "y": 81.0, + "width": 0.346, + "keypointlabels": ["LEye"], + }, + "from_name": "label", + "type": "keypointlabels", + }, + { + "original_width": 600, + "original_height": 403, + "image_rotation": 0, + "value": { + "x": 33.3, + "y": 77.1, + "width": 0.346, + "keypointlabels": ["LEye"], + }, + "from_name": "label", + "type": "keypointlabels", + }, + { + "original_width": 600, + "original_height": 403, + "image_rotation": 0, + "value": { + "x": 42.4, + "y": 89.4, + "width": 0.346, + "keypointlabels": ["REye"], + }, + "from_name": "label", + "type": "keypointlabels", + }, + { + "original_width": 600, + "original_height": 403, + "image_rotation": 0, + "value": { + "x": 42.4, + "y": 74.5, + "width": 0.346, + "keypointlabels": ["REye"], + }, + "from_name": "label", + "type": "keypointlabels", + }, ], + "fiftyone": fol.Keypoints( + keypoints=[ + fol.Keypoint( + label="LEye", + points=[ + (0.39, 0.81), + (0.333, 0.771), + ], + ), + fol.Keypoint( + label="REye", + points=[ + (0.424, 0.894), + (0.424, 0.745), + ], + ), + ] + ), }, { "labelstudio": { @@ -324,7 +475,7 @@ def label_mappings(): def test_import_labels(label_mappings): for case in label_mappings: - label = fouls.import_label_studio_annotation(case["labelstudio"]) + label = fouls.import_label_studio_annotation(case["labelstudio"])[1] expected = case["fiftyone"] if isinstance(expected, (list, tuple)): @@ -389,6 +540,81 @@ def test_annotate_simple(backend_config, setup, label_type): assert len(labelled) == len(dummy_predictions) +def test_annotate_not_all(backend_config, setup): + # check that we can load partially annotated data + label_type = "classification" + dataset, anno_key, label_field, labels = setup + label_field += f"-{label_type}" + dataset.annotate( + anno_key, + label_field=label_field, + project_name=f"labelstudio_{label_type}_tests", + label_type=label_type, + classes=labels if label_type != "scalar" else None, + backend="labelstudio", + **backend_config, + ) + + # create dummy prediction and convert to annotations + dummy_predictions = _generate_dummy_predictions( + label_type, labels, len(dataset) - 1 + ) + _add_dummy_annotations( + dataset, anno_key, dummy_predictions, backend_config + ) + + dataset.load_annotations(anno_key, cleanup=False, **backend_config) + + labelled = dataset.exists(label_field) + assert len(labelled) == len(dummy_predictions) + assert len(labelled) == len(dataset) - 1 + + +def test_annotate_multiple_fields(backend_config, setup): + # check that we can load partially annotated data + dataset, anno_key, label_field, labels = setup + label_schema = { + "cls_field": { + "type": "classification", + "labels": ["yes", "no"], + "classes": ["yes", "no"], + }, + "det_field": { + "type": "detections", + "classes": ["obj"], + }, + } + + dataset.annotate( + anno_key, + label_schema=label_schema, + project_name=f"labelstudio_multi-field_tests", + backend="labelstudio", + **backend_config, + ) + + # create dummy prediction and convert to annotations + dummy_predictions_cls = _generate_dummy_predictions( + "classification", labels, len(dataset) + ) + dummy_predictions_det = _generate_dummy_predictions( + "detections", labels, len(dataset) + ) + _add_dummy_annotations( + dataset, anno_key, dummy_predictions_det, backend_config + ) + dataset.load_annotations(anno_key, cleanup=False, **backend_config) + labelled = dataset.exists("det_field") + assert len(labelled) == len(dataset) + # can't load them together, so do it one by one + _add_dummy_annotations( + dataset, anno_key, dummy_predictions_cls, backend_config + ) + dataset.load_annotations(anno_key, cleanup=False, **backend_config) + labelled = dataset.exists("cls_field") + assert len(labelled) == len(dataset) + + @pytest.mark.parametrize( "label_type", ["classification", "detections", "polylines", "keypoints"] ) @@ -442,6 +668,9 @@ def _assert_labels_equal(converted, expected): np.testing.assert_allclose( converted.points, expected.points, atol=0.01, rtol=0.01 ) + elif expected._cls == "Keypoints": + for pair in zip(converted.keypoints, expected.keypoints): + _assert_labels_equal(*pair) elif expected._cls == "Regression": assert expected.value == converted.value else: @@ -509,7 +738,7 @@ def generate_keypoint(): return [ { "type": "keypointlabels", - "from_name": "label", + "from_name": "new_field-keypoints", "to_name": "image", "value": { "x": random.randint(0, 50), From 31874ae250673edfadce90424a5d97c9d6b57b97 Mon Sep 17 00:00:00 2001 From: bonlime Date: Thu, 7 Dec 2023 13:11:24 +0000 Subject: [PATCH 54/59] support instances --- fiftyone/utils/labelstudio.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/fiftyone/utils/labelstudio.py b/fiftyone/utils/labelstudio.py index 162ec95f4f..20ac754c72 100644 --- a/fiftyone/utils/labelstudio.py +++ b/fiftyone/utils/labelstudio.py @@ -448,7 +448,6 @@ def download_annotations(self, results): labeled_tasks, results.uploaded_tasks ) annotations = defaultdict(lambda: defaultdict(dict)) - # TODO: where to fix label type for "instances" to convert segm -> detection? for task_id, task_labels in all_labels.items(): for label_id, (label_field, label) in task_labels.items(): annotations[label_field][task_id][label_id] = label @@ -457,6 +456,17 @@ def download_annotations(self, results): final_annotations = {} for label_field, label_info in results.config.label_schema.items(): return_type = foua._RETURN_TYPES_MAP[label_info["type"]] + # need to convert masks to detections + if label_info["type"] == "instances": + new_label_field = defaultdict(dict) + for task_id, task_labels in annotations[label_field].items(): + for label_id, label in task_labels.items(): + detections = label.to_detections( + {255: label.label} + ).detections + detections = detections[0] if detections else None + new_label_field[task_id][label_id] = detections + annotations[label_field] = new_label_field final_annotations[label_field] = { return_type: annotations[label_field] } @@ -889,11 +899,6 @@ def _from_brushlabels(result): label = label_values[0] segmentation = fol.Segmentation(label=label, mask=mask) - - # if label_type == "instances": - # detections = segmentation.to_detections({255: label}).detections - # return detections[0] if detections else None - return segmentation From 9c939c4dbfca97a056f192cc97e330f976100ce3 Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 16:25:26 -0500 Subject: [PATCH 55/59] acknowleding bonlime! --- docs/source/integrations/labelstudio.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/integrations/labelstudio.rst b/docs/source/integrations/labelstudio.rst index 4ffc294234..17e245e4a3 100644 --- a/docs/source/integrations/labelstudio.rst +++ b/docs/source/integrations/labelstudio.rst @@ -644,5 +644,5 @@ ________________ .. note:: - Special thanks to `Rustem Galiullin `_ for - making this integration happen! + Special thanks to `Rustem Galiullin `_ and + `Emil Zakirov `_ for building this integration! From fd72aec5ab8fd155f64ee5116910f186d0240d9c Mon Sep 17 00:00:00 2001 From: brimoor Date: Wed, 20 Dec 2023 19:28:20 -0500 Subject: [PATCH 56/59] editing pass --- docs/source/release-notes.rst | 99 +++++++++++++++++++++-------------- 1 file changed, 61 insertions(+), 38 deletions(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index a1e833b827..7889e5a7fd 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -3,50 +3,37 @@ FiftyOne Release Notes .. default-role:: code -.. _release-notes-teams-v1.5.2: +.. _release-notes-teams-v1.5.3: FiftyOne Teams 1.5.3 -------------------- -*Released December 20, 2023* +*Released December 21, 2023* Includes all updates from :ref:`FiftyOne 0.23.2 `, plus: General -- Added 'label' as searchable field for delegated operations +- Improved performance of + :meth:`add_samples() `, + :meth:`set_values() `, + :meth:`compute_metadata() `, + and other large batched computations when using + :ref:`API connections ` +- Added `label` as a searchable field for delegated operations - Fixed issue where invalid tokens were not causing redirects -- Re-running a delegated operation now uses dataset id instead of name -- Improved performance of ``add_samples()``, ``set_values()``, - ``compute_metadata()``, and other large batched computations, through - API-connected SDK. -- Trim API logging of large batch SDK operations. +- Re-running a delegated operation now uses dataset ID instead of name +- Trimmed API logging of large batch SDK operations .. _release-notes-v0.23.2: FiftyOne 0.23.2 --------------- -*Released December 20, 2023* +*Released December 21, 2023* -Core +News -- Fixed an issue where exporting patches would have incorrect path names - `#3921 `_ -- Removed loading from mongoengine cache - `#3922 `_ -- Fixed overwriting dataset metadata with empty values during import - `#3913 `_ -- Fixed issue where users could not select scalar fields in the sidebar - `#3938 `_ -- Optimized reloading data from mongodb when performing bulk writes - `#3942 `_ -- Added configurable batch sizes to `bulk_write()` - `#3944 `_ -- Added configurable batcher choices used in some operations like ``add_samples()`` - `#3923 `_ -- Added built-in Ubuntu 23 support - `#3936 `_ -- Fixed evaluation of unclosed polylines - `#3930 `_ +- Added :ref:`OpenCLIP ` to the FiftyOne Model Zoo! + `#3925 `_ App @@ -54,27 +41,63 @@ App `#3935 `_ - Fixed an issue where embeddigns legend did not display full names `#3927 `_ -- Added a toggle to show/hide fields in the sample modal that have undefined values +- Added a toggle to show/hide fields in the sample modal that have undefined + values `#3937 `_ -- Fixed an issue with the Lightning threshold Reset button +- Fixed an issue with the Lightning threshold reset button `#3933 `_ - Fixed an issue where similarity search only worked on the default group slice `#3912 `_ +- Fixed issue where users could not select scalar fields in the sidebar + `#3938 `_ + +Core + +- Added configurable batching choices to optimize throughput for operations like + :meth:`add_samples() ` + `#3923 `_ +- IoU computations for non-filled polylines now uses keypoint similarity + `#3930 `_ +- Optimized bulk write database operations like + :meth:`set_values() ` + `#3942 `_ +- Added configurable batch sizes to bulk write operations + `#3944 `_ +- Added builtin support for Ubuntu 23 + `#3936 `_ +- Fixed an issue where exporting patches would have incorrect path names + `#3921 `_ +- Removed loading from mongoengine cache + `#3922 `_ +- Fixed overwriting dataset metadata with empty values during import + `#3913 `_ + +Annotation + +- Added support for annotating multiple label fields using the Label Studio + backend + `#3895 `_ Plugins -- Added support for the new `@voxel51/utils/delegate` operator +- Added support for + :ref:`delegating function calls ` via the new + `@voxel51/utils/delegate `_ + operator `#3939 `_ -- Added the ability to to search multiple fields in a delegated operation list query +- Added the ability to to search multiple fields in a delegated operation list + query `#3892 `_ -- Fixed backwards compatibility issue with `ctx.secrets` +- Delegated operators now reference datasets by ID rather than name for + robustness to dataset name changes + `#3920 `_ +- Improved validation for the builtin `delete_selected_samples` and + `clone_selected_samples` operators + `#3914 `_ +- Fixed backwards compatibility issues with `ctx.secrets` `#3908 `_ -- Fixed issue with JS plugin app configs +- Fixed issue with JS plugin App configs `#3924 `_ -- Improved validation for `delete_selected_samples` and `clone_selected_samples` builtin operators - `#3914 `_ -- Python Operators now load datasets by ID instead of name - `#3920 `_ .. _release-notes-teams-v1.5.2: From 076c3ffb616277a00d4bebb997e1a14e81672beb Mon Sep 17 00:00:00 2001 From: Ritchie Martori Date: Wed, 20 Dec 2023 16:47:31 -0800 Subject: [PATCH 57/59] typo --- docs/source/release-notes.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst index 7889e5a7fd..fd0ffdbc07 100644 --- a/docs/source/release-notes.rst +++ b/docs/source/release-notes.rst @@ -85,7 +85,7 @@ Plugins `@voxel51/utils/delegate `_ operator `#3939 `_ -- Added the ability to to search multiple fields in a delegated operation list +- Added the ability to search multiple fields in a delegated operation list query `#3892 `_ - Delegated operators now reference datasets by ID rather than name for From ce9cbaef03ff7810f904ed7de038efc96303c136 Mon Sep 17 00:00:00 2001 From: brimoor Date: Thu, 21 Dec 2023 10:25:35 -0500 Subject: [PATCH 58/59] docs bugfixes --- docs/source/teams/dataset_versioning.rst | 2 +- fiftyone/core/odm/dataset.py | 83 ++++++++++++++---------- 2 files changed, 49 insertions(+), 36 deletions(-) diff --git a/docs/source/teams/dataset_versioning.rst b/docs/source/teams/dataset_versioning.rst index 0bd4197426..dd9073428d 100644 --- a/docs/source/teams/dataset_versioning.rst +++ b/docs/source/teams/dataset_versioning.rst @@ -650,7 +650,7 @@ method in the Management SDK: .. _dataset-versioning-unarchive-snapshot: Unarchive snapshot ----------------- +------------------ To make an archived snapshot browseable again, users with Can Manage permissions to the dataset can unarchive it via the UI or Management SDK. diff --git a/fiftyone/core/odm/dataset.py b/fiftyone/core/odm/dataset.py index 172e1706c1..88939ac5cd 100644 --- a/fiftyone/core/odm/dataset.py +++ b/fiftyone/core/odm/dataset.py @@ -169,7 +169,7 @@ class ColorScheme(EmbeddedDocument): import fiftyone as fo import fiftyone.zoo as foz - dataset=foz.load_zoo_dataset("quickstart") + dataset = foz.load_zoo_dataset("quickstart") # Store a custom color scheme for a dataset dataset.app_config.color_scheme = fo.ColorScheme( @@ -222,54 +222,67 @@ class ColorScheme(EmbeddedDocument): ], default_colorscale={"name": "sunset", "list": None}, ) + session = fo.launch_app(dataset) Args: - color_by (None): an option that annotations can be colored by "field", - "value" or "instance" + color_by (None): whether annotations should be colored by ``"field"``, + ``"value"``, or ``"instance"`` color_pool (None): an optional list of colors to use as a color pool for this dataset multicolor_keypoints (None): whether to use multiple colors for keypoints opacity (None): transparency of the annotation, between 0 and 1 show_skeletons (None): whether to show skeletons of keypoints - fields (None): an optional list of per-field custom colors. - - `path` (required): the fully-qualified path to the field you're - customizing - - `fieldColor` (optional): a color to assign to the field in the - App sidebar - - `colorByAttribute` (optional): the attribute to use to assign + fields (None): an optional list of dicts of per-field custom colors + with the following keys: + + - ``path`` (required): the fully-qualified path to the field + you're customizing + - ``fieldColor`` (optional): a color to assign to the field in + the App sidebar + - ``colorByAttribute`` (optional): the attribute to use to assign per-value colors. Only applicable when the field is an embedded document - - `valueColors` (optional): a list of dicts specifying colors to - use for individual values of this field - - `maskTargetsColors` (optional): a list of dicts specifying + - ``valueColors`` (optional): a list of dicts specifying colors + to use for individual values of this field + - ``maskTargetsColors`` (optional): a list of dicts specifying index and color for 2D masks - default_mask_targets_colors (None): a list of dicts specifying index - and color for 2D masks of the dataset. If a field does not have - field specific mask targets colors, this list will be used. - - `intTarget`: integer target value - - `color`: a color string - default_colorscale (None): dataset default colorscale - - `name` (optional): a named plotly colorscale, e.g. "hsv". - See https://plotly.com/python/builtin-colorscales/ - - `list` (optional): a list of dicts of colorscale values - - `value`: a float number between 0 and 1. A valid list must - have have colors defined for 0 and 1. - - `color`: a rgb color string. - colorscales (None): an optional list of per-field custom colorscale - - `path` (required): the fully-qualified path to the field you're - customizing. use "dataset" if you are setting the default - colorscale for dataset - - `name` (optional): a named colorscale plotly recognizes - - `list` (optional): a list of dicts of colorscale values - - `value`: a float number between 0 and 1. A valid list must - have have colors defined for 0 and 1. - - `color`: a rgb color string. + default_mask_targets_colors (None): a list of dicts with the following + keys specifying index and color for 2D masks of the dataset. If a + field does not have field specific mask targets colors, this list + will be used: + + - ``intTarget``: integer target value + - ``color``: a color string + default_colorscale (None): dataset default colorscale dict with the + following keys: + + - ``name`` (optional): a named plotly colorscale, e.g. ``"hsv"``. + See https://plotly.com/python/builtin-colorscales + - ``list`` (optional): a list of dicts of colorscale values + + - ``value``: a float number between 0 and 1. A valid list + must have have colors defined for 0 and 1 + - ``color``: an rgb color string + colorscales (None): an optional list of dicts of per-field custom + colorscales with the following keys: + + - ``path`` (required): the fully-qualified path to the field + you're customizing. use "dataset" if you are setting the + default colorscale for dataset + - ``name`` (optional): a named colorscale plotly recognizes + - ``list`` (optional): a list of dicts of colorscale values with + the following keys: + + - ``value``: a float number between 0 and 1. A valid list + must have have colors defined for 0 and 1 + - ``color``: an rgb color string label_tags (None): an optional dict specifying custom colors for label tags with the following keys: - - `fieldColor` (optional): a color to assign to all label tags - - `valueColors` (optional): a list of dicts + + - ``fieldColor`` (optional): a color to assign to all label tags + - ``valueColors`` (optional): a list of dicts """ # strict=False lets this class ignore unknown fields from other versions From 7f9ba213af0d4db030d9ff0bda1c0280b0d42ca9 Mon Sep 17 00:00:00 2001 From: Benjamin Kane Date: Thu, 21 Dec 2023 09:45:00 -0600 Subject: [PATCH 59/59] lang data --- docs/theme/pytorch_sphinx_theme/search.html | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/theme/pytorch_sphinx_theme/search.html b/docs/theme/pytorch_sphinx_theme/search.html index 2811947a25..f1cd32860a 100644 --- a/docs/theme/pytorch_sphinx_theme/search.html +++ b/docs/theme/pytorch_sphinx_theme/search.html @@ -12,6 +12,7 @@ {% block footer %} + {{ super() }} {% endblock %} {% block body %}

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