Docstrings for image classification data (#1093)

.pre-commit-config.yaml

@@ -69,7 +69,7 @@ repos:
     rev: v1.11.0
     hooks:
       - id: blacken-docs
-        args: [ --line-length=120 ]
+        args: [ --line-length=120, --skip-errors ]
         additional_dependencies: [ black==21.10b0 ]
 
   - repo: https://github.com/PyCQA/flake8

docs/source/general/classification_targets.rst

@@ -0,0 +1,7 @@
+.. _formatting_classification_targets:
+
+*********************************
+Formatting Classification Targets
+*********************************
+
+.. note:: The contents of this page are currently being updated. Stay tuned!

docs/source/general/customizing_transforms.rst

@@ -0,0 +1,7 @@
+.. _customizing_transforms:
+
+**********************
+Customizing Transforms
+**********************
+
+.. note:: The contents of this page are currently being updated. Stay tuned!

docs/source/index.rst

@@ -42,6 +42,8 @@ Lightning Flash
    general/serve
    general/backbones
    general/optimization
+   general/classification_targets
+   general/customizing_transforms
 
 .. toctree::
    :maxdepth: 1

flash/core/data/data_module.py

@@ -495,6 +495,14 @@ def num_classes(self) -> Optional[int]:
         n_cls_test = getattr(self.test_dataset, "num_classes", None)
         return n_cls_train or n_cls_val or n_cls_test
 
+    @property
+    def labels(self) -> Optional[int]:
+        """Property that returns the labels if this ``DataModule`` contains classification data."""
+        n_cls_train = getattr(self.train_dataset, "labels", None)
+        n_cls_val = getattr(self.val_dataset, "labels", None)
+        n_cls_test = getattr(self.test_dataset, "labels", None)
+        return n_cls_train or n_cls_val or n_cls_test
+
     @property
     def multi_label(self) -> Optional[bool]:
         """Property that returns ``True`` if this ``DataModule`` contains multi-label data."""

requirements/test.txt

@@ -3,6 +3,7 @@ codecov>=2.1
 pytest>=5.0
 pytest-flake8
 flake8
+pytest-doctestplus
 
 # install pkg
 check-manifest

setup.cfg

@@ -8,9 +8,9 @@ norecursedirs =
     .git
     dist
     build
+doctest_plus = enabled
 addopts =
     --strict
-    --doctest-modules
     --durations=0
     --color=yes
 

tests/core/data/test_callback.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from unittest import mock
-from unittest.mock import ANY, call, MagicMock
 
 import torch
 
@@ -29,7 +28,7 @@
 def test_flash_callback(_, __, tmpdir):
     """Test the callback hook system for fit."""
 
-    callback_mock = MagicMock()
+    callback_mock = mock.MagicMock()
 
     inputs = [(torch.rand(1), torch.rand(1))]
     dm = DataModule(
@@ -44,10 +43,10 @@ def test_flash_callback(_, __, tmpdir):
     _ = next(iter(dm.train_dataloader()))
 
     assert callback_mock.method_calls == [
-        call.on_load_sample(ANY, RunningStage.TRAINING),
-        call.on_per_sample_transform(ANY, RunningStage.TRAINING),
-        call.on_collate(ANY, RunningStage.TRAINING),
-        call.on_per_batch_transform(ANY, RunningStage.TRAINING),
+        mock.call.on_load_sample(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_sample_transform(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_collate(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_batch_transform(mock.ANY, RunningStage.TRAINING),
     ]
 
     class CustomModel(Task):
@@ -75,23 +74,23 @@ def step(self, batch, batch_idx, metrics):
     trainer.fit(CustomModel(), datamodule=dm)
 
     assert callback_mock.method_calls == [
-        call.on_load_sample(ANY, RunningStage.TRAINING),
-        call.on_per_sample_transform(ANY, RunningStage.TRAINING),
-        call.on_collate(ANY, RunningStage.TRAINING),
-        call.on_per_batch_transform(ANY, RunningStage.TRAINING),
-        call.on_load_sample(ANY, RunningStage.VALIDATING),
-        call.on_per_sample_transform(ANY, RunningStage.VALIDATING),
-        call.on_collate(ANY, RunningStage.VALIDATING),
-        call.on_per_batch_transform(ANY, RunningStage.VALIDATING),
-        call.on_per_batch_transform_on_device(ANY, RunningStage.VALIDATING),
-        call.on_load_sample(ANY, RunningStage.TRAINING),
-        call.on_per_sample_transform(ANY, RunningStage.TRAINING),
-        call.on_collate(ANY, RunningStage.TRAINING),
-        call.on_per_batch_transform(ANY, RunningStage.TRAINING),
-        call.on_per_batch_transform_on_device(ANY, RunningStage.TRAINING),
-        call.on_load_sample(ANY, RunningStage.VALIDATING),
-        call.on_per_sample_transform(ANY, RunningStage.VALIDATING),
-        call.on_collate(ANY, RunningStage.VALIDATING),
-        call.on_per_batch_transform(ANY, RunningStage.VALIDATING),
-        call.on_per_batch_transform_on_device(ANY, RunningStage.VALIDATING),
+        mock.call.on_load_sample(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_sample_transform(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_collate(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_batch_transform(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_load_sample(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_sample_transform(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_collate(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_batch_transform(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_batch_transform_on_device(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_load_sample(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_sample_transform(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_collate(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_batch_transform(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_per_batch_transform_on_device(mock.ANY, RunningStage.TRAINING),
+        mock.call.on_load_sample(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_sample_transform(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_collate(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_batch_transform(mock.ANY, RunningStage.VALIDATING),
+        mock.call.on_per_batch_transform_on_device(mock.ANY, RunningStage.VALIDATING),
     ]