Adding CDDB dataset

src/renate/benchmark/datasets/vision_datasets.py

@@ -5,6 +5,7 @@
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 
+import gdown
 import pandas as pd
 import torch
 import torchvision
@@ -14,7 +15,13 @@
 from renate.benchmark.datasets.base import DataIncrementalDataModule
 from renate.data import ImageDataset
 from renate.data.data_module import RenateDataModule
-from renate.utils.file import download_and_unzip_file, download_file, download_folder_from_s3
+from renate.utils.file import (
+    download_and_unzip_file,
+    download_file,
+    download_file_from_s3,
+    download_folder_from_s3,
+    extract_file,
+)
 
 
 class TinyImageNetDataModule(RenateDataModule):
@@ -402,3 +409,65 @@ def _get_filepaths_and_labels(self, split: str) -> Tuple[List[str], List[int]]:
         data = list(df.path.apply(lambda x: os.path.join(path, x)))
         labels = list(df.label)
         return data, labels
+
+
+class CDDBDataModule(DataIncrementalDataModule):
+    md5s = {
+        "CDDB.tar.zip": "823b6496270ba03019dbd6af60cbcb6b",
+    }
+
+    domains = ["gaugan", "biggan", "wild", "whichfaceisreal", "san"]
+    dataset_stats = {
+        "CDDB": dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    }
+    google_drive_id = "1NgB8ytBMFBFwyXJQvdVT_yek1EaaEHrg"
+
+    def __init__(
+        self,
+        data_path: Union[Path, str],
+        src_bucket: Optional[str] = None,
+        src_object_name: Optional[str] = None,
+        domain: str = "gaugan",
+        val_size: float = defaults.VALIDATION_SIZE,
+        seed: int = defaults.SEED,
+    ):
+        assert domain in self.domains
+        super().__init__(
+            data_path=data_path,
+            data_id=domain.lower(),
+            src_bucket=src_bucket,
+            src_object_name=src_object_name,
+            val_size=val_size,
+            seed=seed,
+        )
+
+    def prepare_data(self) -> None:
+        """Download DomainNet dataset for given domain."""
+        file_name = "CDDB.tar.zip"
+        self._dataset_name = ""
+        if not self._verify_file(file_name):
+            if self._src_bucket is None:
+                gdown.download(
+                    output=self._data_path,
+                    quiet=False,
+                    url=f"https://drive.google.com/u/0/uc?id={self.google_drive_id}&export=download&confirm=pbef",  # noqa: E501
+                )
+            else:
+                download_file_from_s3(
+                    dst=os.path.join(self._data_path, file_name),
+                    src_bucket=self._src_bucket,
+                    src_object_name=self._src_object_name,
+                )
+            extract_file(data_path=self._data_path, file_name="CDDB.tar.zip", dataset_name="")
+            extract_file(data_path=self._data_path, file_name="CDDB.tar", dataset_name="")
+
+    def setup(self) -> None:
+        self._dataset_name = "CDDB"  # we need this because zip+tar
+        train_path = self._get_filepaths_and_labels("train")
+        train_data = torchvision.datasets.ImageFolder(train_path)
+        self._train_data, self._val_data = self._split_train_val_data(train_data)
+        test_path = self._get_filepaths_and_labels("val")
+        self._test_data = torchvision.datasets.ImageFolder(test_path)
+
+    def _get_filepaths_and_labels(self, split: str) -> Tuple[List[str], List[int]]:
+        return os.path.join(self._data_path, self._dataset_name, self.data_id, split)

src/renate/benchmark/experiment_config.py

@@ -14,6 +14,7 @@
 
 from renate.benchmark.datasets.nlp_datasets import HuggingFaceTextDataModule, MultiTextDataModule
 from renate.benchmark.datasets.vision_datasets import (
+    CDDBDataModule,
     CLEARDataModule,
     DomainNetDataModule,
     TorchVisionDataModule,
@@ -174,6 +175,14 @@ def get_data_module(
             val_size=val_size,
             seed=seed,
         )
+    if dataset_name == "CDDB":
+        return CDDBDataModule(
+            data_path=data_path,
+            src_bucket=src_bucket,
+            src_object_name=src_object_name,
+            val_size=val_size,
+            seed=seed,
+        )
     raise ValueError(f"Unknown dataset `{dataset_name}`.")
 
 
@@ -335,6 +344,11 @@ def _get_normalize_transform(dataset_name):
             DomainNetDataModule.dataset_stats["all"]["mean"],
             DomainNetDataModule.dataset_stats["all"]["std"],
         )
+    if dataset_name == "CDDB":
+        return transforms.Normalize(
+            CDDBDataModule.dataset_stats["CDDB"]["mean"],
+            CDDBDataModule.dataset_stats["CDDB"]["std"],
+        )
 
 
 def train_transform(dataset_name: str, model_name: Optional[str] = None) -> Optional[Callable]:
@@ -391,6 +405,17 @@ def train_transform(dataset_name: str, model_name: Optional[str] = None) -> Opti
                 _get_normalize_transform(dataset_name),
             ]
         )
+    if dataset_name == "CDDB":
+        return transforms.Compose(
+            [
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.ColorJitter(brightness=63 / 255),
+                transforms.ToTensor(),
+                _get_normalize_transform(dataset_name),
+            ]
+        )
+
     raise ValueError(f"Unknown dataset `{dataset_name}`.")
 
 
@@ -442,6 +467,16 @@ def test_transform(
                 _get_normalize_transform(dataset_name),
             ]
         )
+    if dataset_name == "CDDB":
+        return transforms.Compose(
+            [
+                transforms.Resize(256),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                _get_normalize_transform(dataset_name),
+            ]
+        )
+
     raise ValueError(f"Unknown dataset `{dataset_name}`.")
 
 

src/renate/utils/file.py

@@ -4,6 +4,7 @@
 import os
 import shutil
 from pathlib import Path
+from tarfile import TarFile
 from typing import List, Optional, Tuple, Union
 from urllib.parse import urlparse
 from zipfile import ZipFile
@@ -287,9 +288,15 @@ def delete_file_from_s3(bucket: str, object_name: str) -> None:
     s3_client.delete_object(Bucket=bucket, Key=str(object_name))
 
 
-def unzip_file(dataset_name: str, data_path: Union[str, Path], file_name: str) -> None:
-    """Extract .zip files into folder named with dataset name."""
-    with ZipFile(os.path.join(data_path, dataset_name, file_name)) as f:
+def extract_file(dataset_name: str, data_path: Union[str, Path], file_name: str) -> None:
+    """Extract .zip or .tar depending on the flag files into folder named with dataset name."""
+    if file_name.endswith(".zip"):
+        Extractor = ZipFile
+    elif file_name.endswith(".tar"):
+        Extractor = TarFile
+    else:
+        raise ValueError("Unknown compressed format extension. Only Zip/Tar supported.")
+    with Extractor(os.path.join(data_path, dataset_name, file_name)) as f:
         f.extractall(os.path.join(data_path, dataset_name))
 
 
@@ -328,7 +335,7 @@ def download_and_unzip_file(
 ) -> None:
     """A helper function to download data .zips and uncompress them."""
     download_file(dataset_name, data_path, src_bucket, src_object_name, url, file_name)
-    unzip_file(dataset_name, data_path, file_name)
+    extract_file(dataset_name, data_path, file_name)
 
 
 def save_pandas_df_to_csv(df: pd.DataFrame, file_path: Union[str, Path]) -> pd.DataFrame:

test/renate/benchmark/test_experimentation_config.py

@@ -9,7 +9,11 @@
 
 from renate.benchmark import experiment_config
 from renate.benchmark.datasets.nlp_datasets import HuggingFaceTextDataModule, MultiTextDataModule
-from renate.benchmark.datasets.vision_datasets import CLEARDataModule, TorchVisionDataModule
+from renate.benchmark.datasets.vision_datasets import (
+    CDDBDataModule,
+    CLEARDataModule,
+    TorchVisionDataModule,
+)
 from renate.benchmark.experiment_config import (
     data_module_fn,
     get_data_module,
@@ -95,6 +99,7 @@ def test_model_fn_fails_for_unknown_model():
             "label",
         ),
         ("MultiText", MultiTextDataModule, "distilbert-base-uncased", None, None),
+        ("CDDB", CDDBDataModule, None, None, None),
     ),
 )
 def test_get_data_module(
@@ -237,6 +242,13 @@ def test_get_scenario_fails_for_unknown_scenario(tmpdir):
             DataIncrementalScenario,
             2,
         ),
+        (
+            "DataIncrementalScenario",
+            "CDDB",
+            {"data_ids": ("biggan", "wild")},
+            DataIncrementalScenario,
+            2,
+        ),
     ),
     ids=[
         "class_incremental",
@@ -250,6 +262,7 @@ def test_get_scenario_fails_for_unknown_scenario(tmpdir):
         "wild_time_image_all_tasks",
         "domainnet_by data_id",
         "domainnet by groupings",
+        "cddb by dataid",
     ],
 )
 @pytest.mark.parametrize("val_size", (0, 0.5), ids=["no_val", "val"])
@@ -282,7 +295,7 @@ def test_data_module_fn(
     elif expected_scenario_class == DataIncrementalScenario:
         if "pretrained_model_name_or_path" in scenario_kwargs:
             assert scenario._data_module._tokenizer is not None
-        elif dataset_name not in ["CLEAR10", "CLEAR100", "DomainNet"]:
+        elif dataset_name not in ["CLEAR10", "CLEAR100", "DomainNet", "CDDB"]:
             assert scenario._data_module._tokenizer is None
     assert scenario._num_tasks == expected_num_tasks
 
@@ -296,6 +309,7 @@ def test_data_module_fn(
         ("CIFAR100", Compose, Normalize, "ResNet18CIFAR"),
         ("CLEAR10", Compose, Compose, "ResNet18"),
         ("DomainNet", Compose, Compose, "VisionTransformerB16"),
+        ("CDDB", Compose, Compose, None),
         ("hfd-rotten_tomatoes", type(None), type(None), "HuggingFaceTransformer"),
         ("fmow", Compose, Compose, "ResNet18"),
         ("yearbook", ToTensor, ToTensor, "ResNet18CIFAR"),