| import shutil |
| import textwrap |
|
|
| import numpy as np |
| import pytest |
|
|
| from datasets import ClassLabel, Features, Image, Value |
| from datasets.data_files import DataFilesDict, get_data_patterns |
| from datasets.download.streaming_download_manager import StreamingDownloadManager |
| from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder |
|
|
| from ..utils import require_pil |
|
|
|
|
| @pytest.fixture |
| def cache_dir(tmp_path): |
| return str(tmp_path / "imagefolder_cache_dir") |
|
|
|
|
| @pytest.fixture |
| def data_files_with_labels_no_metadata(tmp_path, image_file): |
| data_dir = tmp_path / "data_files_with_labels_no_metadata" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| subdir_class_0 = data_dir / "cat" |
| subdir_class_0.mkdir(parents=True, exist_ok=True) |
| subdir_class_1 = data_dir / "dog" |
| subdir_class_1.mkdir(parents=True, exist_ok=True) |
|
|
| image_filename = subdir_class_0 / "image_cat.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = subdir_class_1 / "image_dog.jpg" |
| shutil.copyfile(image_file, image_filename2) |
|
|
| data_files_with_labels_no_metadata = DataFilesDict.from_patterns( |
| get_data_patterns(str(data_dir)), data_dir.as_posix() |
| ) |
|
|
| return data_files_with_labels_no_metadata |
|
|
|
|
| @pytest.fixture |
| def image_files_with_labels_and_duplicated_label_key_in_metadata(tmp_path, image_file): |
| data_dir = tmp_path / "image_files_with_labels_and_label_key_in_metadata" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| subdir_class_0 = data_dir / "cat" |
| subdir_class_0.mkdir(parents=True, exist_ok=True) |
| subdir_class_1 = data_dir / "dog" |
| subdir_class_1.mkdir(parents=True, exist_ok=True) |
|
|
| image_filename = subdir_class_0 / "image_cat.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = subdir_class_1 / "image_dog.jpg" |
| shutil.copyfile(image_file, image_filename2) |
|
|
| image_metadata_filename = tmp_path / data_dir / "metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"file_name": "cat/image_cat.jpg", "caption": "Nice image of a cat", "label": "Cat"} |
| {"file_name": "dog/image_dog.jpg", "caption": "Nice image of a dog", "label": "Dog"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
|
|
| return str(image_filename), str(image_filename2), str(image_metadata_filename) |
|
|
|
|
| @pytest.fixture |
| def image_file_with_metadata(tmp_path, image_file): |
| image_filename = tmp_path / "image_rgb.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_metadata_filename = tmp_path / "metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
| return str(image_filename), str(image_metadata_filename) |
|
|
|
|
| @pytest.fixture |
| def image_files_with_metadata_that_misses_one_image(tmp_path, image_file): |
| image_filename = tmp_path / "image_rgb.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = tmp_path / "image_rgb2.jpg" |
| shutil.copyfile(image_file, image_filename2) |
| image_metadata_filename = tmp_path / "metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
| return str(image_filename), str(image_filename2), str(image_metadata_filename) |
|
|
|
|
| @pytest.fixture(params=["jsonl", "csv"]) |
| def data_files_with_one_split_and_metadata(request, tmp_path, image_file): |
| data_dir = tmp_path / "imagefolder_data_dir_with_metadata_one_split" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| subdir = data_dir / "subdir" |
| subdir.mkdir(parents=True, exist_ok=True) |
|
|
| image_filename = data_dir / "image_rgb.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = data_dir / "image_rgb2.jpg" |
| shutil.copyfile(image_file, image_filename2) |
| image_filename3 = subdir / "image_rgb3.jpg" |
| shutil.copyfile(image_file, image_filename3) |
|
|
| image_metadata_filename = data_dir / f"metadata.{request.param}" |
| image_metadata = ( |
| textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| {"file_name": "image_rgb2.jpg", "caption": "Nice second image"} |
| {"file_name": "subdir/image_rgb3.jpg", "caption": "Nice third image"} |
| """ |
| ) |
| if request.param == "jsonl" |
| else textwrap.dedent( |
| """\ |
| file_name,caption |
| image_rgb.jpg,Nice image |
| image_rgb2.jpg,Nice second image |
| subdir/image_rgb3.jpg,Nice third image |
| """ |
| ) |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
| data_files_with_one_split_and_metadata = DataFilesDict.from_patterns( |
| get_data_patterns(str(data_dir)), data_dir.as_posix() |
| ) |
| assert len(data_files_with_one_split_and_metadata) == 1 |
| assert len(data_files_with_one_split_and_metadata["train"]) == 4 |
| return data_files_with_one_split_and_metadata |
|
|
|
|
| @pytest.fixture(params=["jsonl", "csv"]) |
| def data_files_with_two_splits_and_metadata(request, tmp_path, image_file): |
| data_dir = tmp_path / "imagefolder_data_dir_with_metadata_two_splits" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| train_dir = data_dir / "train" |
| train_dir.mkdir(parents=True, exist_ok=True) |
| test_dir = data_dir / "test" |
| test_dir.mkdir(parents=True, exist_ok=True) |
|
|
| image_filename = train_dir / "image_rgb.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = train_dir / "image_rgb2.jpg" |
| shutil.copyfile(image_file, image_filename2) |
| image_filename3 = test_dir / "image_rgb3.jpg" |
| shutil.copyfile(image_file, image_filename3) |
|
|
| train_image_metadata_filename = train_dir / f"metadata.{request.param}" |
| image_metadata = ( |
| textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice train image"} |
| {"file_name": "image_rgb2.jpg", "caption": "Nice second train image"} |
| """ |
| ) |
| if request.param == "jsonl" |
| else textwrap.dedent( |
| """\ |
| file_name,caption |
| image_rgb.jpg,Nice train image |
| image_rgb2.jpg,Nice second train image |
| """ |
| ) |
| ) |
| with open(train_image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
| test_image_metadata_filename = test_dir / f"metadata.{request.param}" |
| image_metadata = ( |
| textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb3.jpg", "caption": "Nice test image"} |
| """ |
| ) |
| if request.param == "jsonl" |
| else textwrap.dedent( |
| """\ |
| file_name,caption |
| image_rgb3.jpg,Nice test image |
| """ |
| ) |
| ) |
| with open(test_image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
| data_files_with_two_splits_and_metadata = DataFilesDict.from_patterns( |
| get_data_patterns(str(data_dir)), data_dir.as_posix() |
| ) |
| assert len(data_files_with_two_splits_and_metadata) == 2 |
| assert len(data_files_with_two_splits_and_metadata["train"]) == 3 |
| assert len(data_files_with_two_splits_and_metadata["test"]) == 2 |
| return data_files_with_two_splits_and_metadata |
|
|
|
|
| @pytest.fixture |
| def data_files_with_zip_archives(tmp_path, image_file): |
| from PIL import Image, ImageOps |
|
|
| data_dir = tmp_path / "imagefolder_data_dir_with_zip_archives" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| archive_dir = data_dir / "archive" |
| archive_dir.mkdir(parents=True, exist_ok=True) |
| subdir = archive_dir / "subdir" |
| subdir.mkdir(parents=True, exist_ok=True) |
|
|
| image_filename = archive_dir / "image_rgb.jpg" |
| shutil.copyfile(image_file, image_filename) |
| image_filename2 = subdir / "image_rgb2.jpg" |
| |
| |
| ImageOps.flip(Image.open(image_file)).save(image_filename2) |
|
|
| image_metadata_filename = archive_dir / "metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| {"file_name": "subdir/image_rgb2.jpg", "caption": "Nice second image"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
|
|
| shutil.make_archive(archive_dir, "zip", archive_dir) |
| shutil.rmtree(str(archive_dir)) |
|
|
| data_files_with_zip_archives = DataFilesDict.from_patterns(get_data_patterns(str(data_dir)), data_dir.as_posix()) |
|
|
| assert len(data_files_with_zip_archives) == 1 |
| assert len(data_files_with_zip_archives["train"]) == 1 |
| return data_files_with_zip_archives |
|
|
|
|
| @require_pil |
| |
| def test_generate_examples_with_labels(data_files_with_labels_no_metadata, cache_dir): |
| |
| imagefolder = ImageFolder(data_files=data_files_with_labels_no_metadata, cache_dir=cache_dir, drop_labels=False) |
| imagefolder.download_and_prepare() |
| assert imagefolder.info.features == Features({"image": Image(), "label": ClassLabel(names=["cat", "dog"])}) |
| dataset = list(imagefolder.as_dataset()["train"]) |
| label_feature = imagefolder.info.features["label"] |
|
|
| assert dataset[0]["label"] == label_feature._str2int["cat"] |
| assert dataset[1]["label"] == label_feature._str2int["dog"] |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("drop_metadata", [None, True, False]) |
| @pytest.mark.parametrize("drop_labels", [None, True, False]) |
| def test_generate_examples_duplicated_label_key( |
| image_files_with_labels_and_duplicated_label_key_in_metadata, drop_metadata, drop_labels, cache_dir, caplog |
| ): |
| cat_image_file, dog_image_file, image_metadata_file = image_files_with_labels_and_duplicated_label_key_in_metadata |
| imagefolder = ImageFolder( |
| drop_metadata=drop_metadata, |
| drop_labels=drop_labels, |
| data_files=[cat_image_file, dog_image_file, image_metadata_file], |
| cache_dir=cache_dir, |
| ) |
| if drop_labels is False: |
| |
| imagefolder.download_and_prepare() |
| warning_in_logs = any("ignoring metadata columns" in record.msg.lower() for record in caplog.records) |
| assert warning_in_logs if drop_metadata is not True else not warning_in_logs |
| dataset = imagefolder.as_dataset()["train"] |
| assert imagefolder.info.features["label"] == ClassLabel(names=["cat", "dog"]) |
| assert all(example["label"] in imagefolder.info.features["label"]._str2int.values() for example in dataset) |
| else: |
| imagefolder.download_and_prepare() |
| dataset = imagefolder.as_dataset()["train"] |
| if drop_metadata is not True: |
| |
| assert imagefolder.info.features["label"] == Value("string") |
| assert all(example["label"] in ["Cat", "Dog"] for example in dataset) |
| else: |
| |
| assert imagefolder.info.features == Features({"image": Image()}) |
| assert all(example.keys() == {"image"} for example in dataset) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("drop_metadata", [None, True, False]) |
| @pytest.mark.parametrize("drop_labels", [None, True, False]) |
| def test_generate_examples_drop_labels(data_files_with_labels_no_metadata, drop_metadata, drop_labels): |
| imagefolder = ImageFolder( |
| drop_metadata=drop_metadata, drop_labels=drop_labels, data_files=data_files_with_labels_no_metadata |
| ) |
| gen_kwargs = imagefolder._split_generators(StreamingDownloadManager())[0].gen_kwargs |
| |
| assert gen_kwargs["add_labels"] is not bool(drop_labels) |
| assert gen_kwargs["add_metadata"] is False |
| generator = imagefolder._generate_examples(**gen_kwargs) |
| if not drop_labels: |
| assert all( |
| example.keys() == {"image", "label"} and all(val is not None for val in example.values()) |
| for _, example in generator |
| ) |
| else: |
| assert all( |
| example.keys() == {"image"} and all(val is not None for val in example.values()) |
| for _, example in generator |
| ) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("drop_metadata", [None, True, False]) |
| @pytest.mark.parametrize("drop_labels", [None, True, False]) |
| def test_generate_examples_drop_metadata(image_file_with_metadata, drop_metadata, drop_labels): |
| image_file, image_metadata_file = image_file_with_metadata |
| imagefolder = ImageFolder( |
| drop_metadata=drop_metadata, drop_labels=drop_labels, data_files={"train": [image_file, image_metadata_file]} |
| ) |
| gen_kwargs = imagefolder._split_generators(StreamingDownloadManager())[0].gen_kwargs |
| |
| assert gen_kwargs["add_metadata"] is not bool(drop_metadata) |
| |
| assert gen_kwargs["add_labels"] is (drop_labels is False) |
| generator = imagefolder._generate_examples(**gen_kwargs) |
| expected_columns = {"image"} |
| if gen_kwargs["add_metadata"]: |
| expected_columns.add("caption") |
| if gen_kwargs["add_labels"]: |
| expected_columns.add("label") |
| result = [example for _, example in generator] |
| assert len(result) == 1 |
| example = result[0] |
| assert example.keys() == expected_columns |
| for column in expected_columns: |
| assert example[column] is not None |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("drop_metadata", [None, True, False]) |
| def test_generate_examples_with_metadata_in_wrong_location(image_file, image_file_with_metadata, drop_metadata): |
| _, image_metadata_file = image_file_with_metadata |
| imagefolder = ImageFolder(drop_metadata=drop_metadata, data_files={"train": [image_file, image_metadata_file]}) |
| gen_kwargs = imagefolder._split_generators(StreamingDownloadManager())[0].gen_kwargs |
| generator = imagefolder._generate_examples(**gen_kwargs) |
| if not drop_metadata: |
| with pytest.raises(ValueError): |
| list(generator) |
| else: |
| assert all( |
| example.keys() == {"image"} and all(val is not None for val in example.values()) |
| for _, example in generator |
| ) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("drop_metadata", [None, True, False]) |
| def test_generate_examples_with_metadata_that_misses_one_image( |
| image_files_with_metadata_that_misses_one_image, drop_metadata |
| ): |
| image_file, image_file2, image_metadata_file = image_files_with_metadata_that_misses_one_image |
| if not drop_metadata: |
| features = Features({"image": Image(), "caption": Value("string")}) |
| else: |
| features = Features({"image": Image()}) |
| imagefolder = ImageFolder( |
| drop_metadata=drop_metadata, |
| features=features, |
| data_files={"train": [image_file, image_file2, image_metadata_file]}, |
| ) |
| gen_kwargs = imagefolder._split_generators(StreamingDownloadManager())[0].gen_kwargs |
| generator = imagefolder._generate_examples(**gen_kwargs) |
| if not drop_metadata: |
| with pytest.raises(ValueError): |
| list(generator) |
| else: |
| assert all( |
| example.keys() == {"image"} and all(val is not None for val in example.values()) |
| for _, example in generator |
| ) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("streaming", [False, True]) |
| def test_data_files_with_metadata_and_single_split(streaming, cache_dir, data_files_with_one_split_and_metadata): |
| data_files = data_files_with_one_split_and_metadata |
| imagefolder = ImageFolder(data_files=data_files, cache_dir=cache_dir) |
| imagefolder.download_and_prepare() |
| datasets = imagefolder.as_streaming_dataset() if streaming else imagefolder.as_dataset() |
| for split, data_files in data_files.items(): |
| expected_num_of_images = len(data_files) - 1 |
| assert split in datasets |
| dataset = list(datasets[split]) |
| assert len(dataset) == expected_num_of_images |
| |
| assert len({example["image"].filename for example in dataset}) == expected_num_of_images |
| assert len({example["caption"] for example in dataset}) == expected_num_of_images |
| assert all(example["caption"] is not None for example in dataset) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("streaming", [False, True]) |
| def test_data_files_with_metadata_and_multiple_splits(streaming, cache_dir, data_files_with_two_splits_and_metadata): |
| data_files = data_files_with_two_splits_and_metadata |
| imagefolder = ImageFolder(data_files=data_files, cache_dir=cache_dir) |
| imagefolder.download_and_prepare() |
| datasets = imagefolder.as_streaming_dataset() if streaming else imagefolder.as_dataset() |
| for split, data_files in data_files.items(): |
| expected_num_of_images = len(data_files) - 1 |
| assert split in datasets |
| dataset = list(datasets[split]) |
| assert len(dataset) == expected_num_of_images |
| |
| assert len({example["image"].filename for example in dataset}) == expected_num_of_images |
| assert len({example["caption"] for example in dataset}) == expected_num_of_images |
| assert all(example["caption"] is not None for example in dataset) |
|
|
|
|
| @require_pil |
| @pytest.mark.parametrize("streaming", [False, True]) |
| def test_data_files_with_metadata_and_archives(streaming, cache_dir, data_files_with_zip_archives): |
| imagefolder = ImageFolder(data_files=data_files_with_zip_archives, cache_dir=cache_dir) |
| imagefolder.download_and_prepare() |
| datasets = imagefolder.as_streaming_dataset() if streaming else imagefolder.as_dataset() |
| for split, data_files in data_files_with_zip_archives.items(): |
| num_of_archives = len(data_files) |
| expected_num_of_images = 2 * num_of_archives |
| assert split in datasets |
| dataset = list(datasets[split]) |
| assert len(dataset) == expected_num_of_images |
| |
| assert len({np.array(example["image"])[0, 0, 0] for example in dataset}) == expected_num_of_images |
| assert len({example["caption"] for example in dataset}) == expected_num_of_images |
| assert all(example["caption"] is not None for example in dataset) |
|
|
|
|
| @require_pil |
| def test_data_files_with_wrong_metadata_file_name(cache_dir, tmp_path, image_file): |
| data_dir = tmp_path / "data_dir_with_bad_metadata" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| shutil.copyfile(image_file, data_dir / "image_rgb.jpg") |
| image_metadata_filename = data_dir / "bad_metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
|
|
| data_files_with_bad_metadata = DataFilesDict.from_patterns(get_data_patterns(str(data_dir)), data_dir.as_posix()) |
| imagefolder = ImageFolder(data_files=data_files_with_bad_metadata, cache_dir=cache_dir) |
| imagefolder.download_and_prepare() |
| dataset = imagefolder.as_dataset(split="train") |
| |
| assert "caption" not in dataset.column_names |
|
|
|
|
| @require_pil |
| def test_data_files_with_wrong_image_file_name_column_in_metadata_file(cache_dir, tmp_path, image_file): |
| data_dir = tmp_path / "data_dir_with_bad_metadata" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| shutil.copyfile(image_file, data_dir / "image_rgb.jpg") |
| image_metadata_filename = data_dir / "metadata.jsonl" |
| image_metadata = textwrap.dedent( |
| """\ |
| {"bad_file_name": "image_rgb.jpg", "caption": "Nice image"} |
| """ |
| ) |
| with open(image_metadata_filename, "w", encoding="utf-8") as f: |
| f.write(image_metadata) |
|
|
| data_files_with_bad_metadata = DataFilesDict.from_patterns(get_data_patterns(str(data_dir)), data_dir.as_posix()) |
| imagefolder = ImageFolder(data_files=data_files_with_bad_metadata, cache_dir=cache_dir) |
| with pytest.raises(ValueError) as exc_info: |
| imagefolder.download_and_prepare() |
| assert "`file_name` must be present" in str(exc_info.value) |
|
|
|
|
| @require_pil |
| def test_data_files_with_with_metadata_in_different_formats(cache_dir, tmp_path, image_file): |
| data_dir = tmp_path / "data_dir_with_metadata_in_different_format" |
| data_dir.mkdir(parents=True, exist_ok=True) |
| shutil.copyfile(image_file, data_dir / "image_rgb.jpg") |
| image_metadata_filename_jsonl = data_dir / "metadata.jsonl" |
| image_metadata_jsonl = textwrap.dedent( |
| """\ |
| {"file_name": "image_rgb.jpg", "caption": "Nice image"} |
| """ |
| ) |
| with open(image_metadata_filename_jsonl, "w", encoding="utf-8") as f: |
| f.write(image_metadata_jsonl) |
| image_metadata_filename_csv = data_dir / "metadata.csv" |
| image_metadata_csv = textwrap.dedent( |
| """\ |
| file_name,caption |
| image_rgb.jpg,Nice image |
| """ |
| ) |
| with open(image_metadata_filename_csv, "w", encoding="utf-8") as f: |
| f.write(image_metadata_csv) |
|
|
| data_files_with_bad_metadata = DataFilesDict.from_patterns(get_data_patterns(str(data_dir)), data_dir.as_posix()) |
| imagefolder = ImageFolder(data_files=data_files_with_bad_metadata, cache_dir=cache_dir) |
| with pytest.raises(ValueError) as exc_info: |
| imagefolder.download_and_prepare() |
| assert "metadata files with different extensions" in str(exc_info.value) |
|
|
