From 26ea725bc0d90c75ba20d2f894321aa98b2c6cf2 Mon Sep 17 00:00:00 2001 From: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> Date: Fri, 15 Dec 2023 17:58:36 +0100 Subject: [PATCH] Update fixtures-image-utils (#28080) * fix hf-internal-testing/fixtures_image_utils * fix test * comments --- .../test_image_processing_imagegpt.py | 8 +++--- .../test_pipelines_depth_estimation.py | 10 ++++--- .../test_pipelines_image_classification.py | 10 ++++--- .../test_pipelines_image_segmentation.py | 10 ++++--- .../test_pipelines_object_detection.py | 10 ++++--- tests/utils/test_image_utils.py | 27 ++++++++++++------- 6 files changed, 46 insertions(+), 29 deletions(-) diff --git a/tests/models/imagegpt/test_image_processing_imagegpt.py b/tests/models/imagegpt/test_image_processing_imagegpt.py index a806f03243..4596d742a2 100644 --- a/tests/models/imagegpt/test_image_processing_imagegpt.py +++ b/tests/models/imagegpt/test_image_processing_imagegpt.py @@ -226,10 +226,12 @@ class ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): def prepare_images(): - dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") - image1 = Image.open(dataset[4]["file"]) - image2 = Image.open(dataset[5]["file"]) + image1 = dataset[4]["image"] + image2 = dataset[5]["image"] images = [image1, image2] diff --git a/tests/pipelines/test_pipelines_depth_estimation.py b/tests/pipelines/test_pipelines_depth_estimation.py index 009aa1c942..abc58ca710 100644 --- a/tests/pipelines/test_pipelines_depth_estimation.py +++ b/tests/pipelines/test_pipelines_depth_estimation.py @@ -68,17 +68,19 @@ class DepthEstimationPipelineTests(unittest.TestCase): self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}, outputs) import datasets - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") outputs = depth_estimator( [ Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"), "http://images.cocodataset.org/val2017/000000039769.jpg", # RGBA - dataset[0]["file"], + dataset[0]["image"], # LA - dataset[1]["file"], + dataset[1]["image"], # L - dataset[2]["file"], + dataset[2]["image"], ] ) self.assertEqual( diff --git a/tests/pipelines/test_pipelines_image_classification.py b/tests/pipelines/test_pipelines_image_classification.py index 7af16371a0..bec538d53a 100644 --- a/tests/pipelines/test_pipelines_image_classification.py +++ b/tests/pipelines/test_pipelines_image_classification.py @@ -72,7 +72,9 @@ class ImageClassificationPipelineTests(unittest.TestCase): import datasets - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") # Accepts URL + PIL.Image + lists outputs = image_classifier( @@ -80,11 +82,11 @@ class ImageClassificationPipelineTests(unittest.TestCase): Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"), "http://images.cocodataset.org/val2017/000000039769.jpg", # RGBA - dataset[0]["file"], + dataset[0]["image"], # LA - dataset[1]["file"], + dataset[1]["image"], # L - dataset[2]["file"], + dataset[2]["image"], ] ) self.assertEqual( diff --git a/tests/pipelines/test_pipelines_image_segmentation.py b/tests/pipelines/test_pipelines_image_segmentation.py index 9c5c8fdfd4..23a95570ab 100644 --- a/tests/pipelines/test_pipelines_image_segmentation.py +++ b/tests/pipelines/test_pipelines_image_segmentation.py @@ -113,18 +113,20 @@ class ImageSegmentationPipelineTests(unittest.TestCase): # to make it work self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n, outputs) - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") # RGBA - outputs = image_segmenter(dataset[0]["file"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) + outputs = image_segmenter(dataset[0]["image"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) m = len(outputs) self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs) # LA - outputs = image_segmenter(dataset[1]["file"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) + outputs = image_segmenter(dataset[1]["image"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) m = len(outputs) self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs) # L - outputs = image_segmenter(dataset[2]["file"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) + outputs = image_segmenter(dataset[2]["image"], threshold=0.0, mask_threshold=0, overlap_mask_area_threshold=0) m = len(outputs) self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs) diff --git a/tests/pipelines/test_pipelines_object_detection.py b/tests/pipelines/test_pipelines_object_detection.py index 4196db36d7..ec4984b76f 100644 --- a/tests/pipelines/test_pipelines_object_detection.py +++ b/tests/pipelines/test_pipelines_object_detection.py @@ -73,17 +73,19 @@ class ObjectDetectionPipelineTests(unittest.TestCase): import datasets - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") batch = [ Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"), "http://images.cocodataset.org/val2017/000000039769.jpg", # RGBA - dataset[0]["file"], + dataset[0]["image"], # LA - dataset[1]["file"], + dataset[1]["image"], # L - dataset[2]["file"], + dataset[2]["image"], ] batch_outputs = object_detector(batch, threshold=0.0) diff --git a/tests/utils/test_image_utils.py b/tests/utils/test_image_utils.py index 5d899c2f1d..ee45300a7e 100644 --- a/tests/utils/test_image_utils.py +++ b/tests/utils/test_image_utils.py @@ -538,9 +538,11 @@ class LoadImageTester(unittest.TestCase): self.assertEqual(img_arr.shape, (64, 32, 3)) def test_load_img_rgba(self): - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") - img = load_image(dataset[0]["file"]) # img with mode RGBA + img = load_image(dataset[0]["image"]) # img with mode RGBA img_arr = np.array(img) self.assertEqual( @@ -549,9 +551,11 @@ class LoadImageTester(unittest.TestCase): ) def test_load_img_la(self): - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") - img = load_image(dataset[1]["file"]) # img with mode LA + img = load_image(dataset[1]["image"]) # img with mode LA img_arr = np.array(img) self.assertEqual( @@ -560,9 +564,11 @@ class LoadImageTester(unittest.TestCase): ) def test_load_img_l(self): - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") - img = load_image(dataset[2]["file"]) # img with mode L + img = load_image(dataset[2]["image"]) # img with mode L img_arr = np.array(img) self.assertEqual( @@ -571,10 +577,11 @@ class LoadImageTester(unittest.TestCase): ) def test_load_img_exif_transpose(self): - dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test") - img_file = dataset[3]["file"] + # we use revision="refs/pr/1" until the PR is merged + # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1 + dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1") - img_without_exif_transpose = PIL.Image.open(img_file) + img_without_exif_transpose = dataset[3]["image"] img_arr_without_exif_transpose = np.array(img_without_exif_transpose) self.assertEqual( @@ -582,7 +589,7 @@ class LoadImageTester(unittest.TestCase): (333, 500, 3), ) - img_with_exif_transpose = load_image(img_file) + img_with_exif_transpose = load_image(dataset[3]["image"]) img_arr_with_exif_transpose = np.array(img_with_exif_transpose) self.assertEqual(