Update fixtures-image-utils (#28080)

* fix hf-internal-testing/fixtures_image_utils

* fix test

* comments

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]
 

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(

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(

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)
 

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)
 

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(