[fix] Add DeformableDetrFeatureExtractor (#19140)

* Add DeformableDetrFeatureExtractor

* Fix post_process

* Fix name

* Add tests for feature extractor

* Fix doc tests

* Fix name

* Address comments

* Apply same fix to DETR and YOLOS as well

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

tests/models/deformable_detr/test_feature_extraction_deformable_detr.py

@@ -0,0 +1,341 @@
+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import json
+import pathlib
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision, slow
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import DeformableDetrFeatureExtractor
+
+
+class DeformableDetrFeatureExtractionTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=18,
+        max_size=1333,  # by setting max_size > max_resolution we're effectively not testing this :p
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.max_size = max_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "max_size": self.max_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+        }
+
+    def get_expected_values(self, image_inputs, batched=False):
+        """
+        This function computes the expected height and width when providing images to DeformableDetrFeatureExtractor,
+        assuming do_resize is set to True with a scalar size.
+        """
+        if not batched:
+            image = image_inputs[0]
+            if isinstance(image, Image.Image):
+                w, h = image.size
+            else:
+                h, w = image.shape[1], image.shape[2]
+            if w < h:
+                expected_height = int(self.size * h / w)
+                expected_width = self.size
+            elif w > h:
+                expected_height = self.size
+                expected_width = int(self.size * w / h)
+            else:
+                expected_height = self.size
+                expected_width = self.size
+
+        else:
+            expected_values = []
+            for image in image_inputs:
+                expected_height, expected_width = self.get_expected_values([image])
+                expected_values.append((expected_height, expected_width))
+            expected_height = max(expected_values, key=lambda item: item[0])[0]
+            expected_width = max(expected_values, key=lambda item: item[1])[1]
+
+        return expected_height, expected_width
+
+
+@require_torch
+@require_vision
+class DeformableDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
+
+    feature_extraction_class = DeformableDetrFeatureExtractor if is_vision_available() else None
+
+    def setUp(self):
+        self.feature_extract_tester = DeformableDetrFeatureExtractionTester(self)
+
+    @property
+    def feat_extract_dict(self):
+        return self.feature_extract_tester.prepare_feat_extract_dict()
+
+    def test_feat_extract_properties(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        self.assertTrue(hasattr(feature_extractor, "image_mean"))
+        self.assertTrue(hasattr(feature_extractor, "image_std"))
+        self.assertTrue(hasattr(feature_extractor, "do_normalize"))
+        self.assertTrue(hasattr(feature_extractor, "do_resize"))
+        self.assertTrue(hasattr(feature_extractor, "size"))
+        self.assertTrue(hasattr(feature_extractor, "max_size"))
+
+    def test_batch_feature(self):
+        pass
+
+    def test_call_pil(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
+        for image in image_inputs:
+            self.assertIsInstance(image, Image.Image)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_numpy(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random numpy tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, np.ndarray)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_pytorch(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, torch.Tensor)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_equivalence_pad_and_create_pixel_mask(self):
+        # Initialize feature_extractors
+        feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
+        feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, torch.Tensor)
+
+        # Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors
+        encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
+        encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
+
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
+        )
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
+        )
+
+    @slow
+    def test_call_pytorch_with_coco_detection_annotations(self):
+        # prepare image and target
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
+            target = json.loads(f.read())
+
+        target = {"image_id": 39769, "annotations": target}
+
+        # encode them
+        feature_extractor = DeformableDetrFeatureExtractor()
+        encoding = feature_extractor(images=image, annotations=target, return_tensors="pt")
+
+        # verify pixel values
+        expected_shape = torch.Size([1, 3, 800, 1066])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
+
+        # verify area
+        expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
+        # verify boxes
+        expected_boxes_shape = torch.Size([6, 4])
+        self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
+        expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
+        # verify image_id
+        expected_image_id = torch.tensor([39769])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
+        # verify is_crowd
+        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
+        # verify class_labels
+        expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
+        # verify orig_size
+        expected_orig_size = torch.tensor([480, 640])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
+        # verify size
+        expected_size = torch.tensor([800, 1066])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
+
+    @slow
+    def test_call_pytorch_with_coco_panoptic_annotations(self):
+        # prepare image, target and masks_path
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
+            target = json.loads(f.read())
+
+        target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
+
+        masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
+
+        # encode them
+        feature_extractor = DeformableDetrFeatureExtractor(format="coco_panoptic")
+        encoding = feature_extractor(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
+
+        # verify pixel values
+        expected_shape = torch.Size([1, 3, 800, 1066])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
+
+        # verify area
+        expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
+        # verify boxes
+        expected_boxes_shape = torch.Size([6, 4])
+        self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
+        expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
+        # verify image_id
+        expected_image_id = torch.tensor([39769])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
+        # verify is_crowd
+        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
+        # verify class_labels
+        expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
+        # verify masks
+        expected_masks_sum = 822338
+        self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
+        # verify orig_size
+        expected_orig_size = torch.tensor([480, 640])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
+        # verify size
+        expected_size = torch.tensor([800, 1066])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))

tests/models/detr/test_feature_extraction_detr.py

@@ -240,8 +240,12 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
         encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
 
-        assert torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
-        assert torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
+        )
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
+        )
 
     @slow
     def test_call_pytorch_with_coco_detection_annotations(self):
@@ -261,31 +265,31 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertEqual(encoding["pixel_values"].shape, expected_shape)
 
         expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
-        assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
 
         # verify area
         expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
-        assert torch.allclose(encoding["labels"][0]["area"], expected_area)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
         # verify boxes
         expected_boxes_shape = torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
         expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
-        assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
         # verify image_id
         expected_image_id = torch.tensor([39769])
-        assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
         # verify is_crowd
         expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
-        assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
         # verify class_labels
         expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
-        assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
         # verify orig_size
         expected_orig_size = torch.tensor([480, 640])
-        assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
         # verify size
         expected_size = torch.tensor([800, 1066])
-        assert torch.allclose(encoding["labels"][0]["size"], expected_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
 
     @slow
     def test_call_pytorch_with_coco_panoptic_annotations(self):
@@ -299,8 +303,7 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
 
         # encode them
-        # TODO replace by .from_pretrained facebook/detr-resnet-50-panoptic
-        feature_extractor = DetrFeatureExtractor(format="coco_panoptic")
+        feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
         encoding = feature_extractor(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
 
         # verify pixel values
@@ -308,31 +311,31 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertEqual(encoding["pixel_values"].shape, expected_shape)
 
         expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
-        assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
 
         # verify area
         expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
-        assert torch.allclose(encoding["labels"][0]["area"], expected_area)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
         # verify boxes
         expected_boxes_shape = torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
         expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
-        assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
         # verify image_id
         expected_image_id = torch.tensor([39769])
-        assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
         # verify is_crowd
         expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
-        assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
         # verify class_labels
         expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
-        assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
         # verify masks
         expected_masks_sum = 822338
         self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
         # verify orig_size
         expected_orig_size = torch.tensor([480, 640])
-        assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
         # verify size
         expected_size = torch.tensor([800, 1066])
-        assert torch.allclose(encoding["labels"][0]["size"], expected_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))

tests/models/yolos/test_feature_extraction_yolos.py

@@ -240,7 +240,9 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
         encoded_images_with_method = feature_extractor_1.pad(image_inputs, return_tensors="pt")
         encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
 
-        assert torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
+        )
 
     @slow
     def test_call_pytorch_with_coco_detection_annotations(self):
@@ -260,31 +262,31 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
         self.assertEqual(encoding["pixel_values"].shape, expected_shape)
 
         expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
-        assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
 
         # verify area
         expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
-        assert torch.allclose(encoding["labels"][0]["area"], expected_area)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
         # verify boxes
         expected_boxes_shape = torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
         expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
-        assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
         # verify image_id
         expected_image_id = torch.tensor([39769])
-        assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
         # verify is_crowd
         expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
-        assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
         # verify class_labels
         expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
-        assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
         # verify orig_size
         expected_orig_size = torch.tensor([480, 640])
-        assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
         # verify size
         expected_size = torch.tensor([800, 1066])
-        assert torch.allclose(encoding["labels"][0]["size"], expected_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
 
     @slow
     def test_call_pytorch_with_coco_panoptic_annotations(self):
@@ -306,31 +308,31 @@ class YolosFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
         self.assertEqual(encoding["pixel_values"].shape, expected_shape)
 
         expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
-        assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
 
         # verify area
         expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
-        assert torch.allclose(encoding["labels"][0]["area"], expected_area)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
         # verify boxes
         expected_boxes_shape = torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
         expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
-        assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
         # verify image_id
         expected_image_id = torch.tensor([39769])
-        assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
         # verify is_crowd
         expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
-        assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
         # verify class_labels
         expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
-        assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
         # verify masks
         expected_masks_sum = 822338
         self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
         # verify orig_size
         expected_orig_size = torch.tensor([480, 640])
-        assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
         # verify size
         expected_size = torch.tensor([800, 1066])
-        assert torch.allclose(encoding["labels"][0]["size"], expected_size)
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))