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tests/models/tvp/test_image_processing_tvp.py

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+# Copyright 2023 The Intel Team Authors, The HuggingFace Inc. team. All rights reserved.
+#
+# 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 unittest
+
+import numpy as np
+
+from transformers.image_transforms import PaddingMode
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_video_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class TvpImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        do_resize: bool = True,
+        size: dict[str, int] = {"longest_edge": 40},
+        do_center_crop: bool = False,
+        crop_size: dict[str, int] | None = None,
+        do_rescale: bool = False,
+        rescale_factor: int | float = 1 / 255,
+        do_pad: bool = True,
+        pad_size: dict[str, int] = {"height": 80, "width": 80},
+        fill: int | None = None,
+        pad_mode: PaddingMode | None = None,
+        do_normalize: bool = True,
+        image_mean: float | list[float] | None = [0.48145466, 0.4578275, 0.40821073],
+        image_std: float | list[float] | None = [0.26862954, 0.26130258, 0.27577711],
+        batch_size=2,
+        min_resolution=40,
+        max_resolution=80,
+        num_channels=3,
+        num_frames=2,
+    ):
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_pad = do_pad
+        self.pad_size = pad_size
+        self.fill = fill
+        self.pad_mode = pad_mode
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.num_frames = num_frames
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_rescale": self.do_rescale,
+            "do_center_crop": self.do_center_crop,
+            "do_pad": self.do_pad,
+            "pad_size": self.pad_size,
+        }
+
+    def get_expected_values(self, image_inputs, batched=False):
+        """
+        This function computes the expected height and width when providing images to TvpImageProcessor,
+        assuming do_resize is set to True with a scalar size.
+        """
+        if not batched:
+            return (int(self.pad_size["height"]), int(self.pad_size["width"]))
+
+        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
+
+    def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_video_inputs(
+            batch_size=self.batch_size,
+            num_frames=self.num_frames,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class TvpImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = TvpImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "do_pad"))
+            self.assertTrue(hasattr(image_processing, "pad_size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"longest_edge": 40})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size={"longest_edge": 12})
+            self.assertEqual(image_processor.size, {"longest_edge": 12})
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL videos
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], Image.Image)
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    def test_call_numpy(self):
+        # Test numpy with both processors
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], np.ndarray)
+
+            # For torchvision processor, convert numpy to tensor
+            if backend_name == "torchvision":
+                # Convert numpy arrays to tensors for torchvision processor
+                tensor_video_inputs = []
+                for video in video_inputs:
+                    tensor_video = [torch.from_numpy(frame) for frame in video]
+                    tensor_video_inputs.append(tensor_video)
+                test_inputs = tensor_video_inputs
+            else:  # pil
+                test_inputs = video_inputs
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(test_inputs[0], return_tensors="pt").pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(test_inputs, return_tensors="pt").pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+    def test_call_numpy_4_channels(self):
+        # Test numpy with both processors
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], np.ndarray)
+
+            # For torchvision processor, convert numpy to tensor
+            if backend_name == "torchvision":
+                # Convert numpy arrays to tensors for torchvision processor
+                tensor_video_inputs = []
+                for video in video_inputs:
+                    tensor_video = [torch.from_numpy(frame) for frame in video]
+                    tensor_video_inputs.append(tensor_video)
+                test_inputs = tensor_video_inputs
+            else:  # pil
+                test_inputs = video_inputs
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(
+                test_inputs[0],
+                return_tensors="pt",
+                image_mean=(0.0, 0.0, 0.0),
+                image_std=(1.0, 1.0, 1.0),
+                input_data_format="channels_first",
+            ).pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(
+                test_inputs,
+                return_tensors="pt",
+                image_mean=(0.0, 0.0, 0.0),
+                image_std=(1.0, 1.0, 1.0),
+                input_data_format="channels_first",
+            ).pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+        self.image_processor_tester.num_channels = 3
+
+    def test_call_pytorch(self):
+        # Test PyTorch tensors with both processors
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], torch.Tensor)
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
+        image_processor_torchvision = self.image_processing_classes["torchvision"](**self.image_processor_dict)
+        image_processor_pil = self.image_processing_classes["pil"](**self.image_processor_dict)
+
+        encoding_torchvision = image_processor_torchvision(dummy_images, return_tensors="pt")
+        encoding_pil = image_processor_pil(dummy_images, return_tensors="pt")
+        # Higher max atol for video processing, mean_atol still 5e-3 -> 1e-1
+        self._assert_tensors_equivalence(
+            encoding_torchvision.pixel_values, encoding_pil.pixel_values, atol=10.0, mean_atol=1e-1
+        )