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tests/models/pix2struct/test_image_processing_pix2struct.py

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+# Copyright 2023 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 unittest
+
+import numpy as np
+
+from transformers.image_utils import load_image
+from transformers.testing_utils import require_torch, require_torch_accelerator, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Pix2StructImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        size=None,
+        do_normalize=True,
+        do_convert_rgb=True,
+        patch_size=None,
+    ):
+        size = size if size is not None else {"height": 20, "width": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.size = size
+        self.do_normalize = do_normalize
+        self.do_convert_rgb = do_convert_rgb
+        self.max_patches = [512, 1024, 2048, 4096]
+        self.patch_size = patch_size if patch_size is not None else {"height": 16, "width": 16}
+
+    def prepare_image_processor_dict(self):
+        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
+
+    def prepare_dummy_image(self):
+        img_url = url_to_local_path(
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+        )
+        raw_image = load_image(img_url).convert("RGB")
+        return raw_image
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            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 Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Pix2StructImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        """Override to use flattened_patches instead of pixel_values."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        import io
+
+        import httpx
+        from PIL import Image
+
+        dummy_image = Image.open(
+            io.BytesIO(
+                httpx.get("http://images.cocodataset.org/val2017/000000039769.jpg", follow_redirects=True).content
+            )
+        )
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt", max_patches=2048)
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend].flattened_patches
+        for backend_name in backend_names[1:]:
+            current_encoding = encodings[backend_name].flattened_patches
+            self._assert_tensors_equivalence(reference_encoding, current_encoding)
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        """Override to use flattened_patches instead of pixel_values."""
+        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_image_inputs(equal_resolution=False, torchify=True)
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt", max_patches=2048)
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend].flattened_patches
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name].flattened_patches)
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processor, "do_normalize"))
+            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+
+    def test_expected_patches(self):
+        dummy_image = self.image_processor_tester.prepare_dummy_image()
+
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            max_patch = 2048
+
+            inputs = image_processor(dummy_image, return_tensors="pt", max_patches=max_patch)
+            torch.testing.assert_close(inputs.flattened_patches.mean(), torch.tensor(0.0606), rtol=1e-3, atol=1e-3)
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * self.image_processor_tester.num_channels
+            ) + 2
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+
+    def test_call_vqa(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * self.image_processor_tester.num_channels
+            ) + 2
+
+            image_processor.is_vqa = True
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                with self.assertRaises(ValueError):
+                    encoded_images = image_processor(
+                        image_inputs[0], return_tensors="pt", max_patches=max_patch
+                    ).flattened_patches
+
+                dummy_text = "Hello"
+
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch, header_text=dummy_text
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch, header_text=dummy_text
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * self.image_processor_tester.num_channels
+            ) + 2
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+
+    def test_call_numpy_4_channels(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            self.image_processor_tester.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * self.image_processor_tester.num_channels
+            ) + 2
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+            self.image_processor_tester.num_channels = 3
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * self.image_processor_tester.num_channels
+            ) + 2
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+
+    @slow
+    @require_torch_accelerator
+    @require_vision
+    def test_can_compile_torchvision_backend(self):
+        if "torchvision" not in self.image_processing_classes:
+            self.skipTest("Skipping compilation test as torchvision backend is not available")
+
+        torch.compiler.reset()
+        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
+        image_processor = self.image_processing_classes["torchvision"](**self.image_processor_dict)
+        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        image_processor = torch.compile(image_processor, mode="reduce-overhead")
+        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
+        # Pix2Struct uses flattened_patches instead of pixel_values
+        self._assert_tensors_equivalence(
+            output_eager.flattened_patches, output_compiled.flattened_patches, atol=1e-4, rtol=1e-4, mean_atol=1e-5
+        )
+
+
+@require_torch
+@require_vision
+class Pix2StructImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Pix2StructImageProcessingTester(self, num_channels=4)
+        self.expected_encoded_image_num_channels = 3
+
+    @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_processor = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processor, "do_normalize"))
+            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processor
+            image_processor = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            expected_hidden_dim = (
+                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
+                * (self.image_processor_tester.num_channels - 1)
+            ) + 2
+
+            for max_patch in self.image_processor_tester.max_patches:
+                # Test not batched input
+                encoded_images = image_processor(
+                    image_inputs[0], return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (1, max_patch, expected_hidden_dim),
+                )
+
+                # Test batched
+                encoded_images = image_processor(
+                    image_inputs, return_tensors="pt", max_patches=max_patch
+                ).flattened_patches
+                self.assertEqual(
+                    encoded_images.shape,
+                    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),
+                )
+
+    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
+    def test_call_numpy(self):
+        return super().test_call_numpy()
+
+    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")  # FIXME Amy
+    def test_call_pytorch(self):
+        return super().test_call_torch()
+
+    @unittest.skip(
+        reason="Pix2StructImageProcessor does treat numpy and PIL 4 channel images consistently"
+    )  # FIXME Amy
+    def test_call_numpy_4_channels(self):
+        return super().test_call_torch()
+
+    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
+    def test_backends_equivalence(self):
+        pass
+
+    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
+    def test_backends_equivalence_batched(self):
+        pass
+
+    @unittest.skip(reason="Pix2StructImageProcessor does not support 4 channels yet")
+    def test_can_compile_torchvision_backend(self):
+        pass