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tests/models/idefics/test_image_processing_idefics.py

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+# Copyright 2021 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 io
+import unittest
+
+import httpx
+import pytest
+
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_torchvision,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_torchvision_available():
+    from torchvision import transforms
+
+if is_vision_available():
+    from PIL import Image
+
+
+class IdeficsImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+    ):
+        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.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "image_size": self.image_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        return (self.num_channels, self.image_size, self.image_size)
+
+    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 IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = IdeficsImageProcessingTester(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, "image_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.assertNotEqual(image_processor.image_size, 30)
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, image_size=42)
+            self.assertEqual(image_processor.image_size, 42)
+
+    @require_torchvision
+    def test_torchvision_numpy_transforms_equivalency(self):
+        def convert_to_rgb(image):
+            if image.mode == "RGB":
+                return image
+            image_rgba = image.convert("RGBA")
+            background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
+            alpha_composite = Image.alpha_composite(background, image_rgba)
+            alpha_composite = alpha_composite.convert("RGB")
+            return alpha_composite
+
+        # Verify that the default inference transforms match an equivalent torchvision.Compose pipeline.
+        for image_processing_class in self.image_processing_classes.values():
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            image_size = image_processor.image_size
+            image_mean = image_processor.image_mean
+            image_std = image_processor.image_std
+
+            transform = transforms.Compose(
+                [
+                    convert_to_rgb,
+                    transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
+                    transforms.ToTensor(),
+                    transforms.Normalize(mean=image_mean, std=image_std),
+                ]
+            )
+
+            pixel_values_transform_implied = image_processor(image_inputs, transform=None, return_tensors="pt")
+            pixel_values_transform_supplied = image_processor(image_inputs, transform=transform, return_tensors="pt")
+
+            torch.testing.assert_close(
+                pixel_values_transform_implied, pixel_values_transform_supplied, rtol=1e-2, atol=2e-2
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        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")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name])
+
+    @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_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")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name])
+
+    @slow
+    @require_torch_accelerator
+    @require_vision
+    @pytest.mark.torch_compile_test
+    def test_can_compile_torchvision_backend(self):
+        # Test compilation with torchvision backend (equivalent to fast processor)
+        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")
+        self._assert_tensors_equivalence(output_eager, output_compiled, atol=1e-4, rtol=1e-4, mean_atol=1e-5)
+
+    @unittest.skip(reason="not supported")
+    def test_call_numpy(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_pil(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_pytorch(self):
+        pass