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tests/models/imagegpt/test_image_processing_imagegpt.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 json
+import os
+import tempfile
+import unittest
+
+import numpy as np
+import pytest
+import requests
+from datasets import load_dataset
+
+from transformers import AutoImageProcessor
+from transformers.testing_utils import (
+    check_json_file_has_correct_format,
+    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
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class ImageGPTImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+    ):
+        size = size if size is not None else {"height": 18, "width": 18}
+        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.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+
+    def prepare_image_processor_dict(self):
+        return {
+            # here we create 2 clusters for the sake of simplicity
+            "clusters": np.asarray(
+                [
+                    [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
+                    [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
+                ]
+            ),
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+        }
+
+    def expected_output_image_shape(self, images):
+        return (self.size["height"] * self.size["width"],)
+
+    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 ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = ImageGPTImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    @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.input_ids.float(), output_compiled.input_ids.float(), atol=1e-4, rtol=1e-4, mean_atol=1e-5
+        )
+
+    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, "clusters"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+
+    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, {"height": 18, "width": 18})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    def test_image_processor_to_json_string(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            obj = json.loads(image_processor.to_json_string())
+            for key, value in self.image_processor_dict.items():
+                if key == "clusters":
+                    self.assertTrue(np.array_equal(value, obj[key]))
+                else:
+                    self.assertEqual(obj[key], value)
+
+    def test_image_processor_to_json_file(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_first = image_processing_class(**self.image_processor_dict)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                json_file_path = os.path.join(tmpdirname, "image_processor.json")
+                image_processor_first.to_json_file(json_file_path)
+                image_processor_second = image_processing_class.from_json_file(json_file_path).to_dict()
+
+            image_processor_first = image_processor_first.to_dict()
+            for key, value in image_processor_first.items():
+                if key == "clusters":
+                    self.assertTrue(np.array_equal(value, image_processor_second[key]))
+                else:
+                    self.assertEqual(image_processor_first[key], value)
+
+    def test_image_processor_from_and_save_pretrained(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_first = image_processing_class(**self.image_processor_dict)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                image_processor_first.save_pretrained(tmpdirname)
+                image_processor_second = image_processing_class.from_pretrained(tmpdirname).to_dict()
+
+            image_processor_first = image_processor_first.to_dict()
+            for key, value in image_processor_first.items():
+                if key == "clusters":
+                    self.assertTrue(np.array_equal(value, image_processor_second[key]))
+                else:
+                    self.assertEqual(value, value)
+
+    def test_image_processor_save_load_with_autoimageprocessor(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_first = image_processing_class(**self.image_processor_dict)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
+                check_json_file_has_correct_format(saved_file)
+
+                image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname)
+
+            image_processor_first = image_processor_first.to_dict()
+            image_processor_second = image_processor_second.to_dict()
+
+            for key, value in image_processor_first.items():
+                if key == "clusters":
+                    self.assertTrue(np.array_equal(value, image_processor_second[key]))
+                else:
+                    self.assertEqual(value, value)
+
+    @unittest.skip(reason="ImageGPT requires clusters at initialization")
+    def test_init_without_params(self):
+        pass
+
+    # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
+    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 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
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
+    def test_call_numpy(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 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)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    @unittest.skip(reason="ImageGPT assumes clusters for 3 channels")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    # Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
+    def test_call_pytorch(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 PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+            expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )
+
+    # For quantization-based processors, use absolute tolerance only to avoid infinity issues
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        """Test equivalence across backends for quantization-based processors."""
+        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(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+
+        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")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_input_ids = encodings[reference_backend].input_ids.float()
+
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                reference_input_ids, encodings[backend_name].input_ids.float(), atol=1.0, rtol=0
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        """Test batched equivalence across backends for quantization-based processors."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
+            self.skipTest(
+                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
+            )
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+        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")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_input_ids = encodings[reference_backend].input_ids.float()
+
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                reference_input_ids, encodings[backend_name].input_ids.float(), atol=1.0, rtol=0
+            )
+
+    @slow
+    @require_torch_accelerator
+    @require_vision
+    @pytest.mark.torch_compile_test
+    def test_can_compile_fast_image_processor(self):
+        if "torchvision" not in self.image_processing_classes:
+            self.skipTest("Skipping compilation test as torchvision image processor is not defined")
+
+        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.input_ids.float(), output_compiled.input_ids.float(), atol=1.0, rtol=0
+        )
+
+
+def prepare_images():
+    # 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 = dataset[4]["image"]
+    image2 = dataset[5]["image"]
+
+    images = [image1, image2]
+
+    return images
+
+
+@require_vision
+@require_torch
+class ImageGPTImageProcessorIntegrationTest(unittest.TestCase):
+    @slow
+    def test_image(self):
+        from transformers import ImageGPTImageProcessor
+
+        image_processing = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")
+
+        images = prepare_images()
+
+        # test non-batched
+        encoding = image_processing(images[0], return_tensors="pt")
+
+        self.assertIsInstance(encoding.input_ids, torch.LongTensor)
+        self.assertEqual(encoding.input_ids.shape, (1, 1024))
+
+        expected_slice = [306, 191, 191]
+        self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice)
+
+        # test batched
+        encoding = image_processing(images, return_tensors="pt")
+
+        self.assertIsInstance(encoding.input_ids, torch.LongTensor)
+        self.assertEqual(encoding.input_ids.shape, (2, 1024))
+
+        expected_slice = [303, 13, 13]
+        self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)