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tests/models/flava/test_image_processing_flava.py

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+# Copyright 2022 Meta Platforms authors and 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 random
+import unittest
+
+import httpx
+import numpy as np
+from PIL import Image
+
+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_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    import PIL
+
+    from transformers.image_utils import PILImageResampling
+    from transformers.models.flava.image_processing_flava import (
+        FLAVA_CODEBOOK_MEAN,
+        FLAVA_CODEBOOK_STD,
+        FLAVA_IMAGE_MEAN,
+        FLAVA_IMAGE_STD,
+    )
+else:
+    FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None
+
+
+class FlavaImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_center_crop=True,
+        crop_size=None,
+        resample=None,
+        do_rescale=True,
+        rescale_factor=1 / 255,
+        do_normalize=True,
+        image_mean=FLAVA_IMAGE_MEAN,
+        image_std=FLAVA_IMAGE_STD,
+        input_size_patches=14,
+        total_mask_patches=75,
+        mask_group_min_patches=16,
+        mask_group_min_aspect_ratio=0.3,
+        mask_group_max_aspect_ratio=None,
+        codebook_do_resize=True,
+        codebook_size=None,
+        codebook_resample=None,
+        codebook_do_center_crop=True,
+        codebook_crop_size=None,
+        codebook_do_map_pixels=True,
+        codebook_do_normalize=True,
+        codebook_image_mean=FLAVA_CODEBOOK_MEAN,
+        codebook_image_std=FLAVA_CODEBOOK_STD,
+    ):
+        size = size if size is not None else {"height": 224, "width": 224}
+        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+        codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
+        codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
+
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.do_resize = do_resize
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.size = size
+        self.resample = resample if resample is not None else PILImageResampling.BICUBIC
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+
+        self.input_size_patches = input_size_patches
+        self.total_mask_patches = total_mask_patches
+        self.mask_group_min_patches = mask_group_min_patches
+        self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
+        self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio
+
+        self.codebook_do_resize = codebook_do_resize
+        self.codebook_size = codebook_size
+        # LANCZOS resample does not support torch Tensor. Use BICUBIC as closest alternative
+        self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.BICUBIC
+        self.codebook_do_center_crop = codebook_do_center_crop
+        self.codebook_crop_size = codebook_crop_size
+        self.codebook_do_map_pixels = codebook_do_map_pixels
+        self.codebook_do_normalize = codebook_do_normalize
+        self.codebook_image_mean = codebook_image_mean
+        self.codebook_image_std = codebook_image_std
+
+    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,
+            "resample": self.resample,
+            "do_rescale": self.do_rescale,
+            "rescale_factor": self.rescale_factor,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "input_size_patches": self.input_size_patches,
+            "total_mask_patches": self.total_mask_patches,
+            "mask_group_min_patches": self.mask_group_min_patches,
+            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
+            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
+            "codebook_do_resize": self.codebook_do_resize,
+            "codebook_size": self.codebook_size,
+            "codebook_resample": self.codebook_resample,
+            "codebook_do_center_crop": self.codebook_do_center_crop,
+            "codebook_crop_size": self.codebook_crop_size,
+            "codebook_do_map_pixels": self.codebook_do_map_pixels,
+            "codebook_do_normalize": self.codebook_do_normalize,
+            "codebook_image_mean": self.codebook_image_mean,
+            "codebook_image_std": self.codebook_image_std,
+        }
+
+    def get_expected_image_size(self):
+        return (self.size["height"], self.size["width"])
+
+    def get_expected_mask_size(self):
+        return (
+            (self.input_size_patches, self.input_size_patches)
+            if not isinstance(self.input_size_patches, tuple)
+            else self.input_size_patches
+        )
+
+    def get_expected_codebook_image_size(self):
+        return (self.codebook_size["height"], self.codebook_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 FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    maxDiff = None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = FlavaImageProcessingTester(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, "resample"))
+            self.assertTrue(hasattr(image_processing, "crop_size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "rescale_factor"))
+            self.assertTrue(hasattr(image_processing, "masking_generator"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_resize"))
+            self.assertTrue(hasattr(image_processing, "codebook_size"))
+            self.assertTrue(hasattr(image_processing, "codebook_resample"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "codebook_crop_size"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_map_pixels"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_normalize"))
+            self.assertTrue(hasattr(image_processing, "codebook_image_mean"))
+            self.assertTrue(hasattr(image_processing, "codebook_image_std"))
+
+    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": 224, "width": 224})
+            self.assertEqual(image_processor.crop_size, {"height": 224, "width": 224})
+            self.assertEqual(image_processor.codebook_size, {"height": 112, "width": 112})
+            self.assertEqual(image_processor.codebook_crop_size, {"height": 112, "width": 112})
+
+            image_processor = image_processing_class.from_dict(
+                self.image_processor_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66
+            )
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+            self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33})
+            self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66})
+
+    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, PIL.Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt")
+
+            # Test no bool masked pos
+            self.assertFalse("bool_masked_pos" in encoded_images)
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+
+            # Test no bool masked pos
+            self.assertFalse("bool_masked_pos" in encoded_images)
+
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    def _test_call_framework(self, instance_class, prepare_kwargs):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs)
+            for image in image_inputs:
+                self.assertIsInstance(image, instance_class)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
+            self.assertEqual(
+                encoded_images.bool_masked_pos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test masking
+            encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
+            self.assertEqual(
+                encoded_images.bool_masked_pos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    def test_call_numpy(self):
+        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
+
+    def test_call_numpy_4_channels(self):
+        # Get the first backend class to modify num_channels
+        first_backend_class = list(self.image_processing_classes.values())[0]
+        original_num_channels = (
+            first_backend_class.num_channels if hasattr(first_backend_class, "num_channels") else None
+        )
+        first_backend_class.num_channels = 4
+        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
+        if original_num_channels is not None:
+            first_backend_class.num_channels = original_num_channels
+        else:
+            delattr(first_backend_class, "num_channels")
+
+    def test_call_pytorch(self):
+        self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
+
+    def test_masking(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            random.seed(1234)
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt")
+            self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
+
+    def test_codebook_pixels(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, PIL.Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
+            self.assertEqual(
+                encoded_images.codebook_pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_codebook_pixels=True, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
+            self.assertEqual(
+                encoded_images.codebook_pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_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", return_codebook_pixels=True, return_image_mask=True
+            )
+
+        # 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.pixel_values, encodings[backend_name].pixel_values)
+            self._assert_tensors_equivalence(
+                reference_encoding.codebook_pixel_values, encodings[backend_name].codebook_pixel_values
+            )