init

transformers/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 unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+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
+
+    from transformers import IdeficsImageProcessor
+
+
+class IdeficsImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        size=None,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+    ):
+        size = size if size is not None else {"shortest_edge": 30}
+        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.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 get_expected_values(self, image_inputs, batched=False):
+        """
+        This function computes the expected height and width when providing images to IdeficsImageProcessor,
+        assuming do_resize is set to True with a scalar size and size_divisor.
+        """
+        if not batched:
+            size = self.image_size
+            image = image_inputs[0]
+            if isinstance(image, Image.Image):
+                w, h = image.size
+            elif isinstance(image, np.ndarray):
+                h, w = image.shape[0], image.shape[1]
+            else:
+                h, w = image.shape[1], image.shape[2]
+            scale = size / min(w, h)
+            if h < w:
+                newh, neww = size, scale * w
+            else:
+                newh, neww = scale * h, size
+
+            max_size = int((1333 / 800) * size)
+            if max(newh, neww) > max_size:
+                scale = max_size / max(newh, neww)
+                newh = newh * scale
+                neww = neww * scale
+
+            newh, neww = int(newh + 0.5), int(neww + 0.5)
+            expected_height, expected_width = (
+                newh // self.size_divisor * self.size_divisor,
+                neww // self.size_divisor * self.size_divisor,
+            )
+
+        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 expected_output_image_shape(self, images):
+        height, width = self.get_expected_values(images, batched=True)
+        return (self.num_channels, height, 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 IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = IdeficsImageProcessor if is_vision_available() else None
+
+    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):
+        image_processing = self.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):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        self.assertNotEqual(image_processor.image_size, 30)
+
+        image_processor = self.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):
+        # as we had to reimplement the torchvision transforms using transformers utils we must check
+        # they both do the same
+
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+        image_processor = self.image_processing_class(**self.image_processor_dict, return_tensors="pt")
+
+        print(image_inputs)
+
+        def convert_to_rgb(image):
+            # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
+            # for transparent images. The call to `alpha_composite` handles this case
+            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
+
+        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=0.0, atol=0.0)
+
+    @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