204 lines
7.4 KiB
Python
204 lines
7.4 KiB
Python
# coding=utf-8
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# Copyright 2021 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers.testing_utils import require_torch, require_torchvision, require_vision
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_torchvision_available():
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import torchvision.transforms as transforms
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if is_vision_available():
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from PIL import Image
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from transformers import IdeficsImageProcessor
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class IdeficsImageProcessingTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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size=None,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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):
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size = size if size is not None else {"shortest_edge": 30}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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# self.size = size
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self.image_mean = image_mean
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self.image_std = image_std
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def prepare_image_processor_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"image_size": self.image_size,
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}
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def get_expected_values(self, image_inputs, batched=False):
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"""
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This function computes the expected height and width when providing images to IdeficsImageProcessor,
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assuming do_resize is set to True with a scalar size and size_divisor.
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"""
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if not batched:
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size = self.image_size
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image = image_inputs[0]
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if isinstance(image, Image.Image):
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w, h = image.size
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else:
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h, w = image.shape[1], image.shape[2]
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scale = size / min(w, h)
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if h < w:
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newh, neww = size, scale * w
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else:
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newh, neww = scale * h, size
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max_size = int((1333 / 800) * size)
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if max(newh, neww) > max_size:
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scale = max_size / max(newh, neww)
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newh = newh * scale
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neww = neww * scale
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newh, neww = int(newh + 0.5), int(neww + 0.5)
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expected_height, expected_width = (
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newh // self.size_divisor * self.size_divisor,
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neww // self.size_divisor * self.size_divisor,
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)
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else:
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expected_values = []
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for image in image_inputs:
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expected_height, expected_width = self.get_expected_values([image])
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expected_values.append((expected_height, expected_width))
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expected_height = max(expected_values, key=lambda item: item[0])[0]
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expected_width = max(expected_values, key=lambda item: item[1])[1]
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return expected_height, expected_width
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def expected_output_image_shape(self, images):
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height, width = self.get_expected_values(images, batched=True)
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return (self.num_channels, height, width)
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = IdeficsImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = IdeficsImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "image_size"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertNotEqual(image_processor.image_size, 30)
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, image_size=42)
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self.assertEqual(image_processor.image_size, 42)
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@require_torchvision
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def test_torchvision_numpy_transforms_equivalency(self):
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# as we had to reimplement the torchvision transforms using transformers utils we must check
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# they both do the same
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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image_processor = self.image_processing_class(**self.image_processor_dict)
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print(image_inputs)
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def convert_to_rgb(image):
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# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
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# for transparent images. The call to `alpha_composite` handles this case
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if image.mode == "RGB":
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return image
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image_rgba = image.convert("RGBA")
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background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
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alpha_composite = Image.alpha_composite(background, image_rgba)
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alpha_composite = alpha_composite.convert("RGB")
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return alpha_composite
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image_size = image_processor.image_size
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image_mean = image_processor.image_mean
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image_std = image_processor.image_std
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transform = transforms.Compose(
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[
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convert_to_rgb,
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transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
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transforms.ToTensor(),
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transforms.Normalize(mean=image_mean, std=image_std),
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]
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)
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pixel_values_transform_implied = image_processor(image_inputs, transform=None)
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pixel_values_transform_supplied = image_processor(image_inputs, transform=transform)
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torch.testing.assert_close(pixel_values_transform_implied, pixel_values_transform_supplied, rtol=0.0, atol=0.0)
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@unittest.skip("not supported")
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def test_call_numpy(self):
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pass
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@unittest.skip("not supported")
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def test_call_numpy_4_channels(self):
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pass
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@unittest.skip("not supported")
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def test_call_pil(self):
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pass
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@unittest.skip("not supported")
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def test_call_pytorch(self):
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pass
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