195 lines
7.4 KiB
Python
195 lines
7.4 KiB
Python
# coding=utf-8
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# Copyright 2023 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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import numpy as np
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from huggingface_hub import hf_hub_download
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import cached_property, is_torch_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_vision_available():
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from PIL import Image
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from transformers import NougatImageProcessor
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class NougatImageProcessingTester(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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do_crop_margin=True,
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do_resize=True,
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size=None,
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do_thumbnail=True,
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do_align_long_axis: bool = False,
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do_pad=True,
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do_normalize: bool = True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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):
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size = size if size is not None else {"height": 20, "width": 20}
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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.do_crop_margin = do_crop_margin
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self.do_resize = do_resize
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self.size = size
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self.do_thumbnail = do_thumbnail
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self.do_align_long_axis = do_align_long_axis
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self.do_pad = do_pad
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self.do_normalize = do_normalize
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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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"do_crop_margin": self.do_crop_margin,
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"do_resize": self.do_resize,
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"size": self.size,
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"do_thumbnail": self.do_thumbnail,
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"do_align_long_axis": self.do_align_long_axis,
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"do_pad": self.do_pad,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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}
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def expected_output_image_shape(self, images):
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return self.num_channels, self.size["height"], self.size["width"]
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def prepare_dummy_image(self):
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
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)
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image = Image.open(filepath).convert("RGB")
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return image
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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 NougatImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = NougatImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = NougatImageProcessingTester(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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@cached_property
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def image_processor(self):
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return self.image_processing_class(**self.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, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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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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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.assertEqual(image_processor.size, {"height": 20, "width": 20})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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def test_expected_output(self):
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dummy_image = self.image_processor_tester.prepare_dummy_image()
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image_processor = self.image_processor
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inputs = image_processor(dummy_image, return_tensors="pt")
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self.assertTrue(torch.allclose(inputs["pixel_values"].mean(), torch.tensor(0.4906), atol=1e-3, rtol=1e-3))
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def test_crop_margin_all_white(self):
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image = np.uint8(np.ones((100, 100, 3)) * 255)
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image_processor = self.image_processor
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cropped_image = image_processor.crop_margin(image)
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self.assertTrue(np.array_equal(image, cropped_image))
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def test_crop_margin_centered_black_square(self):
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image = np.ones((100, 100, 3), dtype=np.uint8) * 255
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image[45:55, 45:55, :] = 0
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image_processor = self.image_processor
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cropped_image = image_processor.crop_margin(image)
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expected_cropped = image[45:55, 45:55, :]
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self.assertTrue(np.array_equal(expected_cropped, cropped_image))
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def test_align_long_axis_no_rotation(self):
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image = np.uint8(np.ones((100, 200, 3)) * 255)
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image_processor = self.image_processor
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size = {"height": 200, "width": 300}
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aligned_image = image_processor.align_long_axis(image, size)
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self.assertEqual(image.shape, aligned_image.shape)
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def test_align_long_axis_with_rotation(self):
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image = np.uint8(np.ones((200, 100, 3)) * 255)
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image_processor = self.image_processor
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size = {"height": 300, "width": 200}
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aligned_image = image_processor.align_long_axis(image, size)
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self.assertEqual((200, 100, 3), aligned_image.shape)
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def test_align_long_axis_data_format(self):
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image = np.uint8(np.ones((100, 200, 3)) * 255)
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data_format = "channels_first"
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size = {"height": 200, "width": 300}
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image_processor = self.image_processor
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aligned_image = image_processor.align_long_axis(image, size, data_format=data_format)
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self.assertEqual((3, 100, 200), aligned_image.shape)
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def prepare_dummy_np_image(self):
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_pdf.png", repo_type="dataset"
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)
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image = Image.open(filepath).convert("RGB")
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return np.array(image)
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def test_crop_margin_equality_cv2_python(self):
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image = self.prepare_dummy_np_image()
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image_processor = self.image_processor
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image_cropped_python = image_processor.crop_margin(image)
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self.assertEqual(image_cropped_python.shape, (850, 685, 3))
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self.assertEqual(image_cropped_python.mean(), 237.43881150708458)
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