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https://github.com/huggingface/transformers.git
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4336ecd1ea
* add fast image processor nougat * test fixes * docstring white space * last fixes * docstring_type * tolerance unit test * fix tolerance * fix rtol * remove traling white space * remove white space * note for tolerance unit test * fix tests * remove print --------- Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
321 lines
15 KiB
Python
321 lines
15 KiB
Python
# 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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import requests
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from huggingface_hub import hf_hub_download
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from transformers.image_utils import SizeDict
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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_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_vision_available():
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from PIL import Image
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from transformers import NougatImageProcessor
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if is_torchvision_available():
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from transformers import NougatImageProcessorFast
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class NougatImageProcessingTester:
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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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self.data_format = "channels_first"
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self.input_data_format = "channels_first"
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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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revision = "ec57bf8c8b1653a209c13f6e9ee66b12df0fc2db"
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_docvqa",
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filename="nougat_pdf.png",
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repo_type="dataset",
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revision=revision,
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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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fast_image_processing_class = NougatImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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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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for image_processing_class in self.image_processor_list:
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image_processing = 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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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 20, "width": 20})
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kwargs = dict(self.image_processor_dict)
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kwargs.pop("size", None)
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image_processor = self.image_processing_class(**kwargs, 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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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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inputs = image_processor(dummy_image, return_tensors="pt")
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torch.testing.assert_close(inputs["pixel_values"].mean(), torch.tensor(0.4906), rtol=1e-3, atol=1e-3)
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def test_crop_margin_all_white(self):
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image = np.uint8(np.ones((3, 100, 100)) * 255)
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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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image_processor = image_processing_class(**self.image_processor_dict)
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cropped_image = image_processor.crop_margin(image)
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self.assertTrue(torch.equal(image, cropped_image))
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else:
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image_processor = image_processing_class(**self.image_processor_dict)
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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((3, 100, 100), dtype=np.uint8) * 255
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image[:, 45:55, 45:55] = 0
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expected_cropped = image[:, 45:55, 45:55]
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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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expected_cropped = torch.from_numpy(expected_cropped)
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image_processor = image_processing_class(**self.image_processor_dict)
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cropped_image = image_processor.crop_margin(image)
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self.assertTrue(torch.equal(expected_cropped, cropped_image))
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else:
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image_processor = image_processing_class(**self.image_processor_dict)
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cropped_image = image_processor.crop_margin(image)
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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((3, 100, 200)) * 255)
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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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size = SizeDict(height=200, width=300)
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image_processor = image_processing_class(**self.image_processor_dict)
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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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else:
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size = {"height": 200, "width": 300}
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image_processor = image_processing_class(**self.image_processor_dict)
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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((3, 200, 100)) * 255)
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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size = SizeDict(height=300, width=200)
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image_processor = image_processing_class(**self.image_processor_dict)
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aligned_image = image_processor.align_long_axis(image, size)
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self.assertEqual(torch.Size([3, 200, 100]), aligned_image.shape)
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else:
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size = {"height": 300, "width": 200}
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image_processor = image_processing_class(**self.image_processor_dict)
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aligned_image = image_processor.align_long_axis(image, size)
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self.assertEqual((3, 200, 100), aligned_image.shape)
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def test_align_long_axis_data_format(self):
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image = np.uint8(np.ones((3, 100, 200)) * 255)
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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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image_processor = image_processing_class(**self.image_processor_dict)
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size = SizeDict(height=200, width=300)
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aligned_image = image_processor.align_long_axis(image, size)
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self.assertEqual(torch.Size([3, 100, 200]), aligned_image.shape)
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else:
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size = {"height": 200, "width": 300}
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data_format = "channels_first"
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image_processor = image_processing_class(**self.image_processor_dict)
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aligned_image = image_processor.align_long_axis(image, size, 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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revision = "ec57bf8c8b1653a209c13f6e9ee66b12df0fc2db"
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_docvqa",
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filename="nougat_pdf.png",
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repo_type="dataset",
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revision=revision,
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)
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image = Image.open(filepath).convert("RGB")
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return np.array(image).transpose(2, 0, 1)
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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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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessorFast:
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image = torch.from_numpy(image)
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image_processor = image_processing_class(**self.image_processor_dict)
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image_cropped_python = image_processor.crop_margin(image)
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self.assertEqual(image_cropped_python.shape, torch.Size([3, 850, 685]))
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self.assertAlmostEqual(image_cropped_python.float().mean().item(), 237.43881150708458, delta=0.001)
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else:
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image_processor = image_processing_class(**self.image_processor_dict)
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image_cropped_python = image_processor.crop_margin(image)
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self.assertEqual(image_cropped_python.shape, (3, 850, 685))
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self.assertAlmostEqual(image_cropped_python.mean(), 237.43881150708458, delta=0.001)
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def test_call_numpy_4_channels(self):
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for image_processing_class in self.image_processor_list:
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if image_processing_class == NougatImageProcessor:
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# Test that can process images which have an arbitrary number of channels
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# Initialize image_processing
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image_processor = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0],
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(
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[image_inputs[0]]
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)
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processor(
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image_inputs,
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_slow_fast_equivalence(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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dummy_image = Image.open(
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requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
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)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
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# Adding a larget than usual tolerance because the slow processor uses reducing_gap=2.0 during resizing.
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torch.testing.assert_close(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=2e-1, rtol=0)
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 2e-2
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)
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