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Add feature extractor tests, add token2json method, improve feature e…
…xtractor
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Niels Rogge
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Niels Rogge
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Aug 10, 2022
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# coding=utf-8 | ||
# Copyright 2022 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. | ||
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import unittest | ||
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import numpy as np | ||
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from transformers.testing_utils import require_torch, require_vision | ||
from transformers.utils import is_torch_available, is_vision_available | ||
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from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs | ||
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if is_torch_available(): | ||
import torch | ||
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if is_vision_available(): | ||
from PIL import Image | ||
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from transformers import DonutFeatureExtractor | ||
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class DonutFeatureExtractionTester(unittest.TestCase): | ||
def __init__( | ||
self, | ||
parent, | ||
batch_size=7, | ||
num_channels=3, | ||
image_size=18, | ||
min_resolution=30, | ||
max_resolution=400, | ||
do_resize=True, | ||
size=[20, 18], | ||
do_align_axis=False, | ||
do_pad=True, | ||
do_normalize=True, | ||
image_mean=[0.5, 0.5, 0.5], | ||
image_std=[0.5, 0.5, 0.5], | ||
): | ||
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.do_resize = do_resize | ||
self.size = size | ||
self.do_align_axis = do_align_axis | ||
self.do_pad = do_pad | ||
self.do_normalize = do_normalize | ||
self.image_mean = image_mean | ||
self.image_std = image_std | ||
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def prepare_feat_extract_dict(self): | ||
return { | ||
"do_resize": self.do_resize, | ||
"size": self.size, | ||
"do_align_long_axis": self.do_align_axis, | ||
"do_pad": self.do_pad, | ||
"do_normalize": self.do_normalize, | ||
"image_mean": self.image_mean, | ||
"image_std": self.image_std, | ||
} | ||
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@require_torch | ||
@require_vision | ||
class DonutFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): | ||
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feature_extraction_class = DonutFeatureExtractor if is_vision_available() else None | ||
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def setUp(self): | ||
self.feature_extract_tester = DonutFeatureExtractionTester(self) | ||
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@property | ||
def feat_extract_dict(self): | ||
return self.feature_extract_tester.prepare_feat_extract_dict() | ||
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def test_feat_extract_properties(self): | ||
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | ||
self.assertTrue(hasattr(feature_extractor, "do_resize")) | ||
self.assertTrue(hasattr(feature_extractor, "size")) | ||
self.assertTrue(hasattr(feature_extractor, "do_align_long_axis")) | ||
self.assertTrue(hasattr(feature_extractor, "do_pad")) | ||
self.assertTrue(hasattr(feature_extractor, "do_normalize")) | ||
self.assertTrue(hasattr(feature_extractor, "image_mean")) | ||
self.assertTrue(hasattr(feature_extractor, "image_std")) | ||
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def test_batch_feature(self): | ||
pass | ||
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def test_call_pil(self): | ||
# Initialize feature_extractor | ||
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | ||
# create random PIL images | ||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False) | ||
for image in image_inputs: | ||
self.assertIsInstance(image, Image.Image) | ||
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# Test not batched input | ||
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
1, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) | ||
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# Test batched | ||
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
self.feature_extract_tester.batch_size, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) | ||
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def test_call_numpy(self): | ||
# Initialize feature_extractor | ||
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | ||
# create random numpy tensors | ||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) | ||
for image in image_inputs: | ||
self.assertIsInstance(image, np.ndarray) | ||
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# Test not batched input | ||
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
1, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) | ||
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# Test batched | ||
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
self.feature_extract_tester.batch_size, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) | ||
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def test_call_pytorch(self): | ||
# Initialize feature_extractor | ||
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) | ||
# create random PyTorch tensors | ||
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) | ||
for image in image_inputs: | ||
self.assertIsInstance(image, torch.Tensor) | ||
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# Test not batched input | ||
encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
1, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) | ||
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# Test batched | ||
encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values | ||
self.assertEqual( | ||
encoded_images.shape, | ||
( | ||
self.feature_extract_tester.batch_size, | ||
self.feature_extract_tester.num_channels, | ||
self.feature_extract_tester.size[1], | ||
self.feature_extract_tester.size[0], | ||
), | ||
) |