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https://github.com/huggingface/transformers.git
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e2bd7f80d0
* Update imports and test fetcher * Revert but keep test fetcher update * Fix imports * Fix all imports * Replace fe with ip names * Add generate kwargs to `AutomaticSpeechRecognitionPipeline` (#20952) * Add generate kwargs to AutomaticSpeechRecognitionPipeline * Add test for generation kwargs * Update image processor parameters if creating with kwargs (#20866) * Update parameters if creating with kwargs * Shallow copy to prevent mutating input * Pass all args in constructor dict - warnings in init * Fix typo * Rename tester class * Rebase and tidy up * Fixup * Use ImageProcessingSavingTestMixin * Update property ref in tests * Update property ref in tests * Update recently merged in models * Small fix Co-authored-by: bofeng huang <bofenghuang7@gmail.com>
128 lines
4.9 KiB
Python
128 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2022 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 transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingSavingTestMixin, 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 GLPNImageProcessor
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class GLPNImageProcessingTester(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_resize=True,
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size_divisor=32,
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do_rescale=True,
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):
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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_resize = do_resize
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self.size_divisor = size_divisor
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self.do_rescale = do_rescale
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size_divisor": self.size_divisor,
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"do_rescale": self.do_rescale,
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}
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@require_torch
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@require_vision
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class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
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image_processing_class = GLPNImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = GLPNImageProcessingTester(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, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size_divisor"))
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self.assertTrue(hasattr(image_processing, "resample"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input (GLPNImageProcessor doesn't support batching)
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
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def test_call_numpy(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input (GLPNImageProcessor doesn't support batching)
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
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def test_call_pytorch(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input (GLPNImageProcessor doesn't support batching)
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
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