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https://github.com/huggingface/transformers.git
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guarin
89b6b17574
* Add shared ImageProcessingTester * fix typo * remove strict check * update test * use crop_size if available * inline get_expected_values where possible * remove more get_expected_values * handle get_expected_values for shortest_edge * refactor prepare_semantic_single_inputs * add ImageProcessingTester.__init__ * convert more __init__ * cleanup * refactor * move redundant tests to base class * refactor redundant test_image_processor_from_dict_with_kwargs tests * revert renames to keep diff smaller * cleanup * simplify test * remove unused overrides * move to class attributes * remove redundant overrides * remove redundant mean/std * remove whitspace * add comments * cleanup * update docs * fix repo * add comment * cleanup * cleanup docs * handle longest edge * list defaults
108 lines
4.9 KiB
Python
108 lines
4.9 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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from transformers.image_utils import PILImageResampling
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available
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from ...test_image_processing_common import ImageProcessingTester, ImageProcessingTestMixin
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if is_torch_available():
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import torch
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class EfficientNetImageProcessingTester(ImageProcessingTester):
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def __init__(self, **kwargs):
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# Random test inputs kwargs
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kwargs.setdefault("batch_size", 13)
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# Image processor init kwargs
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kwargs.setdefault("rescale_offset", True)
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kwargs.setdefault("rescale_factor", 1 / 127.5)
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kwargs.setdefault("size", {"height": 18, "width": 18})
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kwargs.setdefault("resample", PILImageResampling.BILINEAR)
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super().__init__(**kwargs)
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@require_torch
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@require_vision
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class EfficientNetImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processor_tester_class = EfficientNetImageProcessingTester
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def test_rescale(self):
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# EfficientNet optionally rescales between -1 and 1 instead of the usual 0 and 1
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image_np = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
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for backend_name, image_processing_class in self.image_processing_classes.items():
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image_processor = image_processing_class(**self.image_processor_dict)
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if backend_name == "torchvision":
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image = torch.from_numpy(image_np)
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# Scale between [-1, 1] with rescale_factor 1/127.5 and rescale_offset=True
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rescaled_image = image_processor.rescale(image, scale=1 / 127.5, offset=True)
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expected_image = (image * (1 / 127.5)) - 1
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self.assertTrue(torch.allclose(rescaled_image, expected_image))
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# Scale between [0, 1] with rescale_factor 1/255 and rescale_offset=False
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rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False)
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expected_image = image / 255.0
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self.assertTrue(torch.allclose(rescaled_image, expected_image))
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else:
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image = image_np
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rescaled_image = image_processor.rescale(image, scale=1 / 127.5, offset=True)
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expected_image = (image.astype(np.float64) * (1 / 127.5)) - 1
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self.assertTrue(np.allclose(rescaled_image, expected_image, rtol=1e-5, atol=1e-5))
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rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False)
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expected_image = image.astype(np.float64) / 255.0
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self.assertTrue(np.allclose(rescaled_image, expected_image, rtol=1e-5, atol=1e-5))
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@require_vision
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@require_torch
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def test_rescale_normalize(self):
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if "torchvision" not in self.image_processing_classes:
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self.skipTest(reason="Skipping rescale_normalize test as torchvision backend is not available")
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image = torch.arange(0, 256, 1, dtype=torch.uint8).reshape(1, 8, 32).repeat(3, 1, 1)
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image_mean_0 = (0.0, 0.0, 0.0)
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image_std_0 = (1.0, 1.0, 1.0)
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image_mean_1 = (0.5, 0.5, 0.5)
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image_std_1 = (0.5, 0.5, 0.5)
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image_processor = self.image_processing_classes["torchvision"](**self.image_processor_dict)
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# Rescale between [-1, 1] with rescale_factor=1/127.5 and rescale_offset=True. Then normalize
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rescaled_normalized = image_processor.rescale_and_normalize_efficientnet(
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image, True, 1 / 127.5, True, image_mean_0, image_std_0, True
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)
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expected_image = (image * (1 / 127.5)) - 1
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expected_image = (expected_image - torch.tensor(image_mean_0).view(3, 1, 1)) / torch.tensor(image_std_0).view(
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3, 1, 1
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)
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self.assertTrue(torch.allclose(rescaled_normalized, expected_image, rtol=1e-3))
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# Rescale between [0, 1] with rescale_factor=1/255 and rescale_offset=False. Then normalize
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rescaled_normalized = image_processor.rescale_and_normalize_efficientnet(
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image, True, 1 / 255, True, image_mean_1, image_std_1, False
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)
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expected_image = image * (1 / 255.0)
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expected_image = (expected_image - torch.tensor(image_mean_1).view(3, 1, 1)) / torch.tensor(image_std_1).view(
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3, 1, 1
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)
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self.assertTrue(torch.allclose(rescaled_normalized, expected_image, rtol=1e-3))
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