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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 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 (
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is_torch_available,
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is_torchvision_available,
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is_vision_available,
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)
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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 transformers import EfficientNetImageProcessor
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if is_torchvision_available():
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from transformers import EfficientNetImageProcessorFast
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class EfficientNetImageProcessorTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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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=None,
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do_normalize=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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do_rescale=True,
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rescale_offset=True,
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rescale_factor=1 / 127.5,
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resample=PILImageResampling.BILINEAR, # NEAREST is too different between PIL and torchvision
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):
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size = size if size is not None else {"height": 18, "width": 18}
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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 = size
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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.resample = resample
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def prepare_image_processor_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_normalize": self.do_normalize,
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"do_resize": self.do_resize,
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"size": self.size,
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"resample": self.resample,
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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_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 EfficientNetImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = EfficientNetImageProcessor if is_vision_available() else None
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fast_image_processing_class = EfficientNetImageProcessorFast 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 = EfficientNetImageProcessorTester(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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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, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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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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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.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 18, "width": 18})
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image_processor = 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_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.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
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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 == EfficientNetImageProcessorFast:
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image = torch.from_numpy(image)
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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=True
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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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rescaled_image = image_processor.rescale(image, scale=1 / 127.5, dtype=np.float64)
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expected_image = (image * (1 / 127.5)).astype(np.float64) - 1
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self.assertTrue(np.allclose(rescaled_image, expected_image))
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rescaled_image = image_processor.rescale(image, scale=1 / 255, offset=False, dtype=np.float64)
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expected_image = (image / 255.0).astype(np.float64)
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self.assertTrue(np.allclose(rescaled_image, expected_image))
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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 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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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_fast = self.fast_image_processing_class(**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_fast.rescale_and_normalize(
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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_fast.rescale_and_normalize(
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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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