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transformers/tests/models/efficientnet/__init__.py
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transformers/tests/models/efficientnet/__init__.py
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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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@@ -0,0 +1,263 @@
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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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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"""Testing suite for the PyTorch EfficientNet model."""
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import unittest
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from functools import cached_property
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from transformers import EfficientNetConfig
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from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import EfficientNetForImageClassification, EfficientNetModel
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if is_vision_available():
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from PIL import Image
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from transformers import AutoImageProcessor
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class EfficientNetModelTester:
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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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image_size=32,
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num_channels=3,
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kernel_sizes=[3, 3, 5],
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in_channels=[32, 16, 24],
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out_channels=[16, 24, 20],
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strides=[1, 1, 2],
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num_block_repeats=[1, 1, 2],
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expand_ratios=[1, 6, 6],
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is_training=True,
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use_labels=True,
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intermediate_size=37,
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hidden_act="gelu",
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num_labels=10,
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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.image_size = image_size
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self.num_channels = num_channels
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self.kernel_sizes = kernel_sizes
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.strides = strides
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self.num_block_repeats = num_block_repeats
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self.expand_ratios = expand_ratios
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self.is_training = is_training
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self.hidden_act = hidden_act
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self.num_labels = num_labels
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self.use_labels = use_labels
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_labels)
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config = self.get_config()
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return config, pixel_values, labels
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def get_config(self):
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return EfficientNetConfig(
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image_size=self.image_size,
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num_channels=self.num_channels,
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kernel_sizes=self.kernel_sizes,
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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strides=self.strides,
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num_block_repeats=self.num_block_repeats,
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expand_ratios=self.expand_ratios,
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hidden_act=self.hidden_act,
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num_labels=self.num_labels,
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)
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def create_and_check_model(self, config, pixel_values, labels):
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model = EfficientNetModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values)
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# expected last hidden states: B, C, H // 4, W // 4
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4),
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)
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def create_and_check_for_image_classification(self, config, pixel_values, labels):
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model = EfficientNetForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, labels=labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values, labels = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as EfficientNet does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"image-feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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has_attentions = False
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test_torch_exportable = True
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def setUp(self):
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self.model_tester = EfficientNetModelTester(self)
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self.config_tester = ConfigTester(
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self,
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config_class=EfficientNetConfig,
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has_text_modality=False,
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hidden_size=37,
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common_properties=["num_channels", "image_size", "hidden_dim"],
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="EfficientNet does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="EfficientNet does not support input and output embeddings")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="EfficientNet does not use feedforward chunking")
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def test_feed_forward_chunking(self):
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pass
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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num_blocks = sum(config.num_block_repeats) * 4
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self.assertEqual(len(hidden_states), num_blocks)
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# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.image_size // 2, self.model_tester.image_size // 2],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_for_image_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "google/efficientnet-b7"
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model = EfficientNetModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@is_pipeline_test
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@require_vision
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@slow
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def test_pipeline_image_feature_extraction(self):
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super().test_pipeline_image_feature_extraction()
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@is_pipeline_test
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@require_vision
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@slow
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def test_pipeline_image_feature_extraction_fp16(self):
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super().test_pipeline_image_feature_extraction_fp16()
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@is_pipeline_test
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@require_vision
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@slow
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def test_pipeline_image_classification(self):
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super().test_pipeline_image_classification()
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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@require_vision
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class EfficientNetModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return AutoImageProcessor.from_pretrained("google/efficientnet-b7") if is_vision_available() else None
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||||
|
||||
@slow
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||||
def test_inference_image_classification_head(self):
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model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7").to(torch_device)
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||||
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||||
image_processor = self.default_image_processor
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image = prepare_img()
|
||||
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
||||
|
||||
# forward pass
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
# verify the logits
|
||||
expected_shape = torch.Size((1, 1000))
|
||||
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||
|
||||
expected_slice = torch.tensor([-0.2962, 0.4487, 0.4499]).to(torch_device)
|
||||
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
|
||||
Reference in New Issue
Block a user