255 lines
11 KiB
Python
255 lines
11 KiB
Python
# Copyright 2021 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_torchvision_available, is_vision_available
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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 PIL import Image
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from transformers import Glm4vImageProcessor
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from transformers.models.glm4v.image_processing_glm4v import smart_resize
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if is_torchvision_available():
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from transformers import Glm4vImageProcessorFast
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class Glm4vImageProcessingTester:
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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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min_resolution=30,
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max_resolution=80,
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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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temporal_patch_size=2,
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patch_size=14,
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merge_size=2,
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):
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size = size if size is not None else {"longest_edge": 20, "shortest_edge": 10}
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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.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.temporal_patch_size = temporal_patch_size
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self.patch_size = patch_size
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self.merge_size = merge_size
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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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"temporal_patch_size": self.temporal_patch_size,
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"patch_size": self.patch_size,
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"merge_size": self.merge_size,
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}
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def expected_output_image_shape(self, images):
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grid_t = 1
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hidden_dim = self.num_channels * self.temporal_patch_size * self.patch_size * self.patch_size
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seq_len = 0
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for image in images:
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if isinstance(image, list) and isinstance(image[0], Image.Image):
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image = np.stack([np.array(frame) for frame in image])
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elif hasattr(image, "shape"):
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pass
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else:
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image = np.array(image)
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if hasattr(image, "shape") and len(image.shape) >= 3:
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if isinstance(image, np.ndarray):
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if len(image.shape) == 4:
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height, width = image.shape[1:3]
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elif len(image.shape) == 3:
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height, width = image.shape[:2]
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else:
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height, width = self.min_resolution, self.min_resolution
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else:
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height, width = image.shape[-2:]
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else:
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height, width = self.min_resolution, self.min_resolution
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resized_height, resized_width = smart_resize(
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self.temporal_patch_size,
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height,
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width,
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factor=self.patch_size * self.merge_size,
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min_pixels=self.size["shortest_edge"],
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max_pixels=self.size["longest_edge"],
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)
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grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
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seq_len += grid_t * grid_h * grid_w
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return (seq_len, hidden_dim)
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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 ViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Glm4vImageProcessor if is_vision_available() else None
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fast_image_processing_class = Glm4vImageProcessorFast 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 = Glm4vImageProcessingTester(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, {"shortest_edge": 10, "longest_edge": 20})
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image_processor = image_processing_class.from_dict(
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self.image_processor_dict, size={"shortest_edge": 42, "longest_edge": 42}
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)
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self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 42})
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# batch size is flattened
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def test_call_pil(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(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
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(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
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(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
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy_4_channels(self):
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for image_processing_class in self.image_processor_list:
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# Test that can process images which have an arbitrary number of channels
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# Initialize image_processing
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image_processor = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0],
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processor(
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image_inputs,
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return_tensors="pt",
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input_data_format="channels_last",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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