mirror of
https://github.com/huggingface/transformers.git
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0ba95564b7
* add args support to fast image processors * add comment for clarity * fix-copies * Handle child class args passed as both args or kwargs in call and preprocess functions * revert support args passed as kwargs in overwritten preprocess * fix image processor errors
359 lines
17 KiB
Python
359 lines
17 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 time
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import unittest
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import warnings
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import numpy as np
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import requests
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from packaging import version
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from transformers.testing_utils import is_flaky, require_torch, require_torch_gpu, require_vision, slow, torch_device
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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 VitMatteImageProcessor
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if is_torchvision_available():
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from transformers import VitMatteImageProcessorFast
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class VitMatteImageProcessingTester:
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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_rescale=True,
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rescale_factor=0.5,
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do_pad=True,
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size_divisibility=10,
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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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):
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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_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_pad = do_pad
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self.size_divisibility = size_divisibility
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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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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_rescale": self.do_rescale,
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"rescale_factor": self.rescale_factor,
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"do_pad": self.do_pad,
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"size_divisibility": self.size_divisibility,
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}
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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 VitMatteImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = VitMatteImageProcessor if is_vision_available() else None
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fast_image_processing_class = VitMatteImageProcessorFast 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 = VitMatteImageProcessingTester(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_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "size_divisibility"))
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def test_call_numpy(self):
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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 (image processor does not support batched inputs)
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image = image_inputs[0]
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trimap = np.random.randint(0, 3, size=image.shape[:2])
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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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encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
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# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-3] == 4)
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def test_call_pytorch(self):
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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 (image processor does not support batched inputs)
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image = image_inputs[0]
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trimap = np.random.randint(0, 3, size=image.shape[1:])
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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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encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
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# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-3] == 4)
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# create batched tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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image_input = torch.stack(image_inputs, dim=0)
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self.assertIsInstance(image_input, torch.Tensor)
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self.assertTrue(image_input.shape[1] == 3)
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trimap_shape = [image_input.shape[0]] + [1] + list(image_input.shape)[2:]
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trimap_input = torch.randint(0, 3, trimap_shape, dtype=torch.uint8)
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self.assertIsInstance(trimap_input, torch.Tensor)
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self.assertTrue(trimap_input.shape[1] == 1)
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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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encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
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# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-3] == 4)
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def test_call_pil(self):
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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 (image processor does not support batched inputs)
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image = image_inputs[0]
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trimap = np.random.randint(0, 3, size=image.size[::-1])
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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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encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
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# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-3] == 4)
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def test_call_numpy_4_channels(self):
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# Test that can process images which have an arbitrary number of channels
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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 (image processor does not support batched inputs)
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image = image_inputs[0]
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trimap = np.random.randint(0, 3, size=image.shape[:2])
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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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encoded_images = image_processor(
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images=image,
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trimaps=trimap,
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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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return_tensors="pt",
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).pixel_values
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# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
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self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
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self.assertTrue(encoded_images.shape[-3] == 5)
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def test_padding_slow(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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image = np.random.randn(3, 249, 491)
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images = image_processing.pad_image(image)
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assert images.shape == (3, 256, 512)
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image = np.random.randn(3, 249, 512)
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images = image_processing.pad_image(image)
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assert images.shape == (3, 256, 512)
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def test_padding_fast(self):
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# extra test because name is different for fast image processor
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image_processing = self.fast_image_processing_class(**self.image_processor_dict)
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image = torch.rand(3, 249, 491)
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images = image_processing._pad_image(image)
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assert images.shape == (3, 256, 512)
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image = torch.rand(3, 249, 512)
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images = image_processing._pad_image(image)
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assert images.shape == (3, 256, 512)
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def test_image_processor_preprocess_arguments(self):
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# vitmatte require additional trimap input for image_processor
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# that is why we override original common test
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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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image = self.image_processor_tester.prepare_image_inputs()[0]
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trimap = np.random.randint(0, 3, size=image.size[::-1])
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with warnings.catch_warnings(record=True) as raised_warnings:
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warnings.simplefilter("always")
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image_processor(image, trimaps=trimap, extra_argument=True)
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messages = " ".join([str(w.message) for w in raised_warnings])
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self.assertGreaterEqual(len(raised_warnings), 1)
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self.assertIn("extra_argument", messages)
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@is_flaky()
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def test_fast_is_faster_than_slow(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping speed test")
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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 speed test as one of the image processors is not defined")
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def measure_time(image_processor, images, trimaps):
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# Warmup
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for _ in range(5):
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_ = image_processor(images, trimaps=trimaps, return_tensors="pt")
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all_times = []
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for _ in range(10):
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start = time.time()
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_ = image_processor(images, trimaps=trimaps, return_tensors="pt")
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all_times.append(time.time() - start)
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# Take the average of the fastest 3 runs
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avg_time = sum(sorted(all_times[:3])) / 3.0
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return avg_time
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dummy_images = torch.randint(0, 255, (4, 3, 400, 800), dtype=torch.uint8)
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dummy_trimaps = torch.randint(0, 3, (4, 400, 800), dtype=torch.uint8)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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fast_time = measure_time(image_processor_fast, dummy_images, dummy_trimaps)
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slow_time = measure_time(image_processor_slow, dummy_images, dummy_trimaps)
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self.assertLessEqual(fast_time, slow_time)
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def test_slow_fast_equivalence(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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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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dummy_image = Image.open(
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requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
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)
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dummy_trimap = np.random.randint(0, 3, size=dummy_image.size[::-1])
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow(dummy_image, trimaps=dummy_trimap, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_image, trimaps=dummy_trimap, return_tensors="pt")
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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def test_slow_fast_equivalence_batched(self):
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# this only checks on equal resolution, since the slow processor doesn't work otherwise
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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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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if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
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self.skipTest(
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reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
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)
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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dummy_trimaps = [np.random.randint(0, 3, size=image.shape[1:]) for image in dummy_images]
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow(dummy_images, trimaps=dummy_trimaps, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_images, trimaps=dummy_trimaps, return_tensors="pt")
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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@slow
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@require_torch_gpu
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@require_vision
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def test_can_compile_fast_image_processor(self):
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# override as trimaps are needed for the image processor
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if self.fast_image_processing_class is None:
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self.skipTest("Skipping compilation test as fast image processor is not defined")
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if version.parse(torch.__version__) < version.parse("2.3"):
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self.skipTest(reason="This test requires torch >= 2.3 to run.")
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torch.compiler.reset()
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input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
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dummy_trimap = np.random.randint(0, 3, size=input_image.shape[1:])
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image_processor = self.fast_image_processing_class(**self.image_processor_dict)
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output_eager = image_processor(input_image, dummy_trimap, device=torch_device, return_tensors="pt")
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image_processor = torch.compile(image_processor, mode="reduce-overhead")
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output_compiled = image_processor(input_image, dummy_trimap, device=torch_device, return_tensors="pt")
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torch.testing.assert_close(output_eager.pixel_values, output_compiled.pixel_values, rtol=1e-4, atol=1e-4)
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