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https://github.com/huggingface/transformers.git
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392 lines
16 KiB
Python
392 lines
16 KiB
Python
# Copyright 2023 The Intel Team Authors, 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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import unittest
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import numpy as np
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from transformers.image_transforms import PaddingMode
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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_video_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 TvpImageProcessor, TvpImageProcessorFast
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class TvpImageProcessingTester:
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def __init__(
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self,
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parent,
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do_resize: bool = True,
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size: dict[str, int] = {"longest_edge": 40},
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do_center_crop: bool = False,
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crop_size: dict[str, int] | None = None,
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do_rescale: bool = False,
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rescale_factor: int | float = 1 / 255,
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do_pad: bool = True,
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pad_size: dict[str, int] = {"height": 80, "width": 80},
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fill: int | None = None,
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pad_mode: PaddingMode | None = None,
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do_normalize: bool = True,
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image_mean: float | list[float] | None = [0.48145466, 0.4578275, 0.40821073],
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image_std: float | list[float] | None = [0.26862954, 0.26130258, 0.27577711],
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batch_size=2,
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min_resolution=40,
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max_resolution=80,
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num_channels=3,
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num_frames=2,
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):
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self.do_resize = do_resize
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self.size = size
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self.do_center_crop = do_center_crop
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self.crop_size = crop_size
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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.pad_size = pad_size
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self.fill = fill
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self.pad_mode = pad_mode
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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.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.num_frames = num_frames
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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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"do_rescale": self.do_rescale,
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"do_center_crop": self.do_center_crop,
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"do_pad": self.do_pad,
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"pad_size": self.pad_size,
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}
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def get_expected_values(self, image_inputs, batched=False):
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"""
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This function computes the expected height and width when providing images to TvpImageProcessor,
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assuming do_resize is set to True with a scalar size.
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"""
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if not batched:
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return (int(self.pad_size["height"]), int(self.pad_size["width"]))
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else:
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expected_values = []
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for image in image_inputs:
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expected_height, expected_width = self.get_expected_values([image])
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expected_values.append((expected_height, expected_width))
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expected_height = max(expected_values, key=lambda item: item[0])[0]
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expected_width = max(expected_values, key=lambda item: item[1])[1]
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return expected_height, expected_width
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def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_video_inputs(
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batch_size=self.batch_size,
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num_frames=self.num_frames,
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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 TvpImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = TvpImageProcessor if is_vision_available() else None
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fast_image_processing_class = (
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TvpImageProcessorFast if is_vision_available() and is_torchvision_available() else None
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)
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def setUp(self):
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super().setUp()
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self.image_processor_tester = TvpImageProcessingTester(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, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "pad_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, {"longest_edge": 40})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size={"longest_edge": 12})
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self.assertEqual(image_processor.size, {"longest_edge": 12})
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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 videos
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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], Image.Image)
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# Test not batched input
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expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
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encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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# Test batched
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expected_height, expected_width = self.image_processor_tester.get_expected_values(
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video_inputs, batched=True
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)
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encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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def test_call_numpy(self):
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# Test numpy with both processors
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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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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# For fast processor, convert numpy to tensor
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if image_processing_class == self.fast_image_processing_class:
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# Convert numpy arrays to tensors for fast processor
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tensor_video_inputs = []
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for video in video_inputs:
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tensor_video = [torch.from_numpy(frame) for frame in video]
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tensor_video_inputs.append(tensor_video)
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test_inputs = tensor_video_inputs
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else:
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test_inputs = video_inputs
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# Test not batched input
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expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
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encoded_videos = image_processing(test_inputs[0], return_tensors="pt").pixel_values
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self.assertListEqual(
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list(encoded_videos.shape),
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[
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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],
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)
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# Test batched
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expected_height, expected_width = self.image_processor_tester.get_expected_values(
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video_inputs, batched=True
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)
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encoded_videos = image_processing(test_inputs, return_tensors="pt").pixel_values
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self.assertListEqual(
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list(encoded_videos.shape),
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[
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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],
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)
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def test_call_numpy_4_channels(self):
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# Test numpy with both processors
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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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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# For fast processor, convert numpy to tensor
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if image_processing_class == self.fast_image_processing_class:
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# Convert numpy arrays to tensors for fast processor
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tensor_video_inputs = []
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for video in video_inputs:
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tensor_video = [torch.from_numpy(frame) for frame in video]
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tensor_video_inputs.append(tensor_video)
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test_inputs = tensor_video_inputs
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else:
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test_inputs = video_inputs
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# Test not batched input
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expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
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encoded_videos = image_processing(
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test_inputs[0],
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return_tensors="pt",
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image_mean=(0.0, 0.0, 0.0),
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image_std=(1.0, 1.0, 1.0),
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input_data_format="channels_first",
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).pixel_values
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self.assertListEqual(
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list(encoded_videos.shape),
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[
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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],
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)
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# Test batched
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expected_height, expected_width = self.image_processor_tester.get_expected_values(
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video_inputs, batched=True
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)
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encoded_videos = image_processing(
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test_inputs,
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return_tensors="pt",
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image_mean=(0.0, 0.0, 0.0),
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image_std=(1.0, 1.0, 1.0),
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input_data_format="channels_first",
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).pixel_values
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self.assertListEqual(
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list(encoded_videos.shape),
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[
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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],
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)
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self.image_processor_tester.num_channels = 3
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def test_call_pytorch(self):
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# Test PyTorch tensors with both processors
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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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video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], torch.Tensor)
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# Test not batched input
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expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
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encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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# Test batched
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expected_height, expected_width = self.image_processor_tester.get_expected_values(
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video_inputs, batched=True
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)
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encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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@require_vision
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@require_torch
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@unittest.skip(
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reason="FIXME: @yoni probably because of an extra 'time' dimension and since image processors don't handle it well?"
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)
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def test_slow_fast_equivalence(self):
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super().test_slow_fast_equivalence()
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@require_vision
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@require_torch
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@unittest.skip(
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reason="FIXME: @yoni probably because of an extra 'time' dimension and since image processors don't handle it well?"
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)
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def test_slow_fast_equivalence_batched(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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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_video_inputs(equal_resolution=False, torchify=True)
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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, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
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# Higher max atol for video processing, mean_atol still 5e-3 -> 1e-2
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self._assert_slow_fast_tensors_equivalence(
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encoding_slow.pixel_values, encoding_fast.pixel_values, atol=10.0, mean_atol=1e-2
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)
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