mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 04:40:06 +06:00
d29482cc91
* add working idefics2 fast and improvements for fast nested images processing * add fast image processors idefics 3 and smolvlm * cleanup tests * fic doc idefics2 * PR review and fix issues after merge * Force providing disable_grouping to group_images_by_shape * simplify group_images_by_shape * fix modular * Fix nits after review
660 lines
31 KiB
Python
660 lines
31 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 inspect
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import json
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import os
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import pathlib
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import tempfile
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import time
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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 import AutoImageProcessor, BatchFeature
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from transformers.image_utils import AnnotationFormat, AnnotionFormat
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from transformers.testing_utils import (
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check_json_file_has_correct_format,
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is_flaky,
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require_torch,
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require_torch_accelerator,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available, is_vision_available
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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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def prepare_image_inputs(
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batch_size,
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min_resolution,
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max_resolution,
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num_channels,
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size_divisor=None,
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equal_resolution=False,
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numpify=False,
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torchify=False,
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):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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One can specify whether the images are of the same resolution or not.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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image_inputs = []
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for i in range(batch_size):
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if equal_resolution:
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width = height = max_resolution
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else:
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# To avoid getting image width/height 0
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if size_divisor is not None:
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# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
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min_resolution = max(size_divisor, min_resolution)
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width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
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image_inputs.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(image) for image in image_inputs]
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if numpify:
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# Numpy images are typically in channels last format
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image_inputs = [image.transpose(1, 2, 0) for image in image_inputs]
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return image_inputs
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def prepare_video(num_frames, num_channels, width=10, height=10, numpify=False, torchify=False):
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"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
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video = []
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for i in range(num_frames):
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video.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
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if torchify:
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video = [torch.from_numpy(frame) for frame in video]
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return video
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def prepare_video_inputs(
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batch_size,
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num_frames,
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num_channels,
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min_resolution,
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max_resolution,
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equal_resolution=False,
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numpify=False,
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torchify=False,
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):
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"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
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one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
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One can specify whether the videos are of the same resolution or not.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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video_inputs = []
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for _ in range(batch_size):
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if equal_resolution:
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width = height = max_resolution
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else:
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width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
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video = prepare_video(
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num_frames=num_frames,
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num_channels=num_channels,
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width=width,
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height=height,
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numpify=numpify,
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torchify=torchify,
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)
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video_inputs.append(video)
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return video_inputs
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class ImageProcessingTestMixin:
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test_cast_dtype = None
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image_processing_class = None
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fast_image_processing_class = None
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image_processors_list = None
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test_slow_image_processor = True
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test_fast_image_processor = True
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def setUp(self):
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image_processor_list = []
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if self.test_slow_image_processor and self.image_processing_class:
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image_processor_list.append(self.image_processing_class)
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if self.test_fast_image_processor and self.fast_image_processing_class:
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image_processor_list.append(self.fast_image_processing_class)
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self.image_processor_list = image_processor_list
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def _assert_slow_fast_tensors_equivalence(self, slow_tensor, fast_tensor, atol=1e-1, rtol=1e-3, mean_atol=5e-3):
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torch.testing.assert_close(slow_tensor, fast_tensor, atol=atol, rtol=rtol)
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self.assertLessEqual(torch.mean(torch.abs(slow_tensor - fast_tensor)).item(), mean_atol)
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@require_vision
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@require_torch
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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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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, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
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self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
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@require_vision
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@require_torch
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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_image_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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self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
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@require_vision
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@require_torch
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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, image):
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# Warmup
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for _ in range(5):
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_ = image_processor(image, 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(image, 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, (3, 224, 224), dtype=torch.uint8) for _ in range(4)]
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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)
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slow_time = measure_time(image_processor_slow, dummy_images)
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self.assertLessEqual(fast_time, slow_time)
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def test_image_processor_to_json_string(self):
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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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obj = json.loads(image_processor.to_json_string())
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for key, value in self.image_processor_dict.items():
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self.assertEqual(obj[key], value)
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def test_image_processor_to_json_file(self):
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for image_processing_class in self.image_processor_list:
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "image_processor.json")
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image_processor_first.to_json_file(json_file_path)
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image_processor_second = image_processing_class.from_json_file(json_file_path)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_from_and_save_pretrained(self):
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for image_processing_class in self.image_processor_list:
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = image_processing_class.from_pretrained(tmpdirname)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_save_load_with_autoimageprocessor(self):
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for i, image_processing_class in enumerate(self.image_processor_list):
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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use_fast = i == 1 or not self.test_slow_image_processor
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image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=use_fast)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_save_load_fast_slow(self):
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"Test that we can load a fast image processor from a slow one and vice-versa."
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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("Skipping slow/fast save/load test as one of the image processors is not defined")
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image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
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image_processor_slow_0 = self.image_processing_class(**image_processor_dict)
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# Load fast image processor from slow one
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with tempfile.TemporaryDirectory() as tmpdirname:
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image_processor_slow_0.save_pretrained(tmpdirname)
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image_processor_fast_0 = self.fast_image_processing_class.from_pretrained(tmpdirname)
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image_processor_fast_1 = self.fast_image_processing_class(**image_processor_dict)
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# Load slow image processor from fast one
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with tempfile.TemporaryDirectory() as tmpdirname:
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image_processor_fast_1.save_pretrained(tmpdirname)
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image_processor_slow_1 = self.image_processing_class.from_pretrained(tmpdirname)
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dict_slow_0 = image_processor_slow_0.to_dict()
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dict_slow_1 = image_processor_slow_1.to_dict()
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difference = {
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key: dict_slow_0.get(key) if key in dict_slow_0 else dict_slow_1.get(key)
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for key in set(dict_slow_0) ^ set(dict_slow_1)
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}
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dict_slow_0 = {key: dict_slow_0[key] for key in set(dict_slow_0) & set(dict_slow_1)}
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dict_slow_1 = {key: dict_slow_1[key] for key in set(dict_slow_0) & set(dict_slow_1)}
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# check that all additional keys are None, except for `default_to_square` and `data_format` which are only set in fast processors
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self.assertTrue(
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all(value is None for key, value in difference.items() if key not in ["default_to_square", "data_format"])
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)
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# check that the remaining keys are the same
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self.assertEqual(dict_slow_0, dict_slow_1)
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dict_fast_0 = image_processor_fast_0.to_dict()
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dict_fast_1 = image_processor_fast_1.to_dict()
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difference = {
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key: dict_fast_0.get(key) if key in dict_fast_0 else dict_fast_1.get(key)
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for key in set(dict_fast_0) ^ set(dict_fast_1)
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}
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dict_fast_0 = {key: dict_fast_0[key] for key in set(dict_fast_0) & set(dict_fast_1)}
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dict_fast_1 = {key: dict_fast_1[key] for key in set(dict_fast_0) & set(dict_fast_1)}
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# check that all additional keys are None, except for `default_to_square` and `data_format` which are only set in fast processors
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self.assertTrue(
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all(value is None for key, value in difference.items() if key not in ["default_to_square", "data_format"])
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)
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# check that the remaining keys are the same
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self.assertEqual(dict_fast_0, dict_fast_1)
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def test_save_load_fast_slow_auto(self):
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"Test that we can load a fast image processor from a slow one and vice-versa using AutoImageProcessor."
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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("Skipping slow/fast save/load test as one of the image processors is not defined")
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image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
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image_processor_slow_0 = self.image_processing_class(**image_processor_dict)
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# Load fast image processor from slow one
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with tempfile.TemporaryDirectory() as tmpdirname:
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image_processor_slow_0.save_pretrained(tmpdirname)
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image_processor_fast_0 = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=True)
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image_processor_fast_1 = self.fast_image_processing_class(**image_processor_dict)
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# Load slow image processor from fast one
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with tempfile.TemporaryDirectory() as tmpdirname:
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image_processor_fast_1.save_pretrained(tmpdirname)
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image_processor_slow_1 = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=False)
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dict_slow_0 = image_processor_slow_0.to_dict()
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dict_slow_1 = image_processor_slow_1.to_dict()
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difference = {
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key: dict_slow_0.get(key) if key in dict_slow_0 else dict_slow_1.get(key)
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for key in set(dict_slow_0) ^ set(dict_slow_1)
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}
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dict_slow_0 = {key: dict_slow_0[key] for key in set(dict_slow_0) & set(dict_slow_1)}
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dict_slow_1 = {key: dict_slow_1[key] for key in set(dict_slow_0) & set(dict_slow_1)}
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# check that all additional keys are None, except for `default_to_square` and `data_format` which are only set in fast processors
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self.assertTrue(
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all(value is None for key, value in difference.items() if key not in ["default_to_square", "data_format"])
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)
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# check that the remaining keys are the same
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self.assertEqual(dict_slow_0, dict_slow_1)
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dict_fast_0 = image_processor_fast_0.to_dict()
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dict_fast_1 = image_processor_fast_1.to_dict()
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difference = {
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key: dict_fast_0.get(key) if key in dict_fast_0 else dict_fast_1.get(key)
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for key in set(dict_fast_0) ^ set(dict_fast_1)
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}
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dict_fast_0 = {key: dict_fast_0[key] for key in set(dict_fast_0) & set(dict_fast_1)}
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dict_fast_1 = {key: dict_fast_1[key] for key in set(dict_fast_0) & set(dict_fast_1)}
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# check that all additional keys are None, except for `default_to_square` and `data_format` which are only set in fast processors
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self.assertTrue(
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all(value is None for key, value in difference.items() if key not in ["default_to_square", "data_format"])
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)
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# check that the remaining keys are the same
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self.assertEqual(dict_fast_0, dict_fast_1)
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def test_init_without_params(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class()
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self.assertIsNotNone(image_processor)
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@require_torch
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@require_vision
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def test_cast_dtype_device(self):
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for image_processing_class in self.image_processor_list:
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if self.test_cast_dtype is not None:
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# Initialize image_processor
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image_processor = 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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encoding = image_processor(image_inputs, return_tensors="pt")
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# for layoutLM compatibility
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float32)
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encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
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with self.assertRaises(TypeError):
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_ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
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# Try with text + image feature
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encoding = image_processor(image_inputs, return_tensors="pt")
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encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
|
|
encoding = encoding.to(torch.float16)
|
|
|
|
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
|
|
self.assertEqual(encoding.pixel_values.dtype, torch.float16)
|
|
self.assertEqual(encoding.input_ids.dtype, torch.long)
|
|
|
|
def test_call_pil(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random PIL images
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, Image.Image)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
|
)
|
|
|
|
def test_call_numpy(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random numpy tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, np.ndarray)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
|
)
|
|
|
|
def test_call_pytorch(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Initialize image_processing
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
# create random PyTorch tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
|
|
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, torch.Tensor)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
|
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape),
|
|
(self.image_processor_tester.batch_size, *expected_output_image_shape),
|
|
)
|
|
|
|
def test_call_numpy_4_channels(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
# Test that can process images which have an arbitrary number of channels
|
|
# Initialize image_processing
|
|
image_processor = image_processing_class(**self.image_processor_dict)
|
|
|
|
# create random numpy tensors
|
|
self.image_processor_tester.num_channels = 4
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
|
|
|
|
# Test not batched input
|
|
encoded_images = image_processor(
|
|
image_inputs[0],
|
|
return_tensors="pt",
|
|
input_data_format="channels_last",
|
|
image_mean=0,
|
|
image_std=1,
|
|
).pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
|
|
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
|
|
|
# Test batched
|
|
encoded_images = image_processor(
|
|
image_inputs,
|
|
return_tensors="pt",
|
|
input_data_format="channels_last",
|
|
image_mean=0,
|
|
image_std=1,
|
|
).pixel_values
|
|
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
|
self.assertEqual(
|
|
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
|
)
|
|
|
|
def test_image_processor_preprocess_arguments(self):
|
|
is_tested = False
|
|
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor = image_processing_class(**self.image_processor_dict)
|
|
|
|
# validation done by _valid_processor_keys attribute
|
|
if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
|
|
preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
|
|
preprocess_parameter_names.remove("self")
|
|
preprocess_parameter_names.sort()
|
|
valid_processor_keys = image_processor._valid_processor_keys
|
|
valid_processor_keys.sort()
|
|
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
|
|
is_tested = True
|
|
|
|
# validation done by @filter_out_non_signature_kwargs decorator
|
|
if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"):
|
|
if hasattr(self.image_processor_tester, "prepare_image_inputs"):
|
|
inputs = self.image_processor_tester.prepare_image_inputs()
|
|
elif hasattr(self.image_processor_tester, "prepare_video_inputs"):
|
|
inputs = self.image_processor_tester.prepare_video_inputs()
|
|
else:
|
|
self.skipTest(reason="No valid input preparation method found")
|
|
|
|
with warnings.catch_warnings(record=True) as raised_warnings:
|
|
warnings.simplefilter("always")
|
|
image_processor(inputs, extra_argument=True)
|
|
|
|
messages = " ".join([str(w.message) for w in raised_warnings])
|
|
self.assertGreaterEqual(len(raised_warnings), 1)
|
|
self.assertIn("extra_argument", messages)
|
|
is_tested = True
|
|
|
|
if not is_tested:
|
|
self.skipTest(reason="No validation found for `preprocess` method")
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
@require_vision
|
|
def test_can_compile_fast_image_processor(self):
|
|
if self.fast_image_processing_class is None:
|
|
self.skipTest("Skipping compilation test as fast image processor is not defined")
|
|
if version.parse(torch.__version__) < version.parse("2.3"):
|
|
self.skipTest(reason="This test requires torch >= 2.3 to run.")
|
|
|
|
torch.compiler.reset()
|
|
input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
|
|
image_processor = self.fast_image_processing_class(**self.image_processor_dict)
|
|
output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
|
|
|
|
image_processor = torch.compile(image_processor, mode="reduce-overhead")
|
|
output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
|
|
print(output_eager.pixel_values.dtype, output_compiled.pixel_values.dtype)
|
|
self._assert_slow_fast_tensors_equivalence(
|
|
output_eager.pixel_values, output_compiled.pixel_values, atol=1e-4, rtol=1e-4, mean_atol=1e-5
|
|
)
|
|
|
|
|
|
class AnnotationFormatTestMixin:
|
|
# this mixin adds a test to assert that usages of the
|
|
# to-be-deprecated `AnnotionFormat` continue to be
|
|
# supported for the time being
|
|
|
|
def test_processor_can_use_legacy_annotation_format(self):
|
|
image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
|
|
fixtures_path = pathlib.Path(__file__).parent / "fixtures" / "tests_samples" / "COCO"
|
|
|
|
with open(fixtures_path / "coco_annotations.txt") as f:
|
|
detection_target = json.loads(f.read())
|
|
|
|
detection_annotations = {"image_id": 39769, "annotations": detection_target}
|
|
|
|
detection_params = {
|
|
"images": Image.open(fixtures_path / "000000039769.png"),
|
|
"annotations": detection_annotations,
|
|
"return_tensors": "pt",
|
|
}
|
|
|
|
with open(fixtures_path / "coco_panoptic_annotations.txt") as f:
|
|
panoptic_target = json.loads(f.read())
|
|
|
|
panoptic_annotations = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": panoptic_target}
|
|
|
|
masks_path = pathlib.Path(fixtures_path / "coco_panoptic")
|
|
|
|
panoptic_params = {
|
|
"images": Image.open(fixtures_path / "000000039769.png"),
|
|
"annotations": panoptic_annotations,
|
|
"return_tensors": "pt",
|
|
"masks_path": masks_path,
|
|
}
|
|
|
|
test_cases = [
|
|
("coco_detection", detection_params),
|
|
("coco_panoptic", panoptic_params),
|
|
(AnnotionFormat.COCO_DETECTION, detection_params),
|
|
(AnnotionFormat.COCO_PANOPTIC, panoptic_params),
|
|
(AnnotationFormat.COCO_DETECTION, detection_params),
|
|
(AnnotationFormat.COCO_PANOPTIC, panoptic_params),
|
|
]
|
|
|
|
def _compare(a, b) -> None:
|
|
if isinstance(a, (dict, BatchFeature)):
|
|
self.assertEqual(a.keys(), b.keys())
|
|
for k, v in a.items():
|
|
_compare(v, b[k])
|
|
elif isinstance(a, list):
|
|
self.assertEqual(len(a), len(b))
|
|
for idx in range(len(a)):
|
|
_compare(a[idx], b[idx])
|
|
elif isinstance(a, torch.Tensor):
|
|
torch.testing.assert_close(a, b, rtol=1e-3, atol=1e-3)
|
|
elif isinstance(a, str):
|
|
self.assertEqual(a, b)
|
|
|
|
for annotation_format, params in test_cases:
|
|
with self.subTest(annotation_format):
|
|
image_processor_params = {**image_processor_dict, **{"format": annotation_format}}
|
|
image_processor_first = self.image_processing_class(**image_processor_params)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
image_processor_first.save_pretrained(tmpdirname)
|
|
image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
|
|
|
|
# check the 'format' key exists and that the dicts of the
|
|
# first and second processors are equal
|
|
self.assertIn("format", image_processor_first.to_dict().keys())
|
|
self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
|
|
|
|
# perform encoding using both processors and compare
|
|
# the resulting BatchFeatures
|
|
first_encoding = image_processor_first(**params)
|
|
second_encoding = image_processor_second(**params)
|
|
_compare(first_encoding, second_encoding)
|