mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +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
287 lines
13 KiB
Python
287 lines
13 KiB
Python
# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import requests
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from packaging import version
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from transformers.testing_utils import 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 PixtralImageProcessor
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if is_torchvision_available():
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from transformers import PixtralImageProcessorFast
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class PixtralImageProcessingTester:
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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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max_num_images_per_sample=3,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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patch_size=None,
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do_normalize=True,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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do_convert_rgb=True,
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):
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super().__init__()
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size = size if size is not None else {"longest_edge": 24}
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patch_size = patch_size if patch_size is not None else {"height": 8, "width": 8}
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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.max_num_images_per_sample = max_num_images_per_sample
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.patch_size = patch_size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_convert_rgb = do_convert_rgb
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"patch_size": self.patch_size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_convert_rgb": self.do_convert_rgb,
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}
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def expected_output_image_shape(self, images):
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if not isinstance(images, (list, tuple)):
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images = [images]
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batch_size = len(images)
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return_height, return_width = 0, 0
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for image in images:
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if isinstance(image, Image.Image):
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width, height = image.size
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elif isinstance(image, np.ndarray):
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height, width = image.shape[:2]
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elif isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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max_height = max_width = self.size.get("longest_edge")
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ratio = max(height / max_height, width / max_width)
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if ratio > 1:
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height = int(np.floor(height / ratio))
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width = int(np.floor(width / ratio))
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patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
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num_height_tokens = (height - 1) // patch_height + 1
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num_width_tokens = (width - 1) // patch_width + 1
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return_height = max(num_height_tokens * patch_height, return_height)
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return_width = max(num_width_tokens * patch_width, return_width)
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return batch_size, self.num_channels, return_height, return_width
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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images = 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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return images
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@require_torch
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@require_vision
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class PixtralImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = PixtralImageProcessor if is_vision_available() else None
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fast_image_processing_class = PixtralImageProcessorFast 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 = PixtralImageProcessingTester(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, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "patch_size"))
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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_normalize"))
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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_convert_rgb"))
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# The following tests are overridden as PixtralImageProcessor can return images of different sizes
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# and thus doesn't support returning batched tensors
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def test_call_pil(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs_list = self.image_processor_tester.prepare_image_inputs()
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for image in image_inputs_list:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs_list = self.image_processor_tester.prepare_image_inputs(numpify=True)
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for image in image_inputs_list:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(batch_encoded_images.shape), expected_output_image_shape)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs_list = self.image_processor_tester.prepare_image_inputs(torchify=True)
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for image in image_inputs_list:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs_list[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list[0])
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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batch_encoded_images = image_processing(image_inputs_list, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs_list)
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self.assertEqual(tuple(batch_encoded_images.shape), expected_output_image_shape)
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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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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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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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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[0][0], encoding_fast.pixel_values[0][0])
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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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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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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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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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for i in range(len(encoding_slow.pixel_values)):
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self._assert_slow_fast_tensors_equivalence(
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encoding_slow.pixel_values[i][0], encoding_fast.pixel_values[i][0]
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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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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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image_processor = self.fast_image_processing_class(**self.image_processor_dict)
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output_eager = image_processor(input_image, 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, device=torch_device, return_tensors="pt")
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self._assert_slow_fast_tensors_equivalence(
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output_eager.pixel_values[0][0], output_compiled.pixel_values[0][0], atol=1e-4, rtol=1e-4, mean_atol=1e-5
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)
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@unittest.skip(reason="PixtralImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
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def test_call_numpy_4_channels(self):
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pass
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