mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +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
403 lines
18 KiB
Python
403 lines
18 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin
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if is_vision_available():
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from PIL import Image
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from transformers import Idefics2ImageProcessor
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if is_torchvision_available():
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from transformers import Idefics2ImageProcessorFast
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if is_torch_available():
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import torch
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class Idefics2ImageProcessingTester:
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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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num_images=1,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_rescale=True,
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rescale_factor=1 / 255,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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do_convert_rgb=True,
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do_pad=True,
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do_image_splitting=True,
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):
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size = size if size is not None else {"shortest_edge": 378, "longest_edge": 980}
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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.num_images = num_images
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_convert_rgb = do_convert_rgb
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self.do_pad = do_pad
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self.do_image_splitting = do_image_splitting
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def prepare_image_processor_dict(self):
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return {
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"do_convert_rgb": self.do_convert_rgb,
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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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"rescale_factor": self.rescale_factor,
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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_pad": self.do_pad,
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"do_image_splitting": self.do_image_splitting,
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}
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def get_expected_values(self, image_inputs, batched=False):
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if not batched:
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shortest_edge = self.size["shortest_edge"]
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longest_edge = self.size["longest_edge"]
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image = image_inputs[0]
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if isinstance(image, Image.Image):
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w, h = image.size
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elif isinstance(image, np.ndarray):
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h, w = image.shape[0], image.shape[1]
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else:
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h, w = image.shape[1], image.shape[2]
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aspect_ratio = w / h
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if w > h and w >= longest_edge:
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w = longest_edge
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h = int(w / aspect_ratio)
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elif h > w and h >= longest_edge:
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h = longest_edge
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w = int(h * aspect_ratio)
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w = max(w, shortest_edge)
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h = max(h, shortest_edge)
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expected_height = h
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expected_width = w
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else:
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expected_values = []
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for images in image_inputs:
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for image in images:
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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 expected_output_image_shape(self, images):
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height, width = self.get_expected_values(images, batched=True)
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effective_nb_images = self.num_images * 5 if self.do_image_splitting else 1
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return effective_nb_images, self.num_channels, height, width
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def prepare_image_inputs(
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self,
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batch_size=None,
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min_resolution=None,
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max_resolution=None,
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num_channels=None,
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num_images=None,
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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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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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batch_size = batch_size if batch_size is not None else self.batch_size
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min_resolution = min_resolution if min_resolution is not None else self.min_resolution
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max_resolution = max_resolution if max_resolution is not None else self.max_resolution
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num_channels = num_channels if num_channels is not None else self.num_channels
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num_images = num_images if num_images is not None else self.num_images
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images_list = []
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for i in range(batch_size):
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images = []
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for j in range(num_images):
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if equal_resolution:
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width = height = max_resolution
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else:
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if size_divisor is not None:
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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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images.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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images_list.append(images)
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if not numpify and not torchify:
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images_list = [[Image.fromarray(np.moveaxis(image, 0, -1)) for image in images] for images in images_list]
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if torchify:
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images_list = [[torch.from_numpy(image) for image in images] for images in images_list]
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if numpify:
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images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list]
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return images_list
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@require_torch
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@require_vision
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class Idefics2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Idefics2ImageProcessor if is_vision_available() else None
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fast_image_processing_class = Idefics2ImageProcessorFast 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 = Idefics2ImageProcessingTester(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_convert_rgb"))
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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, "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_pad"))
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self.assertTrue(hasattr(image_processing, "do_image_splitting"))
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for sample_images in image_inputs:
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for image in sample_images:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_numpy_4_channels(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processor_dict = self.image_processor_dict
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image_processor_dict["image_mean"] = [0.5, 0.5, 0.5, 0.5]
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image_processor_dict["image_std"] = [0.5, 0.5, 0.5, 0.5]
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image_processing = image_processing_class(**image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for sample_images in image_inputs:
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for image in sample_images:
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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(
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image_inputs[0], input_data_format="channels_last", return_tensors="pt"
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(
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image_inputs, input_data_format="channels_last", return_tensors="pt"
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pil(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for images in image_inputs:
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for image in images:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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for images in image_inputs:
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for image in images:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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tuple(encoded_images.shape),
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(self.image_processor_tester.batch_size, *expected_output_image_shape),
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)
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def test_image_splitting(self):
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for image_processing_class in self.image_processor_list:
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image_processor_dict = self.image_processor_dict.copy()
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image_processor_dict["do_image_splitting"] = True
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image_processing = image_processing_class(**image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=True, torchify=True, num_images=1
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)
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result = image_processing(image_inputs[0], return_tensors="pt")
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self.assertEqual(result.pixel_values.shape[1], 5)
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image_processor_dict["do_image_splitting"] = False
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image_processing = image_processing_class(**image_processor_dict)
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result = image_processing(image_inputs[0], return_tensors="pt")
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if len(result.pixel_values.shape) == 5:
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self.assertEqual(result.pixel_values.shape[1], 1)
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else:
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self.assertEqual(result.pixel_values.shape[1], self.image_processor_tester.num_channels)
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def test_pixel_attention_mask(self):
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for image_processing_class in self.image_processor_list:
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image_processor_dict = self.image_processor_dict.copy()
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image_processor_dict["do_pad"] = True
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image_processing = image_processing_class(**image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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result = image_processing(image_inputs, return_tensors="pt")
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self.assertIn("pixel_attention_mask", result)
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self.assertEqual(result.pixel_attention_mask.shape[-2:], result.pixel_values.shape[-2:])
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image_processor_dict["do_pad"] = False
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image_processor_dict["do_image_splitting"] = False
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image_processing = image_processing_class(**image_processor_dict)
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equal_size_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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result = image_processing(equal_size_inputs, return_tensors="pt")
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self.assertNotIn("pixel_attention_mask", result)
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def test_convert_rgb(self):
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for image_processing_class in self.image_processor_list:
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rgba_image = Image.new("RGBA", (100, 100), (255, 0, 0, 128))
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# Test with do_convert_rgb=True - this should work for all processors
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image_processor_dict = self.image_processor_dict.copy()
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image_processor_dict["do_convert_rgb"] = True
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image_processing = image_processing_class(**image_processor_dict)
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result = image_processing([rgba_image], return_tensors="pt")
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self.assertIsNotNone(result.pixel_values)
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rgb_image = rgba_image.convert("RGB")
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image_processor_dict["do_convert_rgb"] = False
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image_processing = image_processing_class(**image_processor_dict)
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# Use the RGB image instead of RGBA when do_convert_rgb=False
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result = image_processing([rgb_image], return_tensors="pt")
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self.assertIsNotNone(result.pixel_values)
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# Additional test: verifying proper handling of regular RGB images
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rgb_image = Image.new("RGB", (100, 100), (255, 0, 0))
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result = image_processing([rgb_image], return_tensors="pt")
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self.assertIsNotNone(result.pixel_values)
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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(
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equal_resolution=False, num_images=5, torchify=True
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)
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# pop some images to have non homogenous batches:
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indices_to_pop = [i if np.random.random() < 0.5 else None for i in range(len(dummy_images))]
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for i in indices_to_pop:
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if i is not None:
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dummy_images[i].pop()
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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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self._assert_slow_fast_tensors_equivalence(
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encoding_slow.pixel_attention_mask.float(), encoding_fast.pixel_attention_mask.float()
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)
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