From e7f5724efd6d607805632234fda6a07d5d3f1791 Mon Sep 17 00:00:00 2001 From: "Vinh H. Pham" Date: Mon, 14 Apr 2025 18:49:13 +0700 Subject: [PATCH] Add Fast Image Processor for Perceiver (#37176) * add test and fast image processor * make style * Update src/transformers/models/perceiver/image_processing_perceiver_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * make style --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> --- docs/source/en/model_doc/perceiver.md | 5 + .../models/auto/image_processing_auto.py | 2 +- src/transformers/models/perceiver/__init__.py | 1 + .../image_processing_perceiver_fast.py | 133 ++++++++++ .../test_image_processing_perceiver.py | 227 ++++++++++++++++++ 5 files changed, 367 insertions(+), 1 deletion(-) create mode 100644 src/transformers/models/perceiver/image_processing_perceiver_fast.py create mode 100644 tests/models/perceiver/test_image_processing_perceiver.py diff --git a/docs/source/en/model_doc/perceiver.md b/docs/source/en/model_doc/perceiver.md index 700f49d42d..629f185953 100644 --- a/docs/source/en/model_doc/perceiver.md +++ b/docs/source/en/model_doc/perceiver.md @@ -132,6 +132,11 @@ audio classification, video classification, etc. [[autodoc]] PerceiverImageProcessor - preprocess +## PerceiverImageProcessorFast + +[[autodoc]] PerceiverImageProcessorFast + - preprocess + ## PerceiverTextPreprocessor [[autodoc]] models.perceiver.modeling_perceiver.PerceiverTextPreprocessor diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py index ac4848dafd..0eccd9ae12 100644 --- a/src/transformers/models/auto/image_processing_auto.py +++ b/src/transformers/models/auto/image_processing_auto.py @@ -125,7 +125,7 @@ else: ("owlv2", ("Owlv2ImageProcessor",)), ("owlvit", ("OwlViTImageProcessor",)), ("paligemma", ("SiglipImageProcessor", "SiglipImageProcessorFast")), - ("perceiver", ("PerceiverImageProcessor",)), + ("perceiver", ("PerceiverImageProcessor", "PerceiverImageProcessorFast")), ("phi4_multimodal", "Phi4MultimodalImageProcessorFast"), ("pix2struct", ("Pix2StructImageProcessor",)), ("pixtral", ("PixtralImageProcessor", "PixtralImageProcessorFast")), diff --git a/src/transformers/models/perceiver/__init__.py b/src/transformers/models/perceiver/__init__.py index 0268bda4a1..da8d30a5a9 100644 --- a/src/transformers/models/perceiver/__init__.py +++ b/src/transformers/models/perceiver/__init__.py @@ -21,6 +21,7 @@ if TYPE_CHECKING: from .configuration_perceiver import * from .feature_extraction_perceiver import * from .image_processing_perceiver import * + from .image_processing_perceiver_fast import * from .modeling_perceiver import * from .tokenization_perceiver import * else: diff --git a/src/transformers/models/perceiver/image_processing_perceiver_fast.py b/src/transformers/models/perceiver/image_processing_perceiver_fast.py new file mode 100644 index 0000000000..87b24e7684 --- /dev/null +++ b/src/transformers/models/perceiver/image_processing_perceiver_fast.py @@ -0,0 +1,133 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fast Image processor class for Perceiver.""" + +from typing import Optional, Union + +from ...image_processing_utils_fast import BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, BaseImageProcessorFast, BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling, SizeDict +from ...utils import ( + TensorType, + add_start_docstrings, + is_torch_available, + is_torchvision_available, + is_torchvision_v2_available, +) + + +if is_torch_available(): + import torch + +if is_torchvision_available(): + if is_torchvision_v2_available(): + from torchvision.transforms.v2 import functional as F + else: + from torchvision.transforms import functional as F + + +@add_start_docstrings( + "Constructs a fast Perceiver image processor.", + BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, +) +class PerceiverImageProcessorFast(BaseImageProcessorFast): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 224, "width": 224} + crop_size = {"height": 256, "width": 256} + do_resize = True + do_center_crop = True + do_rescale = True + do_normalize = True + + def center_crop( + self, + image: "torch.Tensor", + crop_size: dict[str, int], + size: dict[str, int], + **kwargs, + ) -> "torch.Tensor": + """ + Center crop an image to `(size["height"] / crop_size["height"] * min_dim, size["width"] / crop_size["width"] * + min_dim)`. Where `min_dim = min(size["height"], size["width"])`. + + If the input size is smaller than `crop_size` along any edge, the image will be padded with zeros and then + center cropped. + + Args: + image (`"torch.Tensor"`): + Image to center crop. + crop_size (`Dict[str, int]`): + Desired output size after applying the center crop. + size (`Dict[str, int]`): + Size of the output image. + + Returns: + `torch.Tensor`: The center cropped image. + """ + if size.height is None or size.width is None: + raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}") + height, width = image.shape[-2:] + min_dim = min(height, width) + cropped_height = int((size.height / crop_size.height) * min_dim) + cropped_width = int((size.width / crop_size.width) * min_dim) + return F.center_crop(image, (cropped_height, cropped_width)) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + interpolation: Optional["F.InterpolationMode"], + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: Optional[Union[float, list[float]]], + image_std: Optional[Union[float, list[float]]], + return_tensors: Optional[Union[str, TensorType]], + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, size=size, crop_size=crop_size) + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PerceiverImageProcessorFast"] diff --git a/tests/models/perceiver/test_image_processing_perceiver.py b/tests/models/perceiver/test_image_processing_perceiver.py new file mode 100644 index 0000000000..4fd7aa28ac --- /dev/null +++ b/tests/models/perceiver/test_image_processing_perceiver.py @@ -0,0 +1,227 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import unittest + +import numpy as np + +from transformers.image_utils import PILImageResampling +from transformers.testing_utils import require_torch, require_vision +from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available + +from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs + + +if is_vision_available(): + from PIL import Image + + from transformers import PerceiverImageProcessor + + if is_torchvision_available(): + from transformers import PerceiverImageProcessorFast + + +if is_torch_available(): + import torch + + +class PerceiverImageProcessingTester: + def __init__( + self, + parent, + batch_size=7, + num_channels=3, + num_images=1, + image_size=18, + min_resolution=30, + max_resolution=40, + do_center_crop=True, + crop_size=None, + do_resize=True, + size=None, + do_rescale=True, + rescale_factor=1 / 255, + do_normalize=True, + image_mean=[0.5, 0.5, 0.5], + image_std=[0.5, 0.5, 0.5], + resample=PILImageResampling.BICUBIC, + ): + self.crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256} + self.size = size if size is not None else {"height": 224, "width": 224} + self.parent = parent + self.batch_size = batch_size + self.num_channels = num_channels + self.num_images = num_images + self.image_size = image_size + self.min_resolution = min_resolution + self.max_resolution = max_resolution + self.do_center_crop = do_center_crop + self.do_resize = do_resize + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean + self.image_std = image_std + + def prepare_image_processor_dict(self): + return { + "do_center_crop": self.do_center_crop, + "crop_size": self.crop_size, + "do_resize": self.do_resize, + "size": self.size, + "do_rescale": self.do_rescale, + "rescale_factor": self.rescale_factor, + "do_normalize": self.do_normalize, + "image_mean": self.image_mean, + "image_std": self.image_std, + "resample": self.resample, + } + + def expected_output_image_shape(self, images): + return self.num_channels, self.size["height"], self.size["width"] + + def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): + return prepare_image_inputs( + batch_size=self.batch_size, + num_channels=self.num_channels, + min_resolution=self.min_resolution, + max_resolution=self.max_resolution, + equal_resolution=equal_resolution, + numpify=numpify, + torchify=torchify, + ) + + +@require_torch +@require_vision +class PerceiverImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): + image_processing_class = PerceiverImageProcessor if is_vision_available() else None + fast_image_processing_class = PerceiverImageProcessorFast if is_torchvision_available() else None + + def setUp(self): + super().setUp() + self.image_processor_tester = PerceiverImageProcessingTester(self) + + @property + def image_processor_dict(self): + return self.image_processor_tester.prepare_image_processor_dict() + + def test_image_processor_properties(self): + for image_processing_class in self.image_processor_list: + image_processing = image_processing_class(**self.image_processor_dict) + self.assertTrue(hasattr(image_processing, "do_center_crop")) + self.assertTrue(hasattr(image_processing, "crop_size")) + self.assertTrue(hasattr(image_processing, "do_resize")) + self.assertTrue(hasattr(image_processing, "size")) + self.assertTrue(hasattr(image_processing, "resample")) + self.assertTrue(hasattr(image_processing, "do_rescale")) + self.assertTrue(hasattr(image_processing, "rescale_factor")) + self.assertTrue(hasattr(image_processing, "do_normalize")) + self.assertTrue(hasattr(image_processing, "image_mean")) + self.assertTrue(hasattr(image_processing, "image_std")) + + 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 sample_images in image_inputs: + for image in sample_images: + 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_numpy_4_channels(self): + # Idefics3 always processes images as RGB, so it always returns images with 3 channels + for image_processing_class in self.image_processor_list: + # Initialize image_processing + image_processor_dict = self.image_processor_dict + image_processing = image_processing_class(**image_processor_dict) + # create random numpy tensors + image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) + + for sample_images in image_inputs: + for image in sample_images: + 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_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_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 images in image_inputs: + for image in images: + 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), + )