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Add Fast Image Processor for Perceiver (#37176)

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* 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>
This commit is contained in:
Vinh H. Pham
2025-04-14 18:49:13 +07:00
committed by GitHub
parent 4b8c6d4cf8
commit e7f5724efd
5 changed files with 367 additions and 1 deletions
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5
docs/source/en/model_doc/perceiver.md
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@@ -132,6 +132,11 @@ audio classification, video classification, etc.
[[autodoc]] PerceiverImageProcessor
- preprocess
## PerceiverImageProcessorFast
[[autodoc]] PerceiverImageProcessorFast
- preprocess
## PerceiverTextPreprocessor
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverTextPreprocessor

2
src/transformers/models/auto/image_processing_auto.py
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@@ -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")),

1
src/transformers/models/perceiver/__init__.py
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@@ -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:

133
src/transformers/models/perceiver/image_processing_perceiver_fast.py Normal file
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@@ -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"]

227
tests/models/perceiver/test_image_processing_perceiver.py Normal file
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@@ -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),
)