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# NVIDIA
# Copyright (c) 2025 NVIDIA
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
from typing import Any, List, Optional, Union
from transformers.image_processing_utils import BatchFeature, get_patch_output_size, select_best_resolution
try:
from transformers.image_processing_utils_fast import (
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
BaseImageProcessorFast,
DefaultFastImageProcessorKwargs,
divide_to_patches,
group_images_by_shape,
reorder_images,
)
except ImportError:
from transformers.image_processing_utils_fast import (
BaseImageProcessorFast,
DefaultFastImageProcessorKwargs,
divide_to_patches,
group_images_by_shape,
reorder_images,
)
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING = ""
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS = ""
try:
from transformers.image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
IMAGENET_STANDARD_MEAN, # 0.5, 0.5, 0.5
IMAGENET_STANDARD_STD, # 0.5, 0.5, 0.5
ChannelDimension,
ImageInput,
VideoInput,
PILImageResampling,
SizeDict,
get_image_size,
make_flat_list_of_images,
make_batched_videos,
validate_kwargs,
)
except ImportError:
from transformers.image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
IMAGENET_STANDARD_MEAN, # 0.5, 0.5, 0.5
IMAGENET_STANDARD_STD, # 0.5, 0.5, 0.5
ChannelDimension,
ImageInput,
PILImageResampling,
SizeDict,
get_image_size,
make_flat_list_of_images,
validate_kwargs,
)
VideoInput = Any
def make_batched_videos(videos):
return videos
from transformers.processing_utils import Unpack
from transformers.utils import TensorType, add_start_docstrings, is_torch_available, is_torchvision_v2_available
if is_torch_available():
import torch
if is_torchvision_v2_available():
from transformers.image_utils import pil_torch_interpolation_mapping
from torchvision.transforms.v2 import functional as F
else:
from torchvision.transforms import functional as F
def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> torch.Tensor:
"""Crop the given numpy array.
Args:
img (torch.Tensor): Image to be cropped. Format should be (C, H, W).
left (int): The left coordinate of the crop box.
top (int): The top coordinate of the crop box.
right (int): The right coordinate of the crop box.
bottom (int): The bottom coordinate of the crop box.
Returns:
torch.Tensor: Cropped image.
"""
if not isinstance(img, torch.Tensor):
raise TypeError('img should be torch.Tensor. Got {}'.format(type(img)))
if img.ndim not in [2, 3]:
raise ValueError('Image should have 2 or 3 dimensions. Got {}'.format(img.ndim))
img_height = img.shape[1]
img_width = img.shape[2]
if top < 0 or left < 0 or bottom > img_height or right > img_width:
raise ValueError('Crop coordinates out of bounds')
if top >= bottom or left >= right:
raise ValueError('Invalid crop coordinates')
return img[:, top:bottom, left:right]
class Eagle3_VLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
do_pad: Optional[bool]
@add_start_docstrings(
"Constructs a fast ConvNeXT image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.",
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
"""
image_grid_pinpoints (`List[List[int]]`, *optional*):
A list of possible resolutions to use for processing high resolution images. The best resolution is selected
based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
method. Not used for processing videos.
do_pad (`bool`, *optional*):
Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
number of patches in the batch. Padding will be applied to the bottom and right with zeros.
""",
)
class Eagle3_VLImageProcessorFast(BaseImageProcessorFast):
resample = PILImageResampling.BICUBIC
image_mean = IMAGENET_STANDARD_MEAN
image_std = IMAGENET_STANDARD_STD
size = {"height": 448, "width": 448}
default_to_square = False
crop_size = None
do_resize = True
do_center_crop = None
do_rescale = True
do_normalize = True
do_convert_rgb = True
do_pad = True
valid_kwargs = Eagle3_VLFastImageProcessorKwargs
model_input_names = ["pixel_values_videos"]
def __init__(self, **kwargs: Unpack[Eagle3_VLFastImageProcessorKwargs]):
super().__init__(**kwargs)
@add_start_docstrings(
BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
"""
do_pad (`bool`, *optional*):
Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
number of patches in the batch. Padding will be applied to the bottom and right with zeros.
""",
)
def preprocess(self, images: ImageInput, **kwargs: Unpack[Eagle3_VLFastImageProcessorKwargs]) -> BatchFeature:
return super().preprocess(images, **kwargs)
def _prepare_images_structure(
self,
images: ImageInput,
expected_ndims: int = 3,
) -> ImageInput:
"""
Prepare the images structure for processing.
Args:
images (`ImageInput`):
The input images to process.
Returns:
`ImageInput`: The images with a valid nesting.
"""
try:
return make_flat_list_of_images(images, expected_ndims=expected_ndims)
except TypeError:
return make_flat_list_of_images(images)
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]]],
do_pad: bool,
return_tensors: Optional[Union[str, TensorType]],
disable_grouping: Optional[bool] = None,
**kwargs,
) -> BatchFeature:
image_sizes = [get_image_size(image, channel_dim=ChannelDimension.FIRST) for image in images]
# Group images by size for further processing
# Needed in case do_resize is False, or resize returns images with different sizes
try:
grouped_images, grouped_images_index = group_images_by_shape(
images, disable_grouping=disable_grouping
)
except TypeError:
grouped_images, grouped_images_index = group_images_by_shape(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)
return BatchFeature(
data={"pixel_values": processed_images, "image_sizes": image_sizes}, tensor_type=return_tensors
)
def preprocess(self, images: ImageInput, videos: VideoInput=None, **kwargs: Unpack[Eagle3_VLFastImageProcessorKwargs]) -> BatchFeature:
valid_keys = getattr(self, "_valid_kwargs_names", list(self.valid_kwargs.__annotations__.keys()))
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=valid_keys)
# Set default kwargs from self. This ensures that if a kwarg is not provided
# by the user, it gets its default value from the instance, or is set to None.
for kwarg_name in valid_keys:
kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
# Extract parameters that are only used for preparing the input images
do_convert_rgb = kwargs.pop("do_convert_rgb")
input_data_format = kwargs.pop("input_data_format")
device = kwargs.pop("device")
# Transformers API compatibility:
# newer versions expose `_prepare_image_like_inputs`, older forks may still use `_prepare_input_images`.
prepare_inputs = getattr(self, "_prepare_image_like_inputs", None)
if prepare_inputs is None:
prepare_inputs = getattr(self, "_prepare_input_images", None)
if prepare_inputs is None:
raise AttributeError("No image preparation helper found on Eagle3_VLImageProcessorFast.")
# Prepare input images/videos
if images is not None:
images = prepare_inputs(
images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
)
if videos is not None:
videos = prepare_inputs(
images=videos, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
)
# Update kwargs that need further processing before being validated
kwargs = self._further_process_kwargs(**kwargs)
# Validate kwargs
self._validate_preprocess_kwargs(**kwargs)
# Some older transformers builds still expect manual `resample` -> `interpolation`.
if "resample" in kwargs:
resample = kwargs.pop("resample")
kwargs["interpolation"] = (
pil_torch_interpolation_mapping[resample]
if isinstance(resample, (PILImageResampling, int))
else resample
)
# Pop kwargs that are not needed in _preprocess
kwargs.pop("default_to_square", None)
kwargs.pop("data_format", None)
if images is not None:
return self._preprocess(images, **kwargs)
if videos is not None:
return self._preprocess(videos, **kwargs)
raise ValueError("Either `images` or `videos` must be provided.")
__all__ = ["Eagle3_VLImageProcessorFast"]
|