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36978 | Fast image processor for DPT model (#37481)

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* chore: ran codegen script

* test: test_image_processor_properties

* test: test_image_processor_from_dict_with_kwargs

* test: wip - test_padding

* test: test_padding

* test: test_keep_aspect_ratio

* wip

* test

* test: wip

* test: wip

* test: test_call_segmentation_maps, wip

* chore: tidy up

* test: test_call_segmentation_maps

* fix: test_save_load_fast_slow

* test: reduce labels

* chore: make fixup

* chore: rm comment

* chore: tidy

* chore remove comment

* refactor: no need to infer channel dimesnion

* refactor: encapsulate logic for preparing segmentation maps

* refactor: improve readability of segmentation_map preparation

* improvement: batched version of pad_image

* chore: fixup

* docs

* chore: make quality

* chore: remove unecessary comment

* fix: add SemanticSegmentationMixin

* feat: add post_process_depth_estimation to fast dpt image processor

* chore: fix formatting

* remove max_height, max_width

* fix: better way of processin segmentation maps
- copied from Beit Fast processor

* chore: formatting + remove TODO

* chore: fixup styles

* chore: remove unecessary line break

* chore: core review suggestion to remove autodocstring

* fix: add do_reduce_labels logic + refactor
- refactor preprocess logic to make it consistent with other processors
- add missing reduce labels logic

* refactor: remove deprecated mixin

* chore: fixup

* use modular for dpt + final nit changes

* fix style

---------

Co-authored-by: Samuel Rae <samuelrae@Samuels-Air.fritz.box>
Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
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6
docs/source/en/model_doc/dpt.md
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@ -78,7 +78,13 @@ If you're interested in submitting a resource to be included here, please feel f
[[autodoc]] DPTImageProcessor
- preprocess
## DPTImageProcessorFast
[[autodoc]] DPTImageProcessorFast
- preprocess
- post_process_semantic_segmentation
- post_process_depth_estimation
## DPTModel

4
src/transformers/models/auto/image_processing_auto.py
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@ -74,14 +74,14 @@ else:
("data2vec-vision", ("BeitImageProcessor", "BeitImageProcessorFast")),
("deformable_detr", ("DeformableDetrImageProcessor", "DeformableDetrImageProcessorFast")),
("deit", ("DeiTImageProcessor", "DeiTImageProcessorFast")),
("depth_anything", ("DPTImageProcessor",)),
("depth_anything", ("DPTImageProcessor", "DPTImageProcessorFast")),
("depth_pro", ("DepthProImageProcessor", "DepthProImageProcessorFast")),
("deta", ("DetaImageProcessor",)),
("detr", ("DetrImageProcessor", "DetrImageProcessorFast")),
("dinat", ("ViTImageProcessor", "ViTImageProcessorFast")),
("dinov2", ("BitImageProcessor", "BitImageProcessorFast")),
("donut-swin", ("DonutImageProcessor", "DonutImageProcessorFast")),
("dpt", ("DPTImageProcessor",)),
("dpt", ("DPTImageProcessor", "DPTImageProcessorFast")),
("efficientformer", ("EfficientFormerImageProcessor",)),
("efficientnet", ("EfficientNetImageProcessor", "EfficientNetImageProcessorFast")),
("flava", ("FlavaImageProcessor", "FlavaImageProcessorFast")),

5
src/transformers/models/beit/image_processing_beit_fast.py
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@ -174,11 +174,6 @@ class BeitImageProcessorFast(BaseImageProcessorFast):
processed_segmentation_maps = processed_segmentation_maps.to(torch.int64)
return processed_segmentation_maps
def __call__(self, images, segmentation_maps=None, **kwargs):
# Overrides the `__call__` method of the `Preprocessor` class such that the images and segmentation maps can both
# be passed in as positional arguments.
return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
@auto_docstring
def preprocess(
self,

1
src/transformers/models/dpt/__init__.py
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@ -21,6 +21,7 @@ if TYPE_CHECKING:
from .configuration_dpt import *
from .feature_extraction_dpt import *
from .image_processing_dpt import *
from .image_processing_dpt_fast import *
from .modeling_dpt import *
else:
import sys

474
src/transformers/models/dpt/image_processing_dpt_fast.py Normal file
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@ -0,0 +1,474 @@
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/dpt/modular_dpt.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_dpt.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 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.
import math
from collections.abc import Iterable
from typing import TYPE_CHECKING, Optional, Union
from transformers.image_processing_base import BatchFeature
from transformers.image_transforms import group_images_by_shape, reorder_images
from ...image_processing_utils_fast import BaseImageProcessorFast, DefaultFastImageProcessorKwargs
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
SizeDict,
is_torch_tensor,
make_list_of_images,
pil_torch_interpolation_mapping,
validate_kwargs,
)
from ...processing_utils import Unpack
from ...utils import (
TensorType,
auto_docstring,
is_torch_available,
is_torchvision_available,
is_torchvision_v2_available,
requires_backends,
)
if TYPE_CHECKING:
from ...modeling_outputs import DepthEstimatorOutput
if is_torch_available():
import torch
if is_torchvision_v2_available():
from torchvision.transforms.v2 import functional as F
elif is_torchvision_available():
from torchvision.transforms import functional as F
class DPTFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
"""
ensure_multiple_of (`int`, *optional*, defaults to 1):
If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden
by `ensure_multiple_of` in `preprocess`.
do_pad (`bool`, *optional*, defaults to `False`):
Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in
combination with DPT.
size_divisor (`int`, *optional*):
If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the
DINOv2 paper, which uses the model in combination with DPT.
keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
be overidden by `keep_aspect_ratio` in `preprocess`.
do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g.
ADE20k). The background label will be replaced by 255.
"""
ensure_multiple_of: Optional[int]
size_divisor: Optional[int]
do_pad: Optional[bool]
keep_aspect_ratio: Optional[bool]
do_reduce_labels: Optional[bool]
def get_resize_output_image_size(
input_image: "torch.Tensor",
output_size: Union[int, Iterable[int]],
keep_aspect_ratio: bool,
multiple: int,
) -> SizeDict:
def constrain_to_multiple_of(val, multiple, min_val=0, max_val=None):
x = round(val / multiple) * multiple
if max_val is not None and x > max_val:
x = math.floor(val / multiple) * multiple
if x < min_val:
x = math.ceil(val / multiple) * multiple
return x
input_height, input_width = input_image.shape[-2:]
output_height, output_width = output_size
# determine new height and width
scale_height = output_height / input_height
scale_width = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
new_height = constrain_to_multiple_of(scale_height * input_height, multiple=multiple)
new_width = constrain_to_multiple_of(scale_width * input_width, multiple=multiple)
return SizeDict(height=new_height, width=new_width)
@auto_docstring
class DPTImageProcessorFast(BaseImageProcessorFast):
resample = PILImageResampling.BICUBIC
image_mean = IMAGENET_STANDARD_MEAN
image_std = IMAGENET_STANDARD_STD
size = {"height": 384, "width": 384}
default_to_square = True
crop_size = None
do_resize = True
do_center_crop = None
do_rescale = True
do_normalize = True
do_reduce_labels = None
valid_kwargs = DPTFastImageProcessorKwargs
do_pad = False
rescale_factor = 1 / 255
ensure_multiple_of = 1
keep_aspect_ratio = False
def __init__(self, **kwargs: Unpack[DPTFastImageProcessorKwargs]):
super().__init__(**kwargs)
def reduce_label(self, labels: list["torch.Tensor"]):
for idx in range(len(labels)):
label = labels[idx]
label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype), label)
label = label - 1
label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype), label)
labels[idx] = label
return label
def _preprocess(
self,
images: list["torch.Tensor"],
do_reduce_labels: bool,
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]],
keep_aspect_ratio: bool,
ensure_multiple_of: Optional[int],
do_pad: bool,
size_divisor: Optional[int],
**kwargs,
) -> BatchFeature:
if do_reduce_labels:
images = self.reduce_label(images)
# 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_resize:
stacked_images = self.resize(
image=stacked_images,
size=size,
interpolation=interpolation,
ensure_multiple_of=ensure_multiple_of,
keep_aspect_ratio=keep_aspect_ratio,
)
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():
if do_center_crop:
stacked_images = self.center_crop(stacked_images, crop_size)
if do_pad:
stacked_images = self.pad_image(stacked_images, size_divisor)
# 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 processed_images
def _preprocess_images(
self,
images,
**kwargs,
):
"""Preprocesses images."""
kwargs["do_reduce_labels"] = False
processed_images = self._preprocess(images=images, **kwargs)
return processed_images
def _preprocess_segmentation_maps(
self,
segmentation_maps,
**kwargs,
):
"""Preprocesses segmentation maps."""
processed_segmentation_maps = []
for segmentation_map in segmentation_maps:
segmentation_map = self._process_image(
segmentation_map, do_convert_rgb=False, input_data_format=ChannelDimension.FIRST
)
if segmentation_map.ndim == 2:
segmentation_map = segmentation_map[None, ...]
processed_segmentation_maps.append(segmentation_map)
kwargs["do_normalize"] = False
kwargs["do_rescale"] = False
kwargs["input_data_format"] = ChannelDimension.FIRST
processed_segmentation_maps = self._preprocess(images=processed_segmentation_maps, **kwargs)
processed_segmentation_maps = processed_segmentation_maps.squeeze(1)
processed_segmentation_maps = processed_segmentation_maps.to(torch.int64)
return processed_segmentation_maps
@auto_docstring
def preprocess(
self,
images: ImageInput,
segmentation_maps: Optional[ImageInput] = None,
**kwargs: Unpack[DPTFastImageProcessorKwargs],
) -> BatchFeature:
r"""
segmentation_maps (`ImageInput`, *optional*):
The segmentation maps to preprocess.
"""
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self.valid_kwargs.__annotations__.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 self.valid_kwargs.__annotations__:
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")
# Prepare input images
images = self._prepare_input_images(
images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
)
# Prepare segmentation maps
if segmentation_maps is not None:
segmentation_maps = make_list_of_images(images=segmentation_maps, expected_ndims=2)
# Update kwargs that need further processing before being validated
kwargs = self._further_process_kwargs(**kwargs)
# Validate kwargs
self._validate_preprocess_kwargs(**kwargs)
# torch resize uses interpolation instead of resample
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")
kwargs.pop("data_format")
images = self._preprocess_images(
images=images,
**kwargs,
)
data = {"pixel_values": images}
if segmentation_maps is not None:
segmentation_maps = self._preprocess_segmentation_maps(
segmentation_maps=segmentation_maps,
**kwargs,
)
data["labels"] = segmentation_maps
return BatchFeature(data=data)
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
"""
Converts the output of [`DPTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`DPTForSemanticSegmentation`]):
Raw outputs of the model.
target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
predictions will not be resized.
Returns:
semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
"""
# TODO: add support for other frameworks
logits = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(logits) != len(target_sizes):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
)
if is_torch_tensor(target_sizes):
target_sizes = target_sizes.numpy()
semantic_segmentation = []
for idx in range(len(logits)):
resized_logits = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
)
semantic_map = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(semantic_map)
else:
semantic_segmentation = logits.argmax(dim=1)
semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
def resize(
self,
image: "torch.Tensor",
size: SizeDict,
interpolation: "F.InterpolationMode" = None,
antialias: bool = True,
ensure_multiple_of: Optional[int] = 1,
keep_aspect_ratio: bool = False,
) -> "torch.Tensor":
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`torch.Tensor`):
Image to resize.
size (`SizeDict`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
`InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
antialias (`bool`, *optional*, defaults to `True`):
Whether to use antialiasing when resizing the image
ensure_multiple_of (`int`, *optional*):
If `do_resize` is `True`, the image is resized to a size that is a multiple of this value
keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved.
Returns:
`torch.Tensor`: The resized image.
"""
if not size.height or not size.width:
raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
output_size = get_resize_output_image_size(
image,
output_size=(size.height, size.width),
keep_aspect_ratio=keep_aspect_ratio,
multiple=ensure_multiple_of,
)
return super().resize(image, output_size, interpolation=interpolation, antialias=antialias)
def pad_image(
self,
image: "torch.Tensor",
size_divisor: int = 1,
) -> "torch.Tensor":
r"""
Center pad a batch of images to be a multiple of `size_divisor`.
Args:
image (`torch.Tensor`):
Image to pad. Can be a batch of images of dimensions (N, C, H, W) or a single image of dimensions (C, H, W).
size_divisor (`int`):
The width and height of the image will be padded to a multiple of this number.
"""
height, width = image.shape[-2:]
def _get_pad(size, size_divisor):
new_size = math.ceil(size / size_divisor) * size_divisor
pad_size = new_size - size
pad_size_left = pad_size // 2
pad_size_right = pad_size - pad_size_left
return pad_size_left, pad_size_right
pad_top, pad_bottom = _get_pad(height, size_divisor)
pad_left, pad_right = _get_pad(width, size_divisor)
padding = (pad_left, pad_top, pad_right, pad_bottom)
return F.pad(image, padding)
def post_process_depth_estimation(
self,
outputs: "DepthEstimatorOutput",
target_sizes: Optional[Union[TensorType, list[tuple[int, int]], None]] = None,
) -> list[dict[str, TensorType]]:
"""
Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images.
Only supports PyTorch.
Args:
outputs ([`DepthEstimatorOutput`]):
Raw outputs of the model.
target_sizes (`TensorType` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
Returns:
`List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth
predictions.
"""
requires_backends(self, "torch")
predicted_depth = outputs.predicted_depth
if (target_sizes is not None) and (len(predicted_depth) != len(target_sizes)):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the predicted depth"
)
results = []
target_sizes = [None] * len(predicted_depth) if target_sizes is None else target_sizes
for depth, target_size in zip(predicted_depth, target_sizes):
if target_size is not None:
depth = torch.nn.functional.interpolate(
depth.unsqueeze(0).unsqueeze(1), size=target_size, mode="bicubic", align_corners=False
).squeeze()
results.append({"predicted_depth": depth})
return results
__all__ = ["DPTImageProcessorFast"]

313
src/transformers/models/dpt/modular_dpt.py Normal file
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@ -0,0 +1,313 @@
# coding=utf-8
# Copyright 2025 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.
import math
from collections.abc import Iterable
from typing import TYPE_CHECKING, Optional, Union
from transformers.image_processing_base import BatchFeature
from transformers.image_transforms import group_images_by_shape, reorder_images
from transformers.models.beit.image_processing_beit_fast import BeitImageProcessorFast
from ...image_processing_utils_fast import (
DefaultFastImageProcessorKwargs,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
PILImageResampling,
SizeDict,
)
from ...utils import (
TensorType,
auto_docstring,
is_torch_available,
is_torchvision_available,
is_torchvision_v2_available,
requires_backends,
)
if TYPE_CHECKING:
from ...modeling_outputs import DepthEstimatorOutput
if is_torch_available():
import torch
if is_torchvision_v2_available():
from torchvision.transforms.v2 import functional as F
elif is_torchvision_available():
from torchvision.transforms import functional as F
def get_resize_output_image_size(
input_image: "torch.Tensor",
output_size: Union[int, Iterable[int]],
keep_aspect_ratio: bool,
multiple: int,
) -> SizeDict:
def constrain_to_multiple_of(val, multiple, min_val=0, max_val=None):
x = round(val / multiple) * multiple
if max_val is not None and x > max_val:
x = math.floor(val / multiple) * multiple
if x < min_val:
x = math.ceil(val / multiple) * multiple
return x
input_height, input_width = input_image.shape[-2:]
output_height, output_width = output_size
# determine new height and width
scale_height = output_height / input_height
scale_width = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
new_height = constrain_to_multiple_of(scale_height * input_height, multiple=multiple)
new_width = constrain_to_multiple_of(scale_width * input_width, multiple=multiple)
return SizeDict(height=new_height, width=new_width)
class DPTFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
"""
ensure_multiple_of (`int`, *optional*, defaults to 1):
If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden
by `ensure_multiple_of` in `preprocess`.
do_pad (`bool`, *optional*, defaults to `False`):
Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in
combination with DPT.
size_divisor (`int`, *optional*):
If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the
DINOv2 paper, which uses the model in combination with DPT.
keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
be overidden by `keep_aspect_ratio` in `preprocess`.
do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g.
ADE20k). The background label will be replaced by 255.
"""
ensure_multiple_of: Optional[int]
size_divisor: Optional[int]
do_pad: Optional[bool]
keep_aspect_ratio: Optional[bool]
do_reduce_labels: Optional[bool]
@auto_docstring
class DPTImageProcessorFast(BeitImageProcessorFast):
resample = PILImageResampling.BICUBIC
image_mean = IMAGENET_STANDARD_MEAN
image_std = IMAGENET_STANDARD_STD
size = {"height": 384, "width": 384}
do_resize = True
do_rescale = True
do_normalize = True
do_pad = False
rescale_factor = 1 / 255
ensure_multiple_of = 1
keep_aspect_ratio = False
do_reduce_labels = False
crop_size = None
do_center_crop = None
do_reduce_labels = None
valid_kwargs = DPTFastImageProcessorKwargs
def from_dict():
raise NotImplementedError("No need to override this method")
def resize(
self,
image: "torch.Tensor",
size: SizeDict,
interpolation: "F.InterpolationMode" = None,
antialias: bool = True,
ensure_multiple_of: Optional[int] = 1,
keep_aspect_ratio: bool = False,
) -> "torch.Tensor":
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`torch.Tensor`):
Image to resize.
size (`SizeDict`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
`InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
antialias (`bool`, *optional*, defaults to `True`):
Whether to use antialiasing when resizing the image
ensure_multiple_of (`int`, *optional*):
If `do_resize` is `True`, the image is resized to a size that is a multiple of this value
keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved.
Returns:
`torch.Tensor`: The resized image.
"""
if not size.height or not size.width:
raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
output_size = get_resize_output_image_size(
image,
output_size=(size.height, size.width),
keep_aspect_ratio=keep_aspect_ratio,
multiple=ensure_multiple_of,
)
return BeitImageProcessorFast().resize(image, output_size, interpolation=interpolation, antialias=antialias)
def pad_image(
self,
image: "torch.Tensor",
size_divisor: int = 1,
) -> "torch.Tensor":
r"""
Center pad a batch of images to be a multiple of `size_divisor`.
Args:
image (`torch.Tensor`):
Image to pad. Can be a batch of images of dimensions (N, C, H, W) or a single image of dimensions (C, H, W).
size_divisor (`int`):
The width and height of the image will be padded to a multiple of this number.
"""
height, width = image.shape[-2:]
def _get_pad(size, size_divisor):
new_size = math.ceil(size / size_divisor) * size_divisor
pad_size = new_size - size
pad_size_left = pad_size // 2
pad_size_right = pad_size - pad_size_left
return pad_size_left, pad_size_right
pad_top, pad_bottom = _get_pad(height, size_divisor)
pad_left, pad_right = _get_pad(width, size_divisor)
padding = (pad_left, pad_top, pad_right, pad_bottom)
return F.pad(image, padding)
def _preprocess(
self,
images: list["torch.Tensor"],
do_reduce_labels: bool,
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]],
keep_aspect_ratio: bool,
ensure_multiple_of: Optional[int],
do_pad: bool,
size_divisor: Optional[int],
**kwargs,
) -> BatchFeature:
if do_reduce_labels:
images = self.reduce_label(images)
# 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_resize:
stacked_images = self.resize(
image=stacked_images,
size=size,
interpolation=interpolation,
ensure_multiple_of=ensure_multiple_of,
keep_aspect_ratio=keep_aspect_ratio,
)
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():
if do_center_crop:
stacked_images = self.center_crop(stacked_images, crop_size)
if do_pad:
stacked_images = self.pad_image(stacked_images, size_divisor)
# 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 processed_images
def post_process_depth_estimation(
self,
outputs: "DepthEstimatorOutput",
target_sizes: Optional[Union[TensorType, list[tuple[int, int]], None]] = None,
) -> list[dict[str, TensorType]]:
"""
Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images.
Only supports PyTorch.
Args:
outputs ([`DepthEstimatorOutput`]):
Raw outputs of the model.
target_sizes (`TensorType` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
Returns:
`List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth
predictions.
"""
requires_backends(self, "torch")
predicted_depth = outputs.predicted_depth
if (target_sizes is not None) and (len(predicted_depth) != len(target_sizes)):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the predicted depth"
)
results = []
target_sizes = [None] * len(predicted_depth) if target_sizes is None else target_sizes
for depth, target_size in zip(predicted_depth, target_sizes):
if target_size is not None:
depth = torch.nn.functional.interpolate(
depth.unsqueeze(0).unsqueeze(1), size=target_size, mode="bicubic", align_corners=False
).squeeze()
results.append({"predicted_depth": depth})
return results
__all__ = ["DPTImageProcessorFast"]

105
tests/models/dpt/test_image_processing_dpt.py
View File

@ -20,6 +20,7 @@ from datasets import load_dataset
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torchvision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@ -32,6 +33,9 @@ if is_vision_available():
from transformers import DPTImageProcessor
if is_torchvision_available():
from transformers import DPTImageProcessorFast
class DPTImageProcessingTester:
def __init__(
@ -114,6 +118,7 @@ def prepare_semantic_batch_inputs():
@require_vision
class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DPTImageProcessor if is_vision_available() else None
fast_image_processing_class = DPTImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
@ -124,7 +129,8 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
@ -137,23 +143,34 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(hasattr(image_processing, "do_reduce_labels"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
for image_processing_class in self.image_processor_list:
image_processing_class = image_processing_class(**self.image_processor_dict)
image_processor = image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_padding(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
for image_processing_class in self.image_processor_list:
if image_processing_class == DPTImageProcessorFast:
image = torch.arange(0, 366777, 1, dtype=torch.uint8).reshape(3, 249, 491)
image_processor = image_processing_class(**self.image_processor_dict)
padded_image = image_processor.pad_image(image, size_divisor=4)
self.assertTrue(padded_image.shape[1] % 4 == 0)
self.assertTrue(padded_image.shape[2] % 4 == 0)
pixel_values = image_processor.preprocess(
image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
).pixel_values
self.assertTrue(pixel_values.shape[2] % 4 == 0)
self.assertTrue(pixel_values.shape[3] % 4 == 0)
else:
image_processor = image_processing_class(**self.image_processor_dict)
image = np.random.randn(3, 249, 491)
# test individual method
image = image_processing.pad_image(image, size_divisor=4)
image = image_processor.pad_image(image, size_divisor=4)
self.assertTrue(image.shape[1] % 4 == 0)
self.assertTrue(image.shape[2] % 4 == 0)
# test by calling
pixel_values = image_processing.preprocess(
pixel_values = image_processor.preprocess(
image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
).pixel_values
self.assertTrue(pixel_values.shape[2] % 4 == 0)
@ -161,7 +178,8 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
def test_keep_aspect_ratio(self):
size = {"height": 512, "width": 512}
image_processor = DPTImageProcessor(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
image = np.zeros((489, 640, 3))
@ -171,8 +189,9 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_call_segmentation_maps
def test_call_segmentation_maps(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
image_processor = 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)
maps = []
@ -276,18 +295,70 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_reduce_labels
def test_reduce_labels(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
image, map = prepare_semantic_single_inputs()
encoding = image_processor(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 150)
labels_no_reduce = encoding["labels"].clone()
self.assertTrue(labels_no_reduce.min().item() >= 0)
self.assertTrue(labels_no_reduce.max().item() <= 150)
# Get the first non-zero label coords and value, for comparison when do_reduce_labels is True
non_zero_positions = (labels_no_reduce > 0).nonzero()
first_non_zero_coords = tuple(non_zero_positions[0].tolist())
first_non_zero_value = labels_no_reduce[first_non_zero_coords].item()
image_processor.do_reduce_labels = True
encoding = image_processor(image, map, return_tensors="pt")
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
# Compare with non-reduced label to see if it's reduced by 1
self.assertEqual(encoding["labels"][first_non_zero_coords].item(), first_non_zero_value - 1)
def test_slow_fast_equivalence(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
dummy_image, dummy_map = prepare_semantic_single_inputs()
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
image_encoding_slow = image_processor_slow(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
image_encoding_fast = image_processor_fast(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
self.assertTrue(torch.allclose(image_encoding_slow.pixel_values, image_encoding_fast.pixel_values, atol=1e-1))
self.assertLessEqual(
torch.mean(torch.abs(image_encoding_slow.pixel_values - image_encoding_fast.pixel_values)).item(), 1e-3
)
self.assertTrue(torch.allclose(image_encoding_slow.labels, image_encoding_fast.labels, atol=1e-1))
def test_slow_fast_equivalence_batched(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
self.skipTest(
reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
)
dummy_images, dummy_maps = prepare_semantic_batch_inputs()
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
encoding_slow = image_processor_slow(dummy_images, segmentation_maps=dummy_maps, return_tensors="pt")
encoding_fast = image_processor_fast(dummy_images, segmentation_maps=dummy_maps, return_tensors="pt")
self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
self.assertLessEqual(
torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
)