# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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, ) 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 @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. """ return super().preprocess(images, segmentation_maps, **kwargs) def _preprocess_image_like_inputs( self, images: ImageInput, segmentation_maps: Optional[ImageInput], do_convert_rgb: bool, input_data_format: ChannelDimension, device: Optional[Union[str, "torch.device"]] = None, **kwargs: Unpack[DPTFastImageProcessorKwargs], ) -> BatchFeature: """ Preprocess image-like inputs. """ images = self._prepare_image_like_inputs( images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device ) images_kwargs = kwargs.copy() images_kwargs["do_reduce_labels"] = False batch_feature = self._preprocess(images, **images_kwargs) if segmentation_maps is not None: processed_segmentation_maps = self._prepare_image_like_inputs( images=segmentation_maps, expected_ndims=2, do_convert_rgb=False, input_data_format=ChannelDimension.FIRST, ) segmentation_maps_kwargs = kwargs.copy() segmentation_maps_kwargs.update({"do_normalize": False, "do_rescale": False}) processed_segmentation_maps = self._preprocess( images=processed_segmentation_maps, **segmentation_maps_kwargs ).pixel_values batch_feature["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64) return batch_feature 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]]], keep_aspect_ratio: bool, ensure_multiple_of: Optional[int], do_pad: bool, size_divisor: Optional[int], disable_grouping: Optional[bool], return_tensors: Optional[Union[str, TensorType]], **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, disable_grouping=disable_grouping) 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, disable_grouping=disable_grouping) 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 BatchFeature(data={"pixel_values": processed_images}) 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"]