# Copyright 2025 The HuggingFace 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. from typing import List, Optional, Tuple, Union import torch import torch.nn as nn from transformers.models.depth_anything.configuration_depth_anything import DepthAnythingConfig from transformers.models.depth_anything.modeling_depth_anything import ( DepthAnythingDepthEstimationHead, DepthAnythingFeatureFusionLayer, DepthAnythingFeatureFusionStage, DepthAnythingForDepthEstimation, DepthAnythingNeck, DepthAnythingReassembleStage, ) from transformers.utils.generic import torch_int from ...file_utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_outputs import DepthEstimatorOutput from ...modeling_utils import PreTrainedModel _CONFIG_FOR_DOC = "PromptDepthAnythingConfig" class PromptDepthAnythingConfig(DepthAnythingConfig): model_type = "prompt_depth_anything" class PromptDepthAnythingLayer(nn.Module): def __init__(self, config: PromptDepthAnythingConfig): super().__init__() self.convolution1 = nn.Conv2d( 1, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=True, ) self.activation1 = nn.ReLU() self.convolution2 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=True, ) self.activation2 = nn.ReLU() self.convolution3 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=True, ) def forward(self, prompt_depth: torch.Tensor) -> torch.Tensor: hidden_state = self.convolution1(prompt_depth) hidden_state = self.activation1(hidden_state) hidden_state = self.convolution2(hidden_state) hidden_state = self.activation2(hidden_state) hidden_state = self.convolution3(hidden_state) return hidden_state class PromptDepthAnythingFeatureFusionLayer(DepthAnythingFeatureFusionLayer): def __init__(self, config: PromptDepthAnythingConfig): super().__init__(config) self.prompt_depth_layer = PromptDepthAnythingLayer(config) def forward(self, hidden_state, residual=None, size=None, prompt_depth=None): if residual is not None: if hidden_state.shape != residual.shape: residual = nn.functional.interpolate( residual, size=hidden_state.shape[2:], mode="bilinear", align_corners=False ) hidden_state = hidden_state + self.residual_layer1(residual) hidden_state = self.residual_layer2(hidden_state) if prompt_depth is not None: prompt_depth = nn.functional.interpolate( prompt_depth, size=hidden_state.shape[2:], mode="bilinear", align_corners=False ) res = self.prompt_depth_layer(prompt_depth) hidden_state = hidden_state + res modifier = {"scale_factor": 2} if size is None else {"size": size} hidden_state = nn.functional.interpolate( hidden_state, **modifier, mode="bilinear", align_corners=True, ) hidden_state = self.projection(hidden_state) return hidden_state class PromptDepthAnythingFeatureFusionStage(DepthAnythingFeatureFusionStage): def forward(self, hidden_states, size=None, prompt_depth=None): # reversing the hidden_states, we start from the last hidden_states = hidden_states[::-1] fused_hidden_states = [] fused_hidden_state = None for idx, (hidden_state, layer) in enumerate(zip(hidden_states, self.layers)): size = hidden_states[idx + 1].shape[2:] if idx != (len(hidden_states) - 1) else None if fused_hidden_state is None: # first layer only uses the last hidden_state fused_hidden_state = layer(hidden_state, size=size, prompt_depth=prompt_depth) else: fused_hidden_state = layer(fused_hidden_state, hidden_state, size=size, prompt_depth=prompt_depth) fused_hidden_states.append(fused_hidden_state) return fused_hidden_states class PromptDepthAnythingDepthEstimationHead(DepthAnythingDepthEstimationHead): def forward(self, hidden_states: List[torch.Tensor], patch_height: int, patch_width: int) -> torch.Tensor: hidden_states = hidden_states[-1] predicted_depth = self.conv1(hidden_states) target_height = torch_int(patch_height * self.patch_size) target_width = torch_int(patch_width * self.patch_size) predicted_depth = nn.functional.interpolate( predicted_depth, (target_height, target_width), mode="bilinear", align_corners=True, ) predicted_depth = self.conv2(predicted_depth) predicted_depth = self.activation1(predicted_depth) predicted_depth = self.conv3(predicted_depth) predicted_depth = self.activation2(predicted_depth) # (batch_size, 1, height, width) -> (batch_size, height, width), which # keeps the same behavior as Depth Anything v1 & v2 predicted_depth = predicted_depth.squeeze(dim=1) return predicted_depth PROMPT_DEPTH_ANYTHING_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`PromptDepthAnythingConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ PROMPT_DEPTH_ANYTHING_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`DPTImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. prompt_depth (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): Prompt depth is the sparse or low-resolution depth obtained from multi-view geometry or a low-resolution depth sensor. It generally has shape (height, width), where height and width can be smaller than those of the images. It is optional and can be None, which means no prompt depth will be used. If it is None, the output will be a monocular relative depth. The values are recommended to be in meters, but this is not necessary. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. """ class PromptDepthAnythingPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = PromptDepthAnythingConfig base_model_prefix = "prompt_depth_anything" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class PromptDepthAnythingReassembleLayer(nn.Module): def __init__(self, config: PromptDepthAnythingConfig, channels: int, factor: int): super().__init__() self.projection = nn.Conv2d(in_channels=config.reassemble_hidden_size, out_channels=channels, kernel_size=1) # up/down sampling depending on factor if factor > 1: self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) elif factor == 1: self.resize = nn.Identity() elif factor < 1: # so should downsample stride = torch_int(1 / factor) self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=stride, padding=1) def forward(self, hidden_state): hidden_state = self.projection(hidden_state) hidden_state = self.resize(hidden_state) return hidden_state class PromptDepthAnythingReassembleStage(DepthAnythingReassembleStage): pass class PromptDepthAnythingNeck(DepthAnythingNeck): def forward( self, hidden_states: List[torch.Tensor], patch_height: Optional[int] = None, patch_width: Optional[int] = None, prompt_depth: Optional[torch.Tensor] = None, ) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`): List of hidden states from the backbone. """ if not isinstance(hidden_states, (tuple, list)): raise TypeError("hidden_states should be a tuple or list of tensors") if len(hidden_states) != len(self.config.neck_hidden_sizes): raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") # postprocess hidden states hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width) features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)] # fusion blocks output = self.fusion_stage(features, prompt_depth=prompt_depth) return output @add_start_docstrings( """ Prompt Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. """, PROMPT_DEPTH_ANYTHING_START_DOCSTRING, ) class PromptDepthAnythingForDepthEstimation(DepthAnythingForDepthEstimation): @add_start_docstrings_to_model_forward(PROMPT_DEPTH_ANYTHING_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, prompt_depth: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]: r""" ```python >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation >>> import torch >>> import numpy as np >>> from PIL import Image >>> import requests >>> url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/image.jpg?raw=true" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") >>> model = AutoModelForDepthEstimation.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") >>> prompt_depth_url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/arkit_depth.png?raw=true" >>> prompt_depth = Image.open(requests.get(prompt_depth_url, stream=True).raw) >>> # prepare image for the model >>> inputs = image_processor(images=image, return_tensors="pt", prompt_depth=prompt_depth) >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # interpolate to original size >>> post_processed_output = image_processor.post_process_depth_estimation( ... outputs, ... target_sizes=[(image.height, image.width)], ... ) >>> # visualize the prediction >>> predicted_depth = post_processed_output[0]["predicted_depth"] >>> depth = predicted_depth * 1000. >>> depth = depth.detach().cpu().numpy() >>> depth = Image.fromarray(depth.astype("uint16")) # mm ```""" loss = None if labels is not None: raise NotImplementedError("Training is not implemented yet") return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions outputs = self.backbone.forward_with_filtered_kwargs( pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions ) hidden_states = outputs.feature_maps _, _, height, width = pixel_values.shape patch_size = self.config.patch_size patch_height = height // patch_size patch_width = width // patch_size if prompt_depth is not None: # normalize prompt depth batch_size = prompt_depth.shape[0] depth_min = torch.min(prompt_depth.reshape(batch_size, -1), dim=1).values depth_max = torch.max(prompt_depth.reshape(batch_size, -1), dim=1).values depth_min, depth_max = depth_min.view(batch_size, 1, 1, 1), depth_max.view(batch_size, 1, 1, 1) prompt_depth = (prompt_depth - depth_min) / (depth_max - depth_min) # normalize done hidden_states = self.neck(hidden_states, patch_height, patch_width, prompt_depth=prompt_depth) predicted_depth = self.head(hidden_states, patch_height, patch_width) if prompt_depth is not None: # denormalize predicted depth depth_min = depth_min.squeeze(1).to(predicted_depth.device) depth_max = depth_max.squeeze(1).to(predicted_depth.device) predicted_depth = predicted_depth * (depth_max - depth_min) + depth_min # denormalize done if not return_dict: if output_hidden_states: output = (predicted_depth,) + outputs[1:] else: output = (predicted_depth,) + outputs[2:] return ((loss,) + output) if loss is not None else output return DepthEstimatorOutput( loss=loss, predicted_depth=predicted_depth, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) __all__ = [ "PromptDepthAnythingConfig", "PromptDepthAnythingForDepthEstimation", "PromptDepthAnythingPreTrainedModel", ]