# coding=utf-8 # Copyright 2024 TikTok and 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. """PyTorch Depth Anything model.""" from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn 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 from ...utils import logging from ...utils.backbone_utils import load_backbone from .configuration_depth_anything import DepthAnythingConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DepthAnythingConfig" 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 ([`DepthAnythingConfig`]): 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. """ 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. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. """ class DepthAnythingReassembleLayer(nn.Module): def __init__(self, config, channels, factor): 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 self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1) # Copied from transformers.models.dpt.modeling_dpt.DPTReassembleLayer.forward def forward(self, hidden_state): hidden_state = self.projection(hidden_state) hidden_state = self.resize(hidden_state) return hidden_state class DepthAnythingReassembleStage(nn.Module): """ This class reassembles the hidden states of the backbone into image-like feature representations at various resolutions. This happens in 3 stages: 1. Take the patch embeddings and reshape them to image-like feature representations. 2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`. 3. Resizing the spatial dimensions (height, width). Args: config (`[DepthAnythingConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.config = config self.layers = nn.ModuleList() for channels, factor in zip(config.neck_hidden_sizes, config.reassemble_factors): self.layers.append(DepthAnythingReassembleLayer(config, channels=channels, factor=factor)) def forward(self, hidden_states: List[torch.Tensor], patch_height=None, patch_width=None) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`): List of hidden states from the backbone. """ out = [] for i, hidden_state in enumerate(hidden_states): # reshape to (batch_size, num_channels, height, width) hidden_state = hidden_state[:, 1:] batch_size, _, num_channels = hidden_state.shape hidden_state = hidden_state.reshape(batch_size, patch_height, patch_width, num_channels) hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() hidden_state = self.layers[i](hidden_state) out.append(hidden_state) return out class DepthAnythingPreActResidualLayer(nn.Module): """ ResidualConvUnit, pre-activate residual unit. Args: config (`[DepthAnythingConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.activation1 = nn.ReLU() self.convolution1 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=True, ) self.activation2 = nn.ReLU() self.convolution2 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=True, ) def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: residual = hidden_state hidden_state = self.activation1(hidden_state) hidden_state = self.convolution1(hidden_state) hidden_state = self.activation2(hidden_state) hidden_state = self.convolution2(hidden_state) return hidden_state + residual class DepthAnythingFeatureFusionLayer(nn.Module): """Feature fusion layer, merges feature maps from different stages. Args: config (`[DepthAnythingConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True) self.residual_layer1 = DepthAnythingPreActResidualLayer(config) self.residual_layer2 = DepthAnythingPreActResidualLayer(config) def forward(self, hidden_state, residual=None, size=None): if residual is not None: if hidden_state.shape != residual.shape: residual = nn.functional.interpolate( residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False ) hidden_state = hidden_state + self.residual_layer1(residual) hidden_state = self.residual_layer2(hidden_state) 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 DepthAnythingFeatureFusionStage(nn.Module): # Copied from transformers.models.dpt.modeling_dpt.DPTFeatureFusionStage.__init__ with DPT->DepthAnything def __init__(self, config): super().__init__() self.layers = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.layers.append(DepthAnythingFeatureFusionLayer(config)) def forward(self, hidden_states, size=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) else: fused_hidden_state = layer(fused_hidden_state, hidden_state, size=size) fused_hidden_states.append(fused_hidden_state) return fused_hidden_states # Modified from transformers.models.dpt.modeling_dpt.DPTPreTrainedModel with DPT->DepthAnything,dpt->depth_anything # avoiding sdpa and flash_attn_2 support, it's done in the backend class DepthAnythingPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DepthAnythingConfig base_model_prefix = "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 DepthAnythingNeck(nn.Module): """ DepthAnythingNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as input and produces another list of tensors as output. For DepthAnything, it includes 2 stages: * DepthAnythingReassembleStage * DepthAnythingFeatureFusionStage. Args: config (dict): config dict. """ def __init__(self, config): super().__init__() self.config = config self.reassemble_stage = DepthAnythingReassembleStage(config) self.convs = nn.ModuleList() for channel in config.neck_hidden_sizes: self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False)) # fusion self.fusion_stage = DepthAnythingFeatureFusionStage(config) def forward(self, hidden_states: List[torch.Tensor], patch_height=None, patch_width=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) return output class DepthAnythingDepthEstimationHead(nn.Module): """ Output head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples the predictions to the input resolution after the first convolutional layer (details can be found in the DPT paper's supplementary material). The final activation function is either ReLU or Sigmoid, depending on the depth estimation type (relative or metric). For metric depth estimation, the output is scaled by the maximum depth used during pretraining. """ def __init__(self, config): super().__init__() self.head_in_index = config.head_in_index self.patch_size = config.patch_size features = config.fusion_hidden_size self.conv1 = nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1) self.conv2 = nn.Conv2d(features // 2, config.head_hidden_size, kernel_size=3, stride=1, padding=1) self.activation1 = nn.ReLU() self.conv3 = nn.Conv2d(config.head_hidden_size, 1, kernel_size=1, stride=1, padding=0) if config.depth_estimation_type == "relative": self.activation2 = nn.ReLU() elif config.depth_estimation_type == "metric": self.activation2 = nn.Sigmoid() else: raise ValueError(f"Unknown depth estimation type: {config.depth_estimation_type}") self.max_depth = config.max_depth def forward(self, hidden_states: List[torch.Tensor], patch_height, patch_width) -> torch.Tensor: hidden_states = hidden_states[self.head_in_index] predicted_depth = self.conv1(hidden_states) predicted_depth = nn.functional.interpolate( predicted_depth, (int(patch_height * self.patch_size), int(patch_width * self.patch_size)), 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) * self.max_depth predicted_depth = predicted_depth.squeeze(dim=1) # shape (batch_size, height, width) return predicted_depth @add_start_docstrings( """ Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. """, DEPTH_ANYTHING_START_DOCSTRING, ) class DepthAnythingForDepthEstimation(DepthAnythingPreTrainedModel): _no_split_modules = ["DPTViTEmbeddings"] def __init__(self, config): super().__init__(config) self.backbone = load_backbone(config) self.neck = DepthAnythingNeck(config) self.head = DepthAnythingDepthEstimationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DEPTH_ANYTHING_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, 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""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth depth estimation maps for computing the loss. Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation >>> import torch >>> import numpy as np >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf") >>> model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf") >>> # prepare image for the model >>> inputs = image_processor(images=image, return_tensors="pt") >>> 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 * 255 / predicted_depth.max() >>> depth = depth.detach().cpu().numpy() >>> depth = Image.fromarray(depth.astype("uint8")) ```""" 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 hidden_states = self.neck(hidden_states, patch_height, patch_width) predicted_depth = self.head(hidden_states, patch_height, patch_width) 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__ = ["DepthAnythingForDepthEstimation", "DepthAnythingPreTrainedModel"]