#!/usr/bin/env python3 # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates # SPDX-License-Identifier: Apache-2.0 import cv2 import torch import torch.nn as nn import torch.nn.functional as F from torchvision.transforms import Compose from .dinov2 import DINOv2 from .flexible_attention import FlexibleCrossAttention from .util.blocks import FeatureFusionBlock, _make_scratch from .util.transform import NormalizeImage, PrepareForNet, Resize def _make_fusion_block(features, use_bn, size=None): return FeatureFusionBlock( features, nn.ReLU(False), deconv=False, bn=use_bn, expand=False, align_corners=True, size=size, ) class ConvBlock(nn.Module): def __init__(self, in_feature, out_feature): super().__init__() self.conv_block = nn.Sequential( nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(out_feature), nn.ReLU(True), ) def forward(self, x): return self.conv_block(x) class DPTHead(nn.Module): def __init__( self, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False, sigact_out=False, ): super(DPTHead, self).__init__() self.use_clstoken = use_clstoken self.projects = nn.ModuleList( [ nn.Conv2d( in_channels=in_channels, out_channels=out_channel, kernel_size=1, stride=1, padding=0, ) for out_channel in out_channels ] ) self.resize_layers = nn.ModuleList( [ nn.ConvTranspose2d( in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0, ), nn.ConvTranspose2d( in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0, ), nn.Identity(), nn.Conv2d( in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1, ), ] ) if use_clstoken: self.readout_projects = nn.ModuleList() for _ in range(len(self.projects)): self.readout_projects.append( nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()) ) self.scratch = _make_scratch( out_channels, features, groups=1, expand=False, ) self.scratch.stem_transpose = None self.scratch.refinenet1 = _make_fusion_block(features, use_bn) self.scratch.refinenet2 = _make_fusion_block(features, use_bn) self.scratch.refinenet3 = _make_fusion_block(features, use_bn) self.scratch.refinenet4 = _make_fusion_block(features, use_bn) head_features_1 = features head_features_2 = 32 self.scratch.output_conv1 = nn.Conv2d( head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1 ) if not sigact_out: self.scratch.output_conv2 = nn.Sequential( nn.Conv2d( head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, ), nn.ReLU(True), nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0), nn.ReLU(True), nn.Identity(), ) else: self.scratch.output_conv2 = nn.Sequential( nn.Conv2d( head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, ), nn.ReLU(True), nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid(), ) def forward(self, out_features, patch_h, patch_w): out = [] for i, x in enumerate(out_features): if self.use_clstoken: x, cls_token = x[0], x[1] readout = cls_token.unsqueeze(1).expand_as(x) x = self.readout_projects[i](torch.cat((x, readout), -1)) else: x = x[0] x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w)) x = self.projects[i](x) x = self.resize_layers[i](x) out.append(x) layer_1, layer_2, layer_3, layer_4 = out layer_1_rn = self.scratch.layer1_rn(layer_1) layer_2_rn = self.scratch.layer2_rn(layer_2) layer_3_rn = self.scratch.layer3_rn(layer_3) layer_4_rn = self.scratch.layer4_rn(layer_4) path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:]) path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:]) path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:]) path_1 = self.scratch.refinenet1(path_2, layer_1_rn) out = self.scratch.output_conv1(path_1) out = F.interpolate( out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True, ) out = self.scratch.output_conv2(out) return out class RGBDDepth(nn.Module): def __init__( self, encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False, max_depth=20.0, use_xformers=False, ): super(RGBDDepth, self).__init__() self.intermediate_layer_idx = { "vits": [2, 5, 8, 11], "vitb": [2, 5, 8, 11], "vitl": [4, 11, 17, 23], "vitg": [9, 19, 29, 39], } self.max_depth = max_depth self.encoder = encoder self.pretrained = DINOv2(model_name=encoder) self.depth_pretrained = DINOv2(model_name=encoder) # self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken, sigact_out=False) self.depth_head_rgbd = DPTHead( self.pretrained.embed_dim * 2, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken, sigact_out=False, ) # cross attention with xFormers support num_heads = 4 self.crossAtts = nn.ModuleList( [ FlexibleCrossAttention( self.pretrained.embed_dim, num_heads, use_xformers=use_xformers ) for _ in range(4) ] ) def forward(self, x): rgb, depth = x[:, :3], x[:, 3:] patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14 with torch.no_grad(): features_rgb = self.pretrained.get_intermediate_layers( rgb, self.intermediate_layer_idx[self.encoder], return_class_token=True ) features_depth = self.depth_pretrained.get_intermediate_layers( depth.repeat(1, 3, 1, 1), self.intermediate_layer_idx[self.encoder], return_class_token=True, ) features = [] for f_rgb, f_depth, crossAtt in zip(features_rgb, features_depth, self.crossAtts): B, N, C = f_rgb[0].shape tf_rgb = f_rgb[0].reshape(B * N, 1, C) tf_depth = f_depth[0].reshape(B * N, 1, C) token_feat = torch.concat((tf_rgb, tf_depth), axis=1) att_feat, _ = crossAtt(token_feat, token_feat, token_feat) att_feat = att_feat.reshape(B * N, 2, C).sum(axis=1).reshape(B, N, C) feat = torch.concat((f_rgb[0], att_feat), axis=2) cls_t = torch.concat((f_rgb[1], f_depth[1]), axis=1) tuples = (feat, cls_t) features.append(tuples) depth = self.depth_head_rgbd(features, patch_h, patch_w) depth = F.relu(depth) return depth.squeeze(1) @torch.no_grad() def infer_image(self, raw_image, depth_low_res, input_size=518): inputs, (h, w) = self.image2tensor(raw_image, depth_low_res, input_size) pred_depth = self.forward(inputs) pred_depth = F.interpolate(pred_depth[:, None], (h, w), mode="nearest")[0, 0] return pred_depth.cpu().numpy() def image2tensor(self, raw_image, depth, input_size=518): transform = Compose( [ Resize( width=input_size, height=input_size, resize_target=True, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method="lower_bound", image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ] ) h, w = raw_image.shape[:2] image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 prepared = transform({"image": image, "depth": depth}) image = prepared["image"] image = torch.from_numpy(image).unsqueeze(0) depth = prepared["depth"] depth = torch.from_numpy(depth).unsqueeze(0).unsqueeze(0) inputs = torch.cat((image, depth), dim=1) # Use the same device as model parameters device = next(self.parameters()).device inputs = inputs.to(device) return inputs, (h, w)