# -*- coding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. 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 .util.blocks import FeatureFusionBlock, _make_scratch from .util.transform import Resize, NormalizeImage, PrepareForNet class DepthAnythingV2(nn.Module): def __init__( self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False ): super(DepthAnythingV2, 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.encoder = encoder self.pretrained = DINOv2(model_name=encoder) self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) def forward(self, x): patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14 features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True) depth = self.depth_head(features, patch_h, patch_w) depth = F.relu(depth) return depth.squeeze(1) @torch.no_grad() def infer_image(self, raw_image, input_size=518): image, (h, w) = self.image2tensor(raw_image, input_size) depth = self.forward(image) depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0] return depth.cpu().numpy() def image2tensor(self, raw_image, input_size=518): transform = Compose([ Resize( width=input_size, height=input_size, resize_target=False, 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 image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0) DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' image = image.to(DEVICE) return image, (h, w) class DPTHead(nn.Module): def __init__( self, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=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) 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(), ) 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 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, )