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| import torch | |
| import torch.nn as nn | |
| from .blocks import FeatureFusionBlock, _make_scratch | |
| import torch.nn.functional as F | |
| 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 DPTHead(nn.Module): | |
| def __init__(self, nclass, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False): | |
| super(DPTHead, self).__init__() | |
| self.nclass = nclass | |
| 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 | |
| if nclass > 1: | |
| self.scratch.output_conv = nn.Sequential( | |
| nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1), | |
| nn.ReLU(True), | |
| nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0), | |
| ) | |
| else: | |
| 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 | |
| class DPT_DINOv2(nn.Module): | |
| def __init__(self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False, localhub=True): | |
| super(DPT_DINOv2, self).__init__() | |
| assert encoder in ['vits', 'vitb', 'vitl'] | |
| # in case the Internet connection is not stable, please load the DINOv2 locally | |
| if localhub: | |
| self.pretrained = torch.hub.load('torchhub/facebookresearch_dinov2_main', 'dinov2_{:}14'.format(encoder), source='local', pretrained=False) | |
| # self.pretrained.load_state_dict(torch.load('checkpoints/dinov2_{:}14_pretrain.pth'.format(encoder))) | |
| else: | |
| self.pretrained = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder)) | |
| dim = self.pretrained.blocks[0].attn.qkv.in_features | |
| self.depth_head = DPTHead(1, dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) | |
| def forward(self, x): | |
| h, w = x.shape[-2:] | |
| features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True) | |
| patch_h, patch_w = h // 14, w // 14 | |
| depth = self.depth_head(features, patch_h, patch_w) | |
| depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True) | |
| depth = F.relu(depth) | |
| return depth.squeeze(1) | |
| if __name__ == '__main__': | |
| depth_anything = DPT_DINOv2() | |
| depth_anything.load_state_dict(torch.load('checkpoints/depth_anything_dinov2_vitl14.pth')) | |