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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchhub.facebookresearch_dinov2_main.hubconf as dinov2
from depth_anything.util.blocksv2 import FeatureFusionBlock, _make_scratch
from torchvision.transforms import Compose
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
import cv2
import numpy as np
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 = nn.Identity()
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,need_fp=False,need_prior=False,teacher_features=None,alpha=0.8):
depth_out={}
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)).contiguous()
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)
depth_out['out']=out
return depth_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, version='v1'):
super(DPT_DINOv2, self).__init__()
assert encoder in ['vits', 'vitb', 'vitl']
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.version = version
# 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=True)
# else:
# self.pretrained = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder))
self.pretrained = dinov2.__dict__['dinov2_{:}14'.format(encoder)](pretrained=True)
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,need_fp=False,teacher_features=None,alpha=0.8,prior_mode='teacher'):
depth_out={}
h, w = x.shape[-2:]
if self.version == 'v1':
features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
else:
features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True)
patch_h, patch_w = h // 14, w // 14
depth_all = self.depth_head(features, patch_h, patch_w,need_fp,teacher_features,alpha)
depth=depth_all['out']
depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
depth = F.relu(depth).squeeze(1)
depth_out['out']=depth
return depth_out
class DepthAnything_AC(DPT_DINOv2):
def __init__(self, config):
super().__init__(**config)
def get_intermediate_features(self, x):
"""
Extract intermediate features from the model
Args:
x: Input tensor of shape (B, C, H, W)
Returns:
dict: Dictionary containing intermediate features including:
- encoder_features: List of encoder feature maps
- decoder_features: List of decoder feature maps
- decoder_features_path: List of decoder path features
- cls_token: List of classification tokens
"""
features = {
'encoder_features': [],
'decoder_features': [],
'decoder_features_path': [],
'cls_token': []
}
h, w = x.shape[-2:]
patch_h, patch_w = h // 14, w // 14
all_features = []
for i in range(len(self.pretrained.blocks)):
feat = self.pretrained.get_intermediate_layers(x, [i], return_class_token=True)[0]
all_features.append(feat)
if i in [2, 5, 8, 11]:
feat_map = feat[0]
B, N, C = feat_map.shape
H = W = int(np.sqrt(N))
features['encoder_features'].append(feat_map.reshape(B, H, W, C).permute(0, 3, 1, 2))
out_features = []
for layer_idx in self.intermediate_layer_idx[self.encoder]:
out_features.append(all_features[layer_idx])
out = []
for i, feat in enumerate(out_features):
if self.depth_head.use_clstoken:
feat_map, cls_token = feat[0], feat[1]
readout = cls_token.unsqueeze(1).expand_as(feat_map)
feat_map = self.depth_head.readout_projects[i](torch.cat((feat_map, readout), -1))
features['cls_token'].append(cls_token)
else:
feat_map = feat[0]
feat_map = feat_map.permute(0, 2, 1).reshape((feat_map.shape[0], feat_map.shape[-1], patch_h, patch_w)).contiguous()
feat_map = self.depth_head.projects[i](feat_map)
feat_map = self.depth_head.resize_layers[i](feat_map)
out.append(feat_map)
layer_1, layer_2, layer_3, layer_4 = out
layer_1_rn = self.depth_head.scratch.layer1_rn(layer_1)
layer_2_rn = self.depth_head.scratch.layer2_rn(layer_2)
layer_3_rn = self.depth_head.scratch.layer3_rn(layer_3)
layer_4_rn = self.depth_head.scratch.layer4_rn(layer_4)
features['decoder_features'].append(layer_1_rn)
features['decoder_features'].append(layer_2_rn)
features['decoder_features'].append(layer_3_rn)
features['decoder_features'].append(layer_4_rn)
path_4 = self.depth_head.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
path_3 = self.depth_head.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
path_2 = self.depth_head.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
path_1 = self.depth_head.scratch.refinenet1(path_2, layer_1_rn)
features['decoder_features_path'].append(path_1)
features['decoder_features_path'].append(path_2)
features['decoder_features_path'].append(path_3)
features['decoder_features_path'].append(path_4)
return features
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