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Running
on
Zero
File size: 5,118 Bytes
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# Copyright (c) 2024, Depth Anything V2
# https://github.com/DepthAnything/Depth-Anything-V2/blob/main/depth_anything_v2/dpt.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from promptda.model.blocks import _make_scratch, _make_fusion_block
class DPTHead(nn.Module):
def __init__(self,
nclass,
in_channels,
features=256,
out_channels=[256, 512, 1024, 1024],
use_bn=False,
use_clstoken=False,
output_act='sigmoid'):
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
act_func = nn.Sigmoid() if output_act == 'sigmoid' else nn.Identity()
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),
act_func,
)
def forward(self, out_features, patch_h, patch_w, prompt_depth=None):
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:], prompt_depth=prompt_depth)
path_3 = self.scratch.refinenet3(
path_4, layer_3_rn, size=layer_2_rn.shape[2:], prompt_depth=prompt_depth)
path_2 = self.scratch.refinenet2(
path_3, layer_2_rn, size=layer_1_rn.shape[2:], prompt_depth=prompt_depth)
path_1 = self.scratch.refinenet1(
path_2, layer_1_rn, prompt_depth=prompt_depth)
out = self.scratch.output_conv1(path_1)
out_feat = F.interpolate(
out, (int(patch_h * 14), int(patch_w * 14)),
mode="bilinear", align_corners=True)
out = self.scratch.output_conv2(out_feat)
return out
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