Spaces:
Running
on
A10G
Running
on
A10G
File size: 8,822 Bytes
bcec54e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# The deconvolution code is based on Simple Baseline.
# (https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py)
# Modified by Zigang Geng ([email protected]).
# ------------------------------------------------------------------------------
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_, DropPath
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_upsample_layer,
constant_init, normal_init)
from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
import torch.nn.functional as F
from evp.models import UNetWrapper, TextAdapterDepth
class VPDDepthEncoder(nn.Module):
def __init__(self, out_dim=1024, ldm_prior=[320, 640, 1280+1280], sd_path=None, text_dim=768,
dataset='nyu'
):
super().__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(ldm_prior[0], ldm_prior[0], 3, stride=2, padding=1),
nn.GroupNorm(16, ldm_prior[0]),
nn.ReLU(),
nn.Conv2d(ldm_prior[0], ldm_prior[0], 3, stride=2, padding=1),
)
self.layer2 = nn.Sequential(
nn.Conv2d(ldm_prior[1], ldm_prior[1], 3, stride=2, padding=1),
)
self.out_layer = nn.Sequential(
nn.Conv2d(sum(ldm_prior), out_dim, 1),
nn.GroupNorm(16, out_dim),
nn.ReLU(),
)
self.apply(self._init_weights)
### stable diffusion layers
config = OmegaConf.load('./v1-inference.yaml')
if sd_path is None:
config.model.params.ckpt_path = '../checkpoints/v1-5-pruned-emaonly.ckpt'
else:
config.model.params.ckpt_path = f'../{sd_path}'
sd_model = instantiate_from_config(config.model)
self.encoder_vq = sd_model.first_stage_model
self.unet = UNetWrapper(sd_model.model, use_attn=False)
del sd_model.cond_stage_model
del self.encoder_vq.decoder
del self.unet.unet.diffusion_model.out
for param in self.encoder_vq.parameters():
param.requires_grad = False
if dataset == 'nyu':
self.text_adapter = TextAdapterDepth(text_dim=text_dim)
class_embeddings = torch.load('nyu_class_embeddings.pth')
else:
raise NotImplementedError
self.register_buffer('class_embeddings', class_embeddings)
self.gamma = nn.Parameter(torch.ones(text_dim) * 1e-4)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
def forward_features(self, feats):
x = self.ldm_to_net[0](feats[0])
for i in range(3):
if i > 0:
x = x + self.ldm_to_net[i](feats[i])
x = self.layers[i](x)
x = self.upsample_layers[i](x)
return self.out_conv(x)
def forward(self, x, class_ids=None,img_paths=None):
with torch.no_grad():
latents = self.encoder_vq.encode(x).mode().detach()
if class_ids is not None:
class_embeddings = self.class_embeddings[class_ids.tolist()]
else:
class_embeddings = self.class_embeddings
c_crossattn = self.text_adapter(latents, class_embeddings, self.gamma) # NOTE: here the c_crossattn should be expand_dim as latents
t = torch.ones((x.shape[0],), device=x.device).long()
# import pdb; pdb.set_trace()
outs = self.unet(latents, t, c_crossattn=[c_crossattn])
feats = [outs[0], outs[1], torch.cat([outs[2], F.interpolate(outs[3], scale_factor=2)], dim=1)]
x = torch.cat([self.layer1(feats[0]), self.layer2(feats[1]), feats[2]], dim=1)
return self.out_layer(x)
class VPDDepth(nn.Module):
def __init__(self, args=None):
super().__init__()
self.max_depth = args.max_depth
embed_dim = 192
channels_in = embed_dim*8
channels_out = embed_dim
if args.dataset == 'nyudepthv2':
self.encoder = VPDDepthEncoder(out_dim=channels_in, dataset='nyu')
else:
raise NotImplementedError
self.decoder = Decoder(channels_in, channels_out, args)
self.decoder.init_weights()
self.last_layer_depth = nn.Sequential(
nn.Conv2d(channels_out, channels_out, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(channels_out, 1, kernel_size=3, stride=1, padding=1))
for m in self.last_layer_depth.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)
def forward(self, x, class_ids=None,img_paths=None):
# import pdb; pdb.set_trace()
b, c, h, w = x.shape
x = x*2.0 - 1.0 # normalize to [-1, 1]
if h == 480 and w == 480:
new_x = torch.zeros(b, c, 512, 512, device=x.device)
new_x[:, :, 0:480, 0:480] = x
x = new_x
elif h==352 and w==352:
new_x = torch.zeros(b, c, 384, 384, device=x.device)
new_x[:, :, 0:352, 0:352] = x
x = new_x
elif h == 512 and w == 512:
pass
else:
raise NotImplementedError
conv_feats = self.encoder(x, class_ids)
if h == 480 or h == 352:
conv_feats = conv_feats[:, :, :-1, :-1]
out = self.decoder([conv_feats])
out_depth = self.last_layer_depth(out)
out_depth = torch.sigmoid(out_depth) * self.max_depth
return {'pred_d': out_depth}
class Decoder(nn.Module):
def __init__(self, in_channels, out_channels, args):
super().__init__()
self.deconv = args.num_deconv
self.in_channels = in_channels
# import pdb; pdb.set_trace()
self.deconv_layers = self._make_deconv_layer(
args.num_deconv,
args.num_filters,
args.deconv_kernels,
)
conv_layers = []
conv_layers.append(
build_conv_layer(
dict(type='Conv2d'),
in_channels=args.num_filters[-1],
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1))
conv_layers.append(
build_norm_layer(dict(type='BN'), out_channels)[1])
conv_layers.append(nn.ReLU(inplace=True))
self.conv_layers = nn.Sequential(*conv_layers)
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
def forward(self, conv_feats):
# import pdb; pdb.set_trace()
out = self.deconv_layers(conv_feats[0])
out = self.conv_layers(out)
out = self.up(out)
out = self.up(out)
return out
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
"""Make deconv layers."""
layers = []
in_planes = self.in_channels
for i in range(num_layers):
kernel, padding, output_padding = \
self._get_deconv_cfg(num_kernels[i])
planes = num_filters[i]
layers.append(
build_upsample_layer(
dict(type='deconv'),
in_channels=in_planes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False))
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
in_planes = planes
return nn.Sequential(*layers)
def _get_deconv_cfg(self, deconv_kernel):
"""Get configurations for deconv layers."""
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Not supported num_kernels ({deconv_kernel}).')
return deconv_kernel, padding, output_padding
def init_weights(self):
"""Initialize model weights."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.ConvTranspose2d):
normal_init(m, std=0.001)
|