Spaces:
Runtime error
Runtime error
File size: 14,229 Bytes
4d1ebf3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 |
''' Towards An End-to-End Framework for Video Inpainting
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.modules.flow_comp import SPyNet
from model.modules.feat_prop import BidirectionalPropagation, SecondOrderDeformableAlignment
from model.modules.tfocal_transformer import TemporalFocalTransformerBlock, SoftSplit, SoftComp
from model.modules.spectral_norm import spectral_norm as _spectral_norm
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
def print_network(self):
if isinstance(self, list):
self = self[0]
num_params = 0
for param in self.parameters():
num_params += param.numel()
print(
'Network [%s] was created. Total number of parameters: %.1f million. '
'To see the architecture, do print(network).' %
(type(self).__name__, num_params / 1000000))
def init_weights(self, init_type='normal', gain=0.02):
'''
initialize network's weights
init_type: normal | xavier | kaiming | orthogonal
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39
'''
def init_func(m):
classname = m.__class__.__name__
if classname.find('InstanceNorm2d') != -1:
if hasattr(m, 'weight') and m.weight is not None:
nn.init.constant_(m.weight.data, 1.0)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
elif hasattr(m, 'weight') and (classname.find('Conv') != -1
or classname.find('Linear') != -1):
if init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
nn.init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'xavier_uniform':
nn.init.xavier_uniform_(m.weight.data, gain=1.0)
elif init_type == 'kaiming':
nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
nn.init.orthogonal_(m.weight.data, gain=gain)
elif init_type == 'none': # uses pytorch's default init method
m.reset_parameters()
else:
raise NotImplementedError(
'initialization method [%s] is not implemented' %
init_type)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
self.apply(init_func)
# propagate to children
for m in self.children():
if hasattr(m, 'init_weights'):
m.init_weights(init_type, gain)
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.group = [1, 2, 4, 8, 1]
self.layers = nn.ModuleList([
nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1),
nn.LeakyReLU(0.2, inplace=True)
])
def forward(self, x):
bt, c, h, w = x.size()
h, w = h // 4, w // 4
out = x
for i, layer in enumerate(self.layers):
if i == 8:
x0 = out
if i > 8 and i % 2 == 0:
g = self.group[(i - 8) // 2]
x = x0.view(bt, g, -1, h, w)
o = out.view(bt, g, -1, h, w)
out = torch.cat([x, o], 2).view(bt, -1, h, w)
out = layer(out)
return out
class deconv(nn.Module):
def __init__(self,
input_channel,
output_channel,
kernel_size=3,
padding=0):
super().__init__()
self.conv = nn.Conv2d(input_channel,
output_channel,
kernel_size=kernel_size,
stride=1,
padding=padding)
def forward(self, x):
x = F.interpolate(x,
scale_factor=2,
mode='bilinear',
align_corners=True)
return self.conv(x)
class InpaintGenerator(BaseNetwork):
def __init__(self, init_weights=True):
super(InpaintGenerator, self).__init__()
channel = 256
hidden = 512
# encoder
self.encoder = Encoder()
# decoder
self.decoder = nn.Sequential(
deconv(channel // 2, 128, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
deconv(64, 64, kernel_size=3, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1))
# feature propagation module
self.feat_prop_module = BidirectionalPropagation(channel // 2)
# soft split and soft composition
kernel_size = (7, 7)
padding = (3, 3)
stride = (3, 3)
output_size = (60, 108)
t2t_params = {
'kernel_size': kernel_size,
'stride': stride,
'padding': padding,
'output_size': output_size
}
self.ss = SoftSplit(channel // 2,
hidden,
kernel_size,
stride,
padding,
t2t_param=t2t_params)
self.sc = SoftComp(channel // 2, hidden, output_size, kernel_size,
stride, padding)
n_vecs = 1
for i, d in enumerate(kernel_size):
n_vecs *= int((output_size[i] + 2 * padding[i] -
(d - 1) - 1) / stride[i] + 1)
blocks = []
depths = 8
num_heads = [4] * depths
window_size = [(5, 9)] * depths
focal_windows = [(5, 9)] * depths
focal_levels = [2] * depths
pool_method = "fc"
for i in range(depths):
blocks.append(
TemporalFocalTransformerBlock(dim=hidden,
num_heads=num_heads[i],
window_size=window_size[i],
focal_level=focal_levels[i],
focal_window=focal_windows[i],
n_vecs=n_vecs,
t2t_params=t2t_params,
pool_method=pool_method))
self.transformer = nn.Sequential(*blocks)
if init_weights:
self.init_weights()
# Need to initial the weights of MSDeformAttn specifically
for m in self.modules():
if isinstance(m, SecondOrderDeformableAlignment):
m.init_offset()
# flow completion network
self.update_spynet = SPyNet()
def forward_bidirect_flow(self, masked_local_frames):
b, l_t, c, h, w = masked_local_frames.size()
# compute forward and backward flows of masked frames
masked_local_frames = F.interpolate(masked_local_frames.view(
-1, c, h, w),
scale_factor=1 / 4,
mode='bilinear',
align_corners=True,
recompute_scale_factor=True)
masked_local_frames = masked_local_frames.view(b, l_t, c, h // 4,
w // 4)
mlf_1 = masked_local_frames[:, :-1, :, :, :].reshape(
-1, c, h // 4, w // 4)
mlf_2 = masked_local_frames[:, 1:, :, :, :].reshape(
-1, c, h // 4, w // 4)
pred_flows_forward = self.update_spynet(mlf_1, mlf_2)
pred_flows_backward = self.update_spynet(mlf_2, mlf_1)
pred_flows_forward = pred_flows_forward.view(b, l_t - 1, 2, h // 4,
w // 4)
pred_flows_backward = pred_flows_backward.view(b, l_t - 1, 2, h // 4,
w // 4)
return pred_flows_forward, pred_flows_backward
def forward(self, masked_frames, num_local_frames):
l_t = num_local_frames
b, t, ori_c, ori_h, ori_w = masked_frames.size()
# normalization before feeding into the flow completion module
masked_local_frames = (masked_frames[:, :l_t, ...] + 1) / 2
pred_flows = self.forward_bidirect_flow(masked_local_frames)
# extracting features and performing the feature propagation on local features
enc_feat = self.encoder(masked_frames.view(b * t, ori_c, ori_h, ori_w))
_, c, h, w = enc_feat.size()
local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...]
ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...]
local_feat = self.feat_prop_module(local_feat, pred_flows[0],
pred_flows[1])
enc_feat = torch.cat((local_feat, ref_feat), dim=1)
# content hallucination through stacking multiple temporal focal transformer blocks
trans_feat = self.ss(enc_feat.view(-1, c, h, w), b)
trans_feat = self.transformer(trans_feat)
trans_feat = self.sc(trans_feat, t)
trans_feat = trans_feat.view(b, t, -1, h, w)
enc_feat = enc_feat + trans_feat
# decode frames from features
output = self.decoder(enc_feat.view(b * t, c, h, w))
output = torch.tanh(output)
return output, pred_flows
# ######################################################################
# Discriminator for Temporal Patch GAN
# ######################################################################
class Discriminator(BaseNetwork):
def __init__(self,
in_channels=3,
use_sigmoid=False,
use_spectral_norm=True,
init_weights=True):
super(Discriminator, self).__init__()
self.use_sigmoid = use_sigmoid
nf = 32
self.conv = nn.Sequential(
spectral_norm(
nn.Conv3d(in_channels=in_channels,
out_channels=nf * 1,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=1,
bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(64, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(
nn.Conv3d(nf * 1,
nf * 2,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=(1, 2, 2),
bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(128, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(
nn.Conv3d(nf * 2,
nf * 4,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=(1, 2, 2),
bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(
nn.Conv3d(nf * 4,
nf * 4,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=(1, 2, 2),
bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(
nn.Conv3d(nf * 4,
nf * 4,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=(1, 2, 2),
bias=not use_spectral_norm), use_spectral_norm),
# nn.InstanceNorm2d(256, track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(nf * 4,
nf * 4,
kernel_size=(3, 5, 5),
stride=(1, 2, 2),
padding=(1, 2, 2)))
if init_weights:
self.init_weights()
def forward(self, xs):
# T, C, H, W = xs.shape (old)
# B, T, C, H, W (new)
xs_t = torch.transpose(xs, 1, 2)
feat = self.conv(xs_t)
if self.use_sigmoid:
feat = torch.sigmoid(feat)
out = torch.transpose(feat, 1, 2) # B, T, C, H, W
return out
def spectral_norm(module, mode=True):
if mode:
return _spectral_norm(module)
return module
|