Spaces:
Running
on
L40S
Running
on
L40S
File size: 9,223 Bytes
c42db24 |
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 |
import math
import torch
from torch import nn
import torch.nn.functional as F
from .ops import (ConstantInput, ConvLayer, StyledConv, ToFlow, ToRGB, Direction)
class FlowResBlock(nn.Module):
def __init__(self, in_channel, out_channel, style_dim):
super().__init__()
self.norm = nn.GroupNorm(32, out_channel)
self.conv1 = StyledConv(in_channel, out_channel, 3, style_dim, False)
self.conv2 = StyledConv(out_channel, out_channel, 3, style_dim, False)
self.gamma = nn.Parameter(1e-5 * torch.ones([1, out_channel, 1, 1]))
def forward(self, x, style):
h = x
h = self.conv1(h, style)
skip = h
h = self.norm(h)
h = self.conv2(h, style)
h = self.gamma * h
return h + skip
class ResBlock(nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.conv1 = ConvLayer(in_channel, out_channel, 3, upsample=False)
self.conv2 = ConvLayer(out_channel, out_channel, 3, upsample=False)
if in_channel != out_channel:
self.skip = ConvLayer(in_channel, out_channel, 1, upsample=False, activate=False, bias=False)
else:
self.skip = torch.nn.Identity()
def forward(self, x):
h = x
h = self.conv1(h)
h = self.conv2(h)
skip = self.skip(x)
return (h + skip) / math.sqrt(2)
class Decoder(nn.Module):
def __init__(self, style_dim, motion_dim, scale=1):
super().__init__()
channels = [512*scale, 256 * scale, 128 * scale, 64 * scale]
self.direction = Direction(style_dim, motion_dim)
self.input = ConstantInput(channels[0], size=4) # 4
# block1, 4
self.conv1 = StyledConv(channels[0], channels[0], 3, style_dim, False)
# for 512
self.conv_512_1 = StyledConv(channels[0], channels[0], 3, style_dim, True)
self.conv_512_2 = nn.ModuleList([
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
])
self.conv_512_2_rgb = nn.ModuleList([
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
])
self.rgb_512 = ToRGB(channels[0])
self.flow_512 = ToFlow(channels[0], style_dim) # 16
# block2, 8
self.conv2_1 = StyledConv(channels[0], channels[0], 3, style_dim, True)
self.conv2_2 = nn.ModuleList([
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
])
self.conv2_2_up = ConvLayer(channels[0], channels[0], 3, upsample=True)
self.conv2_2_rgb = nn.ModuleList([
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
])
self.rgb2 = ToRGB(channels[0])
self.flow2 = ToFlow(channels[0], style_dim) # 16
# block3, 16
self.conv3_1 = StyledConv(channels[0], channels[0], 3, style_dim, True)
self.conv3_2 = nn.ModuleList([
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
])
self.conv3_2_up = ConvLayer(channels[0], channels[0], 3, upsample=True)
self.conv3_2_rgb = nn.ModuleList([
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
])
self.rgb3 = ToRGB(channels[0])
self.flow3 = ToFlow(channels[0], style_dim) # 32
# block4, 32
self.conv4_1 = StyledConv(channels[0], channels[0], 3, style_dim, True)
self.conv4_2 = nn.ModuleList([
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
FlowResBlock(channels[0], channels[0], style_dim),
])
self.conv4_2_up = ConvLayer(channels[0], channels[0], 3, upsample=True)
self.conv4_2_rgb = nn.ModuleList([
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
ResBlock(channels[0], channels[0]),
])
self.rgb4 = ToRGB(channels[0])
self.flow4 = ToFlow(channels[0], style_dim) # 64
# block5, 64
self.conv5_1 = StyledConv(channels[0], channels[1], 3, style_dim, True)
self.conv5_2 = nn.ModuleList([
FlowResBlock(channels[1], channels[1], style_dim),
FlowResBlock(channels[1], channels[1], style_dim),
FlowResBlock(channels[1], channels[1], style_dim),
FlowResBlock(channels[1], channels[1], style_dim),
])
self.conv5_2_up = ConvLayer(channels[0], channels[1], 3, upsample=True)
self.conv5_2_rgb = nn.ModuleList([
ResBlock(channels[1], channels[1]),
ResBlock(channels[1], channels[1]),
ResBlock(channels[1], channels[1]),
ResBlock(channels[1], channels[1]),
])
self.rgb5 = ToRGB(channels[1])
self.flow5 = ToFlow(channels[1], style_dim) # 128
# block6, 128
self.conv6_1 = StyledConv(channels[1], channels[2], 3, style_dim, True)
self.conv6_2 = nn.ModuleList([
FlowResBlock(channels[2], channels[2], style_dim),
FlowResBlock(channels[2], channels[2], style_dim),
FlowResBlock(channels[2], channels[2], style_dim),
FlowResBlock(channels[2], channels[2], style_dim),
])
self.conv6_2_up = ConvLayer(channels[1], channels[2], 3, upsample=True)
self.conv6_2_rgb = nn.ModuleList([
ResBlock(channels[2], channels[2]),
ResBlock(channels[2], channels[2]),
ResBlock(channels[2], channels[2]),
ResBlock(channels[2], channels[2]),
])
self.rgb6 = ToRGB(channels[2])
self.flow6 = ToFlow(channels[2], style_dim) # 128
# block7, 256
self.conv7_1 = StyledConv(channels[2], channels[3], 3, style_dim, True)
self.conv7_2 = nn.ModuleList([
FlowResBlock(channels[3], channels[3], style_dim),
FlowResBlock(channels[3], channels[3], style_dim),
FlowResBlock(channels[3], channels[3], style_dim),
FlowResBlock(channels[3], channels[3], style_dim),
])
self.conv7_2_up = ConvLayer(channels[2], channels[3], 3, upsample=True)
self.conv7_2_rgb = nn.ModuleList([
ResBlock(channels[3], channels[3]),
ResBlock(channels[3], channels[3]),
ResBlock(channels[3], channels[3]),
ResBlock(channels[3], channels[3]),
])
self.rgb7 = ToRGB(channels[3])
self.flow7 = ToFlow(channels[3], style_dim) # 128
def navigation(self, z_s2r, alpha):
if alpha is not None:
# generating moving directions
if len(alpha) > 1:
z_r2t = self.direction(alpha[0]) # target
z_r2s = self.direction(alpha[1]) # source
z_start = self.direction(alpha[2]) # start
z_s2t = z_s2r + (z_r2t - z_start) + z_r2s
else:
z_r2t = self.direction(alpha[0])
z_s2t = z_s2r + z_r2t # wa + directions
else:
z_s2t = z_s2r
return z_s2t
def apply_flow(self, h, mask, flow, feat):
feat_warp = F.grid_sample(feat, flow) * mask
h = feat_warp + (1 - mask) * h
return feat_warp, h
def forward(self, z_s2r, alpha, feats):
# z_s2r: bs x style_dim
# alpha: bs x style_dim
z_s2t = self.navigation(z_s2r, alpha)
h = self.input(z_s2t)
h = self.conv1(h, z_s2t)
#for 512
h = self.conv_512_1(h, z_s2t)
for conv in self.conv_512_2:
h = conv(h, z_s2t)
h_warp_512, h, h_flow_512 = self.flow_512(h, z_s2t, feats[0])
for conv in self.conv_512_2_rgb:
h_warp_512 = conv(h_warp_512)
rgb_512 = self.rgb_512(h_warp_512)
h = self.conv2_1(h, z_s2t)
for conv in self.conv2_2:
h = conv(h, z_s2t)
h_warp2, h, h_flow2 = self.flow2(h, z_s2t, feats[1], h_flow_512)
h_warp2 = h_warp2 + self.conv2_2_up(h_warp_512)
for conv in self.conv2_2_rgb:
h_warp2 = conv(h_warp2)
rgb2 = self.rgb2(h_warp2, rgb_512)
h = self.conv3_1(h, z_s2t)
for conv in self.conv3_2:
h = conv(h, z_s2t)
h_warp3, h, h_flow3 = self.flow3(h, z_s2t, feats[2], h_flow2)
h_warp3 = h_warp3 + self.conv3_2_up(h_warp2)
for conv in self.conv3_2_rgb:
h_warp3 = conv(h_warp3)
rgb3 = self.rgb3(h_warp3, rgb2)
h = self.conv4_1(h, z_s2t)
for conv in self.conv4_2:
h = conv(h, z_s2t)
h_warp4, h, h_flow4 = self.flow4(h, z_s2t, feats[3], h_flow3)
h_warp4 = h_warp4 + self.conv4_2_up(h_warp3)
for conv in self.conv4_2_rgb:
h_warp4 = conv(h_warp4)
rgb4 = self.rgb4(h_warp4, rgb3)
h = self.conv5_1(h, z_s2t)
for conv in self.conv5_2:
h = conv(h, z_s2t)
h_warp5, h, h_flow5 = self.flow5(h, z_s2t, feats[4], h_flow4)
h_warp5 = h_warp5 + self.conv5_2_up(h_warp4)
for conv in self.conv5_2_rgb:
h_warp5 = conv(h_warp5)
rgb5 = self.rgb5(h_warp5, rgb4)
h = self.conv6_1(h, z_s2t)
for conv in self.conv6_2:
h = conv(h, z_s2t)
h_warp6, h, h_flow6 = self.flow6(h, z_s2t, feats[5], h_flow5)
h_warp6 = h_warp6 + self.conv6_2_up(h_warp5)
for conv in self.conv6_2_rgb:
h_warp6 = conv(h_warp6)
rgb6 = self.rgb6(h_warp6, rgb5)
h = self.conv7_1(h, z_s2t)
for conv in self.conv7_2:
h = conv(h, z_s2t)
h_warp7, h, h_flow7 = self.flow7(h, z_s2t, feats[6], h_flow6)
h_warp7 = h_warp7 + self.conv7_2_up(h_warp6)
for conv in self.conv7_2_rgb:
h_warp7 = conv(h_warp7)
out = self.rgb7(h_warp7, rgb6)
return out
|