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9400036 8471f73 9400036 8471f73 9400036 | 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 | import torch
import torch.nn as nn
from renderer.modules import DownConvResBlock, ResBlock, UpConvResBlock, ConvResBlock
from renderer.attention_modules import CrossAttention, SelfAttention
from renderer.lia_resblocks import StyledConv,EqualConv2d,EqualLinear
class IdentityEncoder(nn.Module):
def __init__(self, in_channels=3, output_channels=[64, 128, 256, 512, 512, 512], initial_channels=32, dm=512):
super(IdentityEncoder, self).__init__()
self.initial_conv = nn.Sequential(
nn.Conv2d(in_channels, initial_channels, kernel_size=7, stride=1, padding=3),
nn.BatchNorm2d(initial_channels),
nn.ReLU(inplace=True)
)
self.down_block_0 = DownConvResBlock(initial_channels, initial_channels)
self.down_blocks = nn.ModuleList()
current_channels = initial_channels
for out_channels in output_channels:
if out_channels==32:continue
self.down_blocks.append(DownConvResBlock(current_channels, out_channels))
current_channels = out_channels
self.equalconv = EqualConv2d(output_channels[-1], output_channels[-1], kernel_size=3, stride=1, padding=1)
self.linear_layers = nn.ModuleList([EqualLinear(output_channels[-1], output_channels[-1]) for _ in range(4)])
self.final_linear = EqualLinear(output_channels[-1], dm)
self.activation = nn.LeakyReLU(0.2)
def forward(self, x):
features = []
x = self.initial_conv(x)
x = self.down_block_0(x)
features.append(x)
for block in self.down_blocks:
x = block(x)
features.append(x)
x = x.view(x.size(0), x.size(1), -1).mean(dim=2)
for linear_layer in self.linear_layers:
x = self.activation(linear_layer(x))
x = self.final_linear(x)
return features[::-1], x
class MotionEncoder(nn.Module):
def __init__(self, initial_channels=64, output_channels=[64, 128, 256, 512, 512, 512], dm=32):
super(MotionEncoder, self).__init__()
self.conv1 = nn.Conv2d(3, initial_channels, kernel_size=3, stride=1, padding=1)
self.activation = nn.LeakyReLU(0.2)
self.res_blocks = nn.ModuleList()
in_channels = initial_channels
for out_channels in output_channels:
self.res_blocks.append(ResBlock(in_channels, out_channels))
in_channels = out_channels
self.equalconv = EqualConv2d(output_channels[-1], output_channels[-1], kernel_size=3, stride=1, padding=1)
self.linear_layers = nn.ModuleList([EqualLinear(output_channels[-1], output_channels[-1]) for _ in range(4)])
self.final_linear = EqualLinear(output_channels[-1], dm)
def forward(self, x):
x = self.activation(self.conv1(x))
for res_block in self.res_blocks:
x = res_block(x)
x = self.equalconv(x)
x = x.view(x.size(0), x.size(1), -1).mean(dim=2)
for linear_layer in self.linear_layers:
x = self.activation(linear_layer(x))
x = self.final_linear(x)
return x
class MotionDecoder(nn.Module):
def __init__(self, latent_dim=32, const_dim=32):
super().__init__()
self.const = nn.Parameter(torch.randn(1, const_dim, 4, 4))
self.style_conv_layers = nn.ModuleList([
StyledConv(const_dim, 512, 3, latent_dim),
StyledConv(512, 512, 3, latent_dim, upsample=True),
StyledConv(512, 512, 3, latent_dim),
StyledConv(512, 512, 3, latent_dim),
StyledConv(512, 512, 3, latent_dim, upsample=True),
StyledConv(512, 512, 3, latent_dim),
StyledConv(512, 512, 3, latent_dim),
StyledConv(512, 256, 3, latent_dim, upsample=True),
StyledConv(256, 256, 3, latent_dim),
StyledConv(256, 256, 3, latent_dim),
StyledConv(256, 128, 3, latent_dim, upsample=True),
StyledConv(128, 128, 3, latent_dim),
StyledConv(128, 128, 3, latent_dim)
])
def forward(self, t):
x = self.const.repeat(t.shape[0], 1, 1, 1)
m1, m2, m3, m4 = None, None, None, None
for i, layer in enumerate(self.style_conv_layers):
x = layer(x, t)
if i == 3:
m1 = x
elif i == 6:
m2 = x
elif i == 9:
m3 = x
elif i == 12:
m4 = x
return m1, m2, m3, m4
class SynthesisNetwork(nn.Module):
def __init__(self, args, feature_dims, spatial_dims):
super().__init__()
self.args = args
feature_dims_rev = feature_dims[::-1]
spatial_dims_rev = spatial_dims[::-1]
self.upconv_blocks = nn.ModuleList([
UpConvResBlock(feature_dims_rev[i], feature_dims_rev[i+1]) for i in range(len(feature_dims_rev) - 1)
])
self.resblocks = nn.ModuleList([
ConvResBlock(feature_dims_rev[i+1]*2, feature_dims_rev[i+1]) for i in range(len(feature_dims_rev) - 1)
])
self.transformer_blocks = nn.ModuleList()
for i in range(len(spatial_dims_rev) - 1):
s_dim = spatial_dims_rev[i+1]
f_dim = feature_dims_rev[i+1]
self.transformer_blocks.append(
SelfAttention(args=args, dim=f_dim, resolution=(s_dim, s_dim))
)
self.final_conv = nn.Sequential(
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(feature_dims_rev[-1], 3*4, kernel_size=3, padding=1),
nn.PixelShuffle(upscale_factor=2),
nn.Sigmoid()
)
def forward(self, features_align):
x = features_align[0]
for i in range(len(self.upconv_blocks)):
x = self.upconv_blocks[i](x)
x = torch.cat([x, features_align[i + 1]], dim=1)
x = self.resblocks[i](x)
x = self.transformer_blocks[i](x)
return self.final_conv(x)
class IdentidyAdaptive(nn.Module):
def __init__(self, dim_mot=32, dim_app=512, depth=4):
super().__init__()
self.in_layer = EqualLinear(dim_app+dim_mot, dim_app)
self.linear_layers = nn.ModuleList([EqualLinear(dim_app, dim_app) for _ in range(depth)])
self.final_linear = EqualLinear(dim_app, dim_mot)
self.activation = nn.LeakyReLU(0.2)
self.scale_activation = nn.Sigmoid()
def forward(self, mot, app):
x = torch.cat((mot, app), dim=-1)
x = self.in_layer(x)
for linear_layer in self.linear_layers:
x = self.activation(linear_layer(x))
out = self.final_linear(x)
return out
class IMTRenderer(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.feature_dims = [32, 64, 128, 256, 512, 512]
self.motion_dims = self.feature_dims
self.spatial_dims = [256, 128, 64, 32, 16, 8]
self.dense_feature_encoder = IdentityEncoder(output_channels=self.feature_dims)
self.latent_token_encoder = MotionEncoder(initial_channels=64, output_channels=[128, 256, 512, 512, 512])
self.latent_token_decoder = MotionDecoder()
self.frame_decoder = SynthesisNetwork(args, self.feature_dims, self.spatial_dims)
self.adapt = IdentidyAdaptive()
self.imt = nn.ModuleList()
for dim, s_dim in zip(self.feature_dims[::-1], self.spatial_dims[::-1]):
self.imt.append(CrossAttention(args=args, dim=dim, resolution=(s_dim, s_dim)))
def decode(self, A, B, C):
num_levels = len(self.spatial_dims)
aligned_features = [None] * num_levels
attention_map = None
for i in range(num_levels):
attention_block = self.imt[i]
if attention_block.is_standard_attention:
aligned_feature, attention_map = attention_block.coarse_stage(A[i], B[i], C[i])
aligned_features[i] = aligned_feature
else:
aligned_feature = attention_block.fine_stage(C[i], attn=attention_map)
aligned_features[i] = aligned_feature
output_frame = self.frame_decoder(aligned_features)
return output_frame
def app_encode(self, x):
f_r, id = self.dense_feature_encoder(x)
return f_r, id
def mot_encode(self, x):
mot_latent = self.latent_token_encoder(x)
return mot_latent
def mot_decode(self, x):
mot_map = self.latent_token_decoder(x)
return mot_map
def id_adapt(self, t, id):
return self.adapt(t, id)
def forward(self, x_current, x_reference):
f_r, i_r = self.app_encode(x_reference)
t_r = self.mot_encode(x_reference)
t_c = self.mot_encode(x_current)
ta_r = self.adapt(t_r, i_r)
ta_c = self.adapt(t_c, i_r)
ma_r = self.mot_decode(ta_r)
ma_c = self.mot_decode(ta_c)
output_frame = self.decode(ma_c, ma_r, f_r)
return output_frame, t_c |