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c3ec853 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
def get_crop_config(crop):
"""
Get configuration parameters based on crop size.
Args:
crop: List of [height, width] or None
Returns:
dict: Configuration including alpha, h, w, diffx, diffy
"""
if crop is None:
# Default values for TIP dataset (56x40)
return {
'alpha': 6,
'h': 3,
'w': 2,
'diffx': [1, 0, 0, 0],
'diffy': [1, 0, 0, 0]
}
elif crop == [64, 27]: # PressurePose dataset
return {
'alpha': 4,
'h': 4,
'w': 1,
'diffx': [1, 0, 1, 1],
'diffy': [0, 0, 0, 0]
}
elif crop == [110, 37]: # MOYO dataset
return {
'alpha': 12,
'h': 6,
'w': 2,
'diffx': [0, 1, 0, 1],
'diffy': [1, 1, 1, 0]
}
else: # Default for other sizes
return {
'alpha': 6,
'h': 3,
'w': 2,
'diffx': [1, 0, 0, 0],
'diffy': [1, 0, 0, 0]
}
class DoubleConv(nn.Module):
"""Double Convolutional Block with BN and ReLU"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class ConditionalBatchNorm2d(nn.Module):
"""Conditional BatchNorm2d to modulate output with condition vector"""
def __init__(self, num_features, cond_dim):
super(ConditionalBatchNorm2d, self).__init__()
self.num_features = num_features
self.bn = nn.BatchNorm2d(num_features, affine=False)
self.gamma = nn.Linear(cond_dim, num_features)
self.beta = nn.Linear(cond_dim, num_features)
def forward(self, x, cond):
gamma = self.gamma(cond).view(-1, self.num_features, 1, 1)
beta = self.beta(cond).view(-1, self.num_features, 1, 1)
return self.bn(x) * gamma + beta
class UpWithCondition(nn.Module):
"""Upscaling then double conv with condition modulation"""
def __init__(self, in_channels, out_channels, cond_dim, diffX, diffY, bilinear=True):
super(UpWithCondition, self).__init__()
self.diffX = diffX
self.diffY = diffY
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels // 2, out_channels)
# Conditional batch norm for output modulation
self.cond_bn = ConditionalBatchNorm2d(out_channels, cond_dim)
def forward(self, x, cond):
x = self.up(x)
x = F.pad(x, [self.diffX // 2, self.diffX - self.diffX // 2, self.diffY // 2, self.diffY - self.diffY // 2])
x = self.conv(x)
x = self.cond_bn(x, cond) # Modulate with condition
return x
class UNetEncoder(nn.Module):
def __init__(self, cond_dim=256, embed_dim=256, dp_rate=0.0, bilinear=False, crop=None):
super(UNetEncoder, self).__init__()
self.cond_dim = cond_dim
self.crop = crop
# Get crop-specific configuration
crop_config = get_crop_config(crop)
alpha = crop_config['alpha']
# Encoder
self.inc = DoubleConv(1, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 // factor)
self.dropout = nn.Dropout(dp_rate)
# VAE latent space parameters
self.fc_mu = nn.Linear((1024 // factor) * alpha + cond_dim, embed_dim)
self.fc_log_var = nn.Linear((1024 // factor) * alpha + cond_dim, embed_dim)
def forward(self, x, cond):
# Encoder path
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4) # B x (1024 // factor) x H x W
# Flatten and concatenate with condition vector
x5_flat = x5.view(x5.size(0), -1)
x5_cond = torch.cat([x5_flat, cond], dim=1)
# Compute mu and log_var for latent space
mu = self.fc_mu(x5_cond)
log_var = self.fc_log_var(x5_cond)
return mu, log_var
class UNetDecoder(nn.Module):
def __init__(self, cond_dim=256, embed_dim=256, dp_rate=0.0, bilinear=False, crop=None):
super(UNetDecoder, self).__init__()
self.bilinear = bilinear
self.cond_dim = cond_dim
self.crop = crop
# Get crop-specific configuration
crop_config = get_crop_config(crop)
alpha = crop_config['alpha']
self.h = crop_config['h']
self.w = crop_config['w']
diffx = crop_config['diffx']
diffy = crop_config['diffy']
factor = 2 if bilinear else 1
# Map latent vector and condition back to decoder size
self.fc_z = nn.Linear(embed_dim + cond_dim, (1024 // factor) * alpha)
self.dropout = nn.Dropout(dp_rate)
# Decoder with conditional batch norm
self.up1 = UpWithCondition(1024, 512 // factor, cond_dim, diffX=diffx[0], diffY=diffy[0], bilinear=bilinear)
self.up2 = UpWithCondition(512, 256 // factor, cond_dim, diffX=diffx[1], diffY=diffy[1], bilinear=bilinear)
self.up3 = UpWithCondition(256, 128 // factor, cond_dim, diffX=diffx[2], diffY=diffy[2], bilinear=bilinear)
self.up4 = UpWithCondition(128, 64, cond_dim, diffX=diffx[3], diffY=diffy[3], bilinear=bilinear)
self.outc = OutConv(64, 1)
def forward(self, z, cond):
b, _ = z.shape
factor = 2 if self.bilinear else 1
# Decode latent vector concatenated with condition
z_cond = torch.cat([z, cond], dim=1)
z_decoded = self.fc_z(z_cond).view(b, 1024 // factor, self.h, self.w)
z_decoded = self.dropout(z_decoded)
# Decoder path with conditional modulation
x = self.up1(z_decoded, cond)
x = self.up2(x, cond)
x = self.up3(x, cond)
x = self.up4(x, cond)
x = self.outc(x)
return x.view(b, self.crop[0], self.crop[1])
if __name__ == "__main__":
def reparameterize(mu, logvar):
"""VAE reparameterization trick"""
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
# Test data
data = torch.randn(4, 1, 56, 40)
condition = torch.randn(4, 85)
# Initialize encoder and decoder
encoder = UNetEncoder(cond_dim=85, embed_dim=256, bilinear=False, crop=[56, 40])
decoder = UNetDecoder(cond_dim=85, embed_dim=256, bilinear=False, crop=[56, 40])
# Forward pass through encoder
mu, logvar = encoder(data, condition)
print(f"Mu shape: {mu.shape}, Logvar shape: {logvar.shape}")
# Reparameterization trick
z = reparameterize(mu, logvar)
print(f"Latent z shape: {z.shape}")
# Forward pass through decoder
reconstructed = decoder(z, condition)
print(f"Reconstructed shape: {reconstructed.shape}")
print(f"Expected shape: [4, 56, 40]")
assert reconstructed.shape == (4, 56, 40), f"Shape mismatch: {reconstructed.shape} vs (4, 56, 40)"
print("Test passed!")
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