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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.layers import VGGConv2d, DCGANConvTranspose2d, BasicLinear
class Encoder(nn.Module):
"""Squeeze input feature map to lower dimension"""
def __init__(
self,
in_channels: int = 3,
feature_channels: int = 64,
output_dims: tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.feature_channels = feature_channels
# Appearance features, canonical features, pose features
(self.f_a_dim, self.f_c_dim, self.f_p_dim) = output_dims
# Conv1 in_channels x 64 x 32
# -> feature_map_size x 64 x 32
self.conv1 = VGGConv2d(in_channels, feature_channels)
# MaxPool1 feature_map_size x 64 x 32
# -> feature_map_size x 32 x 16
self.max_pool1 = nn.AdaptiveMaxPool2d((32, 16))
# Conv2 feature_map_size x 32 x 16
# -> (feature_map_size*4) x 32 x 16
self.conv2 = VGGConv2d(feature_channels, feature_channels * 4)
# MaxPool2 (feature_map_size*4) x 32 x 16
# -> (feature_map_size*4) x 16 x 8
self.max_pool2 = nn.AdaptiveMaxPool2d((16, 8))
# Conv3 (feature_map_size*4) x 16 x 8
# -> (feature_map_size*8) x 16 x 8
self.conv3 = VGGConv2d(feature_channels * 4, feature_channels * 8)
# Conv4 (feature_map_size*8) x 16 x 8
# -> (feature_map_size*8) x 16 x 8 (for large dataset)
self.conv4 = VGGConv2d(feature_channels * 8, feature_channels * 8)
# MaxPool3 (feature_map_size*8) x 16 x 8
# -> (feature_map_size*8) x 4 x 2
self.max_pool3 = nn.AdaptiveMaxPool2d((4, 2))
embedding_dim = sum(output_dims)
# FC (feature_map_size*8) * 4 * 2 -> 320
self.fc = BasicLinear(feature_channels * 8 * 2 * 4, embedding_dim)
def forward(self, x):
x = self.conv1(x)
x = self.max_pool1(x)
x = self.conv2(x)
x = self.max_pool2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.max_pool3(x)
x = x.view(-1, (self.feature_channels * 8) * 2 * 4)
embedding = self.fc(x)
f_appearance, f_canonical, f_pose = embedding.split(
(self.f_a_dim, self.f_c_dim, self.f_p_dim), dim=1
)
return f_appearance, f_canonical, f_pose
class Decoder(nn.Module):
"""Upscale embedding to original image"""
def __init__(
self,
input_dims: tuple[int, int, int] = (128, 128, 64),
feature_channels: int = 64,
out_channels: int = 3,
):
super().__init__()
self.feature_channels = feature_channels
embedding_dim = sum(input_dims)
# FC 320 -> (feature_map_size*8) * 4 * 2
self.fc = BasicLinear(embedding_dim, feature_channels * 8 * 2 * 4)
# TransConv1 (feature_map_size*8) x 4 x 2
# -> (feature_map_size*4) x 8 x 4
self.trans_conv1 = DCGANConvTranspose2d(feature_channels * 8,
feature_channels * 4)
# TransConv2 (feature_map_size*4) x 8 x 4
# -> (feature_map_size*2) x 16 x 8
self.trans_conv2 = DCGANConvTranspose2d(feature_channels * 4,
feature_channels * 2)
# TransConv3 (feature_map_size*2) x 16 x 8
# -> feature_map_size x 32 x 16
self.trans_conv3 = DCGANConvTranspose2d(feature_channels * 2,
feature_channels)
# TransConv4 feature_map_size x 32 x 16
# -> in_channels x 64 x 32
self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
is_last_layer=True)
def forward(self, f_appearance, f_canonical, f_pose, no_trans_conv=False):
x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
x = self.fc(x)
x = F.relu(x.view(-1, self.feature_channels * 8, 4, 2), inplace=True)
# Decode canonical features without transpose convolutions
if no_trans_conv:
return x
x = self.trans_conv1(x)
x = self.trans_conv2(x)
x = self.trans_conv3(x)
x = torch.sigmoid(self.trans_conv4(x))
return x
class AutoEncoder(nn.Module):
def __init__(
self,
num_class: int = 74,
channels: int = 3,
feature_channels: int = 64,
embedding_dims: tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.encoder = Encoder(channels, feature_channels, embedding_dims)
self.decoder = Decoder(embedding_dims, feature_channels, channels)
f_c_dim = embedding_dims[1]
self.classifier = nn.Sequential(
nn.LeakyReLU(0.2, inplace=True),
BasicLinear(f_c_dim, num_class)
)
def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=None, y=None):
# x_c1_t2 is the frame for later module
(f_a_c1_t2, f_c_c1_t2, f_p_c1_t2) = self.encoder(x_c1_t2)
# Decode canonical features for HPM
x_c_c1_t2 = self.decoder(
torch.zeros_like(f_a_c1_t2), f_c_c1_t2, torch.zeros_like(f_p_c1_t2),
no_trans_conv=True
)
# Decode pose features for Part Net
x_p_c1_t2 = self.decoder(
torch.zeros_like(f_a_c1_t2), torch.zeros_like(f_c_c1_t2), f_p_c1_t2
)
if self.training:
# t1 is random time step, c2 is another condition
(f_a_c1_t1, f_c_c1_t1, _) = self.encoder(x_c1_t1)
(_, f_c_c2_t2, f_p_c2_t2) = self.encoder(x_c2_t2)
x_c1_t2_ = self.decoder(f_a_c1_t1, f_c_c1_t1, f_p_c1_t2)
xrecon_loss_t2 = F.mse_loss(x_c1_t2, x_c1_t2_)
y_ = self.classifier(f_c_c1_t2.contiguous())
cano_cons_loss_t2 = (F.mse_loss(f_c_c1_t1, f_c_c1_t2)
+ F.mse_loss(f_c_c1_t2, f_c_c2_t2)
+ F.cross_entropy(y_, y))
return (
(x_c_c1_t2, x_p_c1_t2),
(f_p_c1_t2, f_p_c2_t2),
(xrecon_loss_t2, cano_cons_loss_t2)
)
else: # evaluating
return x_c_c1_t2, x_p_c1_t2
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