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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,
frame_size: tuple[int, int] = (64, 48),
feature_channels: int = 64,
output_dims: tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.feature_channels = feature_channels
h_0, w_0 = frame_size
h_1, w_1 = h_0 // 2, w_0 // 2
h_2, w_2 = h_1 // 2, w_1 // 2
self.feature_size = self.h_3, self.w_3 = h_2 // 4, w_2 // 4
# Appearance features, canonical features, pose features
(self.f_a_dim, self.f_c_dim, self.f_p_dim) = output_dims
# Conv1 in_channels x H x W
# -> feature_map_size x H x W
self.conv1 = VGGConv2d(in_channels, feature_channels)
# MaxPool1 feature_map_size x H x W
# -> feature_map_size x H//2 x W//2
self.max_pool1 = nn.AdaptiveMaxPool2d((h_1, w_1))
# Conv2 feature_map_size x H//2 x W//2
# -> feature_map_size*4 x H//2 x W//2
self.conv2 = VGGConv2d(feature_channels, feature_channels * 4)
# MaxPool2 feature_map_size*4 x H//2 x W//2
# -> feature_map_size*4 x H//4 x W//4
self.max_pool2 = nn.AdaptiveMaxPool2d((h_2, w_2))
# Conv3 feature_map_size*4 x H//4 x W//4
# -> feature_map_size*8 x H//4 x W//4
self.conv3 = VGGConv2d(feature_channels * 4, feature_channels * 8)
# Conv4 feature_map_size*8 x H//4 x W//4
# -> feature_map_size*8 x H//4 x W//4 (for large dataset)
self.conv4 = VGGConv2d(feature_channels * 8, feature_channels * 8)
# MaxPool3 feature_map_size*8 x H//4 x W//4
# -> feature_map_size*8 x H//16 x W//16
self.max_pool3 = nn.AdaptiveMaxPool2d(self.feature_size)
embedding_dim = sum(output_dims)
# FC feature_map_size*8 * H//16 * W//16 -> embedding_dim
self.fc = BasicLinear(
(feature_channels * 8) * self.h_3 * self.w_3, 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) * self.h_3 * self.w_3)
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,
feature_size: tuple[int, int] = (4, 3),
out_channels: int = 3,
):
super().__init__()
self.feature_channels = feature_channels
self.h_0, self.w_0 = feature_size
embedding_dim = sum(input_dims)
# FC 320 -> feature_map_size*8 * H * W
self.fc = BasicLinear(
embedding_dim, (feature_channels * 8) * self.h_0 * self.w_0
)
# TransConv1 feature_map_size*8 x H x W
# -> feature_map_size*4 x H*2 x W*2
self.trans_conv1 = DCGANConvTranspose2d(feature_channels * 8,
feature_channels * 4)
# TransConv2 feature_map_size*4 x H*2 x W*2
# -> feature_map_size*2 x H*4 x W*4
self.trans_conv2 = DCGANConvTranspose2d(feature_channels * 4,
feature_channels * 2)
# TransConv3 feature_map_size*2 x H*4 x W*4
# -> feature_map_size x H*8 x W*8
self.trans_conv3 = DCGANConvTranspose2d(feature_channels * 2,
feature_channels)
# TransConv4 feature_map_size x H*8 x W*8
# -> in_channels x H*16 x W*16
self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
is_last_layer=True)
def forward(self, f_appearance, f_canonical, f_pose):
x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
x = self.fc(x)
x = x.view(-1, self.feature_channels * 8, self.h_0, self.w_0)
x = F.leaky_relu(x, 0.2, inplace=True)
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,
channels: int = 3,
frame_size: tuple[int, int] = (64, 48),
feature_channels: int = 64,
embedding_dims: tuple[int, int, int] = (128, 128, 64)
):
super().__init__()
self.encoder = Encoder(channels, frame_size,
feature_channels, embedding_dims)
self.decoder = Decoder(embedding_dims, feature_channels,
self.encoder.feature_size, channels)
self.classifier = BasicLinear(embedding_dims[1], num_class)
def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=None, y=None):
n, t, c, h, w = x_c1_t2.size()
# x_c1_t2 is the frame for later module
x_c1_t2_ = x_c1_t2.view(n * t, c, h, w)
(f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_) = self.encoder(x_c1_t2_)
if self.training:
# t1 is random time step, c2 is another condition
x_c1_t1 = x_c1_t1.view(n * t, c, h, w)
(f_a_c1_t1_, f_c_c1_t1_, _) = self.encoder(x_c1_t1)
x_c2_t2 = x_c2_t2.view(n * t, c, h, w)
(_, f_c_c2_t2_, f_p_c2_t2_) = self.encoder(x_c2_t2)
x_c1_t2_pred_ = self.decoder(f_a_c1_t1_, f_c_c1_t1_, f_p_c1_t2_)
x_c1_t2_pred = x_c1_t2_pred_.view(n, t, c, h, w)
xrecon_loss = torch.stack([
F.mse_loss(x_c1_t2[:, i, :, :, :], x_c1_t2_pred[:, i, :, :, :])
for i in range(t)
]).sum()
f_c_c1_t1 = f_c_c1_t1_.view(n, t, -1)
f_c_c1_t2 = f_c_c1_t2_.view(n, t, -1)
f_c_c2_t2 = f_c_c2_t2_.view(n, t, -1)
cano_cons_loss = torch.stack([
F.mse_loss(f_c_c1_t1[:, i, :], f_c_c1_t2[:, i, :])
+ F.mse_loss(f_c_c1_t2[:, i, :], f_c_c2_t2[:, i, :])
+ F.cross_entropy(self.classifier(
F.leaky_relu(f_c_c1_t2[:, i, :], 0.2)
), y)
for i in range(t)
]).mean()
f_p_c1_t2 = f_p_c1_t2_.view(n, t, -1)
f_p_c2_t2 = f_p_c2_t2_.view(n, t, -1)
pose_sim_loss = F.mse_loss(f_p_c1_t2.mean(1), f_p_c2_t2.mean(1))
return (
(f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_),
(xrecon_loss, cano_cons_loss, pose_sim_loss * 10)
)
else: # evaluating
return f_c_c1_t2_, f_p_c1_t2_
|
