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import torch
import torch.nn as nn
import torch.nn.functional as F
from models.layers import VGGConv2d, DCGANConvTranspose2d
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] = (192, 192, 128)
):
super().__init__()
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
# 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)
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)
f_appearance, f_canonical, f_pose = x.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,
feature_channels: int = 64,
out_channels: int = 3,
):
super().__init__()
self.feature_channels = feature_channels
# TransConv1 feature_map_size*8 x H x W
# -> feature_map_size*4 x H x W
self.trans_conv1 = DCGANConvTranspose2d(feature_channels * 8,
feature_channels * 4,
kernel_size=3,
stride=1,
padding=1)
# TransConv2 feature_map_size*4 x H x W
# -> feature_map_size*2 x H*2 x W*2
self.trans_conv2 = DCGANConvTranspose2d(feature_channels * 4,
feature_channels * 2)
# TransConv3 feature_map_size*2 x H*2 x W*2
# -> feature_map_size x H*2 x W*2
self.trans_conv3 = DCGANConvTranspose2d(feature_channels * 2,
feature_channels,
kernel_size=3,
stride=1,
padding=1)
# TransConv4 feature_map_size x H*2 x W*2
# -> in_channels x H*4 x W*4
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.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,
channels: int = 3,
frame_size: tuple[int, int] = (64, 48),
feature_channels: int = 64,
embedding_dims: tuple[int, int, int] = (192, 192, 128)
):
super().__init__()
self.embedding_dims = embedding_dims
self.encoder = Encoder(channels, frame_size,
feature_channels, embedding_dims)
self.decoder = Decoder(feature_channels, channels)
def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=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_)
f_size = [torch.Size([n, t, embedding_dim, h // 4, w // 4])
for embedding_dim in self.embedding_dims]
f_a_c1_t2 = f_a_c1_t2_.view(f_size[0])
f_c_c1_t2 = f_c_c1_t2_.view(f_size[1])
f_p_c1_t2 = f_p_c1_t2_.view(f_size[2])
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(f_size[1])
f_c_c2_t2 = f_c_c2_t2_.view(f_size[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])
for i in range(t)
]).mean()
f_p_c2_t2 = f_p_c2_t2_.view(f_size[2])
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 / 10, cano_cons_loss, pose_sim_loss * 10)
)
else: # evaluating
return f_a_c1_t2, f_c_c1_t2, f_p_c1_t2
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