from typing import Tuple 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.relu(x, 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, 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) 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_) 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, :]) 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_