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| author | Jordan Gong <jordan.gong@protonmail.com> | 2021-01-23 22:19:51 +0800 |
|---|---|---|
| committer | Jordan Gong <jordan.gong@protonmail.com> | 2021-01-23 22:19:51 +0800 |
| commit | 507e1d163aaa6ea4be23e7f08ff6ce0ef58c830b (patch) | |
| tree | 1e3c722bb63e3873464296121ec290bd3e64ad14 /models/auto_encoder.py | |
| parent | 59ccf61fed4d95b7fe91bb9552f0deb2f2c75b76 (diff) | |
Remove the third term in canonical consistency loss
Diffstat (limited to 'models/auto_encoder.py')
| -rw-r--r-- | models/auto_encoder.py | 22 |
1 files changed, 8 insertions, 14 deletions
diff --git a/models/auto_encoder.py b/models/auto_encoder.py index 36be868..35cb629 100644 --- a/models/auto_encoder.py +++ b/models/auto_encoder.py @@ -113,7 +113,6 @@ class Decoder(nn.Module): 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) @@ -122,25 +121,23 @@ class AutoEncoder(nn.Module): 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): + def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=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) with torch.no_grad(): # 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), + 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 + torch.zeros_like(f_a_c1_t2), + torch.zeros_like(f_c_c1_t2), + f_p_c1_t2 ) if self.training: @@ -150,11 +147,8 @@ class AutoEncoder(nn.Module): 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)) + + F.mse_loss(f_c_c1_t2, f_c_c2_t2)) return ( (x_c_c1_t2, x_p_c1_t2), |