Merge branch 'disentangling_only' into disentangling_only_py3.8disentangling_only_py3.8

1 files changed, 10 insertions, 5 deletions

diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index 1c7a1a2..6be6b0a 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -10,6 +10,7 @@ from models.auto_encoder import AutoEncoder
 class RGBPartNet(nn.Module):
     def __init__(
             self,
+            num_class: int,
             ae_in_channels: int = 3,
             ae_in_size: Tuple[int, int] = (64, 48),
             ae_feature_channels: int = 64,
@@ -22,11 +23,15 @@ class RGBPartNet(nn.Module):
         self.image_log_on = image_log_on
 
         self.ae = AutoEncoder(
-            ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
+            num_class,
+            ae_in_channels,
+            ae_in_size,
+            ae_feature_channels,
+            f_a_c_p_dims
         )
 
-    def forward(self, x_c1, x_c2=None):
-        losses, features, images = self._disentangle(x_c1, x_c2)
+    def forward(self, x_c1, x_c2=None, y=None):
+        losses, features, images = self._disentangle(x_c1, x_c2, y)
 
         if self.training:
             losses = torch.stack(losses)
@@ -34,11 +39,11 @@ class RGBPartNet(nn.Module):
         else:
             return features
 
-    def _disentangle(self, x_c1_t2, x_c2_t2=None):
+    def _disentangle(self, x_c1_t2, x_c2_t2=None, y=None):
         n, t, c, h, w = x_c1_t2.size()
         if self.training:
             x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :]
-            ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
+            ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2, y)
             f_a = f_a_.view(n, t, -1)
             f_c = f_c_.view(n, t, -1)
             f_p = f_p_.view(n, t, -1)