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

3 files changed, 36 insertions, 12 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index b1d51ef..61622eb 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -112,7 +112,7 @@ class Decoder(nn.Module):
         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 = F.leaky_relu(x, 0.2, inplace=True)
         x = self.trans_conv1(x)
         x = self.trans_conv2(x)
         x = self.trans_conv3(x)
@@ -124,6 +124,7 @@ class Decoder(nn.Module):
 class AutoEncoder(nn.Module):
     def __init__(
             self,
+            num_class: int,
             channels: int = 3,
             frame_size: Tuple[int, int] = (64, 48),
             feature_channels: int = 64,
@@ -134,8 +135,9 @@ class AutoEncoder(nn.Module):
                                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):
+    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)
@@ -162,6 +164,9 @@ class AutoEncoder(nn.Module):
             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()
 
diff --git a/models/model.py b/models/model.py
index 46987ca..ebaaaf1 100644
--- a/models/model.py
+++ b/models/model.py
@@ -54,6 +54,7 @@ class Model:
         self.total_iters = self.meta.get('total_iters', (self.total_iter,))
 
         self.is_train: bool = True
+        self.num_class: Optional[int] = None
         self.in_channels: int = 3
         self.in_size: Tuple[int, int] = (64, 48)
         self.batch_size: Optional[int] = None
@@ -160,8 +161,13 @@ class Model:
         optim_hp: Dict = self.hp.get('optimizer', {}).copy()
         sched_hp = self.hp.get('scheduler', {})
 
-        self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp,
-                                 image_log_on=self.image_log_on)
+        self.rgb_pn = RGBPartNet(
+            self.num_class,
+            self.in_channels,
+            self.in_size,
+            **model_hp,
+            image_log_on=self.image_log_on
+        )
 
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = self.rgb_pn.to(self.device)
@@ -202,7 +208,8 @@ class Model:
             # forward + backward + optimize
             x_c1 = batch_c1['clip'].to(self.device)
             x_c2 = batch_c2['clip'].to(self.device)
-            losses, features, images = self.rgb_pn(x_c1, x_c2)
+            y = batch_c1['label'].to(self.device)
+            losses, features, images = self.rgb_pn(x_c1, x_c2, y)
             loss = losses.sum()
             loss.backward()
             self.optimizer.step()
@@ -254,8 +261,9 @@ class Model:
                 batch_c1, batch_c2 = next(val_dataloader)
                 x_c1 = batch_c1['clip'].to(self.device)
                 x_c2 = batch_c2['clip'].to(self.device)
+                y = batch_c1['label'].to(self.device)
                 with torch.no_grad():
-                    losses, _, _ = self.rgb_pn(x_c1, x_c2)
+                    losses, _, _ = self.rgb_pn(x_c1, x_c2, y)
                 loss = losses.sum()
 
                 self._write_stat('Val', loss, losses)
@@ -302,7 +310,12 @@ class Model:
 
         # Init models
         model_hp: dict = self.hp.get('model', {}).copy()
-        self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp)
+        self.rgb_pn = RGBPartNet(
+            self.num_class,
+            self.in_channels,
+            self.in_size,
+            **model_hp
+        )
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = self.rgb_pn.to(self.device)
         self.rgb_pn.eval()
@@ -419,6 +432,7 @@ class Model:
             self,
             dataset_config: DatasetConfiguration
     ) -> Union[CASIAB]:
+        self.num_class = dataset_config.get('train_size', 74)
         self.in_channels = dataset_config.get('num_input_channels', 3)
         self.in_size = dataset_config.get('frame_size', (64, 48))
         self._dataset_sig = self._make_signature(
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)