DPI/models.py

@@ -258,40 +258,4 @@ class CLUBSample(nn.Module):  # Sampled version of the CLUB estimator
 
     def forward(self, x_samples, y_samples):
         mu, logvar = self.get_mu_logvar(x_samples)
-        return - self.loglikeli(x_samples, y_samples)
-    
-
-class ProjectionHead(nn.Module):
-    def __init__(self, state_size, action_size, hidden_size):
-        super(ProjectionHead, self).__init__()
-        self.state_size = state_size
-        self.action_size = action_size
-        self.hidden_size = hidden_size
-
-        self.projection_model = nn.Sequential(
-            nn.Linear(state_size + action_size, hidden_size),
-            nn.LayerNorm(hidden_size),
-            nn.ReLU(),
-            nn.Linear(hidden_size, hidden_size),
-            nn.LayerNorm(hidden_size),
-            )
-    
-    def forward(self, state, action):
-        x = torch.cat([state, action], dim=-1)
-        x = self.projection_model(x)
-        return x
-    
-
-class ContrastiveHead(nn.Module):
-    def __init__(self, hidden_size, temperature=1):
-        super(ContrastiveHead, self).__init__()
-        self.hidden_size = hidden_size
-        self.temperature = temperature
-        self.W = nn.Parameter(torch.rand(self.hidden_size, self.hidden_size))
-
-    def forward(self, z_a, z_pos):
-        Wz = torch.matmul(self.W, z_pos.T)  # (z_dim,B)
-        logits = torch.matmul(z_a, Wz)  # (B,B)
-        logits = logits - torch.max(logits, 1)[0][:, None]
-        logits = logits * self.temperature
-        return logits
+        return - self.loglikeli(x_samples, y_samples)

DPI/train.py

@@ -155,17 +155,17 @@ class DPI:
                             obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (12,84,84)
                             state_size=self.args.state_size # 128
                             )
-        
-        self.obs_encoder_momentum = ObservationEncoder(
-                            obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (12,84,84)
-                            state_size=self.args.state_size # 128
-                            )
 
         self.obs_decoder = ObservationDecoder(
                             state_size=self.args.state_size, # 128
                             output_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size) # (12,84,84)
                             )
 
+        self.obs_encoder_momentum = ObservationEncoder(
+                            obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (12,84,84)
+                            state_size=self.args.state_size # 128
+                            )
+        
         self.transition_model = TransitionModel(
                             state_size=self.args.state_size, # 128
                             hidden_size=self.args.hidden_size, # 256