Extend reward classifier for multiple camera views (#626)

lerobot/common/policies/hilserl/classifier/configuration_classifier.py

@@ -13,6 +13,7 @@ class ClassifierConfig:
     model_name: str = "microsoft/resnet-50"
     device: str = "cpu"
     model_type: str = "cnn"  # "transformer" or "cnn"
+    num_cameras: int = 2
 
     def save_pretrained(self, save_dir):
         """Save config to json file."""

lerobot/common/policies/hilserl/classifier/modeling_classifier.py

@@ -97,7 +97,7 @@ class Classifier(
                 raise ValueError("Unsupported transformer architecture since hidden_size is not found")
 
         self.classifier_head = nn.Sequential(
-            nn.Linear(input_dim, self.config.hidden_dim),
+            nn.Linear(input_dim * self.config.num_cameras, self.config.hidden_dim),
             nn.Dropout(self.config.dropout_rate),
             nn.LayerNorm(self.config.hidden_dim),
             nn.ReLU(),
@@ -130,11 +130,11 @@ class Classifier(
                     return outputs.pooler_output
                 return outputs.last_hidden_state[:, 0, :]
 
-    def forward(self, x: torch.Tensor) -> ClassifierOutput:
+    def forward(self, xs: torch.Tensor) -> ClassifierOutput:
         """Forward pass of the classifier."""
         # For training, we expect input to be a tensor directly from LeRobotDataset
-        encoder_output = self._get_encoder_output(x)
-        logits = self.classifier_head(encoder_output)
+        encoder_outputs = torch.hstack([self._get_encoder_output(x) for x in xs])
+        logits = self.classifier_head(encoder_outputs)
 
         if self.config.num_classes == 2:
             logits = logits.squeeze(-1)
@@ -142,4 +142,10 @@ class Classifier(
         else:
             probabilities = torch.softmax(logits, dim=-1)
 
-        return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_output)
+        return ClassifierOutput(logits=logits, probabilities=probabilities, hidden_states=encoder_outputs)
+
+    def predict_reward(self, x):
+        if self.config.num_classes == 2:
+            return (self.forward(x).probabilities > 0.5).float()
+        else:
+            return torch.argmax(self.forward(x).probabilities, dim=1)