Add basic setup.py

interimit/policies/policy.py

@@ -0,0 +1,42 @@
+
+import torch
+from torch import nn
+
+from interimit.nets.deepsets import DeepSetsModule, Phi
+
+class Policy:
+    pass
+
+class DeepSetsPolicy(Policy, nn.Module):
+    def __init__(self, ego_config, dynamic_config, path_config, head_config):
+        """
+        Args:
+            ego_config (dict): dictionary for configuring the ego network
+            dynamic_config (dict): dictionary for configuring the dynamic input (deepsets) network
+            path_config (dict): dictionary for configuring the path network
+            head_config (dict): dictionary for configuring the common head network
+        """
+        super(DeepSetsPolicy, self).__init__()
+        self.ego_net = Phi.from_config(ego_config)
+        self.deepsets = DeepSetsModule.from_config(dynamic_config)
+        self.path_net = Phi.from_config(path_config)
+        cat_dim = self.ego_net.output_dim + self.deepsets.output_dim + self.path_net.output_dim
+        # head has number of concatenated features as input
+        head_config["input_dim"] = cat_dim
+        self.head = Phi.from_config(head_config)
+
+    def forward(self, ego_state, relative_states, path):
+        """
+        Args:
+            ego_state (torch.tensor): (ns,) state of ego vehicle
+            relative_states (torch.tensor): (nv, ns) relative states of other vehicles (dynamic size)
+            path (torch.tensor): (path_length, 2) coordinates (x,y) of path
+        Returns:
+            x (torch.tensor): (head_output_dim,) output of common head network
+        """
+        x_ego = self.ego_net(ego_state)
+        x_relative = self.deepsets(relative_states)
+        x_path = self.path_net(path.flatten())
+        x = torch.cat([x_ego, x_relative, x_path])
+        x = self.head(x)
+        return x