Modified pyTorch call to API to make it work with torch 1.3.1

src/doom/actions.py

@@ -88,8 +88,9 @@ def get_action(self, action):
                            for k in self.available_buttons]
             return doom_action
         else:
-            assert type(action) is int
-            return self.doom_actions[action]
+            a = action if type(action) == int else action.item()
+            assert type(a) is int
+            return self.doom_actions[a]
 
 
 action_categories_discrete = {

src/doom/scenarios/deathmatch.py

@@ -174,6 +174,7 @@ def evaluate_deathmatch(game, network, params, n_train_iter=None):
             # observe the game state / select the next action
             game.observe_state(params, last_states)
             action = network.next_action(last_states)
+            action = action.tolist()
             pred_features = network.pred_features
 
             # game features

src/model/dqn/base.py

@@ -78,7 +78,7 @@ def base_forward(self, x_screens, x_variables):
 
         # create state input
         if self.n_variables:
-            output = torch.cat([conv_output] + embeddings, 1)
+            output = torch.cat([conv_output] + embeddings, dim=1)
         else:
             output = conv_output
 
@@ -185,8 +185,8 @@ def prepare_f_train_args(self, screens, variables, features,
         return screens, variables, features, actions, rewards, isfinal
 
     def register_loss(self, loss_history, loss_sc, loss_gf):
-        loss_history['dqn_loss'].append(loss_sc.data[0])
-        loss_history['gf_loss'].append(loss_gf.data[0]
+        loss_history['dqn_loss'].append(loss_sc.data)
+        loss_history['gf_loss'].append(loss_gf.data
                                        if self.n_features else 0)
 
     def next_action(self, last_states, save_graph=False):
@@ -205,7 +205,7 @@ def next_action(self, last_states, save_graph=False):
             if pred_features is not None:
                 assert pred_features.size() == (1, seq_len, self.module.n_features)
                 pred_features = pred_features[0, -1]
-        action_id = scores.data.max(0)[1][0]
+        action_id = scores.data.max(0)[1]
         self.pred_features = pred_features
         return action_id
 

src/model/dqn/feedforward.py

@@ -21,10 +21,15 @@ def forward(self, x_screens, x_variables):
         """
 
         batch_size = x_screens.size(0)
+
+        for x in x_variables:
+            x.unsqueeze_(0)
+
         assert x_screens.ndimension() == 4
         assert len(x_variables) == self.n_variables
-        assert all(x.ndimension() == 1 and x.size(0) == batch_size
-                   for x in x_variables)
+
+        #assert all(x.ndimension() == 0 and len(list(x.size())) == batch_size
+        #            for x in x_variables)
 
         # state input (screen / depth / labels buffer + variables)
         state_input, output_gf = self.base_forward(x_screens, x_variables)
@@ -45,7 +50,6 @@ class DQNFeedforward(DQN):
     def f_eval(self, last_states):
 
         screens, variables = self.prepare_f_eval_args(last_states)
-
         return self.module(
             screens.view(1, -1, *self.screen_shape[1:]),
             [variables[-1, i] for i in range(self.params.n_variables)]