Clean up.

rl_games/common/a2c_common.py

@@ -416,9 +416,6 @@ def get_action_values(self, obs):
             'rnn_states' : self.rnn_states
         }
 
-        # if 'proprio' in obs:
-        #     input_dict['proprio'] = obs['proprio']
-
         with torch.no_grad():
             res_dict = self.model(input_dict)
             if self.has_central_value:
@@ -452,8 +449,6 @@ def get_values(self, obs):
                     'obs' : processed_obs,
                     'rnn_states' : self.rnn_states
                 }
-                # if 'proprio' in obs:
-                #     input_dict['proprio'] = obs['proprio']
 
                 result = self.model(input_dict)
                 value = result['values']
@@ -835,8 +830,6 @@ def play_steps_rnn(self):
 
             self.rnn_states = res_dict['rnn_states']
             self.experience_buffer.update_data('obses', n, self.obs['obs'])
-            # if 'proprio' in self.obs:
-            #     self.experience_buffer.update_data('proprio', n, self.obs['proprio'])
             self.experience_buffer.update_data('dones', n, self.dones.byte())
 
             for k in update_list:
@@ -1030,9 +1023,6 @@ def prepare_dataset(self, batch_dict):
         dataset_dict['rnn_states'] = rnn_states
         dataset_dict['rnn_masks'] = rnn_masks
 
-        # if 'proprio' in batch_dict:
-        #     dataset_dict['proprio'] = batch_dict['proprio']
-
         if self.use_action_masks:
             dataset_dict['action_masks'] = batch_dict['action_masks']
 

rl_games/common/experience.py

@@ -330,11 +330,7 @@ def _init_from_env_info(self, env_info):
         state_base_shape = self.state_base_shape
 
         self.tensor_dict['obses'] = self._create_tensor_from_space(env_info['observation_space'], obs_base_shape)
-        # print("obs base shape", obs_base_shape)
-        # print('obses shape:', self.tensor_dict['obses'].shape)
-        # print('proprioception_space shape:', env_info.get('proprioception_space'))
-        # if env_info.get('proprieception_space') is not None:
-        #     self.tensor_dict['proprio'] = self._create_tensor_from_space(env_info['proprioception_space'], self.obs_base_shape)
+
         if self.has_central_value:
             self.tensor_dict['states'] = self._create_tensor_from_space(env_info['state_space'], state_base_shape)
 

rl_games/networks/vision_networks.py

@@ -1,8 +1,9 @@
 import torch
 from torch import nn
+from torchvision import models
 import torch.nn.functional as F
 import torch_ext
-from rl_games.algos_torch.network_builder import NetworkBuilder
+from rl_games.algos_torch.network_builder import NetworkBuilder, ImpalaSequential
 
 
 class A2CVisionBuilder(NetworkBuilder):
@@ -20,7 +21,8 @@ def __init__(self, params, **kwargs):
             if type(input_shape) is dict:
                 input_shape = input_shape['camera']
                 proprio_shape = input_shape['proprio']
-            self.num_seqs = num_seqs = kwargs.pop('num_seqs', 1)
+
+            self.num_seqs = kwargs.pop('num_seqs', 1)
             self.value_size = kwargs.pop('value_size', 1)
 
             NetworkBuilder.BaseNetwork.__init__(self)
@@ -208,10 +210,6 @@ def build(self, name, **kwargs):
         return net
 
 
-import torch
-import torch.nn as nn
-from torchvision import models
-
 class A2CVisionBackboneBuilder(NetworkBuilder):
     def __init__(self, **kwargs):
         NetworkBuilder.__init__(self)
@@ -227,7 +225,8 @@ def __init__(self, params, **kwargs):
             if isinstance(input_shape, dict):
                 input_shape = input_shape['camera']
                 proprio_shape = input_shape['proprio']
-            self.num_seqs = num_seqs = kwargs.pop('num_seqs', 1)
+
+            self.num_seqs = kwargs.pop('num_seqs', 1)
             self.value_size = kwargs.pop('value_size', 1)
 
             NetworkBuilder.BaseNetwork.__init__(self)