Fix BC and Reward Signals

ml-agents/mlagents/trainers/torch/components/bc/module.py

                 vis_ob = ModelUtils.list_to_tensor(
                     mini_batch_demo["visual_obs%d" % idx]
                 )
-                vis_obs.append(vis_ob)
+                vis_obs.append(ModelUtils.nhwc_to_nchw(vis_ob))
         else:
             vis_obs = []
 

ml-agents/mlagents/trainers/torch/components/reward_providers/curiosity_reward_provider.py

                 ModelUtils.list_to_tensor(mini_batch["vector_obs"], dtype=torch.float)
             ],
             vis_inputs=[
-                ModelUtils.list_to_tensor(
-                    mini_batch["visual_obs%d" % i], dtype=torch.float
+                ModelUtils.nhwc_to_nchw(
+                    ModelUtils.list_to_tensor(
+                        mini_batch["visual_obs%d" % i], dtype=torch.float
+                    )
                 )
                 for i in range(n_vis)
             ],
                 )
             ],
             vis_inputs=[
-                ModelUtils.list_to_tensor(
-                    mini_batch["next_visual_obs%d" % i], dtype=torch.float
+                ModelUtils.nhwc_to_nchw(
+                    ModelUtils.list_to_tensor(
+                        mini_batch["next_visual_obs%d" % i], dtype=torch.float
+                    )
                 )
                 for i in range(n_vis)
             ],

ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py

             else []
         )
         vis_inputs = [
-            ModelUtils.list_to_tensor(mini_batch["visual_obs%d" % i], dtype=torch.float)
+            ModelUtils.nhwc_to_nchw(
+                ModelUtils.list_to_tensor(
+                    mini_batch["visual_obs%d" % i], dtype=torch.float
+                )
+            )
             for i in range(n_vis)
         ]
         return vec_inputs, vis_inputs