training/train_eqa.py [160:201]:
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                                        1, 1), planner_hidden)

                        if controller_step == True:

                            controller_img_feat_var = Variable(
                                controller_img_feat.cuda())
                            controller_action_in_var = Variable(
                                torch.LongTensor(1, 1).fill_(
                                    int(controller_action_in)).cuda())

                            controller_scores = nav_model.controller_step(
                                controller_img_feat_var.view(1, 1, 3200),
                                controller_action_in_var.view(1, 1),
                                planner_hidden[0])

                            prob = F.softmax(controller_scores, dim=1)
                            controller_action = int(
                                prob.max(1)[1].data.cpu().numpy()[0])

                            if controller_action == 1:
                                controller_step = True
                            else:
                                controller_step = False

                            action = int(controller_action_in)
                            action_in = torch.LongTensor(
                                1, 1).fill_(action + 1).cuda()

                        else:

                            prob = F.softmax(planner_scores, dim=1)
                            action = int(prob.max(1)[1].data.cpu().numpy()[0])

                            action_in = torch.LongTensor(
                                1, 1).fill_(action + 1).cuda()

                        h3d.env.reset(
                            x=init_pos[0], y=init_pos[2], yaw=init_pos[3])

                        init_dist_to_target = h3d.get_dist_to_target(
                            h3d.env.cam.pos)
                        if init_dist_to_target < 0:  # unreachable
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training/train_eqa.py [530:571]:
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                                    1, 1), planner_hidden)

                    if controller_step == True:

                        controller_img_feat_var = Variable(
                            controller_img_feat.cuda())
                        controller_action_in_var = Variable(
                            torch.LongTensor(1, 1).fill_(
                                int(controller_action_in)).cuda())

                        controller_scores = nav_model.controller_step(
                            controller_img_feat_var.view(1, 1, 3200),
                            controller_action_in_var.view(1, 1),
                            planner_hidden[0])

                        prob = F.softmax(controller_scores, dim=1)
                        controller_action = int(
                            prob.max(1)[1].data.cpu().numpy()[0])

                        if controller_action == 1:
                            controller_step = True
                        else:
                            controller_step = False

                        action = int(controller_action_in)
                        action_in = torch.LongTensor(
                            1, 1).fill_(action + 1).cuda()

                    else:

                        prob = F.softmax(planner_scores, dim=1)
                        action = int(prob.max(1)[1].data.cpu().numpy()[0])

                        action_in = torch.LongTensor(
                            1, 1).fill_(action + 1).cuda()

                    h3d.env.reset(
                        x=init_pos[0], y=init_pos[2], yaw=init_pos[3])

                    init_dist_to_target = h3d.get_dist_to_target(
                        h3d.env.cam.pos)
                    if init_dist_to_target < 0:  # unreachable
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