feat: complete adversarial's rollout()
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@ -174,14 +174,14 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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def _nav_vp_variable(self, obs, gmaps, pano_embeds, cand_vpids, view_lens, obj_lens, nav_types):
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batch_size = len(obs)
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print("PANO shape", pano_embeds.shape)
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# print("PANO shape", pano_embeds.shape)
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# add [stop] token & [NOT FOUND] token
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# [STOP] 在最前面, [NOT FOUND] 在最後面
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vp_img_embeds = torch.cat(
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[torch.zeros_like(pano_embeds[:, :1]), pano_embeds, torch.ones_like(pano_embeds[:, :1])], 1
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)
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print("SHAPE:", vp_img_embeds.shape)
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# print("SHAPE:", vp_img_embeds.shape)
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batch_vp_pos_fts = []
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for i, gmap in enumerate(gmaps):
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@ -195,15 +195,15 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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)
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# add [stop] token at beginning
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vp_pos_fts = np.zeros((vp_img_embeds.size(1), 14), dtype=np.float32)
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print("vp_pos_fts:", vp_pos_fts.shape)
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# print("vp_pos_fts:", vp_pos_fts.shape)
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vp_pos_fts[:, :7] = cur_start_pos_fts
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print("vp_pos_fts[:, :7]:", vp_pos_fts[:, :7].shape)
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print("cur_start_pos_fts:", cur_start_pos_fts.shape)
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# print("vp_pos_fts[:, :7]:", vp_pos_fts[:, :7].shape)
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# print("cur_start_pos_fts:", cur_start_pos_fts.shape)
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vp_pos_fts[1:len(cur_cand_pos_fts)+1, 7:] = cur_cand_pos_fts
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print("vp_pos_fts[1:len(), 7:]:", vp_pos_fts[1:len(cur_cand_pos_fts)+1, 7:].shape)
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print("cur_cand_pos_fts:", cur_cand_pos_fts.shape)
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# print("vp_pos_fts[1:len(), 7:]:", vp_pos_fts[1:len(cur_cand_pos_fts)+1, 7:].shape)
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# print("cur_cand_pos_fts:", cur_cand_pos_fts.shape)
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batch_vp_pos_fts.append(torch.from_numpy(vp_pos_fts))
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@ -213,10 +213,10 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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# 這邊把 stop 跟 candidate 放一起、把 not found 跟 object 放一起
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vp_nav_masks = torch.cat([torch.ones(batch_size, 1).bool().cuda(), nav_types == 1, torch.zeros(batch_size, 1).bool().cuda()], 1)
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vp_obj_masks = torch.cat([torch.zeros(batch_size, 1).bool().cuda(), nav_types == 2, torch.ones(batch_size, 1).bool().cuda()], 1)
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print('vp_nav_masks:', vp_nav_masks.shape)
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print('vp_obj_masks:', vp_obj_masks.shape)
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# print('vp_nav_masks:', vp_nav_masks.shape)
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# print('vp_obj_masks:', vp_obj_masks.shape)
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vp_masks = gen_seq_masks(view_lens+obj_lens+2)
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print()
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# print()
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return {
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'vp_img_embeds': vp_img_embeds,
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@ -331,7 +331,9 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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'instr_id': ob['instr_id'],
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'path': [[ob['viewpoint']]],
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'pred_objid': None,
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'gt_objid': None,
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'found': None,
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'gt_found': None,
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'details': {},
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} for ob in obs]
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@ -441,8 +443,6 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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ml_loss += self.criterion(nav_outs['local_logits'], local_nav_targets) # local
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# objec grounding
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obj_targets = self._teacher_object(obs, ended, pano_inputs['view_lens'], obj_logits)
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print("TARGET OBJECT:", obj_targets)
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print('obj logits:', obj_logits)
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# print(t, obj_targets[6], obj_logits[6], obs[6]['obj_ids'], pano_inputs['view_lens'][i], obs[6]['gt_obj_id'])
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og_loss += self.criterion(obj_logits, obj_targets)
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# print(F.cross_entropy(obj_logits, obj_targets, reduction='none'))
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@ -490,9 +490,11 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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else:
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cpu_a_t.append(nav_vpids[i][a_t[i]])
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original_gt_founds = [ ob['gt_found'] for ob in obs ]
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# Make action and get the new state
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self.make_equiv_action(cpu_a_t, gmaps, obs, traj)
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for i in range(batch_size):
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traj[i]['gt_found'] = original_gt_founds[i]
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if (not ended[i]) and just_ended[i]:
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stop_node, stop_score = None, {'stop': -float('inf'), 'og': None}
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for k, v in gmaps[i].node_stop_scores.items():
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@ -502,6 +504,10 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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if stop_node is not None and obs[i]['viewpoint'] != stop_node:
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traj[i]['path'].append(gmaps[i].graph.path(obs[i]['viewpoint'], stop_node))
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traj[i]['pred_objid'] = stop_score['og']
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if stop_score['og'] == -1:
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traj[i]['found'] = False
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else:
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traj[i]['found'] = True
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if self.args.detailed_output:
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for k, v in gmaps[i].node_stop_scores.items():
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traj[i]['details'][k] = {
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@ -531,4 +537,10 @@ class GMapObjectNavAgent(Seq2SeqAgent):
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self.logs['IL_loss'].append(ml_loss.item())
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self.logs['OG_loss'].append(og_loss.item())
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'''
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print("TRAJ:")
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for i in traj:
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print(" GT: {}, PREDICT: {}, SCORE: {}".format(i['gt_found'], i['found'], 1 if i['gt_found']==i['found'] else 0))
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'''
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return traj
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@ -112,7 +112,6 @@ def train(args, train_env, val_envs, aug_env=None, rank=-1):
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)
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# first evaluation
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'''
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if args.eval_first:
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loss_str = "validation before training"
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for env_name, env in val_envs.items():
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@ -136,7 +135,6 @@ def train(args, train_env, val_envs, aug_env=None, rank=-1):
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write_to_record_file(
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'\nListener training starts, start iteration: %s' % str(start_iter), record_file
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)
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'''
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best_val = {'val_unseen': {"spl": 0., "sr": 0., "state":""}}
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