feat: add NOT_FOUND token
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@ -147,7 +147,7 @@ class Seq2SeqAgent(BaseAgent):
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return Variable(torch.from_numpy(features), requires_grad=False).cuda()
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def _candidate_variable(self, obs):
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candidate_leng = [len(ob['candidate']) + 1 for ob in obs] # +1 is for the end
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candidate_leng = [len(ob['candidate']) + 2 for ob in obs] # +1 is for the end
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candidate_feat = np.zeros((len(obs), max(candidate_leng), self.feature_size + args.angle_feat_size), dtype=np.float32)
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# Note: The candidate_feat at len(ob['candidate']) is the feature for the END
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@ -155,6 +155,7 @@ class Seq2SeqAgent(BaseAgent):
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for i, ob in enumerate(obs):
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for j, cc in enumerate(ob['candidate']):
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candidate_feat[i, j, :] = cc['feature']
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candidate_feat[i, -1, :] = np.ones((self.feature_size + args.angle_feat_size))
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return torch.from_numpy(candidate_feat).cuda(), candidate_leng
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@ -186,7 +187,10 @@ class Seq2SeqAgent(BaseAgent):
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break
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else: # Stop here
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assert ob['teacher'] == ob['viewpoint'] # The teacher action should be "STAY HERE"
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a[i] = len(ob['candidate'])
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if ob['swap']: # instruction 有被換過，所以要 not found
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a[i] = len(ob['candidate'])
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else: # STOP
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a[i] = len(ob['candidate'])-1
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return torch.from_numpy(a).cuda()
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def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None):
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@ -205,7 +209,8 @@ class Seq2SeqAgent(BaseAgent):
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for i, idx in enumerate(perm_idx):
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action = a_t[i]
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if action != -1: # -1 is the <stop> action
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print('action: ', action)
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if action != -1 and action != -2: # -1 is the <stop> action
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select_candidate = perm_obs[i]['candidate'][action]
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src_point = perm_obs[i]['viewIndex']
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trg_point = select_candidate['pointId']
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@ -228,6 +233,11 @@ class Seq2SeqAgent(BaseAgent):
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# print("action: {} view_index: {}".format(action, state.viewIndex))
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if traj is not None:
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traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
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elif action == -1:
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print('<STOP>')
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elif action == -2:
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print('<NOT_FOUND>')
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def rollout(self, train_ml=None, train_rl=True, reset=True):
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"""
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@ -253,7 +263,6 @@ class Seq2SeqAgent(BaseAgent):
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sentence, language_attention_mask, token_type_ids, \
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seq_lengths, perm_idx = self._sort_batch(obs)
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print("perm_index:", perm_idx)
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perm_obs = obs[perm_idx]
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@ -297,7 +306,6 @@ class Seq2SeqAgent(BaseAgent):
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input_a_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
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# the first [CLS] token, initialized by the language BERT, serves
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# as the agent's state passing through time steps
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if (t >= 1) or (args.vlnbert=='prevalent'):
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@ -328,7 +336,17 @@ class Seq2SeqAgent(BaseAgent):
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# Supervised training
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target = self._teacher_action(perm_obs, ended)
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print("target: ", target.shape)
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for i, d in enumerate(target):
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print(perm_obs[i]['swap'], perm_obs[i]['instructions'])
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print(d)
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_, at_t = logit.max(1)
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if at_t[i].item() == candidate_leng[i]-1:
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print("-2")
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elif at_t[i].item() == candidate_leng[i]-2:
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print("-1")
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else:
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print(at_t[i].item())
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print()
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ml_loss += self.criterion(logit, target)
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# Determine next model inputs
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@ -349,12 +367,15 @@ class Seq2SeqAgent(BaseAgent):
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else:
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print(self.feedback)
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sys.exit('Invalid feedback option')
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# Prepare environment action
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# NOTE: Env action is in the perm_obs space
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cpu_a_t = a_t.cpu().numpy()
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for i, next_id in enumerate(cpu_a_t):
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if next_id == (candidate_leng[i]-1) or next_id == args.ignoreid or ended[i]: # The last action is <end>
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if next_id == (candidate_leng[i]-2) or next_id == args.ignoreid or ended[i]: # The last action is <end>
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cpu_a_t[i] = -1 # Change the <end> and ignore action to -1
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elif next_id == (candidate_leng[i]-1):
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cpu_a_t[i] = -2
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# Make action and get the new state
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self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj)
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@ -381,8 +402,22 @@ class Seq2SeqAgent(BaseAgent):
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if action_idx == -1: # If the action now is end
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if dist[i] < 3.0: # Correct
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reward[i] = 2.0 + ndtw_score[i] * 2.0
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if ob['swap']:
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reward[i] -= 2
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else:
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reward[i] += 1
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else: # Incorrect
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reward[i] = -2.0
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elif action_idx == -2: # NOT_FOUND reward 設定在這裏
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if dist[i] < 3.0:
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reward[i] = 2.0 + ndtw_score[i] * 2.0
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if ob['swap']:
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reward[i] += 3 # 偵測到錯誤 instruction,多加一分
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else:
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reward[i] -= 2
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else: # Incorrect
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reward[i] = -2.0
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reward[i] += 1 # distance > 3, 確實沒找到東西，從扣二變成扣一
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else: # The action is not end
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# Path fidelity rewards (distance & nDTW)
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reward[i] = - (dist[i] - last_dist[i])
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@ -404,6 +439,7 @@ class Seq2SeqAgent(BaseAgent):
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# Update the finished actions
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# -1 means ended or ignored (already ended)
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ended[:] = np.logical_or(ended, (cpu_a_t == -1))
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ended[:] = np.logical_or(ended, (cpu_a_t == -2))
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# Early exit if all ended
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if ended.all():
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