def validation_step(self, batch, batch_idx):
start, end, q_type, paras, sents, ents, yp1, yp2 = self.forward(batch=batch)
loss_list = compute_loss(self.hparams, batch, start, end, paras, sents, ents, q_type)
loss, loss_span, loss_type, loss_sup, loss_ent, loss_para = loss_list
dict_for_log = {'span_loss': loss_span, 'type_loss': loss_type,
'sent_loss': loss_sup, 'ent_loss': loss_ent,
'para_loss': loss_para,
'step': batch_idx + 1}
#######################################################################
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(self.dev_example_dict,
self.dev_feature_dict,
batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
predict_support_np = torch.sigmoid(sents[:, :, 1]).data.cpu().numpy()
valid_dict = {'answer': answer_dict_, 'ans_type': answer_type_dict_, 'ids': batch['ids'],
'ans_type_pro': answer_type_prob_dict_, 'supp_np': predict_support_np}
#######################################################################
output = {'valid_loss': loss, 'log': dict_for_log, 'valid_dict_output': valid_dict}
# output = {'valid_dict_output': valid_dict}
return output
def jd_train_eval_model(args,
encoder,
model,
dataloader,
example_dict,
feature_dict,
prediction_file,
eval_file,
train_gold_file,
train_type,
output_score_file=None):
encoder.eval()
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
##++++++
prediction_res_score_dict = {}
##++++++
# dataloader.refresh()
#++++++
cut_sentence_count = 0
#++++++
thresholds = np.arange(0.1, 1.0, 0.05)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
for batch in tqdm(dataloader):
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for key, value in batch.items():
if key not in {'ids'}:
batch[key] = value.to(args.device)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with torch.no_grad():
inputs = {
'input_ids':
batch['context_idxs'],
'attention_mask':
batch['context_mask'],
'token_type_ids':
batch['segment_idxs']
if args.model_type in ['bert', 'xlnet', 'electra'] else None
} # XLM don't use segment_ids
outputs = encoder(**inputs)
####++++++++++++++++++++++++++++++++++++++
if args.model_type == 'electra':
batch['context_encoding'] = outputs.last_hidden_state
else:
batch['context_encoding'] = outputs[0]
####++++++++++++++++++++++++++++++++++++++
batch['context_mask'] = batch['context_mask'].float().to(
args.device)
start, end, q_type, paras, sent, ent, yp1, yp2, cls_emb = model(
batch, return_yp=True, return_cls=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(
example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# print('ent_prediction', ent.shape)
# print('ent_mask', batch['ans_cand_mask'])
# print('gold_ent', batch['is_gold_ent'])
ent_pre_prob = torch.sigmoid(ent).data.cpu().numpy()
ent_mask_np = batch['ent_mask'].data.cpu().numpy()
ans_cand_mask_np = batch['ans_cand_mask'].data.cpu().numpy()
is_gold_ent_np = batch['is_gold_ent'].data.cpu().numpy()
_, _, answer_sent_name_dict_ = convert_answer_to_sent_names(
example_dict, feature_dict, batch,
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob, ent_pre_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
####################################################################
support_sent_mask_np = batch['sent_mask'].data.cpu().numpy()
predict_support_para_np = torch.sigmoid(paras[:, :,
1]).data.cpu().numpy()
support_para_mask_np = batch['para_mask'].data.cpu().numpy()
cls_emb_np = cls_emb.data.cpu().numpy()
####################################################################
predict_support_logit_np = sent[:, :, 1].data.cpu().numpy()
predict_support_para_logit_np = paras[:, :, 1].data.cpu().numpy()
ent_pre_logit_np = ent.data.cpu().numpy()
####################################################################
for i in range(predict_support_np.shape[0]):
cur_sp_pred = [[] for _ in range(N_thresh)]
cur_id = batch['ids'][i]
##+++++++++++++++++++++++++
orig_supp_fact_id = example_dict[cur_id].sup_fact_id
prune_supp_fact_id = feature_dict[cur_id].sup_fact_ids
# print('origi supp fact id {}'.format(orig_supp_fact_id))
# print('prune supp fact id {}'.format(prune_supp_fact_id))
##+++++++++++++++++++++++++
topk_score_ref, cut_sent_flag, topk_pred_sent_names, diff_para_sent_names, topk_pred_paras = \
post_process_sent_para(cur_id=cur_id, example_dict=example_dict, feature_dict=feature_dict,
sent_scores_np_i=predict_support_np[i], sent_mask_np_i=support_sent_mask_np[i],
para_scores_np_i=predict_support_para_np[i], para_mask_np_i=support_para_mask_np[i])
ans_sent_name = answer_sent_name_dict_[cur_id]
if cut_sent_flag:
cut_sentence_count += 1
##+++++++++++++++++++++++++
# sent_pred_ = {'sp_score': predict_support_np[i].tolist(), 'sp_mask': support_sent_mask_np[i].tolist(), 'sp_names': example_dict[cur_id].sent_names}
# para_pred_ = {'para_score': predict_support_para_np[i].tolist(), 'para_mask': support_para_mask_np[i].tolist(), 'para_names': example_dict[cur_id].para_names}
# ans_pred_ = {'ans_type': type_prob[i].tolist(), 'ent_score': ent_pre_prob[i].tolist(), 'ent_mask': ent_mask_np[i].tolist(),
# 'query_entity': example_dict[cur_id].ques_entities_text, 'ctx_entity': example_dict[cur_id].ctx_entities_text,
# 'ans_ent_mask': ans_cand_mask_np[i].tolist(), 'is_gold_ent': is_gold_ent_np[i].tolist(), 'answer': answer_dict[cur_id]}
sent_pred_ = {
'sp_score': predict_support_logit_np[i].tolist(),
'sp_mask': support_sent_mask_np[i].tolist(),
'sp_names': example_dict[cur_id].sent_names,
'sup_fact_id': orig_supp_fact_id,
'trim_sup_fact_id': prune_supp_fact_id
}
para_pred_ = {
'para_score': predict_support_para_logit_np[i].tolist(),
'para_mask': support_para_mask_np[i].tolist(),
'para_names': example_dict[cur_id].para_names
}
ans_pred_ = {
'ans_type': type_prob[i].tolist(),
'ent_score': ent_pre_logit_np[i].tolist(),
'ent_mask': ent_mask_np[i].tolist(),
'query_entity': example_dict[cur_id].ques_entities_text,
'ctx_entity': example_dict[cur_id].ctx_entities_text,
'ans_ent_mask': ans_cand_mask_np[i].tolist(),
'is_gold_ent': is_gold_ent_np[i].tolist(),
'answer': answer_dict[cur_id]
}
cls_emb_ = {'cls_emb': cls_emb_np[i].tolist()}
res_pred = {**sent_pred_, **para_pred_, **ans_pred_, **cls_emb_}
prediction_res_score_dict[cur_id] = res_pred
##+++++++++++++++++++++++++
for j in range(predict_support_np.shape[1]):
if j >= len(example_dict[cur_id].sent_names):
break
for thresh_i in range(N_thresh):
# if predict_support_np[i, j] > thresholds[thresh_i]:
if predict_support_np[
i, j] > thresholds[thresh_i] * topk_score_ref:
cur_sp_pred[thresh_i].append(
example_dict[cur_id].sent_names[j])
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
##+++++
# +++++++++++++++++++++++++++
post_process_thresh_i_sp_pred = post_process_technique(
cur_sp_pred=cur_sp_pred[thresh_i],
topk_pred_paras=topk_pred_paras,
topk_pred_sent_names=topk_pred_sent_names,
diff_para_sent_names=diff_para_sent_names,
ans_sent_name=ans_sent_name)
total_sp_dict[thresh_i][cur_id].extend(
post_process_thresh_i_sp_pred)
# # +++++++++++++++++++++++++++
# total_sp_dict[thresh_i][cur_id].extend(cur_sp_pred[thresh_i])
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
for thresh_i in range(N_thresh):
prediction = {
'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
tmp_file = os.path.join(os.path.dirname(pred_file),
'tmp_train.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = train_eval(tmp_file, train_gold_file, train_type)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
best_metrics = metrics
shutil.move(tmp_file, pred_file)
return best_metrics, best_threshold
best_metrics, best_threshold = choose_best_threshold(
answer_dict, prediction_file)
json.dump(best_metrics, open(eval_file, 'w'))
if output_score_file is not None:
with open(output_score_file, 'w') as f:
json.dump(prediction_res_score_dict, f)
#####+++++++++++
with open(train_gold_file) as f:
gold = json.load(f)
for row in gold:
key = row['_id']
print('suppo = {}'.format(row['supporting_facts']))
if key in prediction_res_score_dict:
score_case = prediction_res_score_dict[key]
sp_names = score_case['sp_names']
sup_fact_id = score_case['sup_fact_id']
trim_sup_fact_id = score_case['trim_sup_fact_id']
print('orig', [sp_names[_] for _ in sup_fact_id])
print('trim', [sp_names[_] for _ in trim_sup_fact_id])
#####+++++++++++
print('Number of examples with cutted sentences = {}'.format(
cut_sentence_count))
return best_metrics, best_threshold
def jd_unified_post_feature_collection_model(args,
model,
dataloader,
example_dict,
feature_dict,
prediction_file,
eval_file,
dev_gold_file=None,
output_score_file=None):
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
##++++++
prediction_res_score_dict = {}
##++++++
thresholds = np.arange(0.1, 1.0, 0.05)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
for batch in tqdm(dataloader):
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for key, value in batch.items():
if key not in {'ids'}:
batch[key] = value.to(args.device)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with torch.no_grad():
start, end, q_type, paras, sent, ent, yp1, yp2, cls_emb = model(
batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(
example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob)
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
predict_support_logit_np = sent[:, :, 1].data.cpu().numpy()
support_sent_mask_np = batch['sent_mask'].data.cpu().numpy()
cls_emb_np = cls_emb.data.cpu().numpy()
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for i in range(predict_support_np.shape[0]):
cur_sp_pred = [[] for _ in range(N_thresh)]
cur_id = batch['ids'][i]
# print(cur_id)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
orig_supp_fact_id = example_dict[cur_id].sup_fact_id
prune_supp_fact_id = feature_dict[cur_id].sup_fact_ids
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
sent_pred_ = {
'sp_score': predict_support_logit_np[i].tolist(),
'sp_mask': support_sent_mask_np[i].tolist(),
'sp_names': example_dict[cur_id].sent_names,
'sup_fact_id': orig_supp_fact_id,
'trim_sup_fact_id': prune_supp_fact_id
}
cls_emb_ = {'cls_emb': cls_emb_np[i].tolist()}
res_score = {**sent_pred_, **cls_emb_}
prediction_res_score_dict[cur_id] = res_score
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for j in range(predict_support_np.shape[1]):
if j >= len(example_dict[cur_id].sent_names):
break
for thresh_i in range(N_thresh):
if predict_support_np[i, j] > thresholds[thresh_i]:
cur_sp_pred[thresh_i].append(
example_dict[cur_id].sent_names[j])
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
total_sp_dict[thresh_i][cur_id].extend(cur_sp_pred[thresh_i])
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
for thresh_i in range(N_thresh):
prediction = {
'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
tmp_file = os.path.join(os.path.dirname(pred_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = hotpot_eval(tmp_file, dev_gold_file)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
best_metrics = metrics
shutil.move(tmp_file, pred_file)
return best_metrics, best_threshold
best_metrics, best_threshold = choose_best_threshold(
answer_dict, prediction_file)
json.dump(best_metrics, open(eval_file, 'w'))
if output_score_file is not None:
with open(output_score_file, 'w') as f:
json.dump(prediction_res_score_dict, f)
print('Saving {} score records into {}'.format(
len(prediction_res_score_dict), output_score_file))
return best_metrics, best_threshold
def jd_eval_model(args, encoder, model, dataloader, example_dict, feature_dict,
prediction_file, eval_file, dev_gold_file):
encoder.eval()
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
dataloader.refresh()
thresholds = np.arange(0.1, 1.0, 0.025)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
##++++++++++++++++++++++++++++++++++
total_para_sp_dict = {}
##++++++++++++++++++++++++++++++++++
best_sp_dict = {}
threshold_inter_count = 0
##++++++++++++++++++++++++++++++++++
for batch in tqdm(dataloader):
with torch.no_grad():
inputs = {
'input_ids':
batch['context_idxs'],
'attention_mask':
batch['context_mask'],
'token_type_ids':
batch['segment_idxs']
if args.model_type in ['bert', 'xlnet', 'electra'] else None
} # XLM don't use segment_ids
outputs = encoder(**inputs)
batch['context_encoding'] = outputs[0]
batch['context_mask'] = batch['context_mask'].float().to(
args.device)
start, end, q_type, paras, sent, ent, yp1, yp2 = model(
batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(
example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob)
para_mask = batch['para_mask']
sent_mask = batch['sent_mask']
# print(para_mask.shape, paras.shape)
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
##++++++++++++++++++++++++++++++++++++++++
paras = paras[:, :, 1] - (1 - para_mask) * 1e30
predict_para_support_np = torch.sigmoid(paras).data.cpu().numpy()
# predict_para_support_np = torch.sigmoid(paras[:, :, 1]).data.cpu().numpy()
##++++++++++++++++++++++++++++++++++++++++
# print('sent shape {}'.format(sent.shape))
sent = sent[:, :, 1] - (1 - sent_mask) * 1e30
# predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
predict_support_np = torch.sigmoid(sent).data.cpu().numpy()
# print('supp sent np shape {}'.format(predict_support_np.shape))
for i in range(predict_support_np.shape[0]):
cur_id = batch['ids'][i]
predict_para_support_np_ith = predict_para_support_np[i]
predict_support_np_ith = predict_support_np[i]
# ################################################
cur_para_sp_pred = supp_doc_prediction(
predict_para_support_np_ith=predict_para_support_np_ith,
example_dict=example_dict,
batch_ids_ith=cur_id)
total_para_sp_dict[cur_id] = cur_para_sp_pred
# ################################################
cur_sp_pred = supp_sent_prediction(
predict_support_np_ith=predict_support_np_ith,
example_dict=example_dict,
batch_ids_ith=cur_id,
thresholds=thresholds)
# ###################################
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
total_sp_dict[thresh_i][cur_id].extend(cur_sp_pred[thresh_i])
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
#####
best_threshold_idx = -1
#####
for thresh_i in range(N_thresh):
prediction = {
'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
tmp_file = os.path.join(os.path.dirname(pred_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = hotpot_eval(tmp_file, dev_gold_file)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
#####
best_threshold_idx = thresh_i
#####
best_metrics = metrics
shutil.move(tmp_file, pred_file)
return best_metrics, best_threshold, best_threshold_idx
best_metrics, best_threshold, best_threshold_idx = choose_best_threshold(
answer_dict, prediction_file)
##############++++++++++++
doc_recall_metric = doc_recall_eval(doc_prediction=total_para_sp_dict,
gold_file=dev_gold_file)
##############++++++++++++
json.dump(best_metrics, open(eval_file, 'w'))
# -------------------------------------
best_prediction = {
'answer': answer_dict,
'sp': best_sp_dict,
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
print('Number of inter threshold = {}'.format(threshold_inter_count))
best_tmp_file = os.path.join(os.path.dirname(prediction_file),
'best_tmp.json')
with open(best_tmp_file, 'w') as f:
json.dump(best_prediction, f)
best_th_metrics = hotpot_eval(best_tmp_file, dev_gold_file)
for key, val in best_th_metrics.items():
print("{} = {}".format(key, val))
# -------------------------------------
return best_metrics, best_threshold, doc_recall_metric
def jd_unified_eval_model(args,
model,
dataloader,
example_dict,
feature_dict,
prediction_file,
eval_file,
dev_gold_file=None):
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
# dataloader.refresh()
thresholds = np.arange(0.1, 1.0, 0.05)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
for batch in tqdm(dataloader):
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for key, value in batch.items():
if key not in {'ids'}:
batch[key] = value.to(args.device)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with torch.no_grad():
start, end, q_type, paras, sent, ent, yp1, yp2 = model(
batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(
example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob)
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
for i in range(predict_support_np.shape[0]):
cur_sp_pred = [[] for _ in range(N_thresh)]
cur_id = batch['ids'][i]
for j in range(predict_support_np.shape[1]):
if j >= len(example_dict[cur_id].sent_names):
break
for thresh_i in range(N_thresh):
if predict_support_np[i, j] > thresholds[thresh_i]:
cur_sp_pred[thresh_i].append(
example_dict[cur_id].sent_names[j])
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
total_sp_dict[thresh_i][cur_id].extend(cur_sp_pred[thresh_i])
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
for thresh_i in range(N_thresh):
prediction = {
'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
tmp_file = os.path.join(os.path.dirname(pred_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = hotpot_eval(tmp_file, dev_gold_file)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
best_metrics = metrics
shutil.move(tmp_file, pred_file)
return best_metrics, best_threshold
best_metrics, best_threshold = choose_best_threshold(
answer_dict, prediction_file)
json.dump(best_metrics, open(eval_file, 'w'))
return best_metrics, best_threshold
def jd_at_eval_model(args, encoder, model, dataloader, example_dict,
feature_dict, prediction_file, eval_file, dev_gold_file):
encoder.eval()
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
dataloader.refresh()
##++++++++++++++++++++++++++++++++++
total_sent_sp_dict = {}
##++++++++++++++++++++++++++++++++++
total_para_sp_dict = {}
##++++++++++++++++++++++++++++++++++
for batch in tqdm(dataloader):
with torch.no_grad():
inputs = {
'input_ids':
batch['context_idxs'],
'attention_mask':
batch['context_mask'],
'token_type_ids':
batch['segment_idxs']
if args.model_type in ['bert', 'xlnet'] else None
} # XLM don't use segment_ids
outputs = encoder(**inputs)
batch['context_encoding'] = outputs[0]
batch['context_mask'] = batch['context_mask'].float().to(
args.device)
start, end, q_type, paras, sent, ent, yp1, yp2 = model(
batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(
example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(), type_prob)
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
##++++++++++++++++++++++++++++++++++++++++
predict_para_support_logits = paras
para_mask = batch['para_mask']
batch_size = para_mask.shape[0]
query_para_mask = torch.cat(
[torch.ones(batch_size, 1).to(para_mask), para_mask], dim=-1)
para_pred_out = adaptive_threshold_prediction(
logits=predict_para_support_logits,
number_labels=2,
mask=query_para_mask,
type='topk')
para_pred_out_np = para_pred_out.data.cpu().numpy()
##++++++++++++++++++++++++++++++++++++++++
predict_sent_support_logits = sent
sent_mask = batch['sent_mask']
query_sent_mask = torch.cat(
[torch.ones(batch_size, 1).to(sent_mask), sent_mask], dim=-1)
sent_pred_out = adaptive_threshold_prediction(
logits=predict_sent_support_logits,
number_labels=2,
mask=query_sent_mask,
type='or')
sent_pred_out_np = sent_pred_out.data.cpu().numpy()
##++++++++++++++++++++++++++++++++++++++++
for i in range(sent_pred_out_np.shape[0]):
cur_id = batch['ids'][i]
para_pred_out_np_ith = para_pred_out_np[i]
sent_pred_out_np_ith = sent_pred_out_np[i]
cur_para_sp_pred = supp_para_at_prediction(
predict_para_support_np_ith=para_pred_out_np_ith,
example_dict=example_dict,
batch_ids_ith=cur_id)
cur_sent_sp_pred = supp_sent_at_prediction(
predict_sent_support_np_ith=sent_pred_out_np_ith,
example_dict=example_dict,
batch_ids_ith=cur_id)
total_para_sp_dict[cur_id] = cur_para_sp_pred
total_sent_sp_dict[cur_id] = cur_sent_sp_pred
prediction = {
'answer': answer_dict,
'sp': total_sent_sp_dict,
'type': answer_type_dict,
'type_prob': answer_type_prob_dict
}
tmp_file = os.path.join(os.path.dirname(prediction_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
eval_metrics = hotpot_eval(tmp_file, dev_gold_file)
doc_recall_metric = doc_recall_eval(doc_prediction=total_para_sp_dict,
gold_file=dev_gold_file)
total_inconsistent_number = supp_doc_sent_consistent_checker(
predict_para_dict=total_para_sp_dict,
predicted_supp_sent_dict=total_sent_sp_dict,
gold_file=dev_gold_file)
##++++++++++++++++++++++++++++++++++++++++
json.dump(eval_metrics, open(eval_file, 'w'))
return eval_metrics, doc_recall_metric, total_inconsistent_number
def lightnHGN_test_procedure(model, test_data_loader, dev_feature_dict, dev_example_dict, args, device):
model.freeze()
out_puts = []
start_time = time()
total_steps = len(test_data_loader)
with torch.no_grad():
for batch_idx, batch in tqdm(enumerate(test_data_loader)):
batch = batch2device(batch=batch, device=device)
start, end, q_type, paras, sents, ents, yp1, yp2 = model.forward(batch=batch)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(dev_example_dict,
dev_feature_dict,
batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
predict_support_np = torch.sigmoid(sents[:, :, 1]).data.cpu().numpy()
valid_dict = {'answer': answer_dict_, 'ans_type': answer_type_dict_, 'ids': batch['ids'],
'ans_type_pro': answer_type_prob_dict_, 'supp_np': predict_support_np}
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
if (batch_idx + 1) % args.eval_batch_size == 0:
print('Evaluating the model... {}/{} in {:.4f} seconds'.format(batch_idx + 1, total_steps, time()-start_time))
out_puts.append(valid_dict)
del batch
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
thresholds = np.arange(0.1, 1.0, 0.025)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for batch_idx, valid_dict in tqdm(enumerate(out_puts)):
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = valid_dict['answer'], valid_dict['ans_type'], \
valid_dict['ans_type_pro']
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = valid_dict['supp_np']
batch_ids = valid_dict['ids']
for i in range(predict_support_np.shape[0]):
cur_sp_pred = [[] for _ in range(N_thresh)]
cur_id = batch_ids[i]
for j in range(predict_support_np.shape[1]):
if j >= len(dev_example_dict[cur_id].sent_names):
break
for thresh_i in range(N_thresh):
if predict_support_np[i, j] > thresholds[thresh_i]:
cur_sp_pred[thresh_i].append(dev_example_dict[cur_id].sent_names[j])
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
total_sp_dict[thresh_i][cur_id].extend(cur_sp_pred[thresh_i])
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
#################
metric_dict = {}
#################
for thresh_i in range(N_thresh):
prediction = {'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict}
tmp_file = os.path.join(os.path.dirname(pred_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = hotpot_eval(tmp_file, args.dev_gold_file)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
best_metrics = metrics
shutil.move(tmp_file, pred_file)
#######
metric_dict[thresh_i] = (
metrics['em'], metrics['f1'],
metrics['sp_em'], metrics['sp_f1'],
metrics['joint_em'], metrics['joint_f1'])
#######
return best_metrics, best_threshold, metric_dict
output_pred_file = os.path.join(args.exp_name, f'pred.json')
output_eval_file = os.path.join(args.exp_name, f'eval.txt')
####+++++
best_metrics, best_threshold, metric_dict = choose_best_threshold(answer_dict, output_pred_file)
####++++++
logging.info('Leader board evaluation completed with threshold = {:.4f}'.format(best_threshold))
log_metrics(mode='Evaluation', metrics=best_metrics)
logging.info('*' * 75)
####++++++
for key, value in metric_dict.items():
str_value = ['{:.4f}'.format(_) for _ in value]
logging.info('threshold {:.4f}: \t metrics: {}'.format(thresholds[key], str_value))
####++++++
json.dump(best_metrics, open(output_eval_file, 'w'))
#############################################################################
return best_metrics, best_threshold
def jd_postprecess_unified_test_model(args, model, dataloader, example_dict, feature_dict, threshold=0.35,
output_score_file=None):
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
sp_dict = {}
##++++++
prediction_res_score_dict = {}
##++++++
for batch in tqdm(dataloader):
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for key, value in batch.items():
if key not in {'ids'}:
batch[key] = value.to(args.device)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with torch.no_grad():
start, end, q_type, paras, sent, ent, yp1, yp2, cls_emb = model(batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
_, _, answer_sent_name_dict_ = convert_answer_to_sent_names(example_dict, feature_dict, batch,
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
predict_support_para_np = torch.sigmoid(paras[:, :, 1]).data.cpu().numpy()
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
predict_support_logit_np = sent[:, :, 1].data.cpu().numpy()
support_sent_mask_np = batch['sent_mask'].data.cpu().numpy()
predict_support_para_logit_np = paras[:, :, 1].data.cpu().numpy()
support_para_mask_np = batch['para_mask'].data.cpu().numpy()
cls_emb_np = cls_emb.data.cpu().numpy()
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for i in range(predict_support_np.shape[0]):
cur_id = batch['ids'][i]
cur_sp_pred = []
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
sent_pred_ = {'sp_score': predict_support_logit_np[i].tolist(), 'sp_mask': support_sent_mask_np[i].tolist(),
'sp_names': example_dict[cur_id].sent_names}
para_pred_ = {'sp_para_score': predict_support_para_logit_np[i].tolist(), 'sp_para_mask': support_para_mask_np[i].tolist(),
'sp_para_names': example_dict[cur_id].para_names}
cls_emb_ = {'cls_emb': cls_emb_np[i].tolist()}
res_score = {**sent_pred_, **cls_emb_, **para_pred_}
prediction_res_score_dict[cur_id] = res_score
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
topk_score_ref, cut_sent_flag, topk_pred_sent_names, diff_para_sent_names, topk_pred_paras = \
post_process_sent_para(cur_id=cur_id, example_dict=example_dict, feature_dict=feature_dict,
sent_scores_np_i=predict_support_np[i], sent_mask_np_i=support_sent_mask_np[i],
para_scores_np_i=predict_support_para_np[i], para_mask_np_i=support_para_mask_np[i])
ans_sent_name = answer_sent_name_dict_[cur_id]
for j in range(predict_support_np.shape[1]):
if j >= len(example_dict[cur_id].sent_names):
break
if predict_support_np[i, j] > topk_score_ref * threshold:
cur_sp_pred.append(example_dict[cur_id].sent_names[j])
post_process_sp_pred = post_process_technique(cur_sp_pred=cur_sp_pred,
topk_pred_paras=topk_pred_paras,
topk_pred_sent_names=topk_pred_sent_names,
diff_para_sent_names=diff_para_sent_names,
ans_sent_name=ans_sent_name)
sp_dict[cur_id] = post_process_sp_pred
prediction = {'answer': answer_dict,
'sp': sp_dict,
'type': answer_type_dict,
'type_prob': answer_type_prob_dict}
if output_score_file is not None:
with open(output_score_file, 'w') as f:
json.dump(prediction_res_score_dict, f)
print('Saving {} score records into {}'.format(len(prediction_res_score_dict), output_score_file))
return prediction
def jd_postprocess_unified_eval_model(args, model, dataloader, example_dict, feature_dict, prediction_file, eval_file, dev_gold_file=None):
model.eval()
answer_dict = {}
answer_type_dict = {}
answer_type_prob_dict = {}
thresholds = np.arange(0.1, 1.0, 0.025)
N_thresh = len(thresholds)
total_sp_dict = [{} for _ in range(N_thresh)]
for batch in tqdm(dataloader):
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for key, value in batch.items():
if key not in {'ids'}:
batch[key] = value.to(args.device)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
with torch.no_grad():
start, end, q_type, paras, sent, ent, yp1, yp2, cls_emb = model(batch, return_yp=True)
type_prob = F.softmax(q_type, dim=1).data.cpu().numpy()
answer_dict_, answer_type_dict_, answer_type_prob_dict_ = convert_to_tokens(example_dict, feature_dict, batch['ids'],
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
_, _, answer_sent_name_dict_ = convert_answer_to_sent_names(example_dict, feature_dict, batch,
yp1.data.cpu().numpy().tolist(),
yp2.data.cpu().numpy().tolist(),
type_prob)
##++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
answer_type_dict.update(answer_type_dict_)
answer_type_prob_dict.update(answer_type_prob_dict_)
answer_dict.update(answer_dict_)
predict_support_np = torch.sigmoid(sent[:, :, 1]).data.cpu().numpy()
####################################################################
support_sent_mask_np = batch['sent_mask'].data.cpu().numpy()
predict_support_para_np = torch.sigmoid(paras[:, :, 1]).data.cpu().numpy()
support_para_mask_np = batch['para_mask'].data.cpu().numpy()
####################################################################
for i in range(predict_support_np.shape[0]):
cur_sp_pred = [[] for _ in range(N_thresh)]
cur_id = batch['ids'][i]
##+++++++++++++++++++++++++
topk_score_ref, cut_sent_flag, topk_pred_sent_names, diff_para_sent_names, topk_pred_paras = \
post_process_sent_para(cur_id=cur_id, example_dict=example_dict, feature_dict=feature_dict,
sent_scores_np_i=predict_support_np[i], sent_mask_np_i=support_sent_mask_np[i],
para_scores_np_i=predict_support_para_np[i], para_mask_np_i=support_para_mask_np[i])
ans_sent_name = answer_sent_name_dict_[cur_id]
##+++++++++++++++++++++++++
for j in range(predict_support_np.shape[1]):
if j >= len(example_dict[cur_id].sent_names):
break
for thresh_i in range(N_thresh):
if predict_support_np[i, j] > thresholds[thresh_i] * topk_score_ref:
cur_sp_pred[thresh_i].append(example_dict[cur_id].sent_names[j])
for thresh_i in range(N_thresh):
if cur_id not in total_sp_dict[thresh_i]:
total_sp_dict[thresh_i][cur_id] = []
##+++++
post_process_thresh_i_sp_pred = post_process_technique(cur_sp_pred=cur_sp_pred[thresh_i],
topk_pred_paras=topk_pred_paras,
topk_pred_sent_names=topk_pred_sent_names,
diff_para_sent_names=diff_para_sent_names,
ans_sent_name=ans_sent_name)
total_sp_dict[thresh_i][cur_id].extend(post_process_thresh_i_sp_pred)
# # +++++++++++++++++++++++++++
def choose_best_threshold(ans_dict, pred_file):
best_joint_f1 = 0
best_metrics = None
best_threshold = 0
for thresh_i in range(N_thresh):
prediction = {'answer': ans_dict,
'sp': total_sp_dict[thresh_i],
'type': answer_type_dict,
'type_prob': answer_type_prob_dict}
tmp_file = os.path.join(os.path.dirname(pred_file), 'tmp.json')
with open(tmp_file, 'w') as f:
json.dump(prediction, f)
metrics = hotpot_eval(tmp_file, dev_gold_file)
if metrics['joint_f1'] >= best_joint_f1:
best_joint_f1 = metrics['joint_f1']
best_threshold = thresholds[thresh_i]
best_metrics = metrics
shutil.move(tmp_file, pred_file)
return best_metrics, best_threshold
best_metrics, best_threshold = choose_best_threshold(answer_dict, prediction_file)
json.dump(best_metrics, open(eval_file, 'w'))
return best_metrics, best_threshold
