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Runtime error
| import torch | |
| from args import args, config | |
| from tqdm import tqdm | |
| from items_dataset import items_dataset | |
| def test_predict(test_loader, device, model, min_label=1, max_label=3): | |
| model.eval() | |
| result = [] | |
| for i, test_batch in enumerate(tqdm(test_loader)): | |
| batch_text = test_batch['batch_text'] | |
| input_ids = test_batch['input_ids'].to(device) | |
| token_type_ids = test_batch['token_type_ids'].to(device) | |
| attention_mask = test_batch['attention_mask'].to(device) | |
| #labels = test_batch['labels'].to(device) | |
| crf_mask = test_batch["crf_mask"].to(device) | |
| sample_mapping = test_batch["overflow_to_sample_mapping"] | |
| output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=None, crf_mask=crf_mask) | |
| if args.use_crf: | |
| prediction = model.crf.decode(output[0], crf_mask) | |
| else: | |
| prediction = torch.max(output[0], -1).indices | |
| #make result of every sample | |
| sample_id = -1 | |
| sample_result= {"text_a" : test_batch['batch_text'][0]} | |
| for batch_id in range(len(sample_mapping)): | |
| change_sample = False | |
| if sample_id != sample_mapping[batch_id]: change_sample = True | |
| #print(i, id) | |
| if change_sample: | |
| sample_id = sample_mapping[batch_id] | |
| sample_result= {"text_a" : test_batch['batch_text'][sample_id]} | |
| decode_span_table = torch.zeros(len(test_batch['batch_text'][sample_id])) | |
| spans = items_dataset.cal_agreement_span(None, agreement_table=prediction[batch_id], min_agree=min_label, max_agree=max_label) | |
| #decode spans | |
| for span in spans: | |
| #print(span) | |
| if span[0]==0: span[0]+=1 | |
| if span[1]==1: span[1]+=1 | |
| while(True): | |
| start = test_batch[batch_id].token_to_chars(span[0]) | |
| if start != None or span[0]>=span[1]: | |
| break | |
| span[0]+=1 | |
| while(True): | |
| end = test_batch[batch_id].token_to_chars(span[1]) | |
| if end != None or span[0]>=span[1]: | |
| break | |
| span[1]-=1 | |
| if span[0]<span[1]: | |
| de_start = test_batch[batch_id].token_to_chars(span[0])[0] | |
| de_end = test_batch[batch_id].token_to_chars(span[1]-1)[0] | |
| #print(de_start, de_end) | |
| #if(de_start>512): print(de_start, de_end) | |
| decode_span_table[de_start:de_end]=2 #insite | |
| decode_span_table[de_start]=1 #begin | |
| if change_sample: | |
| sample_result["predict_span_table"] = decode_span_table | |
| #sample_result["boundary"] = test_batch["boundary"][id] | |
| result.append(sample_result) | |
| model.train() | |
| return result | |
| def add_sentence_table(result): | |
| pattern =":；。，？!～！： " | |
| for sample in result: | |
| boundary_list = [] | |
| for i, char in enumerate(sample['text_a']): | |
| if char in pattern: | |
| boundary_list.append(i) | |
| boundary_list.append(len(sample['text_a'])+1) | |
| start=0 | |
| end =0 | |
| pre_states =False | |
| sample["predict_sentence_table"] = torch.zeros(len(sample["predict_span_table"])) | |
| for boundary in boundary_list: | |
| end = boundary | |
| if(sum(sample["predict_span_table"][start:end])>0): | |
| if pre_states: | |
| sample["predict_sentence_table"][start-1:end] = 2 | |
| else: | |
| sample["predict_sentence_table"][start:end] = 2 | |
| sample["predict_sentence_table"][start] = 1 | |
| pre_states=True | |
| else: pre_states =False | |
| start = end+1 |