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eval_roberta.py
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eval_roberta.py
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from file_utils import WEIGHTS_NAME
from configuration_roberta import RobertaConfig
from modeling_roberta import RobertaForSequenceClassification
from tokenization_roberta import RobertaTokenizer
import torch
import numpy as np
from processors.glue import glue_convert_examples_to_features as convert_examples_to_features
from processors.utils import DataProcessor, InputExample, InputFeatures
import xlrd
import json
from sklearn.metrics import f1_score
all_count=0
correct=0
fp=0
fn=0
tp=0
tn=0
results=[]
#logits_all=None
#paths=["D:\数据/data1.xlsx","D:\数据/data2.xlsx"]
paths=["./data/data1.xlsx","./data/data2.xlsx"]
config_class, model_class, tokenizer_class = RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer
config = config_class.from_pretrained(r"./roberta/config.json")
tokenizer = tokenizer_class.from_pretrained(r"./pretrained/robertabase")
model = model_class.from_pretrained(r"./roberta/pytorch_model.bin", from_tf=False,config=config)
#config = config_class.from_pretrained(r"D:\代码\服务器代码中转\transformer\pretrained\checkpoint-500\config.json")
#tokenizer = tokenizer_class.from_pretrained(r"D:\代码\服务器代码中转\transformer\pretrained\robertalarge")
#model = model_class.from_pretrained(r"D:\代码\服务器代码中转\transformer\pretrained\checkpoint-500\pytorch_model.bin", from_tf=False,config=config)
model=model.cuda()
model.eval()
for path in paths:
data = xlrd.open_workbook(path)
table = data.sheets()[0]
nrows = table.nrows
for j in range(1, nrows):
#output =None
#labels=None
all_count += 1
samples = []
sentences = table.row_values(j)[6].split("|")
for sentence in sentences:
sample = []
sample.append(sentence)
sample.append(table.row_values(j)[4])
samples.append(sample)
examples = []
for i in range(len(samples)):
guid = "%s" % (i)
text_a = samples[i][0].lower()
text_b = samples[i][1].lower()
label = str(int(table.row_values(j)[3] >= 0.5))
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
features = convert_examples_to_features(examples,
tokenizer,
label_list=["0", "1"],
max_length=64,
output_mode="classification",
pad_on_left=False,
# pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=0,
)
#for f in features:
#input_ids = torch.tensor(f.input_ids, dtype=torch.long).unsqueeze(0)
#attention_mask = torch.tensor(f.attention_mask, dtype=torch.long).unsqueeze(0)
#align_mask = torch.tensor(f.align_mask, dtype=torch.long).unsqueeze(0)
#label = torch.tensor(f.label, dtype=torch.long).unsqueeze(0)
#input_ids = torch.tensor(f.input_ids, dtype=torch.long).unsqueeze(0).cuda()
#attention_mask = torch.tensor(f.attention_mask, dtype=torch.long).unsqueeze(0).cuda()
#align_mask = torch.tensor(f.align_mask, dtype=torch.long).unsqueeze(0).cuda()
#label = torch.tensor(f.label, dtype=torch.long).unsqueeze(0).cuda()
#print(label.shape)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long).cuda()
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long).cuda()
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long).cuda()
all_labels = torch.tensor([f.label for f in features], dtype=torch.long).cuda()
outputs = model(input_ids=all_input_ids, attention_mask=all_attention_mask,labels=all_labels)
logits = outputs[1]
logits=torch.nn.functional.softmax(logits,dim=-1)
#print(logits.shape)
output = logits.detach().cpu().numpy()
labels = all_labels.detach().cpu().numpy()
"""
if logits_all is None:
logits_all=logits.detach().cpu().numpy()
else:
logits_all=np.append(logits_all,logits.cpu().detach().numpy(),axis=0)
"""
#if output is None:
#output = logits.detach().numpy()
#labels = label.detach().numpy()
# output = logits.detach().cpu().numpy()
# labels = label.detach().cpu().numpy()
#print(output.shape)
#print(labels.shape)
#else:
#output = np.append(output, logits.detach().numpy(), axis=0)
#labels = np.append(labels, label.detach().numpy(), axis=0)
# output = np.append(output, logits.detach().cpu().numpy(), axis=0)
# labels = np.append(labels, label.detach().cpu().numpy(), axis=0)
#print(output.shape)
#print(labels.shape)
output=np.argmax(output,axis=1)
correct+=int(np.any(output==labels))
if int(np.any(output==labels))==1 and labels[0]==1:
tp+=1
if int(np.any(output == labels)) == 1 and labels[0] == 0:
tn+=1
if int(np.any(output == labels)) == 0 and labels[0] == 1:
fn+=1
if int(np.any(output == labels)) == 0 and labels[0] == 0:
fp+=1
"""
if labels[0]==1:
correct+=int(np.any(output==labels))
else:
correct += int(np.all(output == labels))
results.append({"answer": table.row_values(j)[4], "ref": table.row_values(j)[6], "logits": logits.detach().cpu().numpy().tolist(),"label":int(labels[0]),"iscorrect": int(np.any(output == labels)) if labels[0]==1 else int(np.all(output == labels))})
"""
results.append({"answer": table.row_values(j)[4], "ref": table.row_values(j)[6],
"logits": logits.detach().cpu().numpy().tolist(), "label": int(labels[0]),
"iscorrect": int(np.any(output == labels))})
print(correct)
print(all_count)
print(correct / all_count)
#print(correct/all_count)
#np.save("results.npy",results)
#np.save("logits.npy",logits_all)
#np.savetxt("logits.txt",logits_all)
p=tp/(tp+fp)
r=tp/(tp+fn)
print(tp)
print(fp)
print(fn)
print(tn)
print(1.25*p*r/(0.25*p+r))
with open("./results/results_roberta.json","w",encoding="utf8") as fout:
for result in results:
fout.write(json.dumps(result,ensure_ascii=False)+"\n")