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test_all.py
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import time,copy
import argparse
import math
import torch
import torch.nn as nn
from rouge import Rouge
from transformers import T5Tokenizer,BartTokenizer
from transformers import AdamW
from torch.optim import *
import numpy as np
from model.model_T5 import generator,generator_prefix
from data_loader import data_loader
import os
class Test(object):
def __init__(self, config):
x=torch.load('save_model/'+config.test_model,map_location='cpu')
self.generator = x['generator']
self.config = x['config']
self.config.true_batch_size=1
self.config.buffer_size=1
self.config.batch_size=1
self.config.seed=10
self.config.only_target=1
self.config.use_pretrained_seed=0
self.config.use_same_seed=0
self.generator.cuda()
if 'bart' in self.config.pretrained_model:
self.tokenizer = BartTokenizer.from_pretrained(self.config.pretrained_model)
self.raw_rouge=Rouge()
self.can_path = 'result/'+config.test_description+config.test_model+'/'+config.test_model+'_cand.txt'
self.gold_path ='result/'+config.test_description+config.test_model+'/'+config.test_model+'_gold.txt'
self.source_path ='result/'+config.test_description+config.test_model+'/'+config.test_model+'_source.txt'
if not os.path.exists('result/'+config.test_description+config.test_model):
os.mkdir('result/'+config.test_description+config.test_model)
def test(self,test_num=21126):
self.raw_rouge=Rouge()
self.generator.eval()
if (self.config.load_xsum == 1 and self.config.load_debate ==1) or (self.config.load_squad == 1 and self.config.load_debate ==1):
data_loader_val = data_loader('test', self.config, self.tokenizer, load_xsum=0, load_debate=1, load_squad=0, load_kptimes=0, mix=1)
elif self.config.load_squad == 1 and self.config.load_xsum ==1 and self.config.load_debate !=1:
data_loader_val = data_loader('test', self.config, self.tokenizer, load_xsum=1, load_debate=0, load_squad=0, load_kptimes=0, mix=1)
else:
data_loader_val = data_loader('test', self.config, self.tokenizer, load_xsum=self.config.load_xsum, load_debate=self.config.load_debate, load_squad=self.config.load_squad, load_kptimes=self.config.load_kptimes, mix=self.config.mix)
r1=[]
r2=[]
rl=[]
pred_list=[]
gold_list=[]
source_list=[]
with open(self.can_path, 'w', encoding='utf-8') as save_pred:
with open(self.gold_path, 'w', encoding='utf-8') as save_gold:
with open(self.source_path, 'w', encoding='utf-8') as save_source:
for i in range(int(test_num/self.config.batch_size)):
if i%500 == 0:
print(i)
try:
article_id_b,article_id_mask_b,summary_i_b,summary_id_mask_b,summary_b,label1,label2 = \
data_loader_val.load_data()
except:
print('load data fail during the evaluation')
break
if (self.config.load_xsum == 1 and self.config.load_debate ==1) or (self.config.load_squad == 1 and self.config.load_debate ==1):
if label1==0 and label2==0:
divide=1
start=0
for mini in range(int(self.config.batch_size/divide)):
try:
article_id=article_id_b[start:start+divide]
article_id_mask=article_id_mask_b[start:start+divide]
gold=summary_b[start]
gold=summary_b[start]
input_id=article_id
input_id_mask=article_id_mask
start=start+divide
output = self.generator.inference(input_id,input_id_mask,label1,label2,use_beam=2)
pred = self.tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
article=self.tokenizer.decode(article_id[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
scores = self.raw_rouge.get_scores(pred, gold)
r1.append(scores[0]['rouge-1']['f'])
r2.append(scores[0]['rouge-2']['f'])
rl.append(scores[0]['rouge-l']['f'])
pred_list.append(pred)
gold_list.append(gold)
source_list.append(article)
except Exception as e:
print('one test batch fail')
print('Reason for batch fail:', e)
else:
divide=1
start=0
for mini in range(int(self.config.batch_size/divide)):
try:
article_id=article_id_b[start:start+divide]
article_id_mask=article_id_mask_b[start:start+divide]
gold=summary_b[start]
gold=summary_b[start]
input_id=article_id
input_id_mask=article_id_mask
start=start+divide
output = self.generator.inference(input_id,input_id_mask,label1,label2,use_beam=2)
pred = self.tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)
article=self.tokenizer.decode(article_id[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
scores = self.raw_rouge.get_scores(pred, gold)
r1.append(scores[0]['rouge-1']['f'])
r2.append(scores[0]['rouge-2']['f'])
rl.append(scores[0]['rouge-l']['f'])
pred_list.append(pred)
gold_list.append(gold)
source_list.append(article)
except Exception as e:
print('one test batch fail')
print('Reason for batch fail:', e)
if data_loader_val.epoch == 2:
break
for sent in gold_list:
save_gold.write(sent.strip() + '\n')
for sent in pred_list:
save_pred.write(sent.strip() + '\n')
for sent in source_list:
save_source.write(sent.strip() + '\n')
print(np.mean(r1),np.mean(r2),np.mean(rl))
def argLoader():
parser = argparse.ArgumentParser()
#device
parser.add_argument('--device', type=int, default=0)
# Do What
parser.add_argument('--do_train', action='store_true', help="Whether to run training")
parser.add_argument('--do_test', action='store_true', help="Whether to run test")
parser.add_argument('--promote', type=int, default=0)
parser.add_argument('--low_data', type=int, default=1)
parser.add_argument('--low_data_start', type=int, default=0)
parser.add_argument('--low_data_num', type=int, default=288)
parser.add_argument('--seed', type=int, default=10)
parser.add_argument('--use_prefix', type=str, default='')
parser.add_argument('--prefix_length', type=int, default=60)
parser.add_argument('--unq_prefix_length', type=int, default=0)
parser.add_argument('--share_prefix_length', type=int, default=30)
parser.add_argument('--target_prefix_length', type=int, default=30)
parser.add_argument('--only_target', type=int, default=1)
parser.add_argument('--use_pretrained_seed', type=int, default=1)
parser.add_argument('--use_same_seed', type=int, default=0)
parser.add_argument('--mix', type=int, default=1)
parser.add_argument('--load_xsum', type=int, default=0)
parser.add_argument('--load_debate', type=int, default=1)
parser.add_argument('--load_squad', type=int, default=0)
parser.add_argument('--load_kptimes', type=int, default=0)
parser.add_argument('--pretrained_model', type=str, default='facebook/bart-large')
parser.add_argument('--bos_token_id', type=int, default=0)
parser.add_argument('--pad_token_id', type=int, default=1)
parser.add_argument('--eos_token_id', type=int, default=2)
#Preprocess Setting
parser.add_argument('--max_summary', type=int, default=100)
parser.add_argument('--max_article', type=int, default=250)
#Model Setting
parser.add_argument('--hidden_dim', type=int, default=1024)
parser.add_argument('--emb_dim', type=int, default=1024)
parser.add_argument('--vocab_size', type=int, default=50264)
parser.add_argument('--lr', type=float, default=5e-5)
parser.add_argument('--eps', type=float, default=1e-10)
parser.add_argument('--prefix_dropout', type=float, default=0)
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--true_batch_size', type=int, default=48)
parser.add_argument('--buffer_size', type=int, default=144)
parser.add_argument('--scale_embedding', type=int, default=0)
#lr setting
parser.add_argument('--use_lr_decay', type=int, default=0)
parser.add_argument('--lr_decay_step', type=int, default=10000)
parser.add_argument('--lr_decay', type=float, default=1)
# Testing setting
parser.add_argument('--beam_size', type=int, default=2)
parser.add_argument('--max_dec_steps', type=int, default=75)
parser.add_argument('--min_dec_steps', type=int, default=30)
parser.add_argument('--test_model', type=str, default='')
parser.add_argument('--test_description', type=str, default='')
parser.add_argument('--load_model', type=str, default='')
parser.add_argument('--save_path', type=str, default='')
parser.add_argument('--mid_start', type=int, default=0)
# Checkpoint Setting
parser.add_argument('--max_epoch', type=int, default=60)
parser.add_argument('--train_set_len', type=int, default=6)
parser.add_argument('--savefreq', type=int, default=24)
parser.add_argument('--checkfreq', type=int, default=1)
parser.add_argument('--startfreq', type=int, default=48)
args = parser.parse_args()
return args
def main():
args = argLoader()
torch.cuda.set_device(args.device)
test_model_list=[]
for model in test_model_list:
print('test model', model)
print('CUDA', torch.cuda.current_device())
args.test_model=model
x = Test(args)
x.test()
print('finish testing model')
x=0
torch.cuda.empty_cache()
main()