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prediction.py
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prediction.py
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import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import gluonnlp as nlp
import numpy as np
import pandas as pd
from tqdm import tqdm, tqdm_notebook
from kobert.utils import get_tokenizer
from kobert.pytorch_kobert import get_pytorch_kobert_model
from transformers import AdamW
from transformers.optimization import get_cosine_schedule_with_warmup
import argparse
import os
from preprocessing import pre_processing
# from preprocessing
class BERTDataset(Dataset):
def __init__(self, dataset, sent_idx, bert_tokenizer, max_len,
pad, pair):
transform = nlp.data.BERTSentenceTransform(
bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)
self.sentences = [transform([i[sent_idx]]) for i in dataset]
# self.labels = [np.int32(i[label_idx]) for i in dataset]
def __getitem__(self, i):
# return (self.sentences[i] + (self.labels[i], ))
return (self.sentences[i])
def __len__(self):
return (len(self.sentences))
class BERTClassifier(nn.Module):
def __init__(self,
bert,
hidden_size = 768,
num_classes=785,
dr_rate=None,
params=None):
super(BERTClassifier, self).__init__()
self.bert = bert
self.dr_rate = dr_rate
self.classifier = nn.Linear(hidden_size , num_classes)
if dr_rate:
self.dropout = nn.Dropout(p=dr_rate)
def gen_attention_mask(self, token_ids, valid_length):
attention_mask = torch.zeros_like(token_ids)
for i, v in enumerate(valid_length):
attention_mask[i][:v] = 1
return attention_mask.float()
def forward(self, token_ids, valid_length, segment_ids):
attention_mask = self.gen_attention_mask(token_ids, valid_length)
_, pooler = self.bert(input_ids = token_ids, token_type_ids = segment_ids.long(), attention_mask = attention_mask.float().to(token_ids.device))
if self.dr_rate:
out = self.dropout(pooler)
return self.classifier(out)
def load_model(ckpt_dir, bertmodel):
print('Load classifier model ...')
device = torch.device("cuda:0")
model = BERTClassifier(bertmodel, dr_rate=0.6)
checkpoint = torch.load(ckpt_dir, map_location = device)
model.load_state_dict(checkpoint['state_dict'])
model.to(device)
return model
# def calc_accuracy(X,Y):
# max_vals, max_indices = torch.max(X, 1)
# acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]
# return acc
if __name__ == "__main__":
##GPU 사용 시
parser = argparse.ArgumentParser()
parser.add_argument("--input_text", required = True, help="Input text file")
parser.add_argument("--output_text" , required= True, help="output text file")
args = parser.parse_args()
device = torch.device("cuda:0")
pre_processing(args.input_text)
test_tsv_file = 'test.tsv'
bert_model , vocab = get_pytorch_kobert_model()
dataset_test = nlp.data.TSVDataset(test_tsv_file, field_indices=[0], num_discard_samples=0)
tokenizer = get_tokenizer()
tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)
## Setting parameters
max_len = 20
batch_size = 64
# data_test = BERTDataset(dataset_test, 0, 1, tok, max_len, True, False)
data_test = BERTDataset(dataset_test, 0, tok, max_len, True, False)
test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5)
loss_fn = nn.CrossEntropyLoss()
ckpt_dir = 'ckpt/734model.pth'
model = load_model(ckpt_dir, bert_model)
# test_acc = 0.0
output_idx = []
Decoder = pd.read_csv('decoder.txt')
model.eval()
for batch_id, (token_ids, valid_length, segment_ids) in enumerate(tqdm_notebook(test_dataloader)):
token_ids = token_ids.long().to(device)
segment_ids = segment_ids.long().to(device)
valid_length= valid_length
out = model(token_ids, valid_length, segment_ids)
max_vals, max_indices = torch.max(out, 1)
m = max_indices.tolist()
output_idx.append(m)
# print("test acc {}".format(test_acc / (batch_id+1)))
output_sentence = []
for m in output_idx:
for i in m:
# print( Decoder['label_text'][i])
output_sentence.append(Decoder['label_text'][i])
# print('_________________next batch_________________')
with open(args.output_text, 'w') as file:
for i in range(len(output_sentence)):
file.write(output_sentence[i]+'\n')