out = model(q)
_, pred = torch.max(out.data, 1)
total += label.size(0) # batch size
correct += (pred == label).sum()
acc = 100 * (correct.cpu().numpy() / total)
return acc
if __name__ == "__main__":
# 데이터 처리
assert torch.cuda.is_available(), "cuda is not available"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
dataset = custom_dataset.Custom_dataset()
train_data = dataset.get_data()
val_data = train_data[:100]
train_data = train_data[100:]
train_loader = DataLoader(
train_data,
batch_size=c.batch,
shuffle=True,
num_workers=1, #.cpu_processor,
drop_last=True)
# test_loader = DataLoader(test_data,
# batch_size=c.batch,
# shuffle=False,
correct += (pred == label).sum()
acc = 100 * (correct.cpu().numpy() / total)
return acc
if __name__ == "__main__":
path_csv = config.path_csv
# 데이터 처리
start = time.time()
vocab = v.create_vocab(path_csv=path_csv)
word_to_index = vocab.get_data()
print("time vocab load : ", time.time() - start)
start = time.time()
dataset = custom_dataset.Custom_dataset(word_to_index, path_csv=path_csv)
train_data = dataset.get_data()
print("데이터 준비 완료")
print("time data load : ", time.time() - start)
print(len(train_data))
train_loader = DataLoader(
train_data,
batch_size=config.batch,
shuffle=True,
# num_workers=config.cpu_processor,
drop_last=True)
test_dataset = custom_dataset.Custom_dataset(word_to_index,
path_csv="train_data.csv")
def __init__(self):
path = "./train_data.csv"
vocab = v.create_vocab(path_csv=path)
word_to_index = vocab.get_data()
self.dataset = c.Custom_dataset(word_to_index, path)
self.model = torch.load("./model.pth")
if __name__ == "__main__":
# 데이터 처리
start = time.time()
vocab = v.create_vocab(mode=config.vocab_mode)
vocab_list, word_to_index = vocab.get_data()
print("time vocab load : ", time.time() - start)
start = time.time()
glove = custom_glove()
embedding = glove.get_data(vocab_list)
print("time glove emb load : ", time.time() - start)
start = time.time()
dataset = custom_dataset.Custom_dataset(vocab_list, word_to_index)
train_data, test_data, dev_data = dataset.get_data()
print("time data load : ", time.time() - start)
train_loader = DataLoader(train_data,
batch_size=config.batch,
shuffle=True,
num_workers=config.cpu_processor,
drop_last=True)
test_loader = DataLoader(test_data,
batch_size=config.batch,
shuffle=False,
num_workers=config.cpu_processor,
drop_last=True)