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runUtils.py
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runUtils.py
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import torch
from torch import nn
import config
config.setup_seed()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
labelDict = {
0: 'positive',
1: 'negative',
2: 'neutral'
}
def train(model, optimizer, train_loader, val_loader, mode):
criterion = nn.CrossEntropyLoss()
best_accuracy = 0.
for e in range(config.epoch):
for i, data in enumerate(train_loader):
model.train()
labels = data['tag'].to(device)
optimizer.zero_grad()
out = model(data)
loss = criterion(out, labels)
loss.backward()
optimizer.step()
out = out.argmax(dim=1)
accuracy = (out == labels).sum().item() / len(labels)
print('epoch:', e + 1, 'step:', i + 1, 'loss:', loss.item(), 'train accuracy:', accuracy)
if (i + 1) % 10 == 0:
print('validation accuracy:', test(model, val_loader))
accuracy = test(model, val_loader)
print('epoch:', e + 1, 'validation accuracy:', accuracy)
if accuracy >= best_accuracy:
best_accuracy = accuracy
torch.save(model, 'model_' + mode)
print('saved the model')
def test(model, data_loader):
model.eval()
correct = 0
total = 0
for i, data in enumerate(data_loader):
with torch.no_grad():
labels = data['tag'].to(device)
out = model(data)
out = out.argmax(dim=1)
correct += (out == labels).sum().item()
total += len(labels)
return correct / total
def predict(model, data_loader):
model.eval()
result = dict()
for i, data in enumerate(data_loader):
with torch.no_grad():
out = model(data)
out = out.argmax(dim=1)
ids = data['guid']
for j in range(len(ids)):
guid = str(ids[j].item())
tag = labelDict[out[j].item()]
result[guid] = tag
with open('prediction.txt', 'w', encoding='utf-8') as wfs:
wfs.write('guid,tag\n')
with open('./data_pre/test_without_label.txt', 'r', encoding='utf-8') as rfs:
rfs.readline()
for line in rfs:
guid = line[0: line.find(',')]
wfs.write(guid + ',' + result[guid] + '\n')
if __name__ == '__main__':
print(device)