def _infer(data):
model.eval()
# data = dev_data
y_pred = []
with torch.no_grad():
for batch_data in data_iter(data, test_batch_size, shuffle=False):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
y_pred.extend(torch.max(batch_outputs, dim=1)
[1].cpu().numpy().tolist())
print(label_encoder.label2name(y_pred))
def _eval(data):
model.eval() # 不启用 BatchNormalization 和 Dropout
# data = dev_data
y_pred = []
y_true = []
with torch.no_grad():
for batch_data in data_iter(data, test_batch_size, shuffle=False):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
y_pred.extend(torch.max(batch_outputs, dim=1)
[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
score, dev_f1 = get_score(y_true, y_pred)
return score, dev_f1
def run(method="train", save_path=None, infer_texts=[]):
shuffle_slicer = ShuffleSlicer()
# start_time = time.time()
raw_data_path = "/home/wujinjie/kesci_question_multilabel_classification/data/raw_data/baidu/nlp_db.baidu_text.csv"
texts = pd.read_csv(raw_data_path)
train_df, dev_df, test_df = shuffle_slicer.split(texts, dev=True)
# Test_data = {'label': [0] * len(texts), 'text': test_texts}
clip = 5.0
epochs = 100
# log_interval = 50
test_batch_size = 128
train_batch_size = 128
train_texts, train_labels = process_corpus_dl(train_df)
Train_data = {'label': train_labels, 'text': train_texts}
dev_texts, dev_labels = process_corpus_dl(dev_df)
Dev_data = {'label': dev_labels, 'text': dev_texts}
vocab = Vocab(Train_data)
step = 0
def _eval(data):
model.eval() # 不启用 BatchNormalization 和 Dropout
# data = dev_data
y_pred = []
y_true = []
with torch.no_grad():
for batch_data in data_iter(data, test_batch_size, shuffle=False):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
y_pred.extend(torch.max(batch_outputs, dim=1)
[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
score, dev_f1 = get_score(y_true, y_pred)
return score, dev_f1
def _infer(data):
model.eval()
# data = dev_data
y_pred = []
with torch.no_grad():
for batch_data in data_iter(data, test_batch_size, shuffle=False):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
y_pred.extend(torch.max(batch_outputs, dim=1)
[1].cpu().numpy().tolist())
print(label_encoder.label2name(y_pred))
if method == "train":
model = Model(vocab, label_encoder)
# loss
criterion = nn.CrossEntropyLoss() # obj
# 生成模型可处理的格式
train_data = get_examples_bert(Train_data, model.word_encoder, vocab, label_encoder)
dev_data = get_examples_bert(Dev_data, model.word_encoder, vocab, label_encoder)
# 一个epoch的batch个数
batch_num = int(np.ceil(len(train_data) / float(train_batch_size)))
optimizer = Optimizer(model.all_parameters, steps=batch_num * epochs) # 优化器
best_train_f1, best_dev_f1 = 0, 0
early_stop = -1
EarlyStopEpochs = 3 # 当多个epoch，dev的指标都没有提升，则早停
# train
print("start train")
for epoch in range(1, epochs + 1):
optimizer.zero_grad()
model.train() # 启用 BatchNormalization 和 Dropout
overall_losses = 0
losses = 0
# batch_idx = 1
y_pred = []
y_true = []
for batch_data in data_iter(train_data, train_batch_size, shuffle=True):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
loss = criterion(batch_outputs, batch_labels)
loss.backward()
loss_value = loss.detach().cpu().item()
losses += loss_value
overall_losses += loss_value
y_pred.extend(torch.max(batch_outputs, dim=1)
[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
nn.utils.clip_grad_norm_(
optimizer.all_params, max_norm=clip) # 梯度裁剪
for cur_optim, scheduler in zip(optimizer.optims, optimizer.schedulers):
cur_optim.step()
scheduler.step()
optimizer.zero_grad()
step += 1
# print(step)
print(epoch)
overall_losses /= batch_num
overall_losses = reformat(overall_losses, 4)
score, train_f1 = get_score(y_true, y_pred)
print("score:{}, train_f1:{}".format(train_f1, score))
# if set(y_true) == set(y_pred):
# print("report")
# report = classification_report(y_true, y_pred, digits=4, target_names=label_encoder.target_names)
# # logging.info('\n' + report)
# print(report)
# eval
_, dev_f1 = _eval(data=dev_data)
if best_dev_f1 <= dev_f1:
best_dev_f1 = dev_f1
early_stop = 0
best_train_f1 = train_f1
save_path = model_utils.save_checkpoint(
model, epoch, save_folder="/home/wujinjie/kesci_question_multilabel_classification/data/textbert")
print("save_path:{}".format(save_path))
# torch.save(model.state_dict(), save_model)
else:
early_stop += 1
if early_stop == EarlyStopEpochs: # 达到早停次数，则停止训练
break
print("score:{}, dev_f1:{}, best_train_f1:{}, best_dev_f1:{}".format(
dev_f1, score, best_train_f1, best_dev_f1))
else:
model = model_utils.load_checkpoint(save_path)
if method == "test":
test_texts, test_labels = process_corpus_dl(train_df)
Test_data = {'label': test_labels, 'text': test_texts}
test_data = get_examples_bert(Test_data, model.word_encoder, vocab, label_encoder)
# model.load_state_dict(torch.load(save_model))
_, dev_f1 = _eval(data=test_data)
print(dev_f1)
elif method == "infer":
infer_texts = list(map(segment, infer_texts))
# print(infer_texts)
Infer_data = {'label': [0] * len(infer_texts), 'text': infer_texts}
infer_data = get_examples_bert(Infer_data, model.word_encoder, vocab, label_encoder)
_infer(data=infer_data)
batch_num = int(np.ceil(len(train_data) / float(train_batch_size))) # 一个epoch的batch个数
if __name__ == "__main__":
best_train_f1, best_dev_f1 = 0, 0
early_stop = -1
EarlyStopEpochs = 3 # 当多个epoch，dev的指标都没有提升，则早停
# train
print("start train")
for epoch in range(1, epochs + 1):
optimizer.zero_grad()
model.train() # 启用 BatchNormalization 和 Dropout
overall_losses = 0
losses = 0
batch_idx = 1
y_pred = []
y_true = []
for batch_data in data_iter(train_data, train_batch_size, shuffle=True):
torch.cuda.empty_cache()
batch_inputs, batch_labels = batch2tensor(batch_data)
batch_outputs = model(batch_inputs)
loss = criterion(batch_outputs, batch_labels)
loss.backward()
loss_value = loss.detach().cpu().item()
losses += loss_value
overall_losses += loss_value
y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
nn.utils.clip_grad_norm_(optimizer.all_params, max_norm=clip) # 梯度裁剪
for cur_optim, scheduler in zip(optimizer.optims, optimizer.schedulers):