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process.py
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process.py
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import time
import torch
import numpy as np
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from tqdm import tqdm
from loss import CE, Align, Reconstruct
from torch.optim.lr_scheduler import LambdaLR
from classification import fit_lr, get_rep_with_label
class Trainer():
def __init__(self, args, model, train_loader, train_linear_loader, test_loader, verbose=False):
self.args = args
self.verbose = verbose
self.device = args.device
self.print_process(self.device)
self.model = model.to(torch.device(self.device))
# self.model = model.cuda()
print('model cuda')
self.train_loader = train_loader
self.train_linear_loader = train_linear_loader
self.test_loader = test_loader
self.lr_decay = args.lr_decay_rate
self.lr_decay_steps = args.lr_decay_steps
self.cr = CE(self.model)
self.alpha = args.alpha
self.beta = args.beta
self.test_cr = torch.nn.CrossEntropyLoss()
self.num_epoch = args.num_epoch
self.num_epoch_pretrain = args.num_epoch_pretrain
self.eval_per_steps = args.eval_per_steps
self.save_path = args.save_path
if self.num_epoch:
self.result_file = open(self.save_path + '/result.txt', 'w')
self.result_file.close()
self.step = 0
self.best_metric = -1e9
self.metric = 'acc'
def pretrain(self):
print('pretraining')
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.args.lr)
eval_acc = 0
align = Align()
reconstruct = Reconstruct()
self.model.copy_weight()
if self.num_epoch_pretrain:
result_file = open(self.save_path + '/pretrain_result.txt', 'w')
result_file.close()
result_file = open(self.save_path + '/linear_result.txt', 'w')
result_file.close()
for epoch in range(self.num_epoch_pretrain):
self.model.train()
tqdm_dataloader = tqdm(self.train_loader)
loss_sum = 0
loss_mse = 0
loss_ce = 0
hits_sum = 0
NDCG_sum = 0
for idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
self.optimizer.zero_grad()
[rep_mask, rep_mask_prediction], [token_prediction_prob, tokens] = self.model.pretrain_forward(batch[0])
align_loss = align.compute(rep_mask, rep_mask_prediction)
loss_mse += align_loss.item()
reconstruct_loss, hits, NDCG = reconstruct.compute(token_prediction_prob, tokens)
loss_ce += reconstruct_loss.item()
hits_sum += hits.item()
NDCG_sum += NDCG
loss = self.alpha * align_loss + self.beta * reconstruct_loss
loss.backward()
self.optimizer.step()
self.model.momentum_update()
loss_sum += loss.item()
print('pretrain epoch{0}, loss{1}, mse{2}, ce{3}, hits{4}, ndcg{5}'.format(epoch + 1, loss_sum / (idx + 1),
loss_mse / (idx + 1),
loss_ce / (idx + 1), hits_sum,
NDCG_sum / (idx + 1)))
result_file = open(self.save_path + '/pretrain_result.txt', 'a+')
print('pretrain epoch{0}, loss{1}, mse{2}, ce{3}, hits{4}, ndcg{5}'.format(epoch + 1, loss_sum / (idx + 1),
loss_mse / (idx + 1),
loss_ce / (idx + 1), hits_sum,
NDCG_sum / (idx + 1)),
file=result_file)
result_file.close()
if (epoch + 1) % 5 == 0:
self.model.eval()
train_rep, train_label = get_rep_with_label(self.model, self.train_linear_loader)
test_rep, test_label = get_rep_with_label(self.model, self.test_loader)
clf = fit_lr(train_rep, train_label)
acc = clf.score(test_rep, test_label)
print(acc)
result_file = open(self.save_path + '/linear_result.txt', 'a+')
print('epoch{0}, acc{1}'.format(epoch, acc), file=result_file)
result_file.close()
if acc > eval_acc:
eval_acc = acc
torch.save(self.model.state_dict(), self.save_path + '/pretrain_model.pkl')
def finetune(self):
print('finetune')
if self.args.load_pretrained_model:
print('load pretrained model')
state_dict = torch.load(self.save_path + '/pretrain_model.pkl', map_location=self.device)
try:
self.model.load_state_dict(state_dict)
except:
model_state_dict = self.model.state_dict()
for pretrain, random_intial in zip(state_dict, model_state_dict):
assert pretrain == random_intial
if pretrain in ['input_projection.weight', 'input_projection.bias', 'predict_head.weight',
'predict_head.bias', 'position.pe.weight']:
state_dict[pretrain] = model_state_dict[pretrain]
self.model.load_state_dict(state_dict)
self.model.eval()
train_rep, train_label = get_rep_with_label(self.model, self.train_linear_loader)
test_rep, test_label = get_rep_with_label(self.model, self.test_loader)
clf = fit_lr(train_rep, train_label)
acc = clf.score(test_rep, test_label)
pred_label = np.argmax(clf.predict_proba(test_rep), axis=1)
f1 = f1_score(test_label, pred_label, average='macro')
print(acc, f1)
result_file = open(self.save_path + '/linear_result.txt', 'a+')
print('epoch{0}, acc{1}, f1{2}'.format(0, acc, f1), file=result_file)
result_file.close()
self.model.linear_proba = False
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.lr)
self.scheduler = LambdaLR(self.optimizer, lr_lambda=lambda step: self.lr_decay ** step, verbose=self.verbose)
for epoch in range(self.num_epoch):
loss_epoch, time_cost = self._train_one_epoch()
self.result_file = open(self.save_path + '/result.txt', 'a+')
self.print_process(
'Finetune epoch:{0},loss:{1},training_time:{2}'.format(epoch + 1, loss_epoch, time_cost))
print('Finetune train epoch:{0},loss:{1},training_time:{2}'.format(epoch + 1, loss_epoch, time_cost),
file=self.result_file)
self.result_file.close()
self.print_process(self.best_metric)
return self.best_metric
def _train_one_epoch(self):
t0 = time.perf_counter()
self.model.train()
tqdm_dataloader = tqdm(self.train_linear_loader) if self.verbose else self.train_linear_loader
loss_sum = 0
for idx, batch in enumerate(tqdm_dataloader):
batch = [x.to(self.device) for x in batch]
self.optimizer.zero_grad()
loss = self.cr.compute(batch)
loss_sum += loss.item()
loss.backward()
# torch.nn.utils.clip_grad_norm_(self.model.parameters(), 5)
self.optimizer.step()
self.step += 1
# if self.step % self.eval_per_steps == 0:
metric = self.eval_model()
self.print_process(metric)
self.result_file = open(self.save_path + '/result.txt', 'a+')
print('step{0}'.format(self.step), file=self.result_file)
print(metric, file=self.result_file)
self.result_file.close()
if metric[self.metric] >= self.best_metric:
torch.save(self.model.state_dict(), self.save_path + '/model.pkl')
self.result_file = open(self.save_path + '/result.txt', 'a+')
print('saving model of step{0}'.format(self.step), file=self.result_file)
self.result_file.close()
self.best_metric = metric[self.metric]
self.model.train()
return loss_sum / (idx + 1), time.perf_counter() - t0
def eval_model(self):
self.model.eval()
tqdm_data_loader = tqdm(self.test_loader) if self.verbose else self.test_loader
metrics = {'acc': 0, 'f1': 0}
pred = []
label = []
test_loss = 0
with torch.no_grad():
for idx, batch in enumerate(tqdm_data_loader):
batch = [x.to(self.device) for x in batch]
ret = self.compute_metrics(batch)
if len(ret) == 2:
pred_b, label_b = ret
pred += pred_b
label += label_b
else:
pred_b, label_b, test_loss_b = ret
pred += pred_b
label += label_b
test_loss += test_loss_b.cpu().item()
confusion_mat = self._confusion_mat(label, pred)
self.print_process(confusion_mat)
self.result_file = open(self.save_path + '/result.txt', 'a+')
print(confusion_mat, file=self.result_file)
self.result_file.close()
if self.args.num_class == 2:
metrics['f1'] = f1_score(y_true=label, y_pred=pred)
metrics['precision'] = precision_score(y_true=label, y_pred=pred)
metrics['recall'] = recall_score(y_true=label, y_pred=pred)
else:
metrics['f1'] = f1_score(y_true=label, y_pred=pred, average='macro')
metrics['micro_f1'] = f1_score(y_true=label, y_pred=pred, average='micro')
metrics['acc'] = accuracy_score(y_true=label, y_pred=pred)
metrics['test_loss'] = test_loss / (idx + 1)
return metrics
def compute_metrics(self, batch):
if len(batch) == 2:
seqs, label = batch
scores = self.model(seqs)
else:
seqs1, seqs2, label = batch
scores = self.model((seqs1, seqs2))
_, pred = torch.topk(scores, 1)
test_loss = self.test_cr(scores, label.view(-1).long())
pred = pred.view(-1).tolist()
return pred, label.tolist(), test_loss
def _confusion_mat(self, label, pred):
mat = np.zeros((self.args.num_class, self.args.num_class))
for _label, _pred in zip(label, pred):
mat[_label, _pred] += 1
return mat
def print_process(self, *x):
if self.verbose:
print(*x)