def evaluate(model, val_loader, criterion):
# switch to evaluate mode
model.eval()
data_len = 0
total_loss = 0
total_acc = 0
with torch.no_grad():
for batch_x, batch_y in val_loader:
batch_len = len(batch_y)
# batch_len = len(batch_y.size(0))
data_len += batch_len
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
probs = model(batch_x)
loss = criterion(probs, batch_y)
total_loss += loss.item()
_acc = accuracy(probs, batch_y)
total_acc += _acc[0].item() * batch_len
return total_loss / data_len, total_acc / data_len
def train(train_data, val_data, fold_idx=None):
train_data = MyDataset(train_data, train_transform)
train_loader = DataLoader(train_data, batch_size=config.batch_size, shuffle=True)
val_data = MyDataset(val_data, val_transform)
val_loader = DataLoader(val_data, batch_size=config.batch_size, shuffle=False)
model = Net(model_name).to(device)
# criterion = nn.CrossEntropyLoss()
criterion = FocalLoss(0.5)
# optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
# scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
optimizer = Ranger(model.parameters(), lr=1e-3, weight_decay=0.0005)
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=4)
if fold_idx is None:
print('start')
model_save_path = os.path.join(config.model_path, '{}.bin'.format(model_name))
else:
print('start fold: {}'.format(fold_idx + 1))
model_save_path = os.path.join(config.model_path, '{}_fold{}.bin'.format(model_name, fold_idx))
# if os.path.isfile(model_save_path):
# print('加载之前的训练模型')
# model.load_state_dict(torch.load(model_save_path))
best_val_score = 0
best_val_score_cnt = 0
last_improved_epoch = 0
adjust_lr_num = 0
for cur_epoch in range(config.epochs_num):
start_time = int(time.time())
model.train()
print('epoch:{}, step:{}'.format(cur_epoch + 1, len(train_loader)))
cur_step = 0
for batch_x, batch_y in train_loader:
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
optimizer.zero_grad()
probs = model(batch_x)
train_loss = criterion(probs, batch_y)
train_loss.backward()
optimizer.step()
cur_step += 1
if cur_step % config.train_print_step == 0:
train_acc = accuracy(probs, batch_y)
msg = 'the current step: {0}/{1}, train loss: {2:>5.2}, train acc: {3:>6.2%}'
print(msg.format(cur_step, len(train_loader), train_loss.item(), train_acc[0].item()))
val_loss, val_score = evaluate(model, val_loader, criterion)
if val_score >= best_val_score:
if val_score == best_val_score:
best_val_score_cnt += 1
best_val_score = val_score
torch.save(model.state_dict(), model_save_path)
improved_str = '*'
last_improved_epoch = cur_epoch
else:
improved_str = ''
msg = 'the current epoch: {0}/{1}, val loss: {2:>5.2}, val acc: {3:>6.2%}, cost: {4}s {5}'
end_time = int(time.time())
print(msg.format(cur_epoch + 1, config.epochs_num, val_loss, val_score,
end_time - start_time, improved_str))
if cur_epoch - last_improved_epoch >= config.patience_epoch or best_val_score_cnt >= 3:
if adjust_lr_num >= config.adjust_lr_num:
print("No optimization for a long time, auto stopping...")
break
print("No optimization for a long time, adjust lr...")
# scheduler.step()
last_improved_epoch = cur_epoch # 加上，不然会连续更新的
adjust_lr_num += 1
best_val_score_cnt = 0
scheduler.step()
del model
gc.collect()
if fold_idx is not None:
model_score[fold_idx] = best_val_score
def train(train_data, val_data, fold_idx=None):
train_data = MyDataset(train_data, train_transform)
train_loader = DataLoader(train_data, batch_size=config.batch_size, shuffle=True)
val_data = MyDataset(val_data, val_transform)
val_loader = DataLoader(val_data, batch_size=config.batch_size, shuffle=False)
model = Net(model_name).to(device)
criterion = nn.CrossEntropyLoss()
# criterion = FocalLoss(0.5)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
# optimizer = torch.optim.Adagrad(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
# config.model_save_path = os.path.join(config.model_path, '{}.bin'.format(model_name))
best_val_acc = 0
last_improved_epoch = 0
if fold_idx is None:
print('start')
model_save_path = os.path.join(config.model_path, '{}.bin'.format(model_name))
else:
print('start fold: {}'.format(fold_idx + 1))
model_save_path = os.path.join(config.model_path, '{}_fold{}.bin'.format(model_name, fold_idx))
for cur_epoch in range(config.epochs_num):
start_time = int(time.time())
model.train()
print('epoch: ', cur_epoch + 1)
cur_step = 0
for batch_x, batch_y in train_loader:
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
optimizer.zero_grad()
probs = model(batch_x)
train_loss = criterion(probs, batch_y)
train_loss.backward()
optimizer.step()
cur_step += 1
if cur_step % config.train_print_step == 0:
train_acc = accuracy(probs, batch_y)
msg = 'the current step: {0}/{1}, train loss: {2:>5.2}, train acc: {3:>6.2%}'
print(msg.format(cur_step, len(train_loader), train_loss.item(), train_acc[0].item()))
val_loss, val_acc = evaluate(model, val_loader, criterion)
if val_acc >= best_val_acc:
best_val_acc = val_acc
torch.save(model.state_dict(), model_save_path)
improved_str = '*'
last_improved_epoch = cur_epoch
else:
improved_str = ''
# msg = 'the current epoch: {0}/{1}, train loss: {2:>5.2}, train acc: {3:>6.2%}, ' \
# 'val loss: {4:>5.2}, val acc: {5:>6.2%}, {6}'
msg = 'the current epoch: {0}/{1}, val loss: {2:>5.2}, val acc: {3:>6.2%}, cost: {4}s {5}'
end_time = int(time.time())
print(msg.format(cur_epoch + 1, config.epochs_num, val_loss, val_acc,
end_time - start_time, improved_str))
scheduler.step()
if cur_epoch - last_improved_epoch > config.patience_epoch:
print("No optimization for a long time, auto-stopping...")
break
del model
gc.collect()
