关于在函数(PyTorch)中保存state_dict/checkpoint

Question

我试图实现以下函数来保存model_state检查点:

def train_epoch(self):
for epoch in tqdm.trange(self.epoch, self.max_epoch, desc='Train Epoch', ncols=100):
    self.epoch = epoch      # increments the epoch of Trainer
    checkpoint = {} # fixme: here checkpoint!!!
    # model_save_criteria = self.model_save_criteria
    self.train()
    if epoch % 1 == 0:
        self.validate(checkpoint) 
    checkpoint_latest = {
        'epoch': self.epoch,
        'arch': self.model.__class__.__name__,
        'model_state_dict': self.model.state_dict(),
        'optimizer_state_dict': self.optim.state_dict(),
        'model_save_criteria': self.model_save_criteria
    }
    checkpoint['checkpoint_latest'] = checkpoint_latest
    torch.save(checkpoint, self.model_pth)

之前我只运行了一个for循环:

train_states = {}
for epoch in range(max_epochs):
    running_loss = 0
    time_batch_start = time.time()
    model.train()
    for bIdx, sample in enumerate(train_loader):
        ...
        train...
        validation...
        train_states_latest = {
          'epoch': epoch + 1,
          'model_state_dict': model.state_dict(),
          'optimizer_state_dict': optimizer.state_dict(),
          'model_save_criteria': chosen_criteria}
        train_states['train_states_latest'] = train_states_latest
        torch.save(train_states, FILEPATH_MODEL_SAVE)

是否有方法启动checkpoint={}并在每个循环中更新它?或者每个时期的checkpoint={}都可以因为模型本身持有state_dict()。只是我每次都重写检查点。

Answer 1

您可以通过简单地更改FILEPATH_MODEL_SAVE路径并让该路径包含epoch或迭代数的信息来避免覆盖检查点。例如(以原始代码为例)，

train_states = {}
for epoch in range(max_epochs):
    running_loss = 0
    time_batch_start = time.time()
    model.train()
    for bIdx, sample in enumerate(train_loader):
        ...
        train...
        validation...
        train_states_latest = {
          'epoch': epoch + 1,
          'model_state_dict': model.state_dict(),
          'optimizer_state_dict': optimizer.state_dict(),
          'model_save_criteria': chosen_criteria}
        train_states['train_states_latest'] = train_states_latest
        
        # This is the code you can add
        FILEPATH_MODEL_SAVE = "Epoch{}batch{}model_weights.pth".format(epoch, bIdx)
        torch.save(train_states, FILEPATH_MODEL_SAVE)

上面这个新的代码火炬。保存您避免覆盖检查点

Sarthak