How to solve my problem of max_step parameter in pytorch? - machine-learning

Question

I'm trying to train source code.
class mymodel(pl.LightningModule):
def __init__(self, config , learning_rate = 1e-4, max_steps = 100000//2):
super(mymodel, self).__init__()
self.config = config
self.save_hyperparameters()
self.training_losses = []
self.validation_losses = []
self.max_steps = max_steps
def configure_optimizers(self):
return torch.optim.AdamW(self.parameters(), lr = self.hparams['learning_rate'])
def forward(self, batch_dict):
return answer_vector
def calculate_metrics(self, prediction, labels):
batch_size = len(prediction)
ac_score = 0
for (pred, gt) in zip(prediction, labels):
ac_score+= calculate_acc_score(pred.detach().cpu(), gt.detach().cpu())
ac_score = ac_score/batch_size
return ac_score
def training_step(self, batch, batch_idx):
answer_vector = self.forward(batch)
loss = nn.CrossEntropyLoss()(answer_vector.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
_, preds = torch.max(answer_vector, dim = -1)
train_acc = self.calculate_metrics(preds, batch['answer'])
train_acc = torch.tensor(train_acc)
return loss
def validation_step(self, batch, batch_idx):
logits = self.forward(batch)
loss = nn.CrossEntropyLoss()(logits.reshape(-1,self.config['classes']), batch['answer'].reshape(-1))
_, preds = torch.max(logits, dim = -1)
## Validation Accuracy
val_acc = self.calculate_metrics(preds.cpu(), batch['answer'].cpu())
val_acc = torch.tensor(val_acc)
## Logging
self.log('val_ce_loss', loss, prog_bar = True)
self.log('val_acc', val_acc, prog_bar = True)
return {'val_loss': loss, 'val_acc': val_acc}
def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, opt_closure = None, on_tpu=False,
using_native_amp=False, using_lbfgs=False):
## Warmup for 1000 steps
if self.trainer.global_step < 1000:
lr_scale = min(1., float(self.trainer.global_step + 1) / 1000.)
for pg in optimizer.param_groups:
pg['lr'] = lr_scale * self.hparams.learning_rate
## Linear Decay
else:
for pg in optimizer.param_groups:
pg['lr'] = polynomial(self.hparams.learning_rate, self.trainer.global_step, max_iter = self.max_steps)
optimizer.step(opt_closure)
optimizer.zero_grad()
In 5'th epoch (maybe less or more) I encountered error that stop training. so I increase max_step . But when I increase max_step(max_step==100K) I have this problem with loss and acc that
loss>100 && acc==0 .I attach screen of this problem.
enter image description here
What changing I should do in source code to continue training model without this problem?

Answer 1

Updates:
I see. It looks like your optimizer_step is actually for "scheduler," where it messes with the AdamW learning rate. You should directly apply the scheduler to configure_optimizers function. See https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html?highlight=configure_optimizers#configure-optimizers
===
old answer:
By error, do you mean the line of val_ce_loss? If yes, that's not an error. It means the val_ce_loss of the current epoch is not within the top1 of historical epochs so the checkpoint won't be saved to the disk. Please refer to the argument of save_top_k in the checkpoint callback. https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html