45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import math
|
|
from abc import ABC
|
|
from collections import OrderedDict
|
|
|
|
import torch
|
|
|
|
|
|
class TrainingStepVariations(ABC):
|
|
"""
|
|
Houses all variations of training steps
|
|
"""
|
|
test_step_inf_loss = float('inf')
|
|
|
|
def training_step(self, batch, batch_idx, optimizer_idx=None):
|
|
"""Lightning calls this inside the training loop"""
|
|
# forward pass
|
|
x, y = batch
|
|
x = x.view(x.size(0), -1)
|
|
|
|
y_hat = self(x)
|
|
|
|
# calculate loss
|
|
loss_val = self.loss(y, y_hat)
|
|
|
|
# alternate possible outputs to test
|
|
if self.trainer.batch_idx % 1 == 0:
|
|
output = OrderedDict({
|
|
'loss': loss_val,
|
|
'progress_bar': {'some_val': loss_val * loss_val},
|
|
'log': {'train_some_val': loss_val * loss_val},
|
|
})
|
|
return output
|
|
|
|
if self.trainer.batch_idx % 2 == 0:
|
|
return loss_val
|
|
|
|
def training_step__inf_loss(self, batch, batch_idx, optimizer_idx=None):
|
|
output = self.training_step(batch, batch_idx, optimizer_idx)
|
|
if batch_idx == self.test_step_inf_loss:
|
|
if isinstance(output, dict):
|
|
output['loss'] *= torch.tensor(math.inf) # make loss infinite
|
|
else:
|
|
output /= 0
|
|
return output
|