def validate(self, ValDataLoader, Objective, Device='cpu'):
self.eval() #switch to evaluation mode
ValLosses = []
Tic = ptUtils.getCurrentEpochTime()
# print('Val length:', len(ValDataLoader))
for i, (Data, Targets) in enumerate(ValDataLoader,
0): # Get each batch
DataTD = ptUtils.sendToDevice(Data, Device)
TargetsTD = ptUtils.sendToDevice(Targets, Device)
Output = self.forward(DataTD)
Loss = Objective(Output, TargetsTD)
ValLosses.append(Loss.item())
# Print stats
Toc = ptUtils.getCurrentEpochTime()
Elapsed = math.floor((Toc - Tic) * 1e-6)
done = int(50 * (i + 1) / len(ValDataLoader))
sys.stdout.write(
('\r[{}>{}] val loss - {:.8f}, elapsed - {}').format(
'+' * done, '-' * (50 - done),
np.mean(np.asarray(ValLosses)),
ptUtils.getTimeDur(Elapsed)))
sys.stdout.flush()
sys.stdout.write('\n')
self.train() #switch back to train mode
return ValLosses
def fit(self, TrainDataLoader, Optimizer=None, Objective=nn.MSELoss(), TrainDevice='cpu', ValDataLoader=None):
if Optimizer is None:
# Optimizer = optim.SGD(NN.parameters(), lr=Args.learning_rate) # , momentum=0.9)
self.Optimizer = optim.Adam(self.parameters(), lr=self.Config.Args.learning_rate, weight_decay=1e-5) # PARAM
else:
self.Optimizer = Optimizer
self.setupCheckpoint(TrainDevice)
print('[ INFO ]: Training on {}'.format(TrainDevice))
self.to(TrainDevice)
CurrLegend = ['Train loss']
AllTic = ptUtils.getCurrentEpochTime()
for Epoch in range(self.Config.Args.epochs):
try:
EpochLosses = [] # For all batches in an epoch
Tic = ptUtils.getCurrentEpochTime()
for i, (Data, Targets) in enumerate(TrainDataLoader, 0): # Get each batch
DataTD = ptUtils.sendToDevice(Data, TrainDevice)
TargetsTD = ptUtils.sendToDevice(Targets, TrainDevice)
self.Optimizer.zero_grad()
# Forward, backward, optimize
Output = self.forward(DataTD)
Loss = Objective(Output, TargetsTD)
Loss.backward()
self.Optimizer.step()
EpochLosses.append(Loss.item())
gc.collect() # Collect garbage after each batch
# Terminate early if loss is nan
isTerminateEarly = False
if math.isnan(EpochLosses[-1]):
print('[ WARN ]: NaN loss encountered. Terminating training and saving current model checkpoint (might be junk).')
isTerminateEarly = True
break
# Print stats
Toc = ptUtils.getCurrentEpochTime()
Elapsed = math.floor((Toc - Tic) * 1e-6)
TotalElapsed = math.floor((Toc - AllTic) * 1e-6)
# Compute ETA
TimePerBatch = (Toc - AllTic) / ((Epoch * len(TrainDataLoader)) + (i+1)) # Time per batch
ETA = math.floor(TimePerBatch * self.Config.Args.epochs * len(TrainDataLoader) * 1e-6)
done = int(50 * (i+1) / len(TrainDataLoader))
ProgressStr = ('\r[{}>{}] epoch - {}/{}, train loss - {:.8f} | epoch - {}, total - {} ETA - {} |').format('=' * done, '-' * (50 - done), self.StartEpoch + Epoch + 1, self.StartEpoch + self.Config.Args.epochs
, np.mean(np.asarray(EpochLosses)), ptUtils.getTimeDur(Elapsed), ptUtils.getTimeDur(TotalElapsed), ptUtils.getTimeDur(ETA-TotalElapsed))
sys.stdout.write(ProgressStr.ljust(150))
sys.stdout.flush()
sys.stdout.write('\n')
self.LossHistory.append(np.mean(np.asarray(EpochLosses)))
if ValDataLoader is not None:
ValLosses = self.validate(ValDataLoader, Objective, TrainDevice)
self.ValLossHistory.append(np.mean(np.asarray(ValLosses)))
# print('Last epoch val loss - {:.16f}'.format(self.ValLossHistory[-1]))
CurrLegend = ['Train loss', 'Val loss']
# Always save checkpoint after an epoch. Will be replaced each epoch. This is independent of requested checkpointing
self.saveCheckpoint(Epoch, CurrLegend, TimeString='eot', PrintStr='~'*3)
isLastLoop = (Epoch == self.Config.Args.epochs-1) and (i == len(TrainDataLoader)-1)
if (Epoch + 1) % self.SaveFrequency == 0 or isTerminateEarly or isLastLoop:
self.saveCheckpoint(Epoch, CurrLegend)
if isTerminateEarly:
break
except (KeyboardInterrupt, SystemExit):
print('\n[ INFO ]: KeyboardInterrupt detected. Saving checkpoint.')
self.saveCheckpoint(Epoch, CurrLegend, TimeString='eot', PrintStr='$'*3)
break
except Exception as e:
print(traceback.format_exc())
print('\n[ WARN ]: Exception detected. *NOT* saving checkpoint. {}'.format(e))
# self.saveCheckpoint(Epoch, CurrLegend, TimeString='eot', PrintStr='$'*3)
break
AllToc = ptUtils.getCurrentEpochTime()
print('[ INFO ]: All done in {}.'.format(ptUtils.getTimeDur((AllToc - AllTic) * 1e-6)))