def main(args):
model = custom_model.buildModelStage3(args)
optimizer, scheduler, records = solver_utils.configOptimizer(args, model)
optimizers = [optimizer, -1]
criterion = solver_utils.Stage3Crit(args)
recorder = recorders.Records(args.log_dir, records)
train_loader, val_loader = custom_data_loader.reflectanceDataloader(args)
for epoch in range(args.start_epoch, args.epochs + 1):
scheduler.step()
recorder.insertRecord('train', 'lr', epoch, scheduler.get_last_lr())
train_utils.train(args, train_loader, model, criterion, optimizers,
log, epoch, recorder)
if epoch % args.save_intv == 0:
model_utils.saveCheckpoint(args.cp_dir, epoch, model, optimizer,
recorder.records, args)
log.plotCurves(recorder, 'train')
if epoch % args.val_intv == 0:
test_utils.test(args, 'val', val_loader, model, log, epoch,
recorder)
log.plotCurves(recorder, 'val')
def main(args):
train_loader, val_loader = custom_data_loader.customDataloader(args)
model = custom_model.buildModel(args)
optimizer, scheduler, records = solver_utils.configOptimizer(args, model)
criterion = solver_utils.Criterion(args)
recorder = recorders.Records(args.log_dir, records)
tf_train_writer, tf_test_writer = tfboard.tensorboard_init()
for epoch in range(args.start_epoch, args.epochs+1):
scheduler.step()
recorder.insertRecord('train', 'lr', epoch, scheduler.get_lr()[0])
train_utils.train(args, train_loader, model, criterion, optimizer, log, epoch, recorder, tf_train_writer)
if epoch % args.save_intv == 0:
model_utils.saveCheckpoint(args.cp_dir, epoch, model, optimizer, recorder.records, args)
if epoch % args.val_intv == 0:
test_utils.test(args, 'val', val_loader, model, log, epoch, recorder, tf_test_writer)
