start_epoch, best_loss = pt_utils.load_checkpoint(
model, optimizer, filename=args.checkpoint.split(".")[0])
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lbmd,
last_epoch=start_epoch)
bnm_scheduler = pt_utils.BNMomentumScheduler(model,
bnm_lmbd,
last_epoch=start_epoch)
model_fn = model_fn_decorator(nn.CrossEntropyLoss())
trainer = pt_utils.Trainer(model,
model_fn,
optimizer,
checkpoint_name="sem_seg_checkpoint",
best_name="sem_seg_best",
lr_scheduler=lr_scheduler,
bnm_scheduler=bnm_scheduler,
eval_frequency=10)
trainer.train(start_epoch,
args.epochs,
train_loader,
test_loader,
best_loss=best_loss)
if start_epoch == args.epochs:
test_loader.dataset.data_precent = 1.0
_ = trainer.eval_epoch(start_epoch, test_loader)
else:
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lambda=lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bn_lambda=bn_lbmd)
best_loss = 1e10
start_epoch = 1
model_fn = model_fn_decorator(nn.CrossEntropyLoss())
viz = pt_utils.VisdomViz(port=args.visdom_port)
viz.text(str(vars(args)))
trainer = pt_utils.Trainer(model,
model_fn,
optimizer,
checkpoint_name="checkpoints/pointnet2_cls",
best_name="checkpoints/pointnet2_cls_best",
lr_scheduler=lr_scheduler,
bnm_scheduler=bnm_scheduler,
viz=viz)
trainer.train(0,
start_epoch,
args.epochs,
train_loader,
test_loader,
best_loss=best_loss)
if start_epoch == args.epochs:
_ = trainer.eval_epoch(test_loader)
model, bn_lambda=bn_lbmd, last_epoch=start_epoch
)
else:
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lambda=lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bn_lambda=bn_lbmd)
best_loss = 1e10
start_epoch = 1
model_fn = model_fn_decorator(nn.CrossEntropyLoss())
trainer = pt_utils.Trainer(
model,
model_fn,
optimizer,
checkpoint_name="checkpoints/single_layer",
best_name="checkpoints/single_layer_best",
lr_scheduler=lr_scheduler,
bnm_scheduler=bnm_scheduler
)
trainer.train(
start_epoch,
args.epochs,
train_loader,
test_loader,
best_loss=best_loss
)
if start_epoch == args.epochs:
_ = trainer.eval_epoch(start_epoch, test_loader)