last_epoch=start_epoch)
bnm_scheduler = pt_utils.BNMomentumScheduler(model,
bnm_lmbd,
last_epoch=start_epoch)
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=
"/kitti_semantic/Pointnet2_PyTorch-master/pointnet2/train/checkpoints/pointnet2_smeseg",
best_name=
"/kitti_semantic/Pointnet2_PyTorch-master/pointnet2/train/checkpoints/poitnet2_semseg_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)
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())
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_smeseg",
best_name="checkpoints/poitnet2_semseg_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: