def test_xyz():
model = Pointnet2MSG(3, input_channels=3)
pytest.helpers.cls_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss()))
checkpoint_status = pt_utils.load_checkpoint(
model, optimizer, filename=args.checkpoint.split(".")[0])
if checkpoint_status is not None:
it, start_epoch, best_loss = checkpoint_status
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lambda=lr_lbmd,
last_epoch=it)
bnm_scheduler = pt_utils.BNMomentumScheduler(model,
bn_lambda=bn_lbmd,
last_epoch=it)
model = nn.DataParallel(model)
it = max(it, 0) # for the initialize value of `trainer.train`
model_fn = model_fn_decorator(nn.CrossEntropyLoss())
if args.visdom:
viz = pt_utils.VisdomViz(port=args.visdom_port)
else:
viz = pt_utils.CmdLineViz()
viz.text(pprint.pformat(vars(args)))
if not osp.isdir("checkpoints"):
os.makedirs("checkpoints")
trainer = pt_utils.Trainer(
model,
model_fn,
optimizer,
if args.checkpoint is not None:
checkpoint_status = pt_utils.load_checkpoint(
model, optimizer, filename=args.checkpoint.split(".")[0])
if checkpoint_status is not None:
it, start_epoch, best_loss = checkpoint_status
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lambda=lr_lbmd,
last_epoch=it)
bnm_scheduler = pt_utils.BNMomentumScheduler(model,
bn_lambda=bn_lbmd,
last_epoch=it)
it = max(it, 0) # for the initialize value of `trainer.train`
model_fn = model_fn_decorator(nn.CrossEntropyLoss(), args.loss)
if args.visdom:
viz = pt_utils.VisdomViz(port=args.visdom_port)
else:
viz = pt_utils.CmdLineViz()
viz.text(pprint.pformat(vars(args)))
if not osp.isdir("checkpoints"):
os.makedirs("checkpoints")
trainer = pt_utils.Trainer(
model,
model_fn,
optimizer,