'params': model.parameters()
}, {
'params': classifier.parameters()
}],
lr=lr,
weight_decay=args.weight_decay)
# default value
it = -1 # for the initialize value of `LambdaLR` and `BNMomentumScheduler`
best_prec1 = 0
best_auc1 = 0
start_epoch = 1
# load status from checkpoint
if args.model_checkpoint is not None:
checkpoint_status = pt_utils.load_checkpoint(
model, optimizer, filename=args.model_checkpoint.split(".")[0])
if checkpoint_status is not None:
it, start_epoch, best_loss = checkpoint_status
if args.cls_checkpoint is not None:
checkpoint_status = pt_utils.load_checkpoint(
classifier, optimizer, filename=args.cls_checkpoint.split(".")[0])
if checkpoint_status is not None:
it, start_epoch, best_loss = checkpoint_status
for param_group in optimizer.param_groups:
param_group['lr'] = 1e-4
it = max(it, 0) # for the initialize value of `trainer.train`
if not osp.isdir("checkpoints"):
os.makedirs("checkpoints")
## rewrite the training process
lr_clip / args.lr,
)
bn_lbmd = lambda it: max(
args.bn_momentum * args.bnm_decay**
(int(it * args.batch_size / args.decay_step)),
bnm_clip,
)
# default value
it = -1 # for the initialize value of `LambdaLR` and `BNMomentumScheduler`
best_loss = 1e10
start_epoch = 1
# load status from checkpoint
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)
model = nn.DataParallel(model)
it = max(it, 0) # for the initialize value of `trainer.train`
model_fn = model_fn_decorator(nn.CrossEntropyLoss())
model = Pointnet(num_classes=13, input_channels=6, use_xyz=True)
model.cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
lr_lbmd = lambda it: max(args.lr_decay**(int(it * args.batch_size / args.decay_step)), lr_clip / args.lr)
bnm_lmbd = lambda it: max(args.bn_momentum * args.bn_decay**(int(it * args.batch_size / args.decay_step)), bnm_clip)
if args.checkpoint is None:
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
start_epoch = 1
best_prec = 0
best_loss = 1e10
else:
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())
if args.visdom:
viz = pt_utils.VisdomViz(port=args.visdom_port)
else:
viz = pt_utils.CmdLineViz()
viz.text(pprint.pformat(vars(args)))
