num_workers=2,
)
train_set = Indoor3DSemSeg(args.num_points)
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=2,
shuffle=True,
)
model = Pointnet(num_classes=13, input_channels=6, use_xyz=True)
model.cuda()
print(model)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Model size = %i" % params)
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,
)
pin_memory=True,
num_workers=0,
shuffle=True)
model = Pointnet(num_classes=34, input_channels=6, use_xyz=True)
# 设置设备
device_name = "cpu"
if torch.cuda.is_available():
device_name = "cuda"
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(0)
device = torch.device(device_name)
print(device)
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
num_workers=2,
)
train_set = Indoor3DSemSeg(args.num_points)
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
pin_memory=True,
num_workers=2,
shuffle=True,
)
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,
)
# default value
it = -1 # for the initialize value of `LambdaLR` and `BNMomentumScheduler`
best_loss = 1e10