def load_pointnet(model_path):
model = Pointnet(num_classes=4, input_channels=72, use_xyz=False)
if os.path.isfile(model_path):
print("==> Loading from checkpoint '{}'".format(model_path))
checkpoint = torch.load(model_path)
epoch = checkpoint["epoch"]
it = checkpoint.get("it", 0.0)
best_prec = checkpoint["best_prec"]
if model is not None and checkpoint["model_state"] is not None:
model.load_state_dict(checkpoint["model_state"])
print("==> Done")
return model
test_set = Kitti3DSemSeg(args.num_points, train=False)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
# train_set = Indoor3DSemSeg(args.num_points)
train_set = Kitti3DSemSeg(args.num_points)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
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(
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
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**
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
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,
)
visualization.custom_draw_geometry_with_key_callback(point_cloud)
if __name__ == "__main__":
args = parser.parse_args()
test_set = WaymoDatasetLoader(args.test_data_path, args.num_points)
test_loader = DataLoader(
test_set,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=2,
)
model = Pointnet(num_classes=2, input_channels=0, use_xyz=True)
model.cuda()
print("checkpoint filename: {}".format(args.checkpoint.split(".")[0]))
# load status from checkpoint
if args.checkpoint is not None:
checkpoint_status = pt_utils.load_checkpoint(
model, None, filename=args.checkpoint.split(".")[0])
if checkpoint_status is not None:
it, start_epoch, best_loss = checkpoint_status
data_iter = iter(test_loader.__iter__())
for data in data_iter:
inputs, labels = data
gpu_index = paras.gpu
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_index
lr_clip = 1e-5
bnm_clip = 1e-2
if __name__ == "__main__":
args = parser.parse_args()
test_set = Indoor3DSemSeg(args.num_points, train=False)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
model = Pointnet(3, input_channels=6).cuda()
model.load_state_dict(
torch.load(
'/home1/xuhui/Pointnet2_PyTorch-master/pointnet2/train/checkpoints/poitnet2_semseg_best.pth.tar'
)['model_state'])
model.eval()
# for i, data in tqdm.tqdm(enumerate(test_loader, 0), total=len(test_loader),
# leave=False, desc='val'):
for i, data in enumerate(test_loader):
f = open(os.path.join(preds_path, str(i).zfill(2) + '.txt'), 'w')
inputs, labels = data
inputs = inputs.to('cuda', non_blocking=True)
labels = labels.to('cuda', non_blocking=True)
# print(labels)
preds = model(inputs)