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
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)
_, classes = torch.max(preds, -1)
acc = (classes == labels).float().sum() / labels.numel()