def load_pointcloud(pc_filename):
pointcloud = pc_util.read_xyzrgb_ply(pc_filename)
return pointcloud
sampling='seed_fps',
num_class=DC.num_class).to(device)
print('Constructed model.')
# Load checkpoint
optimizer = optim.Adam(net.parameters(), lr=0.001)
checkpoint = torch.load(checkpoint_path)
net.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
print("Loaded checkpoint %s (epoch: %d)" % (checkpoint_path, epoch))
# Load and preprocess input point cloud
net.eval() # set model to eval mode (for bn and dp)
# point_cloud = read_ply(pc_path)
point_cloud = read_xyzrgb_ply(pc_path)
pc = preprocess_point_cloud(point_cloud)
print('Loaded point cloud data: %s' % (pc_path))
# Model inference
inputs = {'point_clouds': torch.from_numpy(pc).to(device)}
tic = time.time()
with torch.no_grad():
end_points = net(inputs)
toc = time.time()
print('Inference time: %f' % (toc - tic))
end_points['point_clouds'] = inputs['point_clouds']
if not os.path.exists(dump_dir): os.mkdir(dump_dir)
MODEL.dump_results(end_points, dump_dir, DC, True)
print('Dumped detection results to folder %s' % (dump_dir))