Inference with custom data

[kaxapatel]

Hi. thank you for your last response. Now I am trying to deploy your model in one of my Jetson board with Velodyne sensor. First I want to try just feeding the single frame into the model. Can you suggest where can I change for input data in the test code. so I can easily generate predictions without giving data_path. My final goal is to have real-time segmentation with Lidar input. In more simplified way, input as a lidar raw data string and output as a predictions.

[edwardzhou130]

You will need to change the dataloader code:

Panoptic-PolarNet/dataloader/dataset.py

Lines 57 to 70 in 3a72f23

	def __getitem__(self, index):
	raw_data = np.fromfile(self.im_idx[index], dtype=np.float32).reshape((-1, 4))
	if self.imageset == 'test':
	sem_data = np.expand_dims(np.zeros_like(raw_data[:,0],dtype=int),axis=1)
	inst_data = np.expand_dims(np.zeros_like(raw_data[:,0],dtype=np.uint32),axis=1)
	else:
	annotated_data = np.fromfile(self.im_idx[index].replace('velodyne','labels')[:-3]+'label', dtype=np.uint32).reshape((-1,1))
	sem_data = annotated_data & 0xFFFF #delete high 16 digits binary
	sem_data = np.vectorize(self.learning_map.__getitem__)(sem_data)
	inst_data = annotated_data
	data_tuple = (raw_data[:,:3], sem_data.astype(np.uint8),inst_data)
	if self.return_ref:
	data_tuple += (raw_data[:,3],)
	return data_tuple

It should work if you can feed the same type of raw data to data_tuple here.