Question about how to get the prediction of all point in each frame?
LeopoldACC opened this issue · comments
Hi,@qiqihaer ,Thanks for your reproducing the work in PyTorch.
Because I want to get the prediction of all point in each frame,but I found that Randla-Net and KPConv both do not support that form of input.The 2 works both use sampler to generate subsampling data in cyclic way,if I change the code as below that use whole of point,the evaluate result is too different as the public result and your test result.
eval mean acc: 0.182902
eval mean loss: 53.351186
mean IoU:4.3
IoU: 0.34 0.13 0.00 0.50 0.13 0.00 0.00 0.00 6.07 2.26 6.12 0.31 26.94 0.13 23.01 2.05 10.04 2.29 0.99 def __getitem__(self, item):
selected_pc, selected_labels, selected_idx, cloud_ind = self.spatially_regular_gen(item)
return selected_pc, selected_labels, selected_idx, cloud_ind
def spatially_regular_gen(self, item):
# Generator loop
if self.mode != 'test':
cloud_ind = item
pc_path = self.data_list[cloud_ind]
pc, tree, labels = self.get_data(pc_path)
# crop a small point cloud
pick_idx = np.random.choice(len(pc), 1)[0]
selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
else:
cloud_ind = int(np.argmin(self.min_possibility))
pick_idx = np.argmin(self.possibility[cloud_ind])
pc_path = path_list[cloud_ind]
pc, tree, labels = self.get_data(pc_path)
selected_pc, selected_labels, selected_idx = self.crop_pc(pc, labels, tree, pick_idx)
# update the possibility of the selected pc
dists = np.sum(np.square((selected_pc - pc[pick_idx]).astype(np.float32)), axis=1)
delta = np.square(1 - dists / np.max(dists))
self.possibility[cloud_ind][selected_idx] += delta
self.min_possibility[cloud_ind] = np.min(self.possibility[cloud_ind])
return selected_pc.astype(np.float32), selected_labels.astype(np.int32), selected_idx.astype(np.int32), np.array([cloud_ind], dtype=np.int32)
def get_data(self, file_path):
seq_id = file_path.split('/')[-3]
frame_id = file_path.split('/')[-1][:-4]
path_list = file_path.split('/')
point_file_path = '/'.join(path_list)
path_list[-2] = "labels"
path_list[-1] = frame_id + '.label'
label_file_path = '/'.join(path_list)
kd_tree_path = join(self.dataset_path, seq_id, 'KDTree', frame_id + '.pkl')
if os.path.exists(kd_tree_path):
# Read pkl with search tree
with open(kd_tree_path, 'rb') as f:
search_tree = pickle.load(f)
points = np.array(search_tree.data, copy=False)
else:
points_origin = DP.load_pc_kitti(point_file_path)
labels = DP.load_label_kitti(join(label_file_path), remap_lut)
sub_points, sub_labels = DP.grid_sub_sampling(points_origin, labels=labels, grid_size=grid_size)
search_tree = KDTree(sub_points)
# KDTree_save = join(KDTree_path_out, str(scan_id[:-4]) + '.pkl')
# np.save(join(pc_path_out, scan_id)[:-4], sub_points)
# np.save(join(label_path_out, scan_id)[:-4], sub_labels)
# with open(KDTree_save, 'wb') as f:
# pickle.dump(search_tree, f)
# if seq_id == '08':
# proj_path = join(seq_path_out, 'proj')
# os.makedirs(proj_path) if not exists(proj_path) else None
# proj_inds = np.squeeze(search_tree.query(points_origin, return_distance=False))
# proj_inds = proj_inds.astype(np.int32)
# proj_save = join(proj_path, str(scan_id[:-4]) + '_proj.pkl')
# with open(proj_save, 'wb') as f:
# pickle.dump([proj_inds], f)
points = np.array(search_tree.data, copy=False)
labels = sub_labels
# Load labels
# if int(seq_id) >= 11:
# labels = np.zeros(np.shape(points)[0], dtype=np.uint8)
# else:
# label_path = join(self.dataset_path, seq_id, 'labels', frame_id + '.npy')
# labels = np.squeeze(np.load(label_path))
return points, search_tree, labels
If you would like, see this repo and this pr. tsunghan-wu/RandLA-Net-pytorch#8 (comment)