preprocessing script fix

README.md

@@ -1,3 +1,22 @@
 # Spatio-Temporal Segmentation
 
 This repository contains the accompanying code for [4D-SpatioTemporal ConvNets: Minkowski Convolutional Neural Networks, CVPR'19](https://arxiv.org/abs/1904.08755).
+
+
+## ScanNet Training
+
+First, preprocess all scannet raw point cloud with the following command after you set the path correctly.
+
+```
+python -m lib.datasets.prepreocessing.scannet
+```
+
+Then, train the scannet network with
+
+```
+./scripts/train_scannet.sh 0 -default "--scannet_path /path/to/preprocessed/scannet"
+```
+
+The first argument is the GPU id and the second argument is the path postfix
+and the last argument is the miscellaneous arguments.
+

lib/datasets/preprocessing/scannet.py

@@ -47,38 +47,11 @@
     # Check that all points are included in the crops.
     assert set(tuple(l) for l in all_points.tolist()) == set(tuple(l) for l in xyz.tolist())
 
-# Split trainval data to train/val according to scene.
-trainval_files = [f.name for f in (SCANNET_OUT_PATH / TRAIN_DEST).glob('*.ply')]
-trainval_scenes = list(set(f.split('_')[0] for f in trainval_files))
-shuffle(trainval_scenes)
-num_train = int(len(trainval_scenes))
-train_scenes = trainval_scenes[:num_train]
-val_scenes = trainval_scenes[num_train:]
-
-# Collect file list for all phase.
-train_files = [f'{TRAIN_DEST}/{f}' for f in trainval_files if any(s in f for s in train_scenes)]
-val_files = [f'{TRAIN_DEST}/{f}' for f in trainval_files if any(s in f for s in val_scenes)]
-test_files = [f'{TEST_DEST}/{f.name}' for f in (SCANNET_OUT_PATH / TEST_DEST).glob('*.ply')]
-
-# Data sanity check.
-assert not set(train_files).intersection(val_files)
-assert all((SCANNET_OUT_PATH / f).is_file() for f in train_files)
-assert all((SCANNET_OUT_PATH / f).is_file() for f in val_files)
-assert all((SCANNET_OUT_PATH / f).is_file() for f in test_files)
-
-# Write file list for all phase.
-with open(SCANNET_OUT_PATH / 'train.txt', 'w') as f:
-  f.writelines([f + '\n' for f in train_files])
-with open(SCANNET_OUT_PATH / 'val.txt', 'w') as f:
-  f.writelines([f + '\n' for f in val_files])
-with open(SCANNET_OUT_PATH / 'test.txt', 'w') as f:
-  f.writelines([f + '\n' for f in test_files])
-
 # Fix bug in the data.
-# for files, bug_index in BUGS.items():
-#   for f in SCANNET_OUT_PATH.glob(files):
-#     pointcloud = read_plyfile(f)
-#     bug_mask = pointcloud[:, -1] == bug_index
-#     print(f'Fixing {f} bugged label {bug_index} x {bug_mask.sum()}')
-#     pointcloud[bug_mask, -1] = 0
-#     save_point_cloud(pointcloud, f, with_label=True, verbose=False)
+for files, bug_index in BUGS.items():
+  for f in SCANNET_OUT_PATH.glob(files):
+    pointcloud = read_plyfile(f)
+    bug_mask = pointcloud[:, -1] == bug_index
+    print(f'Fixing {f} bugged label {bug_index} x {bug_mask.sum()}')
+    pointcloud[bug_mask, -1] = 0
+    save_point_cloud(pointcloud, f, with_label=True, verbose=False)