pointnet++之classification

# Shapenet official train/test split
if FLAGS.normal:
    assert(NUM_POINT<=10000)
    DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled')
    TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
    TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
else:
    assert(NUM_POINT<=2048)
    TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True)
    TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)

训练数据（TRAIN_DATASET）是5个.h5格式的文件：

data/modelnet40_ply_hdf5_2048/ply_data_train0.h5
data/modelnet40_ply_hdf5_2048/ply_data_train1.h5
data/modelnet40_ply_hdf5_2048/ply_data_train2.h5
data/modelnet40_ply_hdf5_2048/ply_data_train3.h5
data/modelnet40_ply_hdf5_2048/ply_data_train4.h5

训练之前把5个训练文件的顺序打乱：

if self.shuffle: np.random.shuffle(self.file_idxs)

测试数据（TEST_DATASET）是2个.h5格式的文件：

data/modelnet40_ply_hdf5_2048/ply_data_test0.h5
data/modelnet40_ply_hdf5_2048/ply_data_test1.h5