Merge branch 'master' into fix-train-runtime

tools/data_converter/sunrgbd_converter.py

-import os
-import pickle
-from pathlib import Path
-
-from tools.data_converter.sunrgbd_data_utils import SUNRGBDData
-
-
-def create_sunrgbd_info_file(data_path,
-                             pkl_prefix='sunrgbd',
-                             save_path=None,
-                             use_v1=False):
-    assert os.path.exists(data_path)
-    if save_path is None:
-        save_path = Path(data_path)
-    else:
-        save_path = Path(save_path)
-    assert os.path.exists(save_path)
-    train_filename = save_path / f'{pkl_prefix}_infos_train.pkl'
-    val_filename = save_path / f'{pkl_prefix}_infos_val.pkl'
-    train_dataset = SUNRGBDData(
-        root_path=data_path, split='train', use_v1=use_v1)
-    val_dataset = SUNRGBDData(root_path=data_path, split='val', use_v1=use_v1)
-    sunrgbd_infos_train = train_dataset.get_sunrgbd_infos(has_label=True)
-    with open(train_filename, 'wb') as f:
-        pickle.dump(sunrgbd_infos_train, f)
-    print('Sunrgbd info train file is saved to %s' % train_filename)
-    sunrgbd_infos_val = val_dataset.get_sunrgbd_infos(has_label=True)
-    with open(val_filename, 'wb') as f:
-        pickle.dump(sunrgbd_infos_val, f)
-    print('Sunrgbd info val file is saved to %s' % val_filename)
-
-
-if __name__ == '__main__':
-    create_sunrgbd_info_file(
-        data_path='./data/sunrgbd/sunrgbd_trainval',
-        save_path='./data/sunrgbd')

tools/data_converter/sunrgbd_data_utils.py

+import concurrent.futures as futures
 import os
 
-import cv2
+import mmcv
 import numpy as np
 import scipy.io as sio
 
 
-def random_sampling(pc, num_sample, replace=None, return_choices=False):
-    """ Input is NxC, output is num_samplexC
+def random_sampling(points, num_points, replace=None, return_choices=False):
+    """Random Sampling.
+
+    Sampling point cloud to a certain number of points.
+
+    Args:
+        points (ndarray): Point cloud.
+        num_points (int): The number of samples.
+        replace (bool): Whether the sample is with or without replacement.
+        return_choices (bool): Whether to return choices.
+
+    Returns:
+        points (ndarray): Point cloud after sampling.
     """
+
     if replace is None:
-        replace = (pc.shape[0] < num_sample)
-    choices = np.random.choice(pc.shape[0], num_sample, replace=replace)
+        replace = (points.shape[0] < num_points)
+    choices = np.random.choice(points.shape[0], num_points, replace=replace)
     if return_choices:
-        return pc[choices], choices
+        return points[choices], choices
     else:
-        return pc[choices]
+        return points[choices]
 
 
 class SUNRGBDInstance(object):
@@ -44,7 +57,15 @@ class SUNRGBDInstance(object):
 
 
 class SUNRGBDData(object):
-    ''' Load and parse object data '''
+    """SUNRGBD Data
+
+    Generate scannet infos for sunrgbd_converter
+
+    Args:
+        root_path (str): Root path of the raw data.
+        split (str): Set split type of the data. Default: 'train'.
+        use_v1 (bool): Whether to use v1. Default: False.
+    """
 
     def __init__(self, root_path, split='train', use_v1=False):
         self.root_dir = root_path
@@ -60,11 +81,9 @@ class SUNRGBDData(object):
             for label in range(len(self.classes))
         }
         assert split in ['train', 'val', 'test']
-        split_dir = os.path.join(self.root_dir, '%s_data_idx.txt' % split)
-        self.sample_id_list = [
-            int(x.strip()) for x in open(split_dir).readlines()
-        ] if os.path.exists(split_dir) else None
-
+        split_file = os.path.join(self.root_dir, f'{split}_data_idx.txt')
+        mmcv.check_file_exist(split_file)
+        self.sample_id_list = map(int, mmcv.list_from_file(split_file))
         self.image_dir = os.path.join(self.split_dir, 'image')
         self.calib_dir = os.path.join(self.split_dir, 'calib')
         self.depth_dir = os.path.join(self.split_dir, 'depth')
@@ -77,20 +96,20 @@ class SUNRGBDData(object):
         return len(self.sample_id_list)
 
     def get_image(self, idx):
-        img_filename = os.path.join(self.image_dir, '%06d.jpg' % (idx))
-        return cv2.imread(img_filename)
+        img_filename = os.path.join(self.image_dir, f'{idx:06d}.jpg')
+        return mmcv.imread(img_filename)
 
     def get_image_shape(self, idx):
         image = self.get_image(idx)
         return np.array(image.shape[:2], dtype=np.int32)
 
     def get_depth(self, idx):
-        depth_filename = os.path.join(self.depth_dir, '%06d.mat' % (idx))
+        depth_filename = os.path.join(self.depth_dir, f'{idx:06d}.mat')
         depth = sio.loadmat(depth_filename)['instance']
         return depth
 
     def get_calibration(self, idx):
-        calib_filepath = os.path.join(self.calib_dir, '%06d.txt' % (idx))
+        calib_filepath = os.path.join(self.calib_dir, f'{idx:06d}.txt')
         lines = [line.rstrip() for line in open(calib_filepath)]
         Rt = np.array([float(x) for x in lines[0].split(' ')])
         Rt = np.reshape(Rt, (3, 3), order='F')
@@ -98,33 +117,43 @@ class SUNRGBDData(object):
         return K, Rt
 
     def get_label_objects(self, idx):
-        label_filename = os.path.join(self.label_dir, '%06d.txt' % (idx))
+        label_filename = os.path.join(self.label_dir, f'{idx:06d}.txt')
         lines = [line.rstrip() for line in open(label_filename)]
         objects = [SUNRGBDInstance(line) for line in lines]
         return objects
 
-    def get_sunrgbd_infos(self,
-                          num_workers=4,
-                          has_label=True,
-                          sample_id_list=None):
-        import concurrent.futures as futures
+    def get_infos(self, num_workers=4, has_label=True, sample_id_list=None):
+        """Get data infos.
+
+        This method gets information from the raw data.
+
+        Args:
+            num_workers (int): Number of threads to be used. Default: 4.
+            has_label (bool): Whether the data has label. Default: True.
+            sample_id_list (List[int]): Index list of the sample.
+                Default: None.
+
+        Returns:
+            infos (List[dict]): Information of the raw data.
+        """
 
         def process_single_scene(sample_idx):
-            print('%s sample_idx: %s' % (self.split, sample_idx))
+            print(f'{self.split} sample_idx: {sample_idx}')
             # convert depth to points
             SAMPLE_NUM = 50000
+            # TODO: Check whether can move the point
+            #  sampling process during training.
             pc_upright_depth = self.get_depth(sample_idx)
-            # TODO : sample points in loading process and test
             pc_upright_depth_subsampled = random_sampling(
                 pc_upright_depth, SAMPLE_NUM)
             np.savez_compressed(
-                os.path.join(self.root_dir, 'lidar', '%06d.npz' % sample_idx),
+                os.path.join(self.root_dir, 'lidar', f'{sample_idx:06d}.npz'),
                 pc=pc_upright_depth_subsampled)
 
             info = dict()
             pc_info = {'num_features': 6, 'lidar_idx': sample_idx}
             info['point_cloud'] = pc_info
-            img_name = os.path.join(self.image_dir, '%06d.jpg' % (sample_idx))
+            img_name = os.path.join(self.image_dir, f'{sample_idx:06d}')
             img_path = os.path.join(self.image_dir, img_name)
             image_info = {
                 'image_idx': sample_idx,
@@ -183,8 +212,7 @@ class SUNRGBDData(object):
             return info
 
         lidar_save_dir = os.path.join(self.root_dir, 'lidar')
-        if not os.path.exists(lidar_save_dir):
-            os.mkdir(lidar_save_dir)
+        mmcv.mkdir_or_exist(lidar_save_dir)
         sample_id_list = sample_id_list if \
             sample_id_list is not None else self.sample_id_list
         with futures.ThreadPoolExecutor(num_workers) as executor: