change docstring

mmdet3d/datasets/pipelines/loading.py

-import os.path as osp
-
-import mmcv
-import numpy as np
-
-from mmdet.datasets.registry import PIPELINES
-
-
-@PIPELINES.register_module
-class LoadPointsFromFile(object):
-
-    def __init__(self, points_dim=4, with_reflectivity=True):
-        self.points_dim = points_dim
-        self.with_reflectivity = with_reflectivity
-
-    def __call__(self, results):
-        if results['pts_prefix'] is not None:
-            filename = osp.join(results['pts_prefix'],
-                                results['img_info']['filename'])
-        else:
-            filename = results['img_info']['filename']
-        points = np.fromfile(
-            filename, dtype=np.float32).reshape(-1, self.points_dim)
-        results['points'] = points
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += '(points_dim={})'.format(self.points_dim)
-        repr_str += '(points_dim={})'.format(self.with_reflectivity)
-        return repr_str
-
-
-@PIPELINES.register_module
-class LoadMultiViewImageFromFiles(object):
-    """ Load multi channel images from a list of separate channel files.
-    Expects results['filename'] to be a list of filenames
-    """
-
-    def __init__(self, to_float32=False, color_type='unchanged'):
-        self.to_float32 = to_float32
-        self.color_type = color_type
-
-    def __call__(self, results):
-        if results['img_prefix'] is not None:
-            filename = [
-                osp.join(results['img_prefix'], fname)
-                for fname in results['img_info']['filename']
-            ]
-        else:
-            filename = results['img_info']['filename']
-        img = np.stack(
-            [mmcv.imread(name, self.color_type) for name in filename], axis=-1)
-        if self.to_float32:
-            img = img.astype(np.float32)
-        results['filename'] = filename
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['ori_shape'] = img.shape
-        # Set initial values for default meta_keys
-        results['pad_shape'] = img.shape
-        results['scale_factor'] = 1.0
-        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
-        results['img_norm_cfg'] = dict(
-            mean=np.zeros(num_channels, dtype=np.float32),
-            std=np.ones(num_channels, dtype=np.float32),
-            to_rgb=False)
-        return results
-
-    def __repr__(self):
-        return "{} (to_float32={}, color_type='{}')".format(
-            self.__class__.__name__, self.to_float32, self.color_type)

tools/data_converter/indoor_converter.py

@@ -9,8 +9,7 @@ def create_indoor_info_file(data_path,
                             pkl_prefix='sunrgbd',
                             save_path=None,
                             use_v1=False):
-    """
-    Create indoor  information file.
+    """Create indoor  information file.
 
     Get information of the raw data and save it to the pkl file.
 
@@ -19,10 +18,6 @@ def create_indoor_info_file(data_path,
         pkl_prefix (str): Prefix of the pkl to be saved. Default: 'sunrgbd'.
         save_path (str): Path of the pkl to be saved. Default: None.
         use_v1 (bool): Whether to use v1. Default: False.
-
-    Returns:
-        None
-
     """
     assert os.path.exists(data_path)
     assert pkl_prefix in ['sunrgbd', 'scannet']

tools/data_converter/scannet_data_utils.py

@@ -5,8 +5,7 @@ import numpy as np
 
 
 class ScanNetData(object):
-    """
-    ScanNet Data
+    """ScanNet Data
 
     Generate scannet infos for scannet_converter
 
@@ -49,8 +48,7 @@ class ScanNetData(object):
         return np.load(box_file)
 
     def get_infos(self, num_workers=4, has_label=True, sample_id_list=None):
-        """
-        Get data infos.
+        """Get data infos.
 
         This method gets information from the raw data.
 

tools/data_converter/sunrgbd_data_utils.py

@@ -7,8 +7,7 @@ import scipy.io as sio
 
 
 def random_sampling(pc, num_samples, replace=None, return_choices=False):
-    """
-    Random Sampling.
+    """Random Sampling.
 
     Sampling point cloud to num_samples points.
 
@@ -58,8 +57,7 @@ class SUNRGBDInstance(object):
 
 
 class SUNRGBDData(object):
-    """
-    SUNRGBD Data
+    """SUNRGBD Data
 
     Generate scannet infos for sunrgbd_converter
 
@@ -127,8 +125,7 @@ class SUNRGBDData(object):
         return objects
 
     def get_infos(self, num_workers=4, has_label=True, sample_id_list=None):
-        """
-        Get data infos.
+        """Get data infos.
 
         This method gets information from the raw data.