lib/model/faster_rcnn/resnet.py
def normal_init(self, m, mean, stddev, truncated=False):
    """
    weight initalizer: truncated normal and random normal.
    """
    # x is a parameter
    if truncated:
      m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)  # not a perfect approximation
    else:
      m.weight.data.normal_(mean, stddev)
      if m.bias is not None:
        m.bias.data.zero_()

    weight initalizer: truncated normal and random normal.
    
weight initalizer: truncated normal and random normal.
weight initalizer : truncated normal and random normal .
def normal_init(self, m, mean, stddev, truncated=False):
    if truncated:
      m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)  
    else:
      m.weight.data.normal_(mean, stddev)
      if m.bias is not None:
        m.bias.data.zero_()
def normal_init ( self , m , mean , stddev , truncated = False ) : if truncated : m . weight . data . normal_ ( ) . fmod_ ( 2 ) . mul_ ( stddev ) . add_ ( mean ) else : m . weight . data . normal_ ( mean , stddev ) if m . bias is not None : m . bias . data . zero_ ( )
weight initalizer: truncated normal and random normal.
weight initalizer : truncated normal and random normal .
"""
    weight initalizer: truncated normal and random normal.
    """ # x is a parameter # not a perfect approximation
self
m
mean
stddev
truncated