fast-reid/fastreid/utils/weight_init.py

# encoding: utf-8
"""
@author:  xingyu liao
@contact: liaoxingyu5@jd.com
"""

import math
from torch import nn

__all__ = [
    'weights_init_classifier',
    'weights_init_kaiming',
]


def weights_init_kaiming(m):
    classname = m.__class__.__name__
    if classname.find('Linear') != -1:
        nn.init.normal_(m.weight, 0, 0.01)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0.0)
    elif classname.find('Conv') != -1:
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        if m.bias is not None:
            nn.init.constant_(m.bias, 0.0)
    elif classname.find('BatchNorm') != -1:
        if m.affine:
            nn.init.normal_(m.weight, 1.0, 0.02)
            nn.init.constant_(m.bias, 0.0)


def weights_init_classifier(m):
    classname = m.__class__.__name__
    if classname.find('Linear') != -1:
        nn.init.normal_(m.weight, std=0.001)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0.0)
    elif classname.find("Arcface") != -1 or classname.find("Circle") != -1:
        nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))