in models/regnet.py [0:0]
def init_weights(self):
# Performs ResNet-style weight initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
# Note that there is no bias due to BN
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1.0)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(mean=0.0, std=0.01)
m.bias.data.zero_()