def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.features.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.Linear):
normal_init(m, std=0.01)
else:
raise TypeError("pretrained must be a str or None")
for m in self.extra.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution="uniform")
constant_init(self.l2_norm, self.l2_norm.scale)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
xavier_init(m, distribution="uniform")