def init_weights(self):
normal_init(self.fc_cls, std=0.01)
normal_init(self.fc_reg, std=0.001)
for m in self.fc_branch.modules():
if isinstance(m, nn.Linear):
xavier_init(m, distribution='uniform')
def init_weights(self):
for m in self.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)
def init_weights(self):
# conv layers are already initialized by ConvModule
normal_init(self.fc_cls, std=0.01)
normal_init(self.fc_reg, std=0.001)
for m in self.fc_branch.modules():
if isinstance(m, nn.Linear):
xavier_init(m, distribution='uniform')
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.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')