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')