def init_weight(self): for layer in self.sublayers(): if isinstance(layer, nn.Conv2D): param_init.normal_init(layer.weight, std=0.001) elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)): param_init.constant_init(layer.weight, value=1.0) param_init.constant_init(layer.bias, value=0.0)
def init_weight(self): """Initialize the parameters of model parts.""" for sublayer in self.sublayers(): if isinstance(sublayer, nn.Conv2D): param_init.normal_init(sublayer.weight, std=0.001) elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)): param_init.constant_init(sublayer.weight, value=1.0) param_init.constant_init(sublayer.bias, value=0.0)
def init_weight(self): for layer in self.sublayers(): if isinstance(layer, nn.Conv2D): param_init.normal_init(layer.weight, std=0.001) elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)): param_init.constant_init(layer.weight, value=1.0) param_init.constant_init(layer.bias, value=0.0) if self.pretrained is not None: utils.load_pretrained_model(self, self.pretrained)
def init_params(self): for m in self.sublayers(): if isinstance(m, nn.Conv2D): param_init.kaiming_normal_init(m.weight) if m.bias is not None: param_init.constant_init(m.bias, value=0.0) elif isinstance(m, nn.BatchNorm2D): param_init.constant_init(m.weight, value=1.0) param_init.constant_init(m.bias, value=0.0) elif isinstance(m, nn.Linear): param_init.normal_init(m.weight, std=0.001) if m.bias is not None: param_init.constant_init(m.bias, value=0.0)