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1.比较常用的就是对每一个类型的网络层进行初始化,并且作为一个函数定义在网络中。具体的初始化,按照先实例化网络再调用初始化函数来达成初始化操作。
def _load_pretrained_model(self): # 加载预训练模型参数 pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/resnet101-5d3b4d8f.pth') model_dict = {} state_dict = self.state_dict() for k, v in pretrain_dict.items(): if k in state_dict: model_dict[k] = v state_dict.update(model_dict) self.load_state_dict(state_dict) def freeze_bn(self): # 冻结BN层,不对该层进行参数更新;同理可用于其他层 for m in self.modules(): if isinstance(m, nn.BatchNorm2d): m.eval() def __init_weight(self): for m in self.modules(): if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) torch.nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
2.使用网络的自有的apply函数进行初始化。
def init_weights(net, init_type='normal', gain=0.02): def init_func(m): classname = m.__class__.__name__ if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': init.normal_(m.weight.data, 0.0, gain) elif init_type == 'xavier': init.xavier_normal_(m.weight.data, gain=gain) elif init_type == 'kaiming': init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': init.orthogonal_(m.weight.data, gain=gain) else: raise NotImplementedError('initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: init.constant_(m.bias.data, 0.0) elif classname.find('BatchNorm2d') != -1: init.normal_(m.weight.data, 1.0, gain) init.constant_(m.bias.data, 0.0) print('initialize network with %s' % init_type) net.apply(init_func) # 使用网络自带的apply函数
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