工具代码粘贴2——pytorch几种初始化方式代码

常用：conv+bn+relu组合

#conv
nn.init.kaiming_normal_(conv.weight, mode = 'fan_in')
nn.init.constant_(conv.bias, 0.)  #如果conv后面有bn， bias=False

#bn
nn.init.normal_(bn.weight, mean = 1., std = 0.02)
nn.init.constant_(bn.bias, 0.)

#fc 
nn.init.kaiming_normal(fc.weight, mode = 'fan_out')
nn.init.constant_(fc.bias, 0.)

resnet:

        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))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

inception:

 for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                import scipy.stats as stats
                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
                X = stats.truncnorm(-2, 2, scale=stddev)
                values = torch.Tensor(X.rvs(m.weight.data.numel()))
                values = values.view(m.weight.data.size())
                m.weight.data.copy_(values)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

vgg:

 def _initialize_weights(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))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()

其他：

def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        weight_shape = list(m.weight.data.size())
        fan_in = np.prod(weight_shape[1:4])
        fan_out = np.prod(weight_shape[2:4]) * weight_shape[0]
        w_bound = np.sqrt(6. / (fan_in + fan_out))
        m.weight.data.uniform_(-w_bound, w_bound)
        if m.bias is not None:
            m.bias.data.fill_(0)

    elif classname.find('Linear') != -1:
        weight_shape = list(m.weight.data.size())
        fan_in = weight_shape[1]
        fan_out = weight_shape[0]
        w_bound = np.sqrt(6. / (fan_in + fan_out))
        m.weight.data.uniform_(-w_bound, w_bound)
        if m.bias is not None:
            m.bias.data.fill_(0)

    elif classname.find('LSTM') != -1:
        for name, param in m.named_parameters():
            if 'bias' in name:
                torch.nn.init.constant(param, 0.0)
            elif 'weight' in name:
                torch.nn.init.orthogonal(param)

        # Initialize biases for LSTM’s forget gate to 1 to remember more by default. Similarly, initialize biases for GRU’s reset gate to -1.
        for names in m._all_weights:
            for name in filter(lambda n: "bias" in n, names):
                bias = getattr(m, name)
                n = bias.size(0)
                start, end = n // 4, n // 2
                bias.data[start:end].fill_(1.)

    elif classname.find('GRU') != -1:
        for name, param in m.named_parameters():
            if 'bias' in name:
                torch.nn.init.constant(param, 0.0)
            elif 'weight' in name:
                torch.nn.init.orthogonal(param)


def initial_model_weight(layers):
    for layer in layers:
        if list(layer.children()) == []:
            weights_init(layer)
            # print('weight initial finished!')
        else:
            for sub_layer in list(layer.children()):
                initial_model_weight([sub_layer])