Hi!
I have some conv layers like bellow:
self.conv_phi= nn.Sequential(
            nn.Conv3d(block_size*block_size,int(np.ceil(SR*block_size*block_size)), kernel_size=(1,1,1))   
        )
self.conv_p1= nn.Sequential(
            nn.Conv3d(int(np.ceil(SR*block_size*block_size)),block_size*block_size, kernel_size=(1,1,1)),
            nn.ReLU()
        )
self.conv_p2= nn.Sequential(
            nn.Conv3d(block_size*block_size,block_size*block_size, kernel_size=(1,1,1)),
            nn.ReLU()
        )
self.conv_p3= nn.Sequential(
            nn.Conv3d(block_size*block_size,block_size*block_size, kernel_size=(1,1,1))
        )

I want to initialize weights of the convolutional layers  by normal distribution and different standard deviation.
I searched and found this code:
def weights_init(m):

    if isinstance(m, nn.Conv3d) :

      m.weight.data.normal_(0.0,0.001)

but how could I set different standard deviation for each conv layer?