关于神经网络初始化 torch.nn.init.xavier_normal_(rand_conv.weight.data)的问题

def random_convolution(imgs):
    '''
    random covolution in "network randomization"
    
    (imbs): B x (C x stack) x H x W, note: imgs should be normalized and torch tensor
    '''
    _device = imgs.device
    
    img_h, img_w = imgs.shape[2], imgs.shape[3]
    num_stack_channel = imgs.shape[1]
    num_batch = imgs.shape[0]

    num_trans = num_batch
    batch_size = int(num_batch / num_trans)

    # initialize random covolution
    rand_conv = nn.Conv2d(3, 3, kernel_size=3, bias=False, padding=1).to(_device)

    for trans_index in range(num_trans):
        torch.nn.init.xavier_normal_(rand_conv.weight.data)
        temp_imgs = imgs[trans_index*batch_size:(trans_index+1)*batch_size]

        temp_imgs = temp_imgs.reshape(-1, 3, img_h, img_w) # (batch x stack, channel, h, w)

        rand_out = rand_conv(temp_imgs)

        rand_out_ = rand_conv(imgs.reshape(-1, 3, 84, 84))

        if trans_index == 0:
            total_out = rand_out
        else:
            total_out = torch.cat((total_out, rand_out), 0)

    return total_out

在上面的代码中，torch.nn.init.xavier_normal_(rand_conv.weight.data)

作为网络的初始化操作，对后续的操作都有影响，注意，这里之所以没有对（128，9，84，84）的图像统一进行操作，就是因为目的是对每个（1，9，84，84）使用不同的网络初始化参数进行处理，
而不是对128个图像stack使用统一的网络参数进行处理。