torch的对比下。

# coding=utf-8
r"""Quantized convolution modules."""

from typing import Optional, List

import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.qat as nniqat

from torch._ops import ops
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.quantized.modules.utils import _pair_from_first
from torch.nn.quantized.modules.utils import _quantize_weight
from torch.nn.utils import fuse_conv_bn_weights

class _ConvNd(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride,
                 padding, dilation,
                 transposed, output_padding,
                 groups, bias,
                 padding_mode='zeros'):
        super(_ConvNd, self).__init__()
        if padding_mode != 'zeros':
            raise NotImplementedError(
                "Currently only zero-padding is supported by quantized conv")
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        self.padding_mode = padding_mode

设置为私有，后面修改很麻烦。。。

灵活性降低了80%估计

封装完善的代价就是不够灵活

是不是推荐使用更基础的算子自己实现啊

有这个困难

其实这个原则是对的，新算子自己造吧

这简直是骨灰级微操作

私有了，以后想对齐精度怎么办啊。。。