Conv2D 属性设置为私有？？？
收藏
如题，为啥设置为私有？相互之间沟通就好难啊。
class _ConvNd(layers.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
transposed,
dims,
stride=1,
padding=0,
padding_mode='zeros',
output_padding=0,
dilation=1,
groups=1,
weight_attr=None,
bias_attr=None,
data_format="NCHW"):
super(_ConvNd, self).__init__()
assert weight_attr is not False, "weight_attr should not be False in Conv."
self._param_attr = weight_attr
self._bias_attr = bias_attr
self._groups = groups
self._in_channels = in_channels
self._out_channels = out_channels
self._data_format = data_format
0
收藏
请登录后评论
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。。。