1. 一维卷积对比：
简单总结：
1.1 torch的1d卷积核在最后一维上滑动，tf的1d卷积核在倒数第2维上滑动。torch的in_channel是倒数第二维，tf的in_channel是倒数第一维。
1.2 torch需要指定in_channel和out_channel，前者是倒数第二维的维度，后者实际就是filter的数量，对应mlp中的输出维度。
torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)
in_channels：在文本应用中，即为词向量的维度（文本经过embedding之后的维度）
out_channels：卷积产生的通道数，相当于是将词向量的维度从in_channels变成了out_channels
kernel_size：卷积核的尺寸；卷积核的第二个维度由in_channels决定，所以实际上卷积核的大小为kernel_size * in_channels
padding：对输入的每一条边，补充0的层数
1.3 tf不需要指定in_channel，in_channel默认就是最后一维（也就是通常HWC中的C），只需要指定out_channel和kernel_size。这样看来tf的设计更好一些，因为in_channle已经通过固定维度指定了，不会发生变化，这一点上来说torch可以向tf学习。
conv1D = tf.keras.layers.Conv1D(1, 3, padding='valid') #1是输出通道数，3是卷积核大小，不使用边界填充
2. 二维卷积对比：
2.1 torch版本：
torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode=’zeros’, device=None, dtype=None)
torch卷积输入数据格式是(N,Cin,H,W) , 输出是 (N,Cout,Hout,Wout)
其中 kernel_size, stride, padding, dilation 三个参数有两种设置方法:
- a single
int– in which case the same value is used for the height and width dimension - a
tupleof two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
2.2 tf.layer版本
tf卷积输入数据格式是 (N,H,W,Cin) , 输出是 (N,Hout,Wout,Cout)
tf.layers.Conv2D(inputs, filters, kernel_size, strides=(1, 1), padding=’valid’)
其中kernel_size和strides都需要设置为一个list或者integer，integer会对所有维度生效相同的值。
Arguments
Tensor input.
Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
, specifying the height and width of the 2D convolution window. Can be
s, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any
value != 1.
One of
or
(case-insensitive).
2.3 tf.nn版本
tf.nn.conv2d(
input, filter=None, strides=None, padding=None, use_cudnn_on_gpu=True,
data_format=’NHWC’, dilations=[1, 1, 1, 1], name=None, filters=None
)
Args
. Must be one of the following types:
. A 4-D tensor. The dimension order is interpreted according to the value of
, see below for details.
. Must have the same type as
. A 4-D tensor of shape
An int or list of
that has length
or
. The stride of the sliding window for each dimension of
. If a single value is given it is replicated in the
and
dimension. By default the
and
dimensions are set to 1. The dimension order is determined by the value of
, see below for details.
Either the
or
indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is
, this should be in the form
. When explicit padding used and data_format is
, this should be in the form
3. 用Conv实现Mlp
（1）torch中对于数据【N,C,H,W】这种四维数据可以用Conv2d实现mlp，只需要将kernal_size和stride_size都设置成【1,1】，filter_size设置成mlp的out_channel即可实现第二维从C变成filter_size的mlp，对于tf来说【N,H,W,C】的数据同理。
（2）torch中对于数据【N,C,H】这种四维数据可以用Conv1d实现mlp，只需要将kernal_size和stride_size都设置成【1】，filter_size设置成mlp的out_channel即可实现第二维从C变成filter_size的mlp，对于tf来说【N,H,C】的数据同理。
Original: https://blog.csdn.net/yumao1/article/details/121795911
Author: 江汉似年
Title: Torch与TensorFlow卷积对比
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