Pytorch中自定义神经网络卷积核权重
- 自定义神经网络卷积核权重
神经网络被深度学习者深深喜爱，究其原因之一是神经网络的便利性，使用者只需要根据自己的需求像搭积木一样搭建神经网络框架即可，搭建过程中我们只需要考虑卷积核的尺寸，输入输出通道数，卷积方式等等。
我们使用惯了自带的参数后，当我们要自定义卷积核参数时，突然有种无从下手的感觉，哈哈哈哈哈哈哈哈~~，请允许我开心下，嘿嘿！因为笔者在初入神经网络时也遇到了同样的问题，当时踩了太多坑了，宝宝想哭(灬ꈍ ꈍ灬)！让我悲伤的是，找遍了各个资源区，也没有找到大家的分享。因此，我想把我的方法写出来，希望能帮助到各位宝宝，开心(^▽^)。
话不多说，正文开始......
- 定义卷积核权重
我这里是自定义的dtt系数卷积核权重，直接上权重代码：
2.1 dtt系数权重Code
def dtt_matrix(n): 这个函数是nn的DTT系数矩阵，笔者的是88的系数矩阵。
def dtt_kernel(out_channels, in_channels, kernel_size): 这个方法是设定权重，权重需要包括4个参数（输出通道数，输入通道数，卷积核高，卷积核宽），这里有很多细节要注意，宝宝们要亲自躺下坑，才能映像深刻也，我就不深究了哈，(#^.^#)。
import numpy as np
import torch
import torch.nn as nn
================================
DTT coefficient matrix of n * n
================================
def dtt_matrix(n):
dtt_coe = np.zeros([n, n], dtype='float32')
for i in range(0, n):
dtt_coe[0, i] = 1/np.sqrt(n)
dtt_coe[1, i] = (2*i + 1 - n)*np.sqrt(3/(n*(np.power(n, 2) - 1)))
for i in range(1, n-1):
dtt_coe[i+1, 0] = -np.sqrt((n-i-1)/(n+i+1)) * np.sqrt((2*(i+1)+1)/(2*(i+1)-1)) * dtt_coe[i, 0]
dtt_coe[i+1, 1] = (1 + (i+1)*(i+2)/(1-n)) * dtt_coe[i+1, 0]
dtt_coe[i+1, n-1] = np.power(-1, i+1) * dtt_coe[i+1, 0]
dtt_coe[i+1, n-2] = np.power(-1, i+1) * dtt_coe[i+1, 1]
for j in range(2, int(n/2)):
t1 = (-(i+1) * (i+2) - (2*j-1) * (j-n-1) - j)/(j*(n-j))
t2 = ((j-1) * (j-n-1))/(j * (n-j))
dtt_coe[i+1, j] = t1 * dtt_coe[i+1, j-1] + t2 * dtt_coe[i+1, j-2]
dtt_coe[i+1, n-j-1] = np.power(-1, i-1) * dtt_coe[i+1, j]
return dtt_coe
===============================================================
DTT coefficient matrix of (out_channels in_channels n * n)
===============================================================
def dtt_kernel(out_channels, in_channels, kernel_size):
dtt_coe = dtt_matrix(kernel_size)
dtt_coe = np.array(dtt_coe)
dtt_weight = np.zeros([out_channels, in_channels, kernel_size, kernel_size], dtype='float32')
temp = np.zeros([out_channels, in_channels, kernel_size, kernel_size], dtype='float32')
order = 0
for i in range(0, kernel_size):
for j in range(0, kernel_size):
dtt_row = dtt_coe[i, :]
dtt_col = dtt_coe[:, j]
dtt_row = dtt_row.reshape(len(dtt_row), 1)
dtt_col = dtt_col.reshape(1, len(dtt_col))
# print("dtt_row: ", dtt_row)
# print("dtt_col: ", dtt_col)
# print("i:", i, "j: ", j)
temp[order, 0, :, :] = np.dot(dtt_row, dtt_col)
order = order + 1
for i in range(0, in_channels):
for j in range(0, out_channels):
# dtt_weight[j, i, :, :] = flip_180(temp[j, 0, :, :])
dtt_weight[j, i, :, :] = temp[j, 0, :, :]
return torch.tensor(dtt_weight)
2.2 'same'方式卷积
如果宝宝需要保持卷积前后的数据尺寸保持不变，即'same'方式卷积，那么你直接使用我这个卷积核（提一下哟，这个我也是借自某位前辈的，我当时没备注哇，先在这里感谢那位前辈，前辈如果路过，还请留言小生哈，(#^.^#)）。
import torch.utils.data
from torch.nn import functional as F
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
from torch.nn.modules.utils import _single, _pair, _triple
class _ConvNd(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding, groups, bias):
super(_ConvNd, self).__init__()
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
if transposed:
self.weight = Parameter(torch.Tensor(
in_channels, out_channels // groups, *kernel_size))
else:
self.weight = Parameter(torch.Tensor(
out_channels, in_channels // groups, *kernel_size))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
n = self.in_channels
for k in self.kernel_size:
n *= k
stdv = 1. / math.sqrt(n)
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def __repr__(self):
s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation={dilation}'
if self.output_padding != (0,) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
s += ')'
return s.format(name=self.__class__.__name__, **self.__dict__)
class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias)
def forward(self, input):
return conv2d_same_padding(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
custom con2d, because pytorch don't have "padding='same'" option.
def conv2d_same_padding(input, weight, bias=None, stride=1, padding=1, dilation=1, groups=1):
input_rows = input.size(2)
filter_rows = weight.size(2)
effective_filter_size_rows = (filter_rows - 1) * dilation[0] + 1
out_rows = (input_rows + stride[0] - 1) // stride[0]
input_cols = input.size(3)
filter_cols = weight.size(3)
effective_filter_size_cols = (filter_cols - 1) * dilation[1] + 1
out_cols = (input_cols + stride[1] - 1) // stride[1]
padding_needed = max(0, (out_rows - 1) * stride[0] + effective_filter_size_rows -input_rows)
padding_rows = max(0, (out_rows - 1) * stride[0] +
(filter_rows - 1) * dilation[0] + 1 - input_rows)
rows_odd = (padding_rows % 2 != 0)
padding_cols = max(0, (out_cols - 1) * stride[1] +
(filter_cols - 1) * dilation[1] + 1 - input_cols)
cols_odd = (padding_cols % 2 != 0)
if rows_odd or cols_odd:
input = pad(input, [0, int(cols_odd), 0, int(rows_odd)])
return F.conv2d(input, weight, bias, stride,
padding=(padding_rows // 2, padding_cols // 2),
dilation=dilation, groups=groups)
2.3 将权重赋给卷积核
此处才是宝宝们最关心的吧，不慌，这就来了哈，开心(^▽^)，进入正文了(#^.^#)。
这里给了一个简单的网络模型（一个固定卷积+3个全连接，全连接是11的Conv2d），代码里我给了注释，宝宝们应该能秒懂滴，(^▽^*)！
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import dtt_kernel
import util
import paddingSame
定义权重
dtt_weight1 = dtt_kernel.dtt_kernel(64, 2, 8)
class DttNet(nn.Module):
def __init__(self):
super(DttNet, self).__init__()
self.conv1 = paddingSame.Conv2d(2, 64, 8)# 将权重赋给卷积核
self.conv1.weight = nn.Parameter(dtt_weight1, requires_grad=False)
self.fc1 = util.fc(64, 512, 1)
self.fc2 = util.fc(512, 128, 1)
self.fc3 = util.fc(128, 2, 1, last=True)
def forward(self, x):
x = self.conv1(x)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
2.4 补充我的util类
import torch.nn as nn
def conv(in_channels, out_channels, kernel_size, stride=1, dilation=1, batch_norm=True):
if batch_norm:
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=(kernel_size // 2)),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
else:
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=(kernel_size // 2)),
nn.ReLU()
)
def fc(in_channels, out_channels, kernel_size, stride=1, bias=True, last=False):
if last:
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=(kernel_size // 2)),
)
else:
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=(kernel_size // 2)),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
- 总结
哇哦，写完了耶，不晓得宝宝们有没得收获呢，o((⊙﹏⊙))o，o((⊙﹏⊙))o。大家不懂的可以再下面留言哟，我会时常关注我家的园子呢。若有不足之处，宝宝们也在留言区吱我一下哟，我们下次再见，┏(＾0＾)┛┏(＾0＾)┛。