了解电商基本知识如何优化seo

1.v8本身的模块存放在nn文件夹下。

2.在nn文件夹下新建一个ASPP.py文件，将新添加的模块写入其中。

3.把下面这段代码复制进去

import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F# without BN version
class ASPP(nn.Module):def __init__(self, in_channel=512, out_channel=256):super(ASPP, self).__init__()self.mean = nn.AdaptiveAvgPool2d((1, 1))  # (1,1)means ouput_dimself.conv = nn.Conv2d(in_channel,out_channel, 1, 1)self.atrous_block1 = nn.Conv2d(in_channel, out_channel, 1, 1)self.atrous_block6 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=6, dilation=6)self.atrous_block12 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=12, dilation=12)self.atrous_block18 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=18, dilation=18)self.conv_1x1_output = nn.Conv2d(out_channel * 5, out_channel, 1, 1)def forward(self, x):size = x.shape[2:]image_features = self.mean(x)image_features = self.conv(image_features)image_features = F.upsample(image_features, size=size, mode='bilinear')atrous_block1 = self.atrous_block1(x)atrous_block6 = self.atrous_block6(x)atrous_block12 = self.atrous_block12(x)atrous_block18 = self.atrous_block18(x)net = self.conv_1x1_output(torch.cat([image_features, atrous_block1, atrous_block6,atrous_block12, atrous_block18], dim=1))return net

import torch
import torch.nn.functional as F
import torch.nn as nnclass ASPP(nn.Module):def __init__(self, in_channel=512, out_channel=256):super(ASPP, self).__init__()self.mean = nn.AdaptiveAvgPool2d((1, 1))  # (1,1)means ouput_dimself.conv = nn.Conv2d(in_channel, out_channel, 1, 1)self.atrous_block1 = nn.Conv2d(in_channel, out_channel, 1, 1)self.atrous_block6 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=6, dilation=6)self.atrous_block12 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=12, dilation=12)self.atrous_block18 = nn.Conv2d(in_channel, out_channel, 3, 1, padding=18, dilation=18)self.conv_1x1_output = nn.Conv2d(out_channel * 5, out_channel, 1, 1)def forward(self, x):size = x.shape[2:]image_features = self.mean(x)image_features = self.conv(image_features)image_features = F.upsample(image_features, size=size, mode='bilinear')atrous_block1 = self.atrous_block1(x)atrous_block6 = self.atrous_block6(x)atrous_block12 = self.atrous_block12(x)atrous_block18 = self.atrous_block18(x)net = self.conv_1x1_output(torch.cat([image_features, atrous_block1, atrous_block6,atrous_block12, atrous_block18], dim=1))return netif __name__ == '__main__':x = torch.randn(1, 256, 16, 16)model = ASPP(256, 256)print(model(x).shape)

SPPFCSPC模块使用以下代码 

import torch
import torch.nn.functional as F
import torch.nn as nn####### SPPFCSPC #####
class SPPFCSPC(nn.Module):def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=5):super(SPPFCSPC, self).__init__()c_ = int(2 * c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(c_, c_, 3, 1)self.cv4 = Conv(c_, c_, 1, 1)self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)self.cv5 = Conv(4 * c_, c_, 1, 1)self.cv6 = Conv(c_, c_, 3, 1)self.cv7 = Conv(2 * c_, c2, 1, 1)def forward(self, x):x1 = self.cv4(self.cv3(self.cv1(x)))x2 = self.m(x1)x3 = self.m(x2)y1 = self.cv6(self.cv5(torch.cat((x1, x2, x3, self.m(x3)), 1)))y2 = self.cv2(x)return self.cv7(torch.cat((y1, y2), dim=1))####### end of SPPFCSPC #####if __name__ == '__main__':x = torch.randn(1, 256, 16, 16)model = SPPFCSPC(256, 256)print(model(x).shape)

4.引入配置好的模块环境。

5.复制一份yolov8.yaml文件，修改模型参数

6. yolov8-ASPP.yaml文件

6.修改配置代码