CV-YOLOV4训练1

import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import math

class VGG(nn.Module):
    def __init__(self):
       super(VGG,self).__init__()
       # the vgg's layers
       #self.features = features
       cfg = [64,64,'M',128,128,'M',256,256,256,'M',512,512,512,'M',512,512,512,'M']
       layers= []
       batch_norm = False
       in_channels = 3
       for v in cfg:
           if v == 'M':
               layers += [nn.MaxPool2d(kernel_size=2,stride = 2)]
           else:
               conv2d = nn.Conv2d(in_channels,v,kernel_size=3,padding = 1)
               if batch_norm:
                   layers += [conv2d,nn.Batchnorm2d(v),nn.ReLU(inplace=True)]
               else:
                   layers += [conv2d,nn.ReLU(inplace=True)]
               in_channels = v
       # use the vgg layers to get the feature
       self.features = nn.Sequential(*layers)
       # 全局池化
       self.avgpool = nn.AdaptiveAvgPool2d((7,7))
       # 决策层：分类层
       self.classifier = nn.Sequential(
           nn.Linear(512*7*7,4096),
           nn.ReLU(True),
           nn.Dropout(),
           nn.Linear(4096,4096),
           nn.ReLU(True),
           nn.Dropout(),
           nn.Linear(4096,1000),
       )

       for m in self.modules():
           if isinstance(m,nn.Conv2d):
               nn.init.kaiming_normal_(m.weight,mode='fan_out',nonlinearity='relu')
               if m.bias is not None: 
                   nn.init.constant_(m.bias,0)
           elif isinstance(m,nn.BatchNorm2d):
               nn.init.constant_(m.weight,1)
               nn.init.constant_(m.bias,1)
           elif isinstance(m,nn.Linear):
               nn.init.normal_(m.weight,0,0.01)
               nn.init.constant_(m.bias,0)

    def forward(self,x):
         x = self.features(x)
         x_fea = x
         x = self.avgpool(x)
         x_avg = x
         x = x.view(x.size(0),-1)
         x = self.classifier(x)
         return x,x_fea,x_avg
    def extractor(self,x):
         x = self.features(x)
         return x

class YOLOV1(nn.Module):
    def __init__(self):
       super(YOLOV1,self).__init__()
       vgg = VGG()
       self.extractor = vgg.extractor
       self.avgpool = nn.AdaptiveAvgPool2d((7,7))
       # 决策层：检测层
       self.detector = nn.Sequential(
          nn.Linear(512*7*7,4096),
          nn.ReLU(True),
          nn.Dropout(),
          nn.Linear(4096,1470),
       )
       for m in self.modules():
           if isinstance(m,nn.Conv2d):
               nn.init.kaiming_normal_(m.weight,mode='fan_out',nonlinearity='relu')
               if m.bias is not None: 
                   nn.init.constant_(m.bias,0)
           elif isinstance(m,nn.BatchNorm2d):
               nn.init.constant_(m.weight,1)
               nn.init.constant_(m.bias,1)
           elif isinstance(m,nn.Linear):
               nn.init.normal_(m.weight,0,0.01)
               nn.init.constant_(m.bias,0)
    def forward(self,x):
        x = self.extractor(x)
        import pdb
        pdb.set_trace()
        x = self.avgpool(x)
        x = x.view(x.size(0),-1)
        x = self.detector(x)
        b,_ = x.shape
        x = x.view(b,7,7,30)
        return x
     
if __name__ == '__main__':
    vgg = VGG()
    x  = torch.randn(1,3,512,512)
    feature,x_fea,x_avg = vgg(x)
    print(feature.shape)
    print(x_fea.shape)
    print(x_avg.shape)
 
    yolov1 = YOLOV1()
    feature = yolov1(x)
    # feature_size b*7*7*30
    print(feature.shape)