Pytorch卷积神经网络resent网络实践

train_paper=r'E:\桌面\资料\cv3\数据集\罚拳_公开\train\paper'
train_rock=r'E:\桌面\资料\cv3\数据集\罚拳_公开\train\rock'
train_scissors=r'E:\桌面\资料\cv3\数据集\罚拳_公开\train\scissors'
test_paper=r'E:\桌面\资料\cv3\数据集\罚拳_公开\test\paper'
test_rock=r'E:\桌面\资料\cv3\数据集\罚拳_公开\test\rock'
test_scission=r'E:\桌面\资料\cv3\数据集\罚拳_公开\test\scissors'
Batch_files=10
transs=trans.Compose([
    trans.ToTensor(),
    trans.NORMalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
def read_img(batch_files):
    images=[]
    labels=[]
    for file in batch_files:
        image=Image.open(file)
        image=image.convert('RGB')
        image=image.resize((64,64))
        tensor=transs(image)
        images.append(tensor)
        if 'rock' in file :
            labels.append(torch.tensor(0,dtype=torch.int64))
        if 'paper' in file:
            labels.append(torch.tensor(1,dtype=torch.int64))
        if 'scissors' in file:
            labels.append(torch.tensor(2,dtype=torch.int64))
    return images,labels
if __name__ == '__main__':

class tiao(nn.Module):
    def __init__(self,shuru,shuchu):
        super(tiao, self).__init__()
        self.conv1=nn.Conv2d(in_channels=shuru,out_channels=shuchu,kernel_size=(3,3),padding=(1,1))
        self.bath=nn.BatchNorm2d(shuchu)
        self.relu=nn.ReLU()
    def forward(self,x):
        x1=self.conv1(x)
        x2=self.bath(x1)
        x3=self.relu(x2)
        x4=self.conv1(x3)
        x5=self.bath(x4)
        x6=self.relu(x5)
        x7=x6+x
        return x7
class tiao2(nn.Module):
    def __init__(self,shuru):
        super(tiao2, self).__init__()
        self.conv1=nn.Conv2d(in_channels=shuru,out_channels=shuru*2,kernel_size=(3,3),stride=(2,2),padding=(1,1))
        self.conv11=nn.Conv2d(in_channels=shuru,out_channels=shuru*2,kernel_size=(1,1),stride=(2,2))
        self.batch=nn.BatchNorm2d(shuru*2)
        self.relu=nn.ReLU()
        self.conv2=nn.Conv2d(in_channels=shuru*2,out_channels=shuru*2,kernel_size=(3,3),stride=(1,1),padding=(1,1))
    def forward(self,x):
        x1=self.conv1(x)
        x2=self.batch(x1)
        x3=self.relu(x2)
        x4=self.conv2(x3)
        x5=self.batch(x4)
        x6=self.relu(x5)
        x11=self.conv11(x)
        x7=x11+x6
        return x7
class resnet18(nn.Module):
    def __init__(self):
        super(resnet18, self).__init__()
        self.conv1=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=(7,7),stride=(2,2),padding=(3,3))
        self.bath=nn.BatchNorm2d(64)
        self.relu=nn.ReLU()
        self.max=nn.MaxPool2d(2,2)
        self.tiao1=tiao(64,64)
        self.tiao2=tiao(64,64)
        self.tiao3=tiao2(64)
        self.tiao4=tiao(128,128)
        self.tiao5=tiao2(128)
        self.tiao6=tiao(256,256)
        self.tiao7=tiao2(256)
        self.tiao8=tiao(512,512)
        self.a=nn.AdaptiveAvgPool2d(output_size=(1,1))
        self.l=nn.Linear(512,3)
    def forward(self,x):
        x1=self.conv1(x)
        x2=self.bath(x1)
        x3=self.relu(x2)
        x4=self.tiao1(x3)
        x5=self.tiao2(x4)
        x6=self.tiao3(x5)
        x7=self.tiao4(x6)
        x8=self.tiao5(x7)
        x9=self.tiao6(x8)
        x10=self.tiao7(x9)
        x11=self.tiao8(x10)
        x12=self.a(x11)
        x13=x12.view(x12.size()[0],-1)
        x14=self.l(x13)
        return x14

 train_rock=[os.path.join(train_rock,file) for file in os.listdir(train_rock)]
    train_paper= [os.path.join(train_paper, file) for file in os.listdir(train_paper)]
    train_scissors = [os.path.join(train_scissors, file) for file in os.listdir(train_scissors)]
    test_rock=[os.path.join(test_rock,file) for file in os.listdir(test_rock)]
    test_paper=[os.path.join(test_paper,file) for file in os.listdir(test_paper)]
    test_scission=[os.path.join(test_scission,file) for file in os.listdir(test_scission)]
    train=train_rock+train_paper+train_scissors
    test=test_rock+test_paper+test_scission
    random.shuffle(train)
    random.shuffle(test)
    model=resnet18().cuda()
    opt = torch.optim.ASGD(model.parameters(), lr=0.001, weight_decay=0.8)
    loss = nn.CrossEntropyLoss()
    print("开始训练")

  for i in range(5):
        running_loss=0
        for index in range(0,len(train),Batch_files):
            images,labels=read_img(train[index:index+Batch_files])
            inputs=torch.stack(images,0).cuda()
            labels=torch.stack(labels,0).cuda()
            inputs, labels = Variable(inputs), Variable(labels)
            opt.zero_grad()
            h=model(inputs)
            loss1=loss(h,labels)
            loss1.backward()
            opt.step()
            running_loss+=loss1.item()
            if index%41==40:
                avg_loos=running_loss/41
                running_loss=0
                print('avg_loss',avg_loos)
            if index%101==99:
                test_files=random.sample(test,100)
                test_image,test_label=read_img(test_files)
                test_images=torch.stack(test_image,0).cuda()
                test_labels=torch.stack(test_label,0).cuda()
                test_h=model(test_images)
                _,prediction=torch.max(test_h.data,1)
                total=test_labels.size(0)
                correct=(prediction==test_labels).sum()
                print('100张测试集准确率%d %%'%(100*correct/total))
    torch.save(model.state_dict(),'resnet_caiq猜拳.pth')

model.load_state_dict(torch.load('resnet_caiq猜拳.pth'))
labels={0:'rock',1:'paper',2:'scissors'}
    images=[]
    image=Image.open(r'E:\桌面\1.png')
    image=image.convert('RGB')
    image=image.resize((64,64))
    image=transs(image)
    images.append(image)
    image= torch.stack(images, 0).cuda()
    label=model(image)
    _,prediction=torch.max(label.data,1)
    print("预测类别",labels[prediction.item()])