resnet50网络实现垃圾分类

一：介绍：

经典resnet网络是由何凯明团队于2015年提出，论文名为《Deep Residual Learning for Image Recognition》

resnet网络所要解决的问题为深度神经网络的“退化”问题，即随着神经网络搭建的越深，拟合效果却越差的问题，并且这个问题不是由过拟合诱发的。

resnet也成为残差网络，网络由残差块构建：

残差块由多个级联的卷积层和一个shortcut connections组成，将二者的输出值累加后，通过ReLU激活层得到残差块的输出。多个残差块可以串联起来，从而实现更深的网络。

残差块有两种设计方式

左图针对较浅的网络，如ResNet-18/34；右图针对较深的网络，又称为”bottleneck” building block，如ResNet-50/101/152，使用此方式的目的就是为了降低参数数目。

论文给出了五种不同层数的resnet

ResNet-18/34对应的每个残差块的卷积kernel大小依次是3*3、3*3，ResNet-50/101/152对应的每个残差块的卷积kernel大小依次是1*1、3*3、1*1。

论文中给出了层数为34的ResNet网络结构

二：实现垃圾分类

1.准备数据集：

2.加载数据集：

class garbage_datasets(Dataset):def __init__(self, filepath):self.images = []self.labels = []self.transform = transformfor filename in tqdm(os.listdir(filepath+'Hazardous waste')):image = Image.open(filepath+'Hazardous waste/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(0)for filename in tqdm(os.listdir(filepath+'Kitchen waste')):image = Image.open(filepath+'Kitchen waste/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(1)  for filename in tqdm(os.listdir(filepath+'Other garbage')):image = Image.open(filepath+'Other garbage/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(2)  for filename in tqdm(os.listdir(filepath+'Recyclable garbage')):image = Image.open(filepath+'Recyclable garbage/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(3)                self.labels = torch.LongTensor(self.labels)def __getitem__(self, index):return self.images[index], self.labels[index]def __len__(self):images = np.array(self.images)len = images.shape[0]return lentrain_data = garbage_datasets('data/train/')
train_loader = DataLoader(train_data,batch_size = batch_size,shuffle = True)val_data = garbage_datasets('data/val/')
val_loader = DataLoader(val_data,batch_size = batch_size)

3.构建网络：

class Bottleneck(nn.Module):extention=4def __init__(self,inplanes,planes,stride,downsample=None):super(Bottleneck, self).__init__()self.conv1=nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False)self.bn1=nn.BatchNorm2d(planes)self.conv2=nn.Conv2d(planes,planes,kernel_size=3,stride=1,padding=1,bias=False)self.bn2=nn.BatchNorm2d(planes)self.conv3=nn.Conv2d(planes,planes*self.extention,kernel_size=1,stride=1,bias=False)self.bn3=nn.BatchNorm2d(planes*self.extention)self.relu=nn.ReLU(  )self.downsample=downsampleself.stride=stridedef forward(self,x):residual=xout=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.conv2(out)out=self.bn2(out)out=self.relu(out)out=self.conv3(out)out=self.bn3(out)out=self.relu(out)if self.downsample is not None:residual=self.downsample(x)out=out + residualout=self.relu(out)return outclass ResNet(nn.Module):def __init__(self,block,layers,num_class):self.inplane=64super(ResNet, self).__init__()self.block=blockself.layers=layersself.conv1=nn.Conv2d(3,self.inplane,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(self.inplane)self.relu=nn.ReLU()self.maxpool=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)self.stage1=self.make_layer(self.block,64,layers[0],stride=1)self.stage2=self.make_layer(self.block,128,layers[1],stride=2)self.stage3=self.make_layer(self.block,256,layers[2],stride=2)self.stage4=self.make_layer(self.block,512,layers[3],stride=2)self.avgpool=nn.AvgPool2d(7)self.fc=nn.Linear(512*block.extention,num_class)def forward(self,x):out=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.maxpool(out)out=self.stage1(out)out=self.stage2(out)out=self.stage3(out)out=self.stage4(out)out=self.avgpool(out)out=torch.flatten(out,1)out=self.fc(out)return outdef make_layer(self,block,plane,block_num,stride=1):block_list=[]downsample=Noneif(stride!=1 or self.inplane!=plane*block.extention):downsample=nn.Sequential(nn.Conv2d(self.inplane,plane*block.extention,stride=stride,kernel_size=1,bias=False),nn.BatchNorm2d(plane*block.extention))conv_block=block(self.inplane,plane,stride=stride,downsample=downsample)block_list.append(conv_block)self.inplane=plane*block.extentionfor i in range(1,block_num):block_list.append(block(self.inplane,plane,stride=1))return nn.Sequential(*block_list)model=ResNet(Bottleneck,[3,4,6,3],4)

4.训练模型：

def train(epoch):model.train()print("epoch:",epoch+1)running_loss = 0.0for batch_idx,data in enumerate(train_loader,0):inputs, targets = datainputs, targets = inputs.to(device),targets.to(device)optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs,targets)loss.backward()optimizer.step()running_loss = running_loss + loss.item()print('train loss: %.3f' % (running_loss/batch_idx))torch.save(model.state_dict(), './model1.pth')

5.验证模型：

def val():model.eval()correct = 0total = 0with torch.no_grad():for data in val_loader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()print('accuracy on test set: %d %% ' % (100*correct/total))return correct/total

6.测试模型：

def test(imgpath):font={    'color': 'red','size': 20,'family': 'Times New Roman','style':'italic'}o_img = Image.open(imgpath)o_img1 = o_img.resize((224,224))img = transform(o_img1)img = img.unsqueeze(0)img = img.cuda()print(img.shape)model = ResNet(Bottleneck,[3,4,6,3],4)model.load_state_dict(torch.load("model.pth")) model = model.cuda()output = model(img)_, predict = torch.max(output,dim=1)if predict == 0:print("Hazardous waste")plt.imshow(o_img)plt.text(0, -6.0, "Hazardous waste", fontdict=font)plt.show()if predict == 1:print("Kitchen waste")plt.imshow(o_img)plt.text(0, -6.0, "Kitchen waste", fontdict=font)plt.show() if predict == 2:print("Other garbage")plt.imshow(o_img)plt.text(0, -6.0, "Other garbage", fontdict=font)plt.show() if predict == 3:print("Recyclable garbage")plt.imshow(o_img)plt.text(0, -6.0, "Recyclable garbage", fontdict=font)plt.show()

源代码：

import torch.nn as nn
import torch
import numpy as np
from torch.utils.data import DataLoader,Dataset
from torchvision import transforms
import torchvision
import torch.nn.functional as F
import torch.optim as optim
import os
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as pltbatch_size = 8transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])])torch.cuda.empty_cache()class garbage_datasets(Dataset):def __init__(self, filepath):self.images = []self.labels = []self.transform = transformfor filename in tqdm(os.listdir(filepath+'Hazardous waste')):image = Image.open(filepath+'Hazardous waste/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(0)for filename in tqdm(os.listdir(filepath+'Kitchen waste')):image = Image.open(filepath+'Kitchen waste/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(1)  for filename in tqdm(os.listdir(filepath+'Other garbage')):image = Image.open(filepath+'Other garbage/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(2)  for filename in tqdm(os.listdir(filepath+'Recyclable garbage')):image = Image.open(filepath+'Recyclable garbage/'+filename)image = image.resize((224,224))image = self.transform(image)self.images.append(image)self.labels.append(3)                self.labels = torch.LongTensor(self.labels)def __getitem__(self, index):return self.images[index], self.labels[index]def __len__(self):images = np.array(self.images)len = images.shape[0]return lentrain_data = garbage_datasets('data/train/')
train_loader = DataLoader(train_data,batch_size = batch_size,shuffle = True)val_data = garbage_datasets('data/val/')
val_loader = DataLoader(val_data,batch_size = batch_size)class Bottleneck(nn.Module):extention=4def __init__(self,inplanes,planes,stride,downsample=None):super(Bottleneck, self).__init__()self.conv1=nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False)self.bn1=nn.BatchNorm2d(planes)self.conv2=nn.Conv2d(planes,planes,kernel_size=3,stride=1,padding=1,bias=False)self.bn2=nn.BatchNorm2d(planes)self.conv3=nn.Conv2d(planes,planes*self.extention,kernel_size=1,stride=1,bias=False)self.bn3=nn.BatchNorm2d(planes*self.extention)self.relu=nn.ReLU(  )self.downsample=downsampleself.stride=stridedef forward(self,x):residual=xout=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.conv2(out)out=self.bn2(out)out=self.relu(out)out=self.conv3(out)out=self.bn3(out)out=self.relu(out)if self.downsample is not None:residual=self.downsample(x)out=out + residualout=self.relu(out)return outclass ResNet(nn.Module):def __init__(self,block,layers,num_class):self.inplane=64super(ResNet, self).__init__()self.block=blockself.layers=layersself.conv1=nn.Conv2d(3,self.inplane,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(self.inplane)self.relu=nn.ReLU()self.maxpool=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)self.stage1=self.make_layer(self.block,64,layers[0],stride=1)self.stage2=self.make_layer(self.block,128,layers[1],stride=2)self.stage3=self.make_layer(self.block,256,layers[2],stride=2)self.stage4=self.make_layer(self.block,512,layers[3],stride=2)self.avgpool=nn.AvgPool2d(7)self.fc=nn.Linear(512*block.extention,num_class)def forward(self,x):out=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.maxpool(out)out=self.stage1(out)out=self.stage2(out)out=self.stage3(out)out=self.stage4(out)out=self.avgpool(out)out=torch.flatten(out,1)out=self.fc(out)return outdef make_layer(self,block,plane,block_num,stride=1):block_list=[]downsample=Noneif(stride!=1 or self.inplane!=plane*block.extention):downsample=nn.Sequential(nn.Conv2d(self.inplane,plane*block.extention,stride=stride,kernel_size=1,bias=False),nn.BatchNorm2d(plane*block.extention))conv_block=block(self.inplane,plane,stride=stride,downsample=downsample)block_list.append(conv_block)self.inplane=plane*block.extentionfor i in range(1,block_num):block_list.append(block(self.inplane,plane,stride=1))return nn.Sequential(*block_list)model=ResNet(Bottleneck,[3,4,6,3],4)device = torch.device('cuda'if torch.cuda.is_available else 'cpu')model.to(device)model.load_state_dict(torch.load("model1.pth")) criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),lr = 0.001)def train(epoch):model.train()print("epoch:",epoch+1)running_loss = 0.0for batch_idx,data in enumerate(train_loader,0):inputs, targets = datainputs, targets = inputs.to(device),targets.to(device)optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs,targets)loss.backward()optimizer.step()running_loss = running_loss + loss.item()print('train loss: %.3f' % (running_loss/batch_idx))torch.save(model.state_dict(), './model1.pth')  def val():model.eval()correct = 0total = 0with torch.no_grad():for data in val_loader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()print('accuracy on test set: %d %% ' % (100*correct/total))return correct/totalif __name__ == '__main__':acc_list = []epoch_list = []for epoch in range(5):train(epoch)acc = val()acc_list.append(acc)epoch_list.append(epoch + 1)plt.plot(epoch_list,acc_list)plt.ylabel("ACC")plt.xlabel("Epoch")plt.show()

测试源码：

from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn as nntransform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])])class Bottleneck(nn.Module):extention=4def __init__(self,inplanes,planes,stride,downsample=None):super(Bottleneck, self).__init__()self.conv1=nn.Conv2d(inplanes,planes,kernel_size=1,stride=stride,bias=False)self.bn1=nn.BatchNorm2d(planes)self.conv2=nn.Conv2d(planes,planes,kernel_size=3,stride=1,padding=1,bias=False)self.bn2=nn.BatchNorm2d(planes)self.conv3=nn.Conv2d(planes,planes*self.extention,kernel_size=1,stride=1,bias=False)self.bn3=nn.BatchNorm2d(planes*self.extention)self.relu=nn.ReLU(  )self.downsample=downsampleself.stride=stridedef forward(self,x):residual=xout=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.conv2(out)out=self.bn2(out)out=self.relu(out)out=self.conv3(out)out=self.bn3(out)out=self.relu(out)if self.downsample is not None:residual=self.downsample(x)out=out + residualout=self.relu(out)return outclass ResNet(nn.Module):def __init__(self,block,layers,num_class):self.inplane=64super(ResNet, self).__init__()self.block=blockself.layers=layersself.conv1=nn.Conv2d(3,self.inplane,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(self.inplane)self.relu=nn.ReLU()self.maxpool=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)self.stage1=self.make_layer(self.block,64,layers[0],stride=1)self.stage2=self.make_layer(self.block,128,layers[1],stride=2)self.stage3=self.make_layer(self.block,256,layers[2],stride=2)self.stage4=self.make_layer(self.block,512,layers[3],stride=2)self.avgpool=nn.AvgPool2d(7)self.fc=nn.Linear(512*block.extention,num_class)def forward(self,x):out=self.conv1(x)out=self.bn1(out)out=self.relu(out)out=self.maxpool(out)out=self.stage1(out)out=self.stage2(out)out=self.stage3(out)out=self.stage4(out)out=self.avgpool(out)out=torch.flatten(out,1)out=self.fc(out)return outdef make_layer(self,block,plane,block_num,stride=1):block_list=[]downsample=Noneif(stride!=1 or self.inplane!=plane*block.extention):downsample=nn.Sequential(nn.Conv2d(self.inplane,plane*block.extention,stride=stride,kernel_size=1,bias=False),nn.BatchNorm2d(plane*block.extention))conv_block=block(self.inplane,plane,stride=stride,downsample=downsample)block_list.append(conv_block)self.inplane=plane*block.extentionfor i in range(1,block_num):block_list.append(block(self.inplane,plane,stride=1))return nn.Sequential(*block_list)def test(imgpath):font={  'color': 'red','size': 20,'family': 'Times New Roman','style':'italic'}o_img = Image.open(imgpath)o_img1 = o_img.resize((224,224))img = transform(o_img1)img = img.unsqueeze(0)img = img.cuda()print(img.shape)model = ResNet(Bottleneck,[3,4,6,3],4)model.load_state_dict(torch.load("model.pth")) model = model.cuda()output = model(img)_, predict = torch.max(output,dim=1)if predict == 0:print("Hazardous waste")plt.imshow(o_img)plt.text(0, -6.0, "Hazardous waste", fontdict=font)plt.show()if predict == 1:print("Kitchen waste")plt.imshow(o_img)plt.text(0, -6.0, "Kitchen waste", fontdict=font)plt.show() if predict == 2:print("Other garbage")plt.imshow(o_img)plt.text(0, -6.0, "Other garbage", fontdict=font)plt.show() if predict == 3:print("Recyclable garbage")plt.imshow(o_img)plt.text(0, -6.0, "Recyclable garbage", fontdict=font)plt.show()   if __name__ == "__main__":test('data/test/Hazardous waste/2.jpg')

最终验证集的准确率可达到70%

附几张测试成功的图片

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