导入基本要的库 

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.optim as optim
import torchvision.models as models
import PIL.Image as Image

图形变tensor的转化 

image_size = (224,224)
data_transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

train_data=dset.ImageFolder(root="data/cat-dog/training_set",transform=data_transform)
# 数据集长度
totallen = len(train_data)
print('train data length:',totallen)
test_data=dset.ImageFolder(root="data/cat-dog/test_set",transform=data_transform)
# 数据集长度
testtotallen = len(test_data)
print('test data length:',testtotallen)

trainlen = int(totallen*0.7)
vallen = totallen - trainlen
train_db,val_db=torch.utils.data.random_split(train_data,[trainlen,vallen])
print('train:',len(train_db),'validation:',len(val_db))
# batch size
bs=16
# 训练集
train_loader=torch.utils.data.DataLoader(train_db,batch_size=bs, shuffle=True,num_workers=2)
# 验证集
val_loader=torch.utils.data.DataLoader(val_db,batch_size=bs, shuffle=True,num_workers=2)

 关键的来了

resnet18 = models.resnet18(pretrained=True)

model = resnet18    #下载使用resent18模型
n_classes = len(train_data.classes)
model.fc = nn.Linear(512, n_classes)

import torch.nn.init as init
for name, module in model._modules.items():
    if(name=='fc'):
        # print(module.weight.shape)
        init.kaiming_uniform_(module.weight, a=0, mode='fan_in')

def get_num_correct(out, labels):
    return out.argmax(dim=1).eq(labels).sum().item()
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)
epoch_num = 5
for epoch in range(epoch_num):    #开始反复训练 epoch为次数
    total_loss=0
    total_correct=0
    val_correct=0
    for batch in train_loader:#GetBatch
        images,labels=batch
        outs=model(images)#PassBatch
        loss=F.cross_entropy(outs,labels)#CalculateLoss
        optimizer.zero_grad()
        loss.backward()#CalculateGradients
        optimizer.step()#UpdateWeights
        total_loss+=loss.item()
        total_correct+=get_num_correct(outs,labels)
    for batch in val_loader:
        images,labels=batch
        outs=model(images)
        val_correct+=get_num_correct(outs,labels)
        print("loss:",total_loss,"train_correct:",total_correct/trainlen,        "    val_correct:",val_correct/vallen)

保存训练的模型，开始预测 

torch.save(model, 'catvsdog.pkl')
model = torch.load('catvsdog.pkl')
model.eval()
test_loader = torch.utils.data.DataLoader(dataset = test_data
,batch_size=100
,shuffle=True
)
batch = next(iter(test_loader))
images, labels = batch 
out = model(images)
grid = torchvision.utils.make_grid(images, nrow=10)
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(10,10))
plt.imshow(np.transpose(grid, (1,2,0)))
print('labels:', labels)
print('predicts:', out.argmax(dim=1))