pytorch采用resnet实现猫狗分类

最近在练习pytorch使用.

首先下载猫狗数据:

链接：https://pan.baidu.com/s/1l1AnBgkAAEhh0vI5_loWKw
提取码：2xq4

然后写代码,感觉这种逐批次从硬盘取数据训练有点慢,但先跑起来吧.感兴趣的可以去看看resnet源码,最好自己手敲一遍,练习效果更好.源码如下:resnet代码分析 - 慢行厚积 - 博客园   先熟悉简单的pytorch接口,后面来搞高级的检测和分割.

import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import Dataset
from torchvision import transforms,datasets,models
import shutil

random_state = 42
np.random.seed(random_state)


original_dataset_dir = '/home/wangyunhao/dc/dogs-vs-cats/train/train'
total_num = int(len(os.listdir(original_dataset_dir))/2)
random_idx = np.array(range(total_num))
np.random.shuffle(random_idx)

base_dir = '/home/wangyunhao/dc/dog_cat_deal'
if not os.path.exists(base_dir):
    os.mkdir(base_dir)

sub_dirs = ['train','test']
animals = ['cats','dogs']
train_idx = random_idx[:int(total_num*0.9)]
test_idx = random_idx[int(total_num*0.9):]
numbers = [train_idx,test_idx]
for idx,sub_dir in enumerate(sub_dirs):
    dir = os.path.join(base_dir,sub_dir)
    if not os.path.exists(dir):
        os.mkdir(dir)
    for animal in animals:
        animal_dir = os.path.join(dir,animal)
        if not os.path.exists(animal_dir):
            os.mkdir(animal_dir)
        fnames = [animal[:-1] + '.{}.jpg'.format(i) for i in numbers[idx]]
        for fname in fnames:
            src = os.path.join(original_dataset_dir,fname)
            dst = os.path.join(animal_dir,fname)
            shutil.copyfile(src,dst)

        print(animal_dir+ '  total images : %d ' %(len(os.listdir(animal_dir))))

    random_state = 1
    torch.manual_seed(random_state)
    torch.cuda.manual_seed(random_state)
    torch.cuda.manual_seed_all(random_state)
    np.random.seed(random_state)

    epochs = 10
    batch_size = 10
    num_workers = 0
    use_gpu = torch.cuda.is_available()
    model_path = '/home/wangyunhao/dc/dc_dog.pt'

    data_transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485,0.456,0.406],std = [0.229, 0.224, 0.225])
    ])

train_dataset = datasets.ImageFolder(root = '/home/wangyunhao/dc/dog_cat_deal/train/',
                                     transform=data_transform)

train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True,
                                           num_workers=num_workers
                                           )
test_dataset = datasets.ImageFolder(root='/home/wangyunhao/dc/dog_cat_deal/test', transform=data_transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

net = models.resnet101(num_classes=2)
if(os.path.exists('/home/wangyunhao/dc/dc_dog.pt')):
    net = torch.load('/home/wangyunhao/dc/dc_dog.pt')

if use_gpu:
    net = net.cuda()
print(net)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr=0.0001, momentum=0.9)

def train():

    for epoch in range(epochs):
        running_loss = 0.0
        train_correct = 0
        train_total = 0

        for i,data in enumerate(train_loader,0):
            inputs,train_labels = data
            print(i,train_labels)
            if use_gpu:
                inputs,labels = Variable(inputs.cuda()),Variable(train_labels.cuda())
            else:
                inputs,labels = Variable(inputs), Variable(train_labels)

            optimizer.zero_grad()
            outputs = net(inputs)
            _,train_predicted = torch.max(outputs.data,1)
            train_correct += (train_predicted==labels.data).sum()
            loss = criterion(outputs,labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            train_total += train_labels.size(0)
        print('train %d epoch loss: %.3f  acc: %.3f ' %(
                epoch+1,running_loss/train_total,100*train_correct / train_total))

        correct = 0
        test_loss = 0.0
        test_total = 0
        net.eval()
        for data in test_loader:
            images,labels = data
            if use_gpu:
                images,labels = Variable(images.cuda()), Variable(labels.cuda())
            else:
                images, labels = Variable(images), Variable(labels)
            outputs = net(images)
            _,predicted = torch.max(outputs.data,1)
            loss = criterion(outputs,labels)
            test_loss += loss.item()
            test_total += labels.size(0)
            correct += (predicted == labels.data).sum()

        print('test  %d epoch loss: %.3f  acc: %.3f' % (epoch+1,test_loss/test_total,100*correct/test_total))
        torch.save(net,'/home/wangyunhao/dc/dc_dog.pt')

train()