pytorch分类cifar-10（多模型对比）

作者: 赖子啊 | 来源:发表于2019-11-15 09:49 被阅读0次

之前沿着这样的路线：AlexNet,VGG,GoogLeNet v1,ResNet,DenseNet把主要的经典的分类网络的paper看完了，主要是人们发现很深的网络很难train，知道之后出现的Batch Normalization和ResNet才解决了深层网络的训练问题，因为网络深了之后准确率上升，所以之后的网络效果在解决了train的问题之后，就明显比之前的好，而且通过Bottleneck的结构，也能够控制参数量，减小过拟合的风险，同时计算量也不会失控。
看完paper，就想实操一下，复现ImageNet有难度的话，那就试试cifar-10吧，找了一个github代码跑一跑，顺便也是当作一个分类任务代码的模板吧，其实这个也可以去pytorch examples里面看那个MNIST的，但是MNIST太简单了，所以就找了下面这个：

质量还可以的，主要通过改pytorch官方网络中的一些参数（因为ImageNet针对的是输入分辨率为224×224的图片，这里是3×32×32的），构建了一些经典的网络，用来分类cifar-10。

A little supplement

我就改了一点点的地方，使她能自动调整学习率了，不用manually去调整（改动的地方非常少），然后用torchsummary来看看每个模型的参数量，这个项目其实非常小，只有一个主文件，但是github上已经有1.7k的stars。

ps:在用VGG16,ResNet18,ResNet50的时候我设置了400个epoch，后来感觉实在太久了，况且后面的提升也不大，所以后面的训练都设置为300个epoch，当然精度可能会受到一点影响

Main code

这里主要放一下主训练函数，其他模型的构建都可以参考torchvision.models里面的源码，虽然没有PreActResNet和DPN，但是PreActResNet和ResNet的搭建非常相似，只是是一个“预激活”的版本，所以可以参考ResNet代码构建，DPN我没看过，但是感觉看一遍论文中的结构部分，参照ResNet代码，构建也不是很难。

'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms

import os
import argparse

from models import *
from utils import progress_bar


parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--trainbs', default=128, type=int, help='trainloader batch size')
parser.add_argument('--testbs', default=100, type=int, help='testloader batch size')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
args = parser.parse_args()

device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.trainbs, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.testbs, shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# Model
print('==> Building model..')
# net = VGG('VGG16')
# net = ResNet18()
net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = ResNeXt29_32x4d()
# net = MobileNet()
# net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# net = ShuffleNetV2(1)
# net = EfficientNetB0()
net_name = net.name
save_path = './checkpoint/{0}_ckpt.pth'.format(net.name)
net = net.to(device)
if device == 'cuda':
    net = torch.nn.DataParallel(net)
    cudnn.benchmark = True

if args.resume:
    # Load best checkpoint trained last time.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load(save_path)
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=70, gamma=0.1)

# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
            % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))

def test(epoch):
    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))

    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        print('Saving ' + net_name + ' ..')
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, save_path)
        best_acc = acc


for epoch in range(start_epoch, 300):
    # In PyTorch 1.1.0 and later,
    # you should call them in the opposite order:
    # `optimizer.step()` before `lr_scheduler.step()`
    train(epoch)
    test(epoch)
    scheduler.step()  # 每隔100 steps学习率乘以0.1

print("\nTesting best accuracy:", best_acc)

Accuracy

Note:下面的Total params，Estimated Total Size (MB)，Trainable params，Params size (MB)均是指改过之后适用cifar-10的相对应的网络模型，不是原ImageNet的网络模型，且batch size大小统一为128，可根据自己的硬件条件进行调整。

Model	My Acc.	Total params	Estimated Total Size (MB)	Trainable params	Params size (MB)	Saved model size (MB)	GPU memory usage(MB)
MobileNetV2	92.64%	2,296,922	36.14	2,296,922	8.76	8.96	3107
VGG16	94.27%	14,728,266	62.77	14,728,266	56.18	59.0	1229
PreActResNet18	94.70%	11,171,146	53.38	11,171,146	42.61	44.7	1665
ResNeXt29(2x64d)	95.09%	9,128,778	99.84	9,128,778	34.82	36.7	5779
ResNet50	95.22%	23,520,842	155.86	23,520,842	89.72	94.4	5723
DPN92	95.42%	34,236,634	243.50	34,236,634	130.60	137.5	10535
ResNeXt29(32x4d)	95.49%	4,774,218	83.22	4,774,218	18.21	19.2	5817
DenseNet121	95.55%	6,956,298	105.05	6,956,298	26.54	28.3	8203
ResNet18	95.59%	11,173,962	53.89	11,173,962	42.63	44.8	1615
ResNet101	95.62%	42,512,970	262.31	42,512,970	162.17	170.6	8857