环境
系统：win10
cpu：i7-6700HQ
gpu：gtx965m
python : 3.6
pytorch ：0.3
数据下载
来源自Sasank Chilamkurthy 的教程； 数据：下载链接。
下载后解压放到项目根目录：
数据集为用来分类 蚂蚁和蜜蜂。有大约120个训练图像，每个类有75个验证图像。
数据导入
可以使用 torchvision.datasets.ImageFolder(root,transforms) 模块 可以将 图片转换为 tensor。
先定义transform：
ata_transforms = {
'train': transforms.Compose([
# 随机切成224x224 大小图片 统一图片格式
transforms.RandomResizedCrop(224),
# 图像翻转
transforms.RandomHorizontalFlip(),
# totensor 归一化(0,255) >> (0,1) normalize channel=（channel-mean）/std
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]),
"val" : transforms.Compose([
# 图片大小缩放 统一图片格式
transforms.Resize(256),
# 以中心裁剪
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
}导入，加载数据：
data_dir = './hymenoptera_data'
# trans data
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
# load data
data_loaders = {x: DataLoader(image_datasets[x], batch_size=BATCH_SIZE, shuffle=True) for x in ['train', 'val']}
data_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
print(data_sizes, class_names){'train': 244, 'val': 153} ['ants', 'bees']训练集 244图片 ， 测试集153图片 。
可视化部分图片看看，由于visdom支持tensor输入 ，不用换成numpy，直接用tensor计算即可 ：
inputs, classes = next(iter(data_loaders['val'])) out = torchvision.utils.make_grid(inputs) inp = torch.transpose(out, 0, 2) mean = torch.FloatTensor([0.485, 0.456, 0.406]) std = torch.FloatTensor([0.229, 0.224, 0.225]) inp = std * inp + mean inp = torch.transpose(inp, 0, 2) viz.images(inp)
创建CNN
net 根据上一篇的处理cifar10的改了一下规格：
class CNN(nn.Module): def __init__(self, in_dim, n_class): super(CNN, self).__init__() self.cnn = nn.Sequential( nn.BatchNorm2d(in_dim), nn.ReLU(True), nn.Conv2d(in_dim, 16, 7), # 224 >> 218 nn.BatchNorm2d(16), nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # 218 >> 109 nn.ReLU(True), nn.Conv2d(16, 32, 5), # 105 nn.BatchNorm2d(32), nn.ReLU(True), nn.Conv2d(32, , 5), # 101 nn.BatchNorm2d(), nn.ReLU(True), nn.Conv2d(, , 3, 1, 1), nn.BatchNorm2d(), nn.ReLU(True), nn.MaxPool2d(2, 2), # 101 >> 50 nn.Conv2d(, 128, 3, 1, 1), # nn.BatchNorm2d(128), nn.ReLU(True), nn.MaxPool2d(3), # 50 >> 16 ) self.fc = nn.Sequential( nn.Linear(128*16*16, 120), nn.BatchNorm1d(120), nn.ReLU(True), nn.Linear(120, n_class)) def forward(self, x): out = self.cnn(x) out = self.fc(out.view(-1, 128*16*16)) return out # 输入3层rgb ，输出 分类 2 model = CNN(3, 2)
loss，优化函数：
line = viz.line(Y=np.arange(10)) loss_f = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=LR, momentum=0.9) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
参数：
BATCH_SIZE = 4 LR = 0.001 EPOCHS = 10
运行 10个 epoch 看看：
[9/10] train_loss:0.650|train_acc:0.639|test_loss:0.621|test_acc0.706 [10/10] train_loss:0.5|train_acc:0.627|test_loss:0.654|test_acc0.686 Training complete in 1m 16s Best val Acc: 0.712418
运行 20个看看：
[19/20] train_loss:0.592|train_acc:0.701|test_loss:0.563|test_acc0.712 [20/20] train_loss:0.5|train_acc:0.721|test_loss:0.571|test_acc0.706 Training complete in 2m 30s Best val Acc: 0.745098
准确率比较低：只有74.5%
我们使用models 里的 resnet18 运行 10个epoch：
model = torchvision.models.resnet18(True) num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, 2)
[9/10] train_loss:0.621|train_acc:0.652|test_loss:0.588|test_acc0.667 [10/10] train_loss:0.610|train_acc:0.680|test_loss:0.561|test_acc0.667 Training complete in 1m 24s Best val Acc: 0.686275
效果也很一般，想要短时间内就训练出效果很好的models，我们可以下载训练好的state，在此基础上训练：
model = torchvision.models.resnet18(pretrained=True) num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, 2)
[9/10] train_loss:0.308|train_acc:0.877|test_loss:0.160|test_acc0.941 [10/10] train_loss:0.267|train_acc:0.885|test_loss:0.148|test_acc0.954 Training complete in 1m 25s Best val Acc: 0.954248
10个epoch直接的到95%的准确率。