pytorch + visdom CNN处理自建图片数据集的方法
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, 64, 5), # 101 nn.batchnorm2d(64), nn.relu(true), nn.conv2d(64, 64, 3, 1, 1), nn.batchnorm2d(64), nn.relu(true), nn.maxpool2d(2, 2), # 101 >> 50 nn.conv2d(64, 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:0.645|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:0.564|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。
示例代码:
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