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:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
|
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:
1
2
3
4
5
6
7
8
9
|
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 :
1
2
3
4
5
6
7
8
9
|
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:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
|
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:
1
2
3
4
|
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:
1
2
3
|
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:
1
2
3
|
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:
1
2
3
|
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:https://github.com/ffzs/ml_pytorch/blob/master/ml_pytorch_hymenoptera
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。
原文链接:https://blog.csdn.net/tonydz0523/article/details/79075077