pytorch + visdom CNN method of processing self-built image data sets

This article mainly introduces the method of pytorch visdom CNN processing self-built image data sets. It has certain reference value. Now I share it with you. Friends in need can refer to it

Environment

System: win10

cpu: i7-6700HQ

gpu: gtx965m

python: 3.6

pytorch: 0.3

Data download

comes from Sasank Chilamkurthy’s tutorial; Data: Download link.

After downloading, unzip and place it in the project root directory:

The data set is used to classify ants and bees. There are about 120 training images and 75 validation images for each class.

Data import

You can use the torchvision.datasets.ImageFolder(root,transforms) module to convert images into tensors.

Define transform first:

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])
  ])
}

Import and load data:

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']

There are 244 images in the training set and 153 images in the test set.

Visualize some pictures. Since visdom supports tensor input, you don’t need to change it to numpy. You can directly use tensor calculation:

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)

Create CNN

net Based on the previous article's processing of cifar10, I changed the specifications:

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, optimization function:

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)

Parameters:

BATCH_SIZE = 4
LR = 0.001
EPOCHS = 10

Run 10 epochs and see:

[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

Run 20 epochs and see:

[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

##The accuracy is relatively low: Only 74.5%

We use resnet18 in models to run 10 epochs:

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

The effect is also very average, and we want to train it with good results in a short time models, we can download the trained state and train on this basis:

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 epochs can directly reach 95% accuracy.

Related recommendations:

pytorch visdom handles simple classification problems

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