数据预处理部分:
- 数据增强:torchvision中transforms模块自带功能,比较实用
- 数据预处理:torchvision中transforms也帮我们实现好了,直接调用即可
- DataLoader模块直接读取batch数据
网络模块设置:
- 加载预训练模型,torchvision中有很多经典网络架构,调用起来十分方便,并且可以用人家训练好的权重参数来继续训练,也就是所谓的迁移学习
- 需要注意的是别人训练好的任务跟咱们的可不是完全一样,需要把最后的head层改一改,一般也就是最后的全连接层,改成咱们自己的任务
- 训练时可以全部重头训练,也可以只训练最后咱们任务的层,因为前几层都是做特征提取的,本质任务目标是一致的
网络模型保存与测试
- 模型保存的时候可以带有选择性,例如在验证集中如果当前效果好则保存
- 读取模型进行实际测试
data_transforms = {
'train':
transforms.Compose([
transforms.Resize([96, 96]),
transforms.RandomRotation(45),
transforms.CenterCrop(64),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(p=0.5),
transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),
transforms.RandomGrayscale(p=0.025),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'valid':
transforms.Compose([
transforms.Resize([64, 64]),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
选择性的权重更新
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
自定义修改模型输出层,以resnet18为例
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
model_ft = models.resnet18(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 102)
input_size = 64
return model_ft, input_size
训练权重 选择
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
model_ft = model_ft.to(device)
filename='checkpoint.pth'
params_to_update = model_ft.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t",name)
else:
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
print("\t",name)
基本训练代码
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25,filename='best.pt'):
since = time.time()
best_acc = 0
model.to(device)
val_acc_history = []
train_acc_history = []
train_losses = []
valid_losses = []
LRs = [optimizer.param_groups[0]['lr']]
best_model_wts = copy.deepcopy(model.state_dict())
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
for phase in ['train', 'valid']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloaders[phase].dataset)
epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
time_elapsed = time.time() - since
print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
state = {
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer' : optimizer.state_dict(),
}
torch.save(state, filename)
if phase == 'valid':
val_acc_history.append(epoch_acc)
valid_losses.append(epoch_loss)
if phase == 'train':
train_acc_history.append(epoch_acc)
train_losses.append(epoch_loss)
print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))
LRs.append(optimizer.param_groups[0]['lr'])
print()
scheduler.step()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
model.load_state_dict(best_model_wts)
return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs
调用训练
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20)
def im_convert(tensor):
""" 展示数据"""
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
image = image.transpose(1,2,0)
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
image = image.clip(0, 1)
return image