Transfer learning

实际工作中，只有很少的人从头开始训练 CNN，因为很难获得大量的样本。一般情况下，会通过调用预训练模型，例如 ConvNet 在 ImageNet（1.2 M 图像 1000 个类别）,可以用 ConvNet 初始化，也可以作为特征提取器用于感兴趣的任务或领域。

有两种主要的迁移学习：

微调 Convnet : 代替随机初始化，用预训练的网络模型进行初始化，就像在 ImageNet1000 的数据集上训练，剩下的训练就很常见了。
ConvNet 作为固定的特征提取器: 冻结网络的权重，除了最后的全连接层，最后的全连接层被替换新的随机权重，然后只训练这一层。

加载数据

我们利用 torchvision 和 torch.utils.data 包加载数据。

今天的任务是训练 ants 和 bees 分类器模型，每个类别 120 张训练图，75 张图验证图，

数据集下载地址

Finetune

from __future__ import print_function, division
import torch 
import torch.nn as nn 
import torch.optim as optim
from torch.optim import lr_scheduler 
import torch.backends.cudnn as cudnn 
import numpy as np import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt 
import time
import os 
import copycudnn.benchmark = True
plt.ion() # interactive mode # 对训练数据进行增强和归一化
# 对验证数据做归一化
data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]),'val': transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]),
}# dataset and dataloader
data_dir = "../../../datasets/hymenoptera_data"
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])for x in ['train', 'val']}dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True,num_workers=4)for x in ['train', 'val']}dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classesdevice = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")# Visualize a few images
# Let’s visualize a few training images so as to understand the data augmentations.
# 
def imshow(inp, title=None):inp = inp.numpy().transpose((1, 2, 0))mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])inp = std * inp + meaninp = np.clip(inp, 0, 1)plt.imshow(inp)if title is not None:plt.title(title)plt.pause(0.001)# inputs, classes = next(iter(dataloaders['train']))
# # make a grid from batch
# out = torchvision.utils.make_grid((inputs))
# imshow(out, title=[class_names[x] for x in classes])# training
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):since = time.time()best_model_wts = copy.deepcopy(model.state_dict())best_acc = 0.0for epoch in range(num_epochs):print('Epoch {}/{}'.format(epoch, num_epochs-1))print('-' * 10)# Each epoch has a training and validation phasefor phase in ['train', 'val']:if phase == 'train':model.train()  # Set model to training modeelse:model.eval()    # Set model to evaluate moderunning_loss = 0.0running_corrects = 0# Iterate over data.for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)# zero the parameter gradientsoptimizer.zero_grad()# forward# track history if only in trainwith torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)# backward + optimize only if in training phaseif phase == 'train':loss.backward()optimizer.step()# statisitcrunning_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)if phase == 'train':scheduler.step()epoch_loss = running_loss / dataset_sizes[phase]epoch_acc = running_corrects.double() / dataset_sizes[phase]print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')# deep copy the modelif phase == 'val' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())print()time_elapsed = time.time() - sinceprint(f'Traing complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f} s')print(f'Best val Acc: {best_acc:.4f}')# load best model weightsmodel.load_state_dict(best_model_wts)return model# Generic function to display predictions for a few images
def visualize_model(model, num_images=6):was_training = model.trainingmodel.eval()images_so_far = 0fig = plt.figure()with torch.no_grad():for i, (inputs, labels) in enumerate(dataloaders['val']):inputs = inputs.to(device)labels = labels.to(device)outputs = model(inputs)_, preds = torch.max(outputs, 1)for j in range(inputs.size()[0]):images_so_far += 1ax = plt.subplot(num_images // 2, 2, images_so_far)ax.axis('off')ax.set_title(f'predicted: {class_names[preds[j]]}')imshow(inputs.cpu().data[j])if images_so_far == num_images:model.train(mode=was_training)returnmodel.train(model=was_training)# Load a pretrained model and reset final fully connected layer.
model_finetune = models.resnet18(pretrained=True)
num_ftrs = model_finetune.fc.in_features# here the size of each output sample is set to 2
# it can be generalized to nn.Linear
model_finetune.fc = nn.Linear(num_ftrs, 2)model_finetune = model_finetune.to(device)criterion = nn.CrossEntropyLoss()# Observe that all parameters are being optimized
optimizer_finetune = optim.SGD(model_finetune.parameters(), lr=0.001, momentum=0.9)# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_finetune, step_size=7, gamma=0.1)# train and evaluate
model_finetune = train_model(model=model_finetune,criterion=criterion, optimizer=optimizer_finetune,scheduler=exp_lr_scheduler, num_epochs=25)# 可视化模型效果
visualize_model(model_finetune)

Output exceeds the size limit. Open the full output data in a text editor
train Loss: 0.6425 Acc: 0.6230
val Loss: 0.2763 Acc: 0.9150Epoch 1/24
----------
train Loss: 0.4966 Acc: 0.8074
val Loss: 0.3128 Acc: 0.8693Epoch 2/24
----------
train Loss: 0.4395 Acc: 0.8402
val Loss: 0.2923 Acc: 0.8562Epoch 3/24
----------
train Loss: 0.3743 Acc: 0.8402
val Loss: 0.2049 Acc: 0.9281Epoch 4/24
----------
train Loss: 0.2655 Acc: 0.8852
val Loss: 0.1841 Acc: 0.9346Epoch 5/24
----------
...
val Loss: 0.1862 Acc: 0.9412Traing complete in 1m 0 s
Best val Acc: 0.9477

在这里插入图片描述

固定为特征提取器

现在我们来冻结网络，除了最后一层。我们需要设置 requires_grad = False 来冻结参数，这样的话就不会计算梯度。查看更多

关键代码

# set requires_grad = False
for param in model_conv.parameters():param.requires_grad = False

from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import numpy as npimport torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copycudnn.benchmark = True
plt.ion()  # interactive mode# 对训练数据进行增强和归一化
# 对验证数据做归一化
data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]),'val': transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),]),
}# dataset and dataloader
data_dir = "../../../datasets/hymenoptera_data"
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])for x in ['train', 'val']}dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True,num_workers=4)for x in ['train', 'val']}dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classesdevice = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")# Visualize a few images
# Let’s visualize a few training images so as to understand the data augmentations.
#def imshow(inp, title=None):inp = inp.numpy().transpose((1, 2, 0))mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])inp = std * inp + meaninp = np.clip(inp, 0, 1)plt.imshow(inp)if title is not None:plt.title(title)plt.pause(0.001)# inputs, classes = next(iter(dataloaders['train']))
# # make a grid from batch
# out = torchvision.utils.make_grid((inputs))
# imshow(out, title=[class_names[x] for x in classes])# trainingdef train_model(model, criterion, optimizer, scheduler, num_epochs=25):since = time.time()best_model_wts = copy.deepcopy(model.state_dict())best_acc = 0.0for epoch in range(num_epochs):print('Epoch {}/{}'.format(epoch, num_epochs-1))print('-' * 10)# Each epoch has a training and validation phasefor phase in ['train', 'val']:if phase == 'train':model.train()  # Set model to training modeelse:model.eval()    # Set model to evaluate moderunning_loss = 0.0running_corrects = 0# Iterate over data.for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)# zero the parameter gradientsoptimizer.zero_grad()# forward# track history if only in trainwith torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)# backward + optimize only if in training phaseif phase == 'train':loss.backward()optimizer.step()# statisitcrunning_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)if phase == 'train':scheduler.step()epoch_loss = running_loss / dataset_sizes[phase]epoch_acc = running_corrects.double() / dataset_sizes[phase]print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')# deep copy the modelif phase == 'val' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())print()time_elapsed = time.time() - sinceprint(f'Traing complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f} s')print(f'Best val Acc: {best_acc:.4f}')# load best model weightsmodel.load_state_dict(best_model_wts)return model# Generic function to display predictions for a few imagesdef visualize_model(model, num_images=6):was_training = model.trainingmodel.eval()images_so_far = 0fig = plt.figure()with torch.no_grad():for i, (inputs, labels) in enumerate(dataloaders['val']):inputs = inputs.to(device)labels = labels.to(device)outputs = model(inputs)_, preds = torch.max(outputs, 1)for j in range(inputs.size()[0]):images_so_far += 1ax = plt.subplot(num_images // 2, 2, images_so_far)ax.axis('off')ax.set_title(f'predicted: {class_names[preds[j]]}')imshow(inputs.cpu().data[j])if images_so_far == num_images:model.train(mode=was_training)returnmodel.train(model=was_training)### 不同的地方
# Load a pretrained model and reset final fully connected layer.
# model_finetune = models.resnet18(pretrained=True)
# num_ftrs = model_finetune.fc.in_featuresmodel_conv = models.resnet18(pretrained=True)
# set requires_grad = False
for param in model_conv.parameters():param.requires_grad = False# 新的结构模块默认 requires_grad=True
num_ftrs = model_conv.fc.in_features
# here the size of each output sample is set to 2
# it can be generalized to nn.Linear
model_conv.fc = nn.Linear(num_ftrs, 2)model_conv = model_conv.to(device)criterion = nn.CrossEntropyLoss()# Observe that all parameters are being optimized
optimizer_finetune = optim.SGD(model_conv.parameters(), lr=0.001, momentum=0.9)# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_finetune, step_size=7, gamma=0.1)# train and evaluate
model_conv = train_model(model=model_conv,criterion=criterion, optimizer=optimizer_finetune,scheduler=exp_lr_scheduler, num_epochs=25)
visualize_model(model_conv)

Output exceeds the size limit. Open the full output data in a text editor
Epoch 0/24
----------
train Loss: 0.6819 Acc: 0.6270
val Loss: 0.1885 Acc: 0.9477Epoch 1/24
----------
train Loss: 0.4212 Acc: 0.7992
val Loss: 0.1760 Acc: 0.9477Epoch 2/24
----------
train Loss: 0.5423 Acc: 0.7828
val Loss: 0.7761 Acc: 0.7059Epoch 3/24
----------
train Loss: 0.6406 Acc: 0.7377
val Loss: 0.2656 Acc: 0.9216Epoch 4/24
----------
train Loss: 0.5401 Acc: 0.7951
val Loss: 0.2485 Acc: 0.8954
...
val Loss: 0.1679 Acc: 0.9542Traing complete in 0m 45 s
Best val Acc: 0.9542