注意
点击 这里 下载完整的示例代码
计算机视觉的迁移学习教程
创建于：2017年3月24日 | 最后更新：2024年8月27日 | 最后验证：2024年11月5日
在本教程中，您将学习如何使用迁移学习训练卷积神经网络进行图像分类。您可以阅读更多关于迁移学习的内容在cs231n notes
引用这些笔记，
在实践中，很少有人从头开始训练整个卷积网络（使用随机初始化），因为拥有足够大小的数据集相对较少。相反，通常的做法是在一个非常大的数据集（例如ImageNet，包含120万张图像和1000个类别）上预训练一个卷积网络，然后将其作为初始化或固定特征提取器用于感兴趣的任务。
这两个主要的迁移学习场景如下所示：
微调卷积网络：我们不是随机初始化，而是使用预训练的网络（如在imagenet 1000数据集上训练的网络）来初始化网络。其余的培训过程看起来像往常一样。
ConvNet 作为固定特征提取器：在这里，我们将冻结网络中除最后一个全连接层之外的所有权重。这个最后的全连接层被替换为一个具有随机权重的新层，并且只训练这一层。
# License: BSD
# Author: Sasank Chilamkurthy
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
from PIL import Image
from tempfile import TemporaryDirectory
cudnn.benchmark = True
plt.ion() # interactive mode
<contextlib.ExitStack object at 0x7effb18a2050>
加载数据
我们将使用torchvision和torch.utils.data包来加载数据。
我们今天要解决的问题是训练一个模型来分类蚂蚁和蜜蜂。我们大约有120张蚂蚁和蜜蜂的训练图像。每个类别有75张验证图像。通常，如果从头开始训练，这是一个非常小的数据集。由于我们使用迁移学习，我们应该能够很好地泛化。
这个数据集是imagenet的一个非常小的子集。
注意
从 这里 下载数据并将其解压到当前目录。
# Data augmentation and normalization for training
# Just normalization for validation
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])
]),
}
data_dir = 'data/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'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
可视化一些图像
让我们可视化一些训练图像，以便理解数据增强。
def imshow(inp, title=None):
"""Display image for Tensor."""
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 + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
# Get a batch of training data
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])
![['ants', 'ants', 'ants', 'ants']](../_images/sphx_glr_transfer_learning_tutorial_001.png)
训练模型
现在，让我们编写一个通用函数来训练模型。在这里，我们将进行说明：
调度学习率
保存最佳模型
在以下内容中，参数 scheduler 是来自
torch.optim.lr_scheduler 的 LR 调度器对象。
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
# Create a temporary directory to save training checkpoints
with TemporaryDirectory() as tempdir:
best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
torch.save(model.state_dict(), best_model_params_path)
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch}/{num_epochs - 1}')
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_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 model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
torch.save(model.state_dict(), best_model_params_path)
print()
time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')
# load best model weights
model.load_state_dict(torch.load(best_model_params_path, weights_only=True))
return model
可视化模型预测
用于显示一些图像预测的通用函数
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = 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 += 1
ax = 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)
return
model.train(mode=was_training)
微调ConvNet
加载一个预训练模型并重置最后的全连接层。
model_ft = models.resnet18(weights='IMAGENET1K_V1')
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``.
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.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_ft, step_size=7, gamma=0.1)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
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100%|##########| 44.7M/44.7M [00:00<00:00, 209MB/s]
训练和评估
在CPU上大约需要15-25分钟。而在GPU上，则不到一分钟。
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
Epoch 0/24
----------
train Loss: 0.4761 Acc: 0.7623
val Loss: 0.2903 Acc: 0.8627
Epoch 1/24
----------
train Loss: 0.5393 Acc: 0.7992
val Loss: 0.6532 Acc: 0.7386
Epoch 2/24
----------
train Loss: 0.4307 Acc: 0.8320
val Loss: 0.2170 Acc: 0.9281
Epoch 3/24
----------
train Loss: 0.6076 Acc: 0.7910
val Loss: 0.3034 Acc: 0.8824
Epoch 4/24
----------
train Loss: 0.3890 Acc: 0.8525
val Loss: 0.2492 Acc: 0.9085
Epoch 5/24
----------
train Loss: 0.4929 Acc: 0.8197
val Loss: 0.2845 Acc: 0.8889
Epoch 6/24
----------
train Loss: 0.3594 Acc: 0.8361
val Loss: 0.2774 Acc: 0.8954
Epoch 7/24
----------
train Loss: 0.4217 Acc: 0.8115
val Loss: 0.2437 Acc: 0.9020
Epoch 8/24
----------
train Loss: 0.2371 Acc: 0.9016
val Loss: 0.2413 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.2811 Acc: 0.8648
val Loss: 0.2473 Acc: 0.9085
Epoch 10/24
----------
train Loss: 0.3361 Acc: 0.8648
val Loss: 0.2125 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.3290 Acc: 0.8484
val Loss: 0.2937 Acc: 0.8954
Epoch 12/24
----------
train Loss: 0.2295 Acc: 0.9016
val Loss: 0.2473 Acc: 0.9085
Epoch 13/24
----------
train Loss: 0.2972 Acc: 0.8648
val Loss: 0.2357 Acc: 0.9216
Epoch 14/24
----------
train Loss: 0.2716 Acc: 0.8811
val Loss: 0.2788 Acc: 0.8954
Epoch 15/24
----------
train Loss: 0.3079 Acc: 0.8443
val Loss: 0.3347 Acc: 0.8824
Epoch 16/24
----------
train Loss: 0.2281 Acc: 0.9139
val Loss: 0.2408 Acc: 0.9020
Epoch 17/24
----------
train Loss: 0.2544 Acc: 0.9057
val Loss: 0.2331 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.2837 Acc: 0.8975
val Loss: 0.2432 Acc: 0.9020
Epoch 19/24
----------
train Loss: 0.1827 Acc: 0.9303
val Loss: 0.2225 Acc: 0.9281
Epoch 20/24
----------
train Loss: 0.2550 Acc: 0.8934
val Loss: 0.2539 Acc: 0.9020
Epoch 21/24
----------
train Loss: 0.2541 Acc: 0.8934
val Loss: 0.2751 Acc: 0.9020
Epoch 22/24
----------
train Loss: 0.3206 Acc: 0.8607
val Loss: 0.2277 Acc: 0.9150
Epoch 23/24
----------
train Loss: 0.2752 Acc: 0.8852
val Loss: 0.2637 Acc: 0.9020
Epoch 24/24
----------
train Loss: 0.2931 Acc: 0.8770
val Loss: 0.2243 Acc: 0.9216
Training complete in 1m 4s
Best val Acc: 0.934641
visualize_model(model_ft)
ConvNet 作为固定特征提取器
在这里，我们需要冻结除最后一层之外的所有网络。我们需要将requires_grad = False设置为冻结参数，以便在backward()中不计算梯度。
你可以在文档中阅读更多关于此的内容 这里。
model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1')
for param in model_conv.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)
model_conv = model_conv.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that only parameters of final layer are being optimized as
# opposed to before.
optimizer_conv = optim.SGD(model_conv.fc.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_conv, step_size=7, gamma=0.1)
训练和评估
在CPU上，这将比之前的情况节省大约一半的时间。这是预期的，因为不需要为网络的大部分计算梯度。然而，前向传播仍然需要计算。
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
Epoch 0/24
----------
train Loss: 0.6996 Acc: 0.6516
val Loss: 0.2014 Acc: 0.9346
Epoch 1/24
----------
train Loss: 0.4233 Acc: 0.8033
val Loss: 0.2656 Acc: 0.8758
Epoch 2/24
----------
train Loss: 0.4603 Acc: 0.7869
val Loss: 0.1847 Acc: 0.9477
Epoch 3/24
----------
train Loss: 0.3096 Acc: 0.8566
val Loss: 0.1747 Acc: 0.9477
Epoch 4/24
----------
train Loss: 0.4427 Acc: 0.8156
val Loss: 0.1630 Acc: 0.9477
Epoch 5/24
----------
train Loss: 0.5505 Acc: 0.7828
val Loss: 0.1643 Acc: 0.9477
Epoch 6/24
----------
train Loss: 0.3004 Acc: 0.8607
val Loss: 0.1744 Acc: 0.9542
Epoch 7/24
----------
train Loss: 0.4083 Acc: 0.8361
val Loss: 0.1892 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.4483 Acc: 0.7910
val Loss: 0.1984 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.3335 Acc: 0.8279
val Loss: 0.1942 Acc: 0.9412
Epoch 10/24
----------
train Loss: 0.2413 Acc: 0.8934
val Loss: 0.2001 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.3107 Acc: 0.8689
val Loss: 0.1801 Acc: 0.9412
Epoch 12/24
----------
train Loss: 0.3032 Acc: 0.8689
val Loss: 0.1669 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.3587 Acc: 0.8525
val Loss: 0.1900 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.2771 Acc: 0.8893
val Loss: 0.2317 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.3064 Acc: 0.8852
val Loss: 0.1909 Acc: 0.9477
Epoch 16/24
----------
train Loss: 0.4243 Acc: 0.8238
val Loss: 0.2227 Acc: 0.9346
Epoch 17/24
----------
train Loss: 0.3297 Acc: 0.8238
val Loss: 0.1916 Acc: 0.9412
Epoch 18/24
----------
train Loss: 0.4235 Acc: 0.8238
val Loss: 0.1766 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.2500 Acc: 0.8934
val Loss: 0.2003 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.2413 Acc: 0.8934
val Loss: 0.1821 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3762 Acc: 0.8115
val Loss: 0.1842 Acc: 0.9412
Epoch 22/24
----------
train Loss: 0.3485 Acc: 0.8566
val Loss: 0.2166 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.3625 Acc: 0.8361
val Loss: 0.1747 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3840 Acc: 0.8320
val Loss: 0.1768 Acc: 0.9412
Training complete in 0m 32s
Best val Acc: 0.954248
visualize_model(model_conv)
plt.ioff()
plt.show()
自定义图像的推理
使用训练好的模型对自定义图像进行预测，并可视化预测的类别标签以及图像。
def visualize_model_predictions(model,img_path):
was_training = model.training
model.eval()
img = Image.open(img_path)
img = data_transforms['val'](img)
img = img.unsqueeze(0)
img = img.to(device)
with torch.no_grad():
outputs = model(img)
_, preds = torch.max(outputs, 1)
ax = plt.subplot(2,2,1)
ax.axis('off')
ax.set_title(f'Predicted: {class_names[preds[0]]}')
imshow(img.cpu().data[0])
model.train(mode=was_training)
visualize_model_predictions(
model_conv,
img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg'
)
plt.ioff()
plt.show()
进一步学习
如果您想了解更多关于迁移学习的应用， 请查看我们的Quantized Transfer Learning for Computer Vision Tutorial。
脚本总运行时间： (1 分钟 39.312 秒)
