注意
单击 此处 下载完整的示例代码
计算机视觉迁移学习教程¶
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以进行图像分类。您可以在 cs231n 笔记 中详细了解迁移学习。
引用这些笔记，
在实践中，很少有人会从头开始训练整个卷积神经网络（使用随机初始化），因为拥有足够大的数据集相对罕见。相反，通常的做法是在非常大的数据集（例如 ImageNet，它包含 120 万张具有 1000 个类别的图像）上预训练一个 ConvNet，然后将 ConvNet 用作目标任务的初始化或固定特征提取器。
这两种主要的迁移学习场景如下所示
微调 ConvNet: 我们不是使用随机初始化，而是使用一个预训练的网络初始化网络，例如在 imagenet 1000 数据集上训练的网络。其余的训练与往常一样。
ConvNet 作为固定特征提取器：在这里，我们将冻结除最终全连接层之外的所有网络的权重。此最后的全连接层被替换为具有随机权重的新层，并且仅训练此层。
# 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 0x7ff3c3224940>
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对蚂蚁和蜜蜂进行分类。我们有大约 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，难以泛化。由于我们使用的是迁移学习，我们应该能够合理地泛化。
此数据集是 ImageNet 的一个非常小的子集。
注意
从 这里 下载数据并将其解压缩到当前目录。
# 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，以便了解数据增强。
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¶
用于显示一些图像的预测的通用函数
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¶
加载预训练模型并重置最终全连接层。
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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42%|####2 | 18.9M/44.7M [00:00<00:00, 197MB/s]
86%|########5 | 38.4M/44.7M [00:00<00:00, 201MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 201MB/s]
训练和评估¶
在 CPU 上大约需要 15-25 分钟。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: 0.4786 Acc: 0.7623
val Loss: 0.2824 Acc: 0.8758
Epoch 1/24
----------
train Loss: 0.5282 Acc: 0.8033
val Loss: 0.5878 Acc: 0.7516
Epoch 2/24
----------
train Loss: 0.4253 Acc: 0.8197
val Loss: 0.2555 Acc: 0.9216
Epoch 3/24
----------
train Loss: 0.6092 Acc: 0.7869
val Loss: 0.3528 Acc: 0.8824
Epoch 4/24
----------
train Loss: 0.4050 Acc: 0.8525
val Loss: 0.3059 Acc: 0.8758
Epoch 5/24
----------
train Loss: 0.5035 Acc: 0.8033
val Loss: 0.2778 Acc: 0.8889
Epoch 6/24
----------
train Loss: 0.4054 Acc: 0.8156
val Loss: 0.2368 Acc: 0.9020
Epoch 7/24
----------
train Loss: 0.4001 Acc: 0.8525
val Loss: 0.2380 Acc: 0.9020
Epoch 8/24
----------
train Loss: 0.2548 Acc: 0.8975
val Loss: 0.2182 Acc: 0.9020
Epoch 9/24
----------
train Loss: 0.2656 Acc: 0.8975
val Loss: 0.2278 Acc: 0.9216
Epoch 10/24
----------
train Loss: 0.3594 Acc: 0.8566
val Loss: 0.2075 Acc: 0.9085
Epoch 11/24
----------
train Loss: 0.3218 Acc: 0.8443
val Loss: 0.2717 Acc: 0.8954
Epoch 12/24
----------
train Loss: 0.2355 Acc: 0.8975
val Loss: 0.2196 Acc: 0.9085
Epoch 13/24
----------
train Loss: 0.3031 Acc: 0.8607
val Loss: 0.2095 Acc: 0.9020
Epoch 14/24
----------
train Loss: 0.2806 Acc: 0.8852
val Loss: 0.2282 Acc: 0.9150
Epoch 15/24
----------
train Loss: 0.3237 Acc: 0.8607
val Loss: 0.2733 Acc: 0.9085
Epoch 16/24
----------
train Loss: 0.2227 Acc: 0.9221
val Loss: 0.2138 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.2611 Acc: 0.8893
val Loss: 0.2062 Acc: 0.9150
Epoch 18/24
----------
train Loss: 0.2713 Acc: 0.8730
val Loss: 0.2236 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.2215 Acc: 0.9057
val Loss: 0.1992 Acc: 0.9281
Epoch 20/24
----------
train Loss: 0.2815 Acc: 0.8770
val Loss: 0.2147 Acc: 0.9216
Epoch 21/24
----------
train Loss: 0.2652 Acc: 0.8770
val Loss: 0.2667 Acc: 0.9085
Epoch 22/24
----------
train Loss: 0.3229 Acc: 0.8770
val Loss: 0.1967 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2817 Acc: 0.8484
val Loss: 0.2168 Acc: 0.9150
Epoch 24/24
----------
train Loss: 0.3143 Acc: 0.8730
val Loss: 0.2091 Acc: 0.9216
Training complete in 1m 4s
Best val Acc: 0.928105
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。我们需要设置 requires_grad = False 来冻结参数，以便在 backward() 中不计算梯度。
您可以在 这里 阅读有关此内容的更多信息。
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，这将比以前的情况快一半时间。这是预期的，因为不需要为大部分网络计算梯度。但是，需要计算前向传播。
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()
在自定义图像上进行推理¶
使用训练后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()
