# 迁移学习教程

• Finetuning the convnet: 我们使用预训练网络初始化网络，而不是随机初始化，就像在imagenet 1000数据集上训练的网络一样。其余训练看起来像往常一样。
• ConvNet as fixed feature extractor: 在这里，我们将冻结除最终完全连接层之外的所有网络的权重。最后一个全连接层被替换为具有随机权重的新层，并且仅训练该层。
# License: BSD
# Author: Sasank Chilamkurthy

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 numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

plt.ion()   # interactive mode



## 加载数据

# 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']}
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

def imshow(inp, title=None):
"""Imshow 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

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])



## 训练模型

• 调度学习率
• 保存最佳的学习模型

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()

best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0

for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)

# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
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.
inputs = inputs.to(device)
labels = labels.to(device)

# forward
# track history if only in 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)

epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]

print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))

# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())

print()

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))

return model



## 可视化模型预测

def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()

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('predicted: {}'.format(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)



## 微调卷积网络

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
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)



### 训练和评估

CPU上需要大约15-25分钟。但是在GPU上，它只需不到一分钟。

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)



Out:

Epoch 0/24
----------
train Loss: 0.7565 Acc: 0.6598
val Loss: 0.2146 Acc: 0.9085

Epoch 1/24
----------
train Loss: 0.4915 Acc: 0.7951
val Loss: 0.3471 Acc: 0.8889

Epoch 2/24
----------
train Loss: 0.7898 Acc: 0.7541
val Loss: 0.4754 Acc: 0.8497

Epoch 3/24
----------
train Loss: 0.7151 Acc: 0.7295
val Loss: 0.5705 Acc: 0.8235

Epoch 4/24
----------
train Loss: 0.8363 Acc: 0.7459
val Loss: 0.2653 Acc: 0.9020

Epoch 5/24
----------
train Loss: 0.6235 Acc: 0.7992
val Loss: 0.4678 Acc: 0.8366

Epoch 6/24
----------
train Loss: 1.0205 Acc: 0.7131
val Loss: 0.5871 Acc: 0.8235

Epoch 7/24
----------
train Loss: 0.4644 Acc: 0.8238
val Loss: 0.2850 Acc: 0.8824

Epoch 8/24
----------
train Loss: 0.3654 Acc: 0.8566
val Loss: 0.2785 Acc: 0.9085

Epoch 9/24
----------
train Loss: 0.3400 Acc: 0.8648
val Loss: 0.2869 Acc: 0.9085

Epoch 10/24
----------
train Loss: 0.2939 Acc: 0.8770
val Loss: 0.2930 Acc: 0.8889

Epoch 11/24
----------
train Loss: 0.3057 Acc: 0.8811
val Loss: 0.2768 Acc: 0.9216

Epoch 12/24
----------
train Loss: 0.3081 Acc: 0.8689
val Loss: 0.3098 Acc: 0.8889

Epoch 13/24
----------
train Loss: 0.3764 Acc: 0.8607
val Loss: 0.2620 Acc: 0.9150

Epoch 14/24
----------
train Loss: 0.3119 Acc: 0.8689
val Loss: 0.2642 Acc: 0.9216

Epoch 15/24
----------
train Loss: 0.2269 Acc: 0.9180
val Loss: 0.2648 Acc: 0.9281

Epoch 16/24
----------
train Loss: 0.3055 Acc: 0.8893
val Loss: 0.2605 Acc: 0.9281

Epoch 17/24
----------
train Loss: 0.3213 Acc: 0.8730
val Loss: 0.2535 Acc: 0.9216

Epoch 18/24
----------
train Loss: 0.3325 Acc: 0.8566
val Loss: 0.2747 Acc: 0.9281

Epoch 19/24
----------
train Loss: 0.3007 Acc: 0.8648
val Loss: 0.2759 Acc: 0.9150

Epoch 20/24
----------
train Loss: 0.3498 Acc: 0.8443
val Loss: 0.2742 Acc: 0.9216

Epoch 21/24
----------
train Loss: 0.3433 Acc: 0.8443
val Loss: 0.2605 Acc: 0.8954

Epoch 22/24
----------
train Loss: 0.2822 Acc: 0.8689
val Loss: 0.2610 Acc: 0.9281

Epoch 23/24
----------
train Loss: 0.3025 Acc: 0.8648
val Loss: 0.2766 Acc: 0.9150

Epoch 24/24
----------
train Loss: 0.3401 Acc: 0.8607
val Loss: 0.2650 Acc: 0.9085

Training complete in 1m 13s
Best val Acc: 0.928105


visualize_model(model_ft)



## ConvNet 作为固定特征提取器

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():

# 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

model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)



Out:

Epoch 0/24
----------
train Loss: 0.6321 Acc: 0.6270
val Loss: 0.2507 Acc: 0.9085

Epoch 1/24
----------
train Loss: 0.6750 Acc: 0.7131
val Loss: 0.1764 Acc: 0.9412

Epoch 2/24
----------
train Loss: 0.4614 Acc: 0.8033
val Loss: 0.1683 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.5286 Acc: 0.7705
val Loss: 0.1566 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.3432 Acc: 0.8566
val Loss: 0.1878 Acc: 0.9281

Epoch 5/24
----------
train Loss: 0.4155 Acc: 0.8402
val Loss: 0.1631 Acc: 0.9412

Epoch 6/24
----------
train Loss: 0.4328 Acc: 0.7951
val Loss: 0.1659 Acc: 0.9542

Epoch 7/24
----------
train Loss: 0.3537 Acc: 0.8566
val Loss: 0.1803 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3448 Acc: 0.8279
val Loss: 0.1681 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.3830 Acc: 0.8402
val Loss: 0.1707 Acc: 0.9412

Epoch 10/24
----------
train Loss: 0.2958 Acc: 0.8689
val Loss: 0.1632 Acc: 0.9542

Epoch 11/24
----------
train Loss: 0.2552 Acc: 0.8893
val Loss: 0.1776 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.2846 Acc: 0.8689
val Loss: 0.1792 Acc: 0.9346

Epoch 13/24
----------
train Loss: 0.3263 Acc: 0.8566
val Loss: 0.1695 Acc: 0.9412

Epoch 14/24
----------
train Loss: 0.3925 Acc: 0.8361
val Loss: 0.2126 Acc: 0.9216

Epoch 15/24
----------
train Loss: 0.3780 Acc: 0.8074
val Loss: 0.1637 Acc: 0.9477

Epoch 16/24
----------
train Loss: 0.3619 Acc: 0.8525
val Loss: 0.1704 Acc: 0.9412

Epoch 17/24
----------
train Loss: 0.2966 Acc: 0.8689
val Loss: 0.1672 Acc: 0.9346

Epoch 18/24
----------
train Loss: 0.2811 Acc: 0.8934
val Loss: 0.1799 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.2484 Acc: 0.8893
val Loss: 0.1859 Acc: 0.9281

Epoch 20/24
----------
train Loss: 0.2696 Acc: 0.8975
val Loss: 0.1903 Acc: 0.9412

Epoch 21/24
----------
train Loss: 0.3679 Acc: 0.8197
val Loss: 0.1866 Acc: 0.9412

Epoch 22/24
----------
train Loss: 0.2854 Acc: 0.8852
val Loss: 0.1814 Acc: 0.9346

Epoch 23/24
----------
train Loss: 0.3550 Acc: 0.8443
val Loss: 0.1677 Acc: 0.9412

Epoch 24/24
----------
train Loss: 0.3928 Acc: 0.8033
val Loss: 0.1571 Acc: 0.9477

Training complete in 0m 34s
Best val Acc: 0.954248


visualize_model(model_conv)

plt.ioff()
plt.show()



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