[学习笔记]Pytorch迁移学习实例

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/cskywit/article/details/90338954

本文参考Pytorch官方教程，个人觉得代码结构写得非常好，很值得借鉴使用，所以转发分享，另外将调试中遇到的问题和解决一起说明一下。

目前在CNN上的迁移学习的主要场景主要有两大类：

1.CNN微调：使用预训练的CNN参数初始化网络，而不是随机初始化网络，如使用在imagenet上进行预训练的网络参数进行初始化；

2.将CNN作为固定的特征提取方式：除了最后的全连接层，其余层全部冻结，最后的全连接层替换为新的层，使用随机权重初始化并进行训练。

实例以训练一个模型来区分蚂蚁和蜜蜂为例，数据可以在https://download.pytorch.org/tutorial/hymenoptera_data.zip下载， 大约各有120张蜜蜂和蚂蚁的训练图片，各75张验证集图片。这个数据集很小，如果从头开始训练，很容易过拟合，因此比较适合迁移学习。

一.Finetuning the convnet

加载必要的头文件：

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

数据加载，个人认为写法很值得借鉴：

# 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")

 图片可视化：

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))#pytorch图像是(C,H,W)，转变为numpy可绘制格式(H,W,C)
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean #还原图像原始值
    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])

训练模型：这里使用了自动减小的学习率，并保存最优模型参数

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.
            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)

            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))

    # load best model weights
    model.load_state_dict(best_model_wts)
    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()

    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('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CNN，加载预训练的resnet18模型，将最后的全连接层重置后训练

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)

训练模型，我这里使用的是NVIDIA 2080Ti GPU进行训练， 总共耗时4m44s，Best val Acc: 0.941176

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

可视化预测：

visualize_model(model_ft)

 

二.ConvNet as fixed feature extractor

这种用途下，需要冻结除了网络最后一层外的其他层，在Pytorch中简单的对要冻结的层参数使用requires_grad == False即可，这样梯度就不用在backward()中计算。

model_conv = torchvision.models.resnet18(pretrained=True)
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)

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

Training complete in 3m 29s
Best val Acc: 0.960784

visualize_model(model_conv)

plt.ioff()
plt.show()

 

三.调试中遇到的问题

训练网络出错，提示：

UnboundLocalError: local variable 'photoshop' referenced before assignment

这个变量来源于加载图片的pillow包，我的pillow版本最新的6.0.0版本，查阅网上资料，该版本有个bug，在Pillow/src/PIL/JpegImagePlugin.py源码的108行，代码只是假定图片是Photoshop3.0版本的，而如果是其他版本的PS就会出错

if s[:14] == b"Photoshop 3.0\x00": 

一种解决方案是将Pillow降级为5.4.1:

conda install pillow=5.4.1

但是降级时会提示一系列有关联的其他包被depressed，考虑到我的虚环境中装了很多包，万一哪个包depressed造成其包用不了更麻烦，所以使用第二种解决方案：

>>> from PIL import JpegImagePlugin
>>> JpegImagePlugin
<module 'PIL.JpegImagePlugin' from '/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/PIL/JpegImagePlugin.py'>

先找到JpegImagePlugin.py路径，打开文件，修改如下：

将139行缩进一个TAB即可。