pytorch之迁移学习

文章目录

1.导入相关的包
2.加载数据
3.可视化部分图像数据
4.训练模型
5.可视化模型的预测结果
6.场景1：微调ConvNet
7.场景2：ConvNet作为固定特征提取器

实际中，基本没有人会从零开始（随机初始化）训练一个完整的卷积网络，因为相对于网络，很难得到一个足够大的数据集[网络很深, 需要足够大数据集]。通常的做法是在一个很大的数据集上进行预训练得到卷积网络ConvNet, 然后将这个ConvNet的参数作为目标任务的初始化参数或者固定这些参数。

转移学习的两个主要场景：

微调Convnet：使用预训练的网络(如在imagenet 1000上训练而来的网络)来初始化自己的网络，而不是随机初始化。其他的训练步骤不变。
将Convnet看成固定的特征提取器:首先固定ConvNet除了最后的全连接层外的其他所有层。最后的全连接层被替换成一个新的随机初始化的层，只有这个新的层会被训练[只有这层参数会在反向传播时更新]

下面是利用PyTorch进行迁移学习步骤，要解决的问题是训练一个模型来对蚂蚁和蜜蜂进行分类。

1.导入相关的包

from __future__ import print_function, divisionimport 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 copyplt.ion()   # 交互模式

注释1：交互模式详情

2.加载数据

今天要解决的问题是训练一个模型来分类蚂蚁ants和蜜蜂bees。ants和bees各有约120张训练图片。每个类有75张验证图片。从零开始在如此小的数据集上进行训练通常是很难泛化的。由于我们使用迁移学习，模型的泛化能力会相当好。该数据集是imagenet的一个非常小的子集。从此处下载数据，并将其解压缩到当前目录。

# 训练集数据扩充和归一化
# 在验证集上仅需要归一化
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])]),
}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'].classesdevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

注释2：ImageFolder
torchvision中有一个更常用的数据集类ImageFolder。它假定了数据集是以如下方式构造的:

root/ants/xxx.png
root/ants/xxy.jpeg
root/ants/xxz.png
.
.
.
root/bees/123.jpg
root/bees/nsdf3.png
root/bees/asd932_.png

也就是"根目录/类别名称/该类别对应的图片"

3.可视化部分图像数据

可视化部分训练图像，以便了解数据扩充。

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 + meaninp = 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# 获得一批训练数据
inputs, classes = next(iter(dataloaders['train']))# 批量制作网格
out = torchvision.utils.make_grid(inputs)imshow(out, title=[class_names[x] for x in classes])

4.训练模型

编写一个通用函数来训练模型。下面将说明：

调整学习速率
保存最好的模型

下面的参数scheduler是一个来自 torch.optim.lr_scheduler的学习速率调整类的对象(LRscheduler object)。

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)# 将参数梯度归零optimizer.zero_grad()# forward# 只在训练上追踪历史with torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)# backward + optimize 只在训练进行if phase == 'train':loss.backward()optimizer.step()# statisticsrunning_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('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))# 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('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 weightsmodel.load_state_dict(best_model_wts)return model

5.可视化模型的预测结果

一个通用的展示少量预测图片的函数

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('predicted: {}'.format(class_names[preds[j]]))imshow(inputs.cpu().data[j])if images_so_far == num_images:model.train(mode=was_training)returnmodel.train(mode=was_training)

6.场景1：微调ConvNet

加载预训练模型并重置最终完全连接的图层。

model_ft = models.resnet18(pretrained=True)
# in_features 是fc线性层的输入数量
num_ftrs = model_ft.fc.in_features
# 这里，每个输出样本的大小设置为2。
# 或者，它可以推广到nn.Linear（num_ftrs，len（类名称））。
model_ft.fc = nn.Linear(num_ftrs, 2)model_ft = model_ft.to(device)criterion = nn.CrossEntropyLoss()# 观察所有参数都正在优化
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)# 每7个epochs衰减LR通过设置gamma=0.1
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

训练和评估模型
（1）训练模型该过程在CPU上需要大约15-25分钟，但是在GPU上，它只需不到一分钟。（我在CPU上跑的）

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

输出

Epoch 0/24
----------
train Loss: 0.6633 Acc: 0.6721
val Loss: 0.2236 Acc: 0.9216Epoch 1/24
----------
train Loss: 0.4831 Acc: 0.7705
val Loss: 0.2813 Acc: 0.9020Epoch 2/24
----------
train Loss: 0.4444 Acc: 0.7828
val Loss: 0.1721 Acc: 0.9477Epoch 3/24
----------
train Loss: 0.4857 Acc: 0.7828
val Loss: 0.1651 Acc: 0.9477Epoch 4/24
----------
train Loss: 0.5882 Acc: 0.7705
val Loss: 0.4952 Acc: 0.8301Epoch 5/24
----------
train Loss: 0.5504 Acc: 0.7869
val Loss: 0.1896 Acc: 0.9412Epoch 6/24
----------
train Loss: 0.5927 Acc: 0.7992
val Loss: 0.3142 Acc: 0.8954Epoch 7/24
----------
train Loss: 0.4391 Acc: 0.8361
val Loss: 0.1705 Acc: 0.9477Epoch 8/24
----------
train Loss: 0.3733 Acc: 0.8566
val Loss: 0.1884 Acc: 0.9346Epoch 9/24
----------
train Loss: 0.3567 Acc: 0.8484
val Loss: 0.2050 Acc: 0.9281Epoch 10/24
----------
train Loss: 0.3769 Acc: 0.8279
val Loss: 0.2070 Acc: 0.9346Epoch 11/24
----------
train Loss: 0.3473 Acc: 0.8648
val Loss: 0.2191 Acc: 0.9281Epoch 12/24
----------
train Loss: 0.3654 Acc: 0.8566
val Loss: 0.1732 Acc: 0.9412Epoch 13/24
----------
train Loss: 0.2885 Acc: 0.8689
val Loss: 0.1959 Acc: 0.9346Epoch 14/24
----------
train Loss: 0.3242 Acc: 0.8525
val Loss: 0.2066 Acc: 0.9281Epoch 15/24
----------
train Loss: 0.3471 Acc: 0.8279
val Loss: 0.1821 Acc: 0.9477Epoch 16/24
----------
train Loss: 0.4058 Acc: 0.8443
val Loss: 0.1773 Acc: 0.9346Epoch 17/24
----------
train Loss: 0.4398 Acc: 0.8279
val Loss: 0.1726 Acc: 0.9477Epoch 18/24
----------
train Loss: 0.3293 Acc: 0.8689
val Loss: 0.1841 Acc: 0.9346Epoch 19/24
----------
train Loss: 0.3484 Acc: 0.8361
val Loss: 0.1846 Acc: 0.9412Epoch 20/24
----------
train Loss: 0.3164 Acc: 0.8402
val Loss: 0.1702 Acc: 0.9542Epoch 21/24
----------
train Loss: 0.3769 Acc: 0.8197
val Loss: 0.1828 Acc: 0.9346Epoch 22/24
----------
train Loss: 0.3204 Acc: 0.8852
val Loss: 0.2065 Acc: 0.9412Epoch 23/24
----------
train Loss: 0.3201 Acc: 0.8852
val Loss: 0.1970 Acc: 0.9346Epoch 24/24
----------
train Loss: 0.2603 Acc: 0.8730
val Loss: 0.2063 Acc: 0.9412Training complete in 33m 18s
Best val Acc: 0.954248Process finished with exit code 0

（2）模型评估结果可视化

visualize_model(model_ft)

输出

7.场景2：ConvNet作为固定特征提取器

在这里需要冻结除最后一层之外的所有网络。通过设置requires_grad == Falsebackward()来冻结参数，这样在反向传播backward()的时候他们的梯度就不会被计算。

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():param.requires_grad = False# 默认情况下，新建模块的参数需要requires_grad=True
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)model_conv = model_conv.to(device)criterion = nn.CrossEntropyLoss()# 请注意，只有最后一层的参数被优化为
# 与以前相反。
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)# 每7个epochs将LR衰减0.1倍
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

训练和评估
（1）训练模型在CPU上，与前一个场景相比，这将花费大约一半的时间，因为不需要为大多数网络计算梯度。但需要计算转发。

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

输出

Epoch 0/24
----------
train Loss: 0.5987 Acc: 0.6393
val Loss: 0.3066 Acc: 0.8693Epoch 1/24
----------
train Loss: 0.5367 Acc: 0.7172
val Loss: 0.1962 Acc: 0.9477Epoch 2/24
----------
train Loss: 0.4591 Acc: 0.7992
val Loss: 0.1974 Acc: 0.9477Epoch 3/24
----------
train Loss: 0.5040 Acc: 0.7787
val Loss: 0.1922 Acc: 0.9346Epoch 4/24
----------
train Loss: 0.5343 Acc: 0.7705
val Loss: 0.2062 Acc: 0.9542Epoch 5/24
----------
train Loss: 0.5048 Acc: 0.7828
val Loss: 0.2255 Acc: 0.9281Epoch 6/24
----------
train Loss: 0.5591 Acc: 0.7951
val Loss: 0.2150 Acc: 0.9346Epoch 7/24
----------
train Loss: 0.3710 Acc: 0.8402
val Loss: 0.2361 Acc: 0.9346Epoch 8/24
----------
train Loss: 0.2645 Acc: 0.8934
val Loss: 0.2024 Acc: 0.9346Epoch 9/24
----------
train Loss: 0.3999 Acc: 0.8402
val Loss: 0.1959 Acc: 0.9477Epoch 10/24
----------
train Loss: 0.3806 Acc: 0.8238
val Loss: 0.2191 Acc: 0.9346Epoch 11/24
----------
train Loss: 0.4044 Acc: 0.8402
val Loss: 0.1941 Acc: 0.9542Epoch 12/24
----------
train Loss: 0.3234 Acc: 0.8648
val Loss: 0.1977 Acc: 0.9477Epoch 13/24
----------
train Loss: 0.3640 Acc: 0.8361
val Loss: 0.2026 Acc: 0.9346Epoch 14/24
----------
train Loss: 0.4070 Acc: 0.8115
val Loss: 0.1912 Acc: 0.9542Epoch 15/24
----------
train Loss: 0.3331 Acc: 0.8484
val Loss: 0.2011 Acc: 0.9346Epoch 16/24
----------
train Loss: 0.3006 Acc: 0.8770
val Loss: 0.1766 Acc: 0.9542Epoch 17/24
----------
train Loss: 0.3397 Acc: 0.8443
val Loss: 0.2180 Acc: 0.9412Epoch 18/24
----------
train Loss: 0.3332 Acc: 0.8443
val Loss: 0.1928 Acc: 0.9477Epoch 19/24
----------
train Loss: 0.3563 Acc: 0.8238
val Loss: 0.1982 Acc: 0.9477Epoch 20/24
----------
train Loss: 0.3222 Acc: 0.8566
val Loss: 0.2268 Acc: 0.9281Epoch 21/24
----------
train Loss: 0.4554 Acc: 0.8115
val Loss: 0.2420 Acc: 0.9281Epoch 22/24
----------
train Loss: 0.3066 Acc: 0.8648
val Loss: 0.1828 Acc: 0.9542Epoch 23/24
----------
train Loss: 0.4099 Acc: 0.8279
val Loss: 0.2061 Acc: 0.9477Epoch 24/24
----------
train Loss: 0.3176 Acc: 0.8648
val Loss: 0.2098 Acc: 0.9346Training complete in 0m 34s
Best val Acc: 0.954248

（2）模型评测结果可视化

visualize_model(model_conv)plt.ioff()
plt.show()

输出