PyTorch之迁移学习
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
# 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
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), #随机裁剪一个area然后再resize
transforms.RandomHorizontalFlip(), #随机水平翻转
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")
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 + 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
# 获取一批训练数据
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(LR scheduler object)。
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 每个epoch都有一个训练和验证阶段
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
# 迭代数据.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# 零参数梯度
optimizer.zero_grad()
# 前向
# 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)
# 后向+仅在训练阶段进行优化
if phase == 'train':
loss.backward()
optimizer.step()
# 统计
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))
# 深度复制mo
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))
# 加载最佳模型权重
model.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.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)
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)
num_ftrs = model_ft.fc.in_features
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。
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
- 输出
Epoch 0/24
----------
train Loss: 0.7032 Acc: 0.6025
val Loss: 0.1698 Acc: 0.9412
Epoch 1/24
----------
train Loss: 0.6411 Acc: 0.7787
val Loss: 0.1981 Acc: 0.9281
·
·
·
Epoch 24/24
----------
train Loss: 0.2812 Acc: 0.8730
val Loss: 0.2647 Acc: 0.9150
Training complete in 1m 7s
Best val Acc: 0.941176
（2）模型评估效果可视化
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
# 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
（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: 0.6400 Acc: 0.6434
val Loss: 0.2539 Acc: 0.9085
·
·
·
Epoch 23/24
----------
train Loss: 0.2988 Acc: 0.8607
val Loss: 0.2151 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3519 Acc: 0.8484
val Loss: 0.2045 Acc: 0.9412
Training complete in 0m 35s
Best val Acc: 0.954248
（2）模型评估效果可视化
visualize_model(model_conv)
plt.ioff()
plt.show()
- 输出
