1、介绍
1.1数据集介绍
flower_data
├── train
│ └── 1-102（102个文件夹）
│ └── XXX.jpg（每个文件夹含若干张图像）
├── valid
│ └── 1-102（102个文件夹）
└── ─── └── XXX.jpg（每个文件夹含若干张图像）
cat_to_name.json：每一类花朵的"名称-编号"对应关系
1.2 任务介绍
实现102种花朵的分类任务，即通过训练train数据集后，从valid数据集中选取某一花朵图像，能准确判别其属于哪一类花朵
1.3Resnet介绍
在ResNet网络中有如下两个亮点：
- 提出residual结构（残差结构），并搭建超深的网络结构(突破1000层)
- 使用Batch Normalization加速训练(丢弃dropout)
在ResNet网络提出之前，传统的卷积神经网络都是通过将一系列卷积层与下采样层进行堆叠得到的。但是当堆叠到一定网络深度时，就会出现两个问题：
- 梯度消失或梯度爆炸
- 退化问题(degradation problem)
2、数据预处理
2.1引入头文件
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
from torchvision import transforms,models,datasets
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image2.2数据读取
#数据读取与预处理操作
data_dir = './flower_data/'
# 训练集
train_dir = data_dir + '/train'
#验证集
valid_ir = data_dir + '/valid'2.3制作数据源
#制作数据源
data_transfroms = {
'train':transforms.Compose([transforms.RandomRotation(45), #随机旋转(-45~45)
transforms.CenterCrop(224), #从中心开始裁剪
transforms.RandomHorizontalFlip(p = 0.5), #随机水平翻转
transforms.RandomVerticalFlip(p = 0.5), #随机垂直翻转
transforms.ColorJitter(brightness=0.2,contrast=0.1,saturation=0.1,hue = 0.1),
transforms.RandomGrayscale(p = 0.025), #概率转换成灰度率，3通道就是R=G=B
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
]),
'valid':transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
]),
}2.4batch数据制作
#batch数据制作
batch_size = 8
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir,x),data_transfroms[x]) for x in ['train','valid']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],batch_size = batch_size,shuffle = True) for x in ['train','valid']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train','valid']}
class_names = image_datasets['train'].classes2.5读取数据标签
#读取标签对应的实际名字
with open('cat_to_name.json','r') as f:
cat_to_name = json.load(f)查看cat_to_name.json文件:
{'21': 'fire lily',
'3': 'canterbury bells',
'45': 'bolero deep blue',
'1': 'pink primrose',
'34': 'mexican aster',
'27': 'prince of wales feathers',
'7': 'moon orchid',
'16': 'globe-flower',
'25': 'grape hyacinth',
'26': 'corn poppy',
'79': 'toad lily',
'39': 'siam tulip',
'24': 'red ginger',
'67': 'spring crocus',
'35': 'alpine sea holly',
'32': 'garden phlox',
'10': 'globe thistle',
'6': 'tiger lily',
'93': 'ball moss',
'33': 'love in the mist',
'9': 'monkshood',
'102': 'blackberry lily',
'14': 'spear thistle',
'19': 'balloon flower',
'100': 'blanket flower',
'13': 'king protea',
'49': 'oxeye daisy',
'15': 'yellow iris',
'61': 'cautleya spicata',
'31': 'carnation',
'64': 'silverbush',
'68': 'bearded iris',
'63': 'black-eyed susan',
'69': 'windflower',
'62': 'japanese anemone',
'20': 'giant white arum lily',
'38': 'great masterwort',
'4': 'sweet pea',
'86': 'tree mallow',
'101': 'trumpet creeper',
'42': 'daffodil',
'22': 'pincushion flower',
'2': 'hard-leaved pocket orchid',
'54': 'sunflower',
'66': 'osteospermum',
'70': 'tree poppy',
'85': 'desert-rose',
'99': 'bromelia',
'87': 'magnolia',
'5': 'english marigold',
'92': 'bee balm',
'28': 'stemless gentian',
'97': 'mallow',
'57': 'gaura',
'40': 'lenten rose',
'47': 'marigold',
'59': 'orange dahlia',
'48': 'buttercup',
'55': 'pelargonium',
'36': 'ruby-lipped cattleya',
'91': 'hippeastrum',
'29': 'artichoke',
'71': 'gazania',
'90': 'canna lily',
'18': 'peruvian lily',
'98': 'mexican petunia',
'8': 'bird of paradise',
'30': 'sweet william',
'17': 'purple coneflower',
'52': 'wild pansy',
'84': 'columbine',
'12': "colt's foot",
'11': 'snapdragon',
'96': 'camellia',
'23': 'fritillary',
'50': 'common dandelion',
'44': 'poinsettia',
'53': 'primula',
'72': 'azalea',
'65': 'californian poppy',
'80': 'anthurium',
'76': 'morning glory',
'37': 'cape flower',
'56': 'bishop of llandaff',
'60': 'pink-yellow dahlia',
'82': 'clematis',
'58': 'geranium',
'75': 'thorn apple',
'41': 'barbeton daisy',
'95': 'bougainvillea',
'43': 'sword lily',
'83': 'hibiscus',
'78': 'lotus lotus',
'88': 'cyclamen',
'94': 'foxglove',
'81': 'frangipani',
'74': 'rose',
'89': 'watercress',
'73': 'water lily',
'46': 'wallflower',
'77': 'passion flower',
'51': 'petunia'}
3、数据展示
3.1图像处理函数
#展示数据
def im_convert(tensor):
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
image = image.transpose(1,2,0)
image = image * np.array((0.229,0.224,0.225)) + np.array((0.485,0.456,0.406))
image = image.clip(0.1)
return image
3.2展示图像
fig=plt.figure(figsize=(20, 12))
columns = 4
rows = 2
dataiter = iter(dataloaders['valid'])
inputs, classes = dataiter.next()
for idx in range (columns*rows):
ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])
ax.set_title(cat_to_name[str(int(class_names[classes[idx]]))])
plt.imshow(im_convert(inputs[idx]))
plt.show()
4、进行迁移学习
迁移学习的关键点:
- 研究可以用哪些知识在不同的领域或者任务中进行迁移学习，即不同领域之间有哪些共有知识可以迁移
- 研究在找到了迁移对象之后，针对具体问题所采用哪种迁移学习的特定算法，即如何设计出合适的算法来提取和迁移共有知识
- 研究什么情况下适合迁移，迁移技巧是否适合具体应用，其中涉及到负迁移的问题。
4.1训练全连接层
加载models中提供的模型，并且直接用训练好的权重当做初始化参数
下载链接:https://download.pytorch.org/models/resnet152-394f9c45.pth
选择resnet网络
model_name = 'resnet' #可选的有： ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception']
#是否用官方训练好的特征来做
feature_extract = True
设置用GPU训练
#是否用GPU来训练
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('cuda is not available. Training on CPU')
else:
print('cuda is available. Training on GPU')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")屏蔽预训练模型的权重，只训练全连接层的权重：
def set_parameter_requires_grad(model,feature_extracting):
if feature_extracting:
for param in model.parameter():
param.requires_grad = False选择resnet152网络
model_ft = models.resnet152()设置优化器:
#优化器设置
optimizer_ft = optim.Adam(params_to_update,lr = 1e-2)
scheduler = optim.lr_scheduler.StepLR(optimizer_ft,step_size=7,gamma=0.1) #学习率每7个epoch衰减成原来的1/10
criterion = nn.NLLLoss()定义训练模块:
# 训练模块
def train_model(model,dataloaders,criterion,optimizer,num_epochs=25,is_inception=False,filename = filename):
since = time.time()
best_acc = 0
model.to(device)
val_acc_history = []
train_acc_history = []
train_losses = []
valid_losses = []
LRs = [optimizer.param_groups[0]['lr']]
best_model_wts = copy.deepcopy(model.state_dict())
for epoch in range(num_epochs):
print('Epoch {} / {}'.format(epoch,num_epochs - 1))
print('-' * 10)
#训练与验证
for phase in ['train','valid']:
if phase == 'train':
model.train() #训练
else:
model.eval() #验证
running_loss = 0.0
running_corrects = 0
#把数据取个遍
for inputs,labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
#清零
optimizer.zero_grad()
#只有训练的时候计算与更新梯度
with torch.set_grad_enabled(phase == 'train'):
if is_inception and phase == 'train':
outputs,aux_outputs = model(inputs)
loss1 = criterion(outputs,labels)
loss2 = criterion(aux_outputs,labels)
loss = loss1 + 0.4 * loss2
else: #resnet执行的是这里
outputs = model(inputs)
loss = criterion(outputs,labels)
_, preds = torch.max(outputs,1)
if phase == 'train':
loss.backward()
optimizer.step()
#计算损失
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloaders[phase].dataset)
epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
time_elapsed = time.time() - since
print('Time elapsed {:.0f}m {:.0f}f'.format(time_elapsed // 60,time_elapsed % 60))
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc))
#得到最好的模型
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
state = {
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer':optimizer.state_dict(),
}
torch.save(state,filename)
if phase == 'valid':
val_acc_history.append(epoch_acc)
valid_losses.append(epoch_loss)
scheduler.step(epoch_loss)
if phase == 'train':
train_acc_history.append(epoch_acc)
train_losses.append(epoch_loss)
print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))
LRs.append(optimizer.param_groups[0]['lr'])
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))
#训练完后用最好的一次当做模型最终的结果
model.load_state_dict(best_model_wts)
return model,val_acc_history,train_acc_history.valid_losses,train_losses,LRs
开始训练:
# 开始训练
model_ft,val_acc_history,train_acc_history,valid_lossea,train_losses,LRs = train_model(model_ft,dataloaders,criterion,optimizer_ft,num_epochs=20,is_inception=(model_name == 'inception'))
4.2训练所有层
我们从上次训练好最优的那个全连接层的参数开始，以此为基础训练所有层，设置param.requires_grad = True表明接下来训练全部网络，之后把学习率调小一点，衰减函数为每7次衰减为原来的1/10，损失函数不变
再继续训练所有层
for param in model_ft.parameters():
param.requires_grad = True
#再继续训练所有的参数，学习率调小一点(lr)
optimizer = optim.Adam(params_to_update,lr = 1e-4)
#衰减函数(每七次衰减为原来的七分之一)
scheduler = optim.lr_scheduler.StepLR(optimizer_ft,step_size=7,gamma=0.1)
#损失函数
criterion = nn.NLLLoss()导入之前的最优结果并开始训练:
#在之前训练得到最好的模型的基础上继续训练
checkpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model_ft.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model_ft,val_acc_history,train_acc_history,valid_lossea,train_losses,LRs = train_model(model_ft,dataloaders,criterion,optimizer_ft,num_epochs=10,is_inception=(model_name == 'inception'))
5、测试网络效果
5.1测试数据预处理
首先将新训练好的checkpoint.pth重命名为serious.pth，之后加载训练好的模型:
#加载训练好的模型
model_ft,input_size = initialize_model(model_name,102,feature_extract,use_pretrained=True)
#GPU模型
model_ft = model_ft.to(device)
#保存文件的名字
filename = 'serious.pth'
#加载模型
checkpoint = torch.load(filename)
best_acc = checkpoint['beat_acc']
model_ft.load_state_dict(checkpoint['state_dict'])定义图像处理函数:
def process_image(image_path):
img = Image.open(image_path)
#Resize,thumbnail方法只能进行缩小，所以进行判断
if img.size[0] > img.size[1]:
img.thumbnail((10000,256))
else:
img.thumbnail((256,10000))
#Crop操作
left_margin = (img.width-224)/2
bottom_margin = (img.height-224)/2
right_margin = (left_margin) + 224
top_margin = bottom_margin + 224
img = img.crop(left_margin,bottom_margin,right_margin,top_margin)
#相同的预处理方法
img = np.array(img)/255
mean = np.array([0.485,0.456,0.406])
std = np.array([0.229,0.224,0.225])
img = (img - mean)/std
#注意颜色通道应该放在第一个位置
img = img.transpose((2,0,1))
return img定义图像展示函数:
#展示数据
def imshow(image,ax = None,title = None):
if ax is None:
fig,ax = plt.subplots()
#颜色通道还原
image = np.array(image).transpose((1,2,0))
#预处理还原
mean = np.array([0.485,0.456,0.406])
std = np.array([0.229,0.224,0.225])
image = std * image + mean
image = np.clip(image,0.1)
ax.imshow(image)
ax.set_title(title)
return ax展示一个数据:
image_path = 'image_06621.jpg'
img = process_image(image_path)
imshow(img) 
得到一个batch测试数据:
#测试一个batch数据
dataiter = iter(dataloaders['valid'])
images,labels = dataiter.next()
model_ft.eval()
if train_on_gpu:
output = model_ft(images.cuda())
else:
output = model_ft(images)利用torch.max()函数计算标签值:
#得到属于类别的八个编号
_,preds_tensor = torch.ax(output,1)
preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())5.2结果可视化
#展示预测结果
fig = plt.figure(figsize=(20,20))
columns = 4
rows = 2
for idx in range(columns * rows):
ax = fig.add_subplot(rows,columns,idx+1,xticks=[],yticks=[])
plt.imshow(im_convert(images[idx]))
ax.set_title("{} {}".format(cat_to_name[str(preds[idx])],cat_to_name[str(labels[idx].item())]),
color = ("green" if cat_to_name[str(preds[idx])] == cat_to_name[str(labels[idx].item())] else "red"))
plt.show()结果如下（绿色标题代表识别成功，红色标题代表识别失败，括号里面为真实值，括号外为预测值）
![linux(进程)[6]](https://img-blog.csdnimg.cn/8041408ab06d4b28995bbcfbcab6d9c6.png)
