抓住11月的尾巴，这里写上昨天做的一个DL的作业吧，作业很简单，基于交通灯的图像分类，但这确是让你从0构建深度学习系统的好例子，很多已有的数据集都封装好了，直接调用，这篇文章将以pytorch这个深度学习框架一步步搭建分类系统。
软件包要求：
pytorch：0.4.0
torchsummary：pip install torchsummary
cv2: pip install opencv-python
matplotlib
numpy
所有代码托管到github上，链接如下：https://github.com/FangYang970206/TL_Dataset_Classification，git clone https://github.com/FangYang970206/TL_Dataset_Classification到本地。
1.数据集简介
数据集有10个类别，分别是红灯的圆球，向左，向右，向上和负例以及绿灯的圆球，向左，向右，向上和负例，如下图所示：
数据集的可通过如下链接进行下载：onedrive，baiduyun，google。
下完数据集后，解压到文件夹TL_Dataset_Classification-master中，得到一个新的文件夹TL_Dataset，可以看到TL_Dataset有以下目录：
2.代码实战
代码是在vscode上编写的，支持flask8，总共有9个文件，下面一一介绍。建议在看代码的时候从main.py文件开始看，大致脉络就清楚了。
2.1 model.py
对于一个深度学习系统来说，model应该是最初的想法，我们想构造什么样的模型来拟合数据集，所以先写model，代码如下：
import torch.nn as nn
from torchsummary import summary
class A2NN(nn.Module):
def __init__(self, ):
super(A2NN, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, 16, 3, 1, 1),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
nn.Conv2d(16, 32, 3, 1, 1),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, 3, 1, 1),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, 3, 1, 1),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.linear = nn.Linear(4*4*64, 9)
def forward(self, inp):
x = self.main(inp)
x = x.view(x.shape[0], -1)
x = self.linear(x)
return x
if __name__ == "__main__":
nn = A2NN()
summary(nn, (3, 32, 32))
model代码不复杂，很简单，这里不多介绍，缺少基础的朋友还请自行补基础。
2.2 dataset.py
第二步我们要构建数据集类，pytorch封装了一个torch.utils.data.Dataset的类，我们可以重载__len__和__getitem__方法，来得到自己的数据集管道，__len__方法是返回数据集的长度，__getitem__是支持从0到len(self)互斥范围内的整数索引，返回的是索引对应的数据和标签。代码如下：
import torch
import cv2
import torch.utils.data as data
class_light = {
'Red Circle': 0,
'Green Circle': 1,
'Red Left': 2,
'Green Left': 3,
'Red Up': 4,
'Green Up': 5,
'Red Right': 6,
'Green Right': 7,
'Red Negative': 8,
'Green Negative': 8
}
class Traffic_Light(data.Dataset):
def __init__(self, dataset_names, img_resize_shape):
super(Traffic_Light, self).__init__()
self.dataset_names = dataset_names
self.img_resize_shape = img_resize_shape
def __getitem__(self, ind):
img = cv2.imread(self.dataset_names[ind])
img = cv2.resize(img, self.img_resize_shape)
img = img.transpose(2, 0, 1)-127.5/127.5
for key in class_light.keys():
if key in self.dataset_names[ind]:
label = class_light[key]
# pylint: disable=E1101,E1102
return torch.from_numpy(img), torch.tensor(label)
# pylint: disable=E1101,E1102
def __len__(self):
return len(self.dataset_names)
if __name__ == '__main__':
from torch.utils.data import DataLoader
from glob import glob
import os
path = 'TL_Dataset/Green Up/'
names = glob(os.path.join(path, '*.png'))
dataset = Traffic_Light(names, (32, 32))
dataload = DataLoader(dataset, batch_size=1)
for ind, (inp, label) in enumerate(dataload):
print("{}-inp_size:{}-label_size:{}".format(ind, inp.numpy().shape,
label.numpy().shape))
2.3 util.py
在上面的dataset.py中，class初始化时，传入了dataset_names，所以utils.py文件中就通过get_train_val_names函数得到训练数据集和验证数据集的names，还有一个函数是检查文件夹是否存在，不存在建立文件夹。代码如下：
import os
from glob import glob
def get_train_val_names(dataset_path, remove_names, radio=0.3):
train_names = []
val_names = []
dataset_paths = os.listdir(dataset_path)
for n in remove_names:
dataset_paths.remove(n)
for path in dataset_paths:
sub_dataset_path = os.path.join(dataset_path, path)
sub_dataset_names = glob(os.path.join(sub_dataset_path, '*.png'))
sub_dataset_len = len(sub_dataset_names)
val_names.extend(sub_dataset_names[:int(radio*sub_dataset_len)])
train_names.extend(sub_dataset_names[int(radio*sub_dataset_len):])
return {'train': train_names, 'val': val_names}
def check_folder(path):
if not os.path.exists(path):
os.mkdir(path)
2.4 trainer.py
model构造好了，数据集也准备好了，现在就需要准备如果训练了，这就是trainer.py文件的作用，trainer.py构建了Trainer类，通过传入训练的一系列参数，调用Trainer.train函数进行训练，并返回loss，代码如下：
import torch.nn as nn
from torch.optim import Adam
class Trainer:
def __init__(self, model, dataload, epoch, lr, device):
self.model = model
self.dataload = dataload
self.epoch = epoch
self.lr = lr
self.device = device
self.optimizer = Adam(self.model.parameters(), lr=self.lr)
self.criterion = nn.CrossEntropyLoss().to(self.device)
def __epoch(self, epoch):
self.model.train()
loss_sum = 0
for ind, (inp, label) in enumerate(self.dataload):
inp = inp.float().to(self.device)
label = label.long().to(self.device)
self.optimizer.zero_grad()
out = self.model.forward(inp)
loss = self.criterion(out, label)
loss.backward()
loss_sum += loss.item()
self.optimizer.step()
print('epoch{}_step{}_train_loss_: {}'.format(epoch,
ind,
loss.item()))
return loss_sum/(ind+1)
def train(self):
train_loss = self.__epoch(self.epoch)
return train_loss
2.5 validator.py
trainer.py文件是用来进行训练数据集的，训练过程中，我们是需要有验证集来判断我们模型的训练效果，所以这里有validator.py文件，里面封装了Validator类，与Trainer.py类似，但不同的是，我们不训练，不更新参数，model处于eval模式，代码上会有一些跟Trainer不一样,通过调用Validator.eval函数返回loss，代码如下：
import torch.nn as nn
class Validator:
def __init__(self, model, dataload, epoch, device, batch_size):
self.model = model
self.dataload = dataload
self.epoch = epoch
self.device = device
self.batch_size = batch_size
self.criterion = nn.CrossEntropyLoss().to(self.device)
def __epoch(self, epoch):
self.model.eval()
loss_sum = 0
for ind, (inp, label) in enumerate(self.dataload):
inp = inp.float().to(self.device)
label = label.long().to(self.device)
out = self.model.forward(inp)
loss = self.criterion(out, label)
loss_sum += loss.item()
return {'val_loss': loss_sum/(ind+1)}
def eval(self):
val_loss = self.__epoch(self.epoch)
return val_loss
2.6 logger.py
我们想看整个学习的过程，可以通过看学习曲线来进行观察。所以这里写了一个logger.py文件，用来对训练loss和验证loss进行统计并画图。代码如下：
import matplotlib.pyplot as plt
import os
class Logger:
def __init__(self, save_path):
self.save_path = save_path
def update(self, Kwarg):
self.__plot(Kwarg)
def __plot(self, Kwarg):
save_img_path = os.path.join(self.save_path, 'learning_curve.png')
plt.clf()
plt.plot(Kwarg['train_losses'], label='Train', color='g')
plt.plot(Kwarg['val_losses'], label='Val', color='b')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend()
plt.title('learning_curve')
plt.savefig(save_img_path)
2.7 main.py
main.py文件将上面所有的东西结合到一起，代码如下：
import torch
import argparse
from model import A2NN
from dataset import Traffic_Light
from utils import get_train_val_names, check_folder
from trainer import Trainer
from validator import Validator
from logger import Logger
from torch.utils.data import DataLoader
def main():
parse = argparse.ArgumentParser()
parse.add_argument('--dataset_path', type=str, default='TL_Dataset/')
parse.add_argument('--remove_names', type=list, default=['README.txt',
'README.png',
'Testset'])
parse.add_argument('--img_resize_shape', type=tuple, default=(32, 32))
parse.add_argument('--batch_size', type=int, default=1024)
parse.add_argument('--lr', type=float, default=0.001)
parse.add_argument('--num_workers', type=int, default=4)
parse.add_argument('--epochs', type=int, default=200)
parse.add_argument('--val_size', type=float, default=0.3)
parse.add_argument('--save_model', type=bool, default=True)
parse.add_argument('--save_path', type=str, default='logs/')
args = vars(parse.parse_args())
check_folder(args['save_path'])
# pylint: disable=E1101
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# pylint: disable=E1101
model = A2NN().to(device)
names = get_train_val_names(args['dataset_path'], args['remove_names'])
train_dataset = Traffic_Light(names['train'], args['img_resize_shape'])
val_dataset = Traffic_Light(names['val'], args['img_resize_shape'])
train_dataload = DataLoader(train_dataset,
batch_size=args['batch_size'],
shuffle=True,
num_workers=args['num_workers'])
val_dataload = DataLoader(val_dataset,
batch_size=args['batch_size'],
shuffle=True,
num_workers=args['num_workers'])
loss_logger = Logger(args['save_path'])
logger_dict = {'train_losses': [],
'val_losses': []}
for epoch in range(args['epochs']):
print('<Main> epoch{}'.format(epoch))
trainer = Trainer(model, train_dataload, epoch, args['lr'], device)
train_loss = trainer.train()
if args['save_model']:
state = model.state_dict()
torch.save(state, 'logs/nn_state.t7')
validator = Validator(model, val_dataload, epoch,
device, args['batch_size'])
val_loss = validator.eval()
logger_dict['train_losses'].append(train_loss)
logger_dict['val_losses'].append(val_loss['val_loss'])
loss_logger.update(logger_dict)
if __name__ == '__main__':
main()
2.8 compute_prec.py和submit.py
其实上面的七个文件，已经是结束了，下面两个文件一个是用来计算精确度的，一个是用来提交答案的。有兴趣可以看看。
compute_prec.py代码如下：
import torch
import numpy as np
import argparse
from model import A2NN
from dataset import Traffic_Light
from torch.utils.data import DataLoader
from utils import get_train_val_names, check_folder
def main():
parse = argparse.ArgumentParser()
parse.add_argument('--dataset_path', type=str, default='TL_Dataset/')
parse.add_argument('--remove_names', type=list, default=['README.txt',
'README.png',
'Testset'])
parse.add_argument('--img_resize_shape', type=tuple, default=(32, 32))
parse.add_argument('--num_workers', type=int, default=4)
parse.add_argument('--val_size', type=float, default=0.3)
parse.add_argument('--save_path', type=str, default='logs/')
args = vars(parse.parse_args())
check_folder(args['save_path'])
# pylint: disable=E1101
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# pylint: disable=E1101
model = A2NN().to(device)
model.load_state_dict(torch.load(args['save_path']+'nn_state.t7'))
model.eval()
names = get_train_val_names(args['dataset_path'], args['remove_names'])
val_dataset = Traffic_Light(names['val'], args['img_resize_shape'])
val_dataload = DataLoader(val_dataset,
batch_size=1,
num_workers=args['num_workers'])
count = 0
for ind, (inp, label) in enumerate(val_dataload):
inp = inp.float().to(device)
label = label.long().to(device)
output = model.forward(inp)
output = np.argmax(output.to('cpu').detach().numpy(), axis=1)
label = label.to('cpu').numpy()
count += 1 if output == label else 0
print('precision: {}'.format(count/(ind+1)))
if __name__ == "__main__":
main()
submit.py代码如下：
import torch
import numpy as np
import argparse
import os
import cv2
from model import A2NN
from utils import check_folder
def main():
parse = argparse.ArgumentParser()
parse.add_argument('--dataset_path', type=str,
default='TL_Dataset/Testset/')
parse.add_argument('--img_resize_shape', type=tuple, default=(32, 32))
parse.add_argument('--num_workers', type=int, default=4)
parse.add_argument('--save_path', type=str, default='logs/')
args = vars(parse.parse_args())
check_folder(args['save_path'])
# pylint: disable=E1101
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# pylint: disable=E1101
model = A2NN().to(device)
model.load_state_dict(torch.load(args['save_path']+'nn_state.t7'))
model.eval()
txt_path = os.path.join(args['save_path'], 'result.txt')
with open(txt_path, 'w') as f:
for i in range(20000):
name = os.path.join(args['dataset_path'], '{}.png'.format(i))
img = cv2.imread(name)
img = cv2.resize(img, args['img_resize_shape'])
img = img.transpose(2, 0, 1)-127.5/127.5
img = torch.unsqueeze(torch.from_numpy(img).float(), dim=0)
img = img.to(device)
output = model.forward(img).to('cpu').detach().numpy()
img_class = np.argmax(output, axis=1)
f.write(name.split('/')[2] + ' ' + str(img_class[0]))
f.write('
')
if __name__ == "__main__":
main()
3. 代码如下运行
将数据集下载在文件夹TL_Dataset_Classification，解压后，在TL_Dataset_Classification文件中进入终端，运行命令：
$ python main.py
如果还想计算精确度，在训练玩数据集之后，运行命令：
$ python compute_prec.py
有运行可以到github上提issue或者在给我的邮箱867540289@qq.com发邮件。
4. 结果
学习曲线：
在测试集中，实现97.425%的精确度。
5. 总结
好了，11月的尾巴到此结束，希望能对你学习深度学习问题和pytorch有所帮助。12月马上到，祝我数学考试顺利，也祝各位开开心心！