- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊
目标
具体实现
（一）环境
语言环境：Python 3.10
编 译 器: PyCharm
框 架: Pytorch
（二）具体步骤
1. 文件结构
2. config.py
import argparse
def get_options(parser=argparse.ArgumentParser()):
parser.add_argument('--workers', type=int, default=0, help='Number of parallel workers')
parser.add_argument('--batch-size', type=int, default=4, help='input batch size, default=32')
parser.add_argument('--size', type=tuple, default=(224, 224), help='input image size')
parser.add_argument('--lr', type=float, default=1e-4, help='learning rate, default=0.0001')
parser.add_argument('--epochs', type=int, default=20, help='number of epochs')
parser.add_argument('--seed', type=int, default=112, help='random seed')
parser.add_argument('--save-path', type=str, default='./models/', help='path to save checkpoints')
opt = parser.parse_args()
if opt:
print(f'num_workers:{opt.workers}')
print(f'batch_size:{opt.batch_size}')
print(f'learn rate:{opt.lr}')
print(f'epochs:{opt.epochs}')
print(f'random seed:{opt.seed}')
print(f'save_path:{opt.save_path}')
return opt
if __name__ == '__main__':
opt = get_options()
3. Utils.py
import torch
import pathlib
import matplotlib.pyplot as plt
from torchvision.transforms import transforms
# 第一步：设置GPU
def USE_GPU():
if torch.cuda.is_available():
print('CUDA is available, will use GPU')
device = torch.device("cuda")
else:
print('CUDA is not available. Will use CPU')
device = torch.device("cpu")
return device
temp_dict = dict()
def recursive_iterate(path):
"""
根据所提供的路径遍历该路径下的所有子目录，列出所有子目录下的文件
:param path: 路径
:return: 返回最后一级目录的数据
""" path = pathlib.Path(path)
for file in path.iterdir():
if file.is_file():
temp_key = str(file).split('\\')[-2]
if temp_key in temp_dict:
temp_dict.update({temp_key: temp_dict[temp_key] + 1})
else:
temp_dict.update({temp_key: 1})
# print(file)
elif file.is_dir():
recursive_iterate(file)
return temp_dict
def data_from_directory(directory, train_dir=None, test_dir=None, show=False):
"""
提供是的数据集是文件形式的，提供目录方式导入数据，简单分析数据并返回数据分类
:param test_dir: 是否设置了测试集目录
:param train_dir: 是否设置了训练集目录
:param directory: 数据集所在目录
:param show: 是否需要以柱状图形式显示数据分类情况,默认显示
:return: 数据分类列表,类型: list
""" global total_image
print("数据目录：{}".format(directory))
data_dir = pathlib.Path(directory)
# for d in data_dir.glob('**/*'): # **/*通配符可以遍历所有子目录
# if d.is_dir():
# print(d) class_name = []
total_image = 0
# temp_sum = 0
if train_dir is None or test_dir is None:
data_path = list(data_dir.glob('*'))
class_name = [str(path).split('\\')[-1] for path in data_path]
print("数据分类: {}, 类别数量：{}".format(class_name, len(list(data_dir.glob('*')))))
total_image = len(list(data_dir.glob('*/*')))
print("图片数据总数: {}".format(total_image))
else:
temp_dict.clear()
train_data_path = directory + '/' + train_dir
train_data_info = recursive_iterate(train_data_path)
print("{}目录:{},{}".format(train_dir, train_data_path, train_data_info))
temp_dict.clear()
test_data_path = directory + '/' + test_dir
print("{}目录:{},{}".format(test_dir, test_data_path, recursive_iterate(test_data_path)))
class_name = temp_dict.keys()
if show:
# 隐藏警告
import warnings
warnings.filterwarnings("ignore") # 忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 # 分辨率
for i in class_name:
data = len(list(pathlib.Path((directory + '\\' + i + '\\')).glob('*')))
plt.title('数据分类情况')
plt.grid(ls='--', alpha=0.5)
plt.bar(i, data)
plt.text(i, data, str(data), ha='center', va='bottom')
print("类别-{}:{}".format(i, data))
# temp_sum += data
plt.show()
# if temp_sum == total_image:
# print("图片数据总数检查一致")
# else: # print("数据数据总数检查不一致，请检查数据集是否正确！")
return class_name
def get_transforms_setting(size):
"""
获取transforms的初始设置
:param size: 图片大小
:return: transforms.compose设置
""" transform_setting = {
'train': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
return transform_setting
# 训练循环
def train(dataloader, device, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 获取图片及其标签
X, y = X.to(device), y.to(device)
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # grad属性归零
loss.backward() # 反向传播
optimizer.step() # 每一步自动更新
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
def test(dataloader, device, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小
num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad():
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
from PIL import Image
def predict_one_image(image_path, device, model, transform, classes):
"""
预测单张图片
:param image_path: 图片路径
:param device: CPU or GPU :param model: cnn模型
:param transform: :param classes: :return:
""" test_img = Image.open(image_path).convert('RGB')
plt.imshow(test_img) # 展示预测的图片
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_, pred = torch.max(output, 1)
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')
4.dataset.py
import os
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms, datasets
from Utils import get_transforms_setting
class CarLicenceDataset(Dataset):
def __init__(self, root_dir, all_labels, transform=None):
self.img_dir = root_dir # 图像目录路径
self.img_labels = all_labels # 获取标签信息
self.transform = transform # 目标转换函数
# self.total_data = datasets.ImageFolder(root_dir, transform=transform)
# print(self.total_data) # # 划分数据集
# train_size = int(0.8 * len(self.total_data))
# test_size = len(self.total_data) - train_size # self.train_dataset, self.test_dataset = torch.utils.data.random_split(self.total_data, [train_size, test_size]) # print(self.train_dataset, self.test_dataset)
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
image = Image.open(self.img_dir[idx]).convert('RGB')
label = self.img_labels[idx]
if self.transform:
image = self.transform(image)
return image, label
# def __getds__(self, dstype):
# if dstype == 'train': # return self.train_dataset # elif dstype == 'test': # return self.test_dataset # else: # pass
5.model.py
import torch
from torch import nn
import torch.nn.functional as F
class Network_bn(nn.Module):
def __init__(self, label_name_len=1, char_set_len=1):
super(Network_bn, self).__init__()
"""
nn.Conv2d()函数：
第一个参数（in_channels）是输入的channel数量
第二个参数（out_channels）是输出的channel数量
第三个参数（kernel_size）是卷积核大小
第四个参数（stride）是步长，默认为1
第五个参数（padding）是填充大小，默认为0
""" self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(12)
self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0)
self.bn2 = nn.BatchNorm2d(12)
self.pool = nn.MaxPool2d(2 ,2)
self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0)
self.bn4 = nn.BatchNorm2d(24)
self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0)
self.bn5 = nn.BatchNorm2d(24)
self.fc1 = nn.Linear(24 *50 *50, label_name_len * char_set_len)
self.reshape = Reshape([label_name_len ,char_set_len])
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool(x)
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = self.pool(x)
x = x.view(-1, 24 *50 *50)
x = self.fc1(x)
# 最终reshape
x = self.reshape(x)
return x
# 定义Reshape层
class Reshape(nn.Module):
def __init__(self, shape):
super(Reshape, self).__init__()
self.shape = shape
def forward(self, x):
return x.view(x.size(0), *self.shape)
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
model = Network_bn().to(device)
model
6. train.py
import torch
import os,PIL,random,pathlib
import matplotlib.pyplot as plt
from torch import nn
import numpy as np
import torchsummary
from dataset import CarLicenceDataset
from Utils import USE_GPU, get_transforms_setting
from config import get_options
from model import Network_bn
device = USE_GPU() # 获取GPU，有则使用GPU，否则使用CPU
opt = get_options() # 获取训练超参数，预设的
transform = get_transforms_setting((224, 224)) # 获取数据转换配置
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
data_dir = './data/licence_plate/' # 数据集路径
data_dir = pathlib.Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2].split("_")[1].split(".")[0] for path in data_paths]
# print(classeNames)
data_paths = list(data_dir.glob('*'))
data_paths_str = [str(path) for path in data_paths]
# print(data_paths_str)
plt.figure(figsize=(14, 5))
plt.suptitle("数据示例", fontsize=15)
for i in range(18):
plt.subplot(3, 6, i + 1)
# plt.xticks([])
# plt.yticks([]) # plt.grid(False)
# 显示图片
images = plt.imread(data_paths_str[i])
plt.imshow(images)
plt.show()
char_enum = ["京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁",\
"豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","军","使"]
number = [str(i) for i in range(0, 10)] # 0 到 9 的数字
alphabet = [chr(i) for i in range(65, 91)] # A 到 Z 的字母
char_set = char_enum + number + alphabet
char_set_len = len(char_set)
label_name_len = len(classeNames[0])
# 将字符串数字化
def text2vec(text):
vector = np.zeros([label_name_len, char_set_len])
for i, c in enumerate(text):
idx = char_set.index(c)
vector[i][idx] = 1.0
return vector
all_labels = [text2vec(i) for i in classeNames]
total_data = CarLicenceDataset(data_paths_str, all_labels, transform['train'])
print(total_data)
train_size = int(0.8 * len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_size,test_size)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=16,
shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
print("The number of images in a training set is: ", len(train_loader)*16)
print("The number of images in a test set is: ", len(test_loader)*16)
print("The number of batches per epoch is: ", len(train_loader))
for X, y in test_loader:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
model = Network_bn(label_name_len, char_set_len).to(device)
torchsummary.summary(model, input_size=(3, 224, 224)) # 打印网络结构
# 创建一个Adam优化器
optimizer = torch.optim.Adam(model.parameters(),
lr=opt.learning_rate, # 从配置文件中取
weight_decay=0.0001)
loss_model = nn.CrossEntropyLoss() # 创建一个交叉熵损失函数
from torch.autograd import Variable
def test(model, test_loader, loss_model):
size = len(test_loader.dataset)
num_batches = len(test_loader)
model.eval()
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in test_loader:
X, y = X.to(device), y.to(device)
pred = model(X)
test_loss += loss_model(pred, y).item()
test_loss /= num_batches
print(f"Avg loss: {test_loss:>8f} \n")
return correct, test_loss
def train(model, train_loader, loss_model, optimizer):
model = model.to(device)
model.train()
for i, (images, labels) in enumerate(train_loader, 0): # 0是标起始位置的值。
images = Variable(images.to(device))
labels = Variable(labels.to(device))
optimizer.zero_grad()
outputs = model(images)
loss = loss_model(outputs, labels)
loss.backward()
optimizer.step()
if i % 1000 == 0:
print('[%5d] loss: %.3f' % (i, loss))
test_acc_list = []
test_loss_list = []
epochs = 30
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(model,train_loader,loss_model,optimizer)
test_acc,test_loss = test(model, test_loader, loss_model)
test_acc_list.append(test_acc)
test_loss_list.append(test_loss)
print("Done!")
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
current_time = datetime.now() # 获取当前时间
x = [i for i in range(1,31)]
plt.plot(x, test_loss_list, label="Loss", alpha=0.8)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title(current_time) # 打卡请带上时间戳，否则代码截图无效
plt.legend()
plt.show()
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