1.背景
在网上找了一些资料用来训练关键点，一般都是人脸或者车牌关键点训练，或者是联合检测一起训练。很少有是单独基于轻量级网络训练单独关键点模型的工程，本文简单介绍一种简单方法和代码。
2.代码模块
（1）网络结构
文件：model.py
import torch.nn as nn
import torch
import torch.nn.functional as F
import torch.nn.init as init
class Fire(nn.Module):
def init(self, inplanes, squeeze_planes,
expand1x1_planes, expand3x3_planes):
super(Fire, self).init()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
self.squeeze_activation = nn.ReLU(inplace=True)
self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
kernel_size=1)
#self.expand1x1_activation = nn.ReLU(inplace=True)
self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
kernel_size=3, padding=1)
#self.expand3x3_activation = nn.ReLU(inplace=True)
def forward(self, x):
x = self.squeeze_activation(self.squeeze(x))
return torch.cat([
self.expand1x1(x),
self.expand3x3(x)
], 1)
class RegressNet(nn.Module):
def init(self,version=1.0,export=False):
super(RegressNet, self).init()
if version not in [1.0, 1.1]:
raise ValueError("Unsupported RegressNet version {version}:"
"1.0 or 1.1 expected".format(version=version))
self.export = export
print(version)
if version == 1.0:
self.features = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3,padding=(1,1), stride=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
Fire(16, 16, 32, 32),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
Fire(64, 32, 32, 32),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
Fire(64, 32, 64, 64),
nn.ReLU(inplace=True),
Fire(128, 32, 64, 64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
nn.Conv2d(128, 128, kernel_size=3,padding=(0,0), stride=2),
)
else:
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(64, 16, 64, 64),
Fire(128, 16, 64, 64),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(128, 32, 128, 128),
Fire(256, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
Fire(512, 64, 256, 256),
)
Final convolution is initialized differently form the rest
#final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
#self.classifier = nn.Sequential(
nn.Dropout(p=0.5),
final_conv,
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1))
#)
self.fc= nn.Linear(128,8)
MAE_Loss = torch.nn.L1Loss()
self.loss = MAE_Loss
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_uniform_(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)
def forward(self, x):
x = self.features(x)
#x = x.squeeze()
#x = x.flatten(0)
x=x.view(-1,128)#使用view函数
x = self.fc(x)
#print(x)
return x
（2）训练工程
文件：train.py 以训练四个关键点为例
import numpy as np
from math import radians, cos, sin
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
#import imutils
import torch
from PIL import Image
import random
import cv2
import xml.etree.ElementTree as ET
from torch.utils.data import Dataset
import os
import torch.nn as nn
import torchvision.models as models
import torch.nn.functional as F
import torch.nn.init as init
import torch.optim as optim
import time
from tqdm import tqdm
from model import RegressNet
class Transforms():
def init(self):
pass
def rotate(self, image, landmarks, angle):
随机生成一个在 -angle 到 +angle 范围内的旋转角度
angle = random.uniform(-angle, +angle)
基于二维平面上的旋转变换的数学特性构建旋转矩阵
transformation_matrix = torch.tensor([
+cos(radians(angle)), -sin(radians(angle))\], \[+sin(radians(angle)), +cos(radians(angle))
])
对图像进行旋转：相比于 PIL 的图像旋转计算开销更小
image = imutils.rotate(np.array(image), angle)
将关键点坐标中心化：简化旋转变换的计算，同时确保关键点的变换和图像变换的对应关系
landmarks = landmarks - 0.5
将关键点坐标应用旋转矩阵
new_landmarks = np.matmul(landmarks, transformation_matrix)
恢复关键点坐标范围
new_landmarks = new_landmarks + 0.5
return Image.fromarray(image), new_landmarks
def resize(self, image, landmarks, img_size):
调整图像大小
image = TF.resize(image, img_size)
return image, landmarks
def color_jitter(self, image, landmarks):
定义颜色调整的参数：亮度、对比度、饱和度和色调
color_jitter = transforms.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=0.1)
对图像进行颜色调整
image = color_jitter(image)
return image, landmarks
def crop_face(self, image, landmarks, crops):
获取裁剪参数
left = int(crops['left'])
top = int(crops['top'])
width = int(crops['width'])
height = int(crops['height'])
对图像进行裁剪
image = TF.crop(image, top, left, height, width)
获取裁剪后的图像形状
img_shape = np.array(image).shape
对关键点坐标进行裁剪后的调整
landmarks = torch.tensor(landmarks) - torch.tensor([[left, top]])
归一化关键点坐标
landmarks = landmarks / torch.tensor([img_shape[1], img_shape[0]])
return image, landmarks
def call(self, image, landmarks):
将图像从数组转换为 PIL 图像对象
image = Image.fromarray(image)
裁剪图像并调整关键点
调整图像大小
image, landmarks = self.resize(image, landmarks, (64, 64))
对图像进行颜色调整
image, landmarks = self.color_jitter(image, landmarks)
对图像和关键点进行旋转变换
#image, landmarks = self.rotate(image, landmarks, angle=10)
将图像从 PIL 图像对象转换为 Torch 张量
image = TF.to_tensor(image)
标准化图像像素值
image = TF.normalize(image, [0.5], [0.5])
return image, landmarks
(3)dataset定义，数据长度为8 x1,y1,x2,y2,x3,y3,x4,y4
#标签排列规则
XXX.jpg x1/width y1/height x2/width y2/height x3/width y3/height x4/width y4/height
class FaceLandmarksDataset(Dataset):
def init(self, transform=None):
#root = os.listdir(r"C:/")
with open(r"C:\DL_Work\test_pics\path.txt", 'r', encoding="utf-8") as r:
root = r.readlines()
初始化变量
self.image_filenames = []
self.landmarks = []
self.crops = []
self.transform = transform
self.root_dir = r'C:\DL_Work\test_pics/'
遍历 XML 数据:root[2] 表示 XML 中的第三个元素，即 <images> 部分，其中包含了每张图像的标注信息
for filename in root:
pic_path = filename.split(" ")[0]
self.image_filenames.append(os.path.join(self.root_dir, pic_path))
#self.crops.append(filename)
landmark = []
for num in range(4):
x_coordinate = int( filename.split(" ")[num*2+1])
y_coordinate = int(filename.split(" ")[num*2+2])
landmark.append([x_coordinate, y_coordinate])
self.landmarks.append(landmark)
self.landmarks = np.array(self.landmarks).astype('float32')
assert len(self.image_filenames) == len(self.landmarks)
def len(self):
return len(self.image_filenames)
def getitem(self, index):
读取图像以及关键点坐标
image = cv2.imread(self.image_filenames[index]) # 以彩色模式读取图像
image = cv2.imread(self.image_filenames[index], 0) # 以灰色模式读取图像
landmarks = self.landmarks[index]
if self.transform:
如果存在预处理变换，应用变换
image, landmarks = self.transform(image, landmarks)
landmarks = landmarks - 0.5 # 进行中心化操作
return image, landmarks
创建数据集对象，并应用预处理变换
dataset = FaceLandmarksDataset(Transforms())
len_valid_set = int(0.1 * len(dataset))
len_train_set = len(dataset) - len_valid_set
#print("The length of Train set is {}".format(len_train_set))
#print("The length of Valid set is {}".format(len_valid_set))
train_dataset, valid_dataset, = torch.utils.data.random_split(dataset, [len_train_set, len_valid_set])
shuffle and batch the datasets
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=1)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, shuffle=True, num_workers=1)
(4)train
def train():
记录每个 epoch 的训练和验证损失
train_losses = []
valid_losses = []
设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.autograd.set_detect_anomaly(True)
#network = Network().to(device)
network = RegressNet().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(network.parameters(), lr=0.0001)
loss_min = np.inf
num_epochs = 10
start_time = time.time()
for epoch in range(1, num_epochs + 1):
loss_train = 0
loss_valid = 0
running_loss = 0
network.train()
for step in tqdm(range(1, len(train_loader) + 1)):
images, landmarks = next(iter(train_loader))
images = images.to(device)
landmarks = landmarks.view(landmarks.size(0), -1).to(device)
predictions = network(images)
optimizer.zero_grad()
loss_train_step = criterion(predictions, landmarks)
loss_train_step.backward()
optimizer.step()
loss_train += loss_train_step.item()
running_loss = loss_train / step
network.eval()
with torch.no_grad():
for step in range(1, len(valid_loader) + 1):
images, landmarks = next(iter(valid_loader))
images = images.to(device)
landmarks = landmarks.view(landmarks.size(0), -1).to(device)
predictions = network(images)
loss_valid_step = criterion(predictions, landmarks)
loss_valid += loss_valid_step.item()
running_loss = loss_valid / step
loss_train /= len(train_loader)
loss_valid /= len(valid_loader)
train_losses.append(loss_train)
valid_losses.append(loss_valid)
print('\n--------------------------------------------------')
print('Epoch: {} Train Loss: {:.4f} Valid Loss: {:.4f}'.format(epoch, loss_train, loss_valid))
print('--------------------------------------------------')
if loss_valid < loss_min:
loss_min = loss_valid
torch.save(network.state_dict(), 'plate_landmark.pth')
print("\nMinimum Validation Loss of {:.4f} at epoch {}/{}".format(loss_min, epoch, num_epochs))
print('Model Saved\n')
print('Training Complete')
print("Total Elapsed Time: {} s".format(time.time() - start_time))
if name == 'main':
train()
3.导出onnx
import torch
import torch.nn
import onnx
from onnxsim import simplify
from model import RegressNet
#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device('cpu')
model = RegressNet()
model_statedict = torch.load(r'./plate_landmark.pth', map_location=device)
#model.eval()
model.load_state_dict(model_statedict)
input_names = ['input0']
output_names = ['output0']
x = torch.randn(1, 3, 64, 64, device=device)
torch.onnx.export(model, x, 'plate_landmark.onnx', opset_version=11, verbose=True, input_names=input_names, output_names = output_names,dynamic_axes={'input0': {0: 'batch'},
'output0': {0: 'batch'}
})
onnx_model = onnx.load("plate_landmark.onnx")# 简化模型
simplified_model, check = simplify(onnx_model)# 保存简化后的模型
onnx.save_model(simplified_model, "plate_landmark_sim.onnx")