行人属性识别，PA100K
1. 前景介绍
最近在调研行人属性识别，有不少研究行人属性识别的论文，阅读多篇论文之后，自己独立写了一个工程，专门做行人属性识别。
工程里面的代码主要参考两篇论文，ResNest对应的论文和Bag of Tricks for Image Classification对应的论文。
主干网络用的是ResNet的最强改进版本：ResNest50，论文详解请看我之前的博客。
训练技巧主要运用了《Bag of Tricks for Image Classification with Convolutional Neural Networks》里面的训练技巧，详细讲解请参考我的专栏：模型训练技巧。
训练数据应用的是行人属性数据集PA100K，关于PA100K的介绍请参考我的博客。
2. 训练部分代码讲解
一共训练10个属性，分别是：性别、年龄、朝向、是否戴帽子、是否戴眼镜、是否有手提包、是否有肩膀包、是否有背包、上衣类型、下衣类型。
for epoch in range(1, args.e + 1):
if epoch > args.warm:
train_scheduler.step(epoch)
#training procedure
net.train()
for batch_index, (images, labels) in enumerate(train_dataloader):
if epoch <= args.warm:
warmup_scheduler.step()
images = images.cuda()
labels = labels.cuda()
optimizer.zero_grad()
predicts = net(images)
loss_gender = cross_entropy(predicts[0], labels[:, 0].long())
loss_age = cross_entropy(predicts[1], labels[:, 1].long())
loss_orientation = cross_entropy(predicts[2], labels[:, 2].long())
loss_hat = cross_entropy(predicts[3], labels[:, 3].long())
loss_glasses = cross_entropy(predicts[4], labels[:, 4].long())
loss_handBag = cross_entropy(predicts[5], labels[:, 5].long())
loss_shoulderBag = cross_entropy(predicts[6], labels[:, 6].long())
loss_backBag = cross_entropy(predicts[7], labels[:, 7].long())
loss_upClothing = cross_entropy(predicts[8], labels[:, 8].long())
loss_downClothing= cross_entropy(predicts[9], labels[:, 9].long())
loss = loss_gender + loss_age + loss_orientation + loss_hat + loss_glasses + loss_handBag + loss_shoulderBag + loss_backBag + loss_upClothing + loss_downClothing
loss.backward()
optimizer.step()
n_iter = (epoch - 1) * len(train_dataloader) + batch_index + 1
if batch_index % 10 == 0:
print('Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLoss_gender: {:0.4f}\tLoss_age: {:0.4f}\tLoss_ori: {:0.4f}\tLoss_hat: {:0.4f}\tLoss_glasses: {:0.4f}\tLoss_handBag: {:0.4f}\t'.format(
loss.item(),
loss_gender.item(),
loss_age.item(),
loss_orientation.item(),
loss_hat.item(),
loss_glasses.item(),
loss_handBag.item(),
epoch=epoch,
trained_samples=batch_index * args.b + len(images),
total_samples=len(train_dataloader.dataset),
))
#visualization
visualize_lastlayer(writer, net, n_iter)
visualize_train_loss(writer, loss.item(), n_iter)
visualize_learning_rate(writer, optimizer.param_groups[0]['lr'], epoch)
visualize_param_hist(writer, net, epoch)
net.eval()
total_loss = 0
correct = np.zeros(10)
ignore = np.zeros(10)
print("=>test model")
for images, labels in tqdm(test_dataloader):
images = images.cuda()
labels = labels.cuda()
predicts = net(images)
for index in range(10):
_, preds = predicts[index].max(1)
ignore[index] += int((labels[:, index]==-1).sum())
correct[index] += preds.eq(labels[:, index]).sum().float()
loss = cross_entropy(predicts[index], labels[:, index].long())
total_loss += loss.item()
test_loss = total_loss / len(test_dataloader)
all_list = np.array([len(test_dataloader.dataset) for i in range(10)])-ignore
acc_list = correct / all_list
print(acc_list.tolist())训练过程如下：
3. 测试代码讲解
"""
author:guopei
"""
import os
import cv2
import torch
from torch.nn import DataParallel
import numpy as np
from PIL import Image,ImageDraw,ImageFont
import transforms
from models import resnest50
class Person_Attribute(object):
def __init__(self, weights="resnest50.pth"):
self.device = torch.device("cuda")
self.net = resnest50().to(self.device)
self.net = DataParallel(self.net)
self.weights = weights
self.net.load_state_dict(torch.load(self.weights))
TRAIN_MEAN = [0.485, 0.499, 0.432]
TRAIN_STD = [0.232, 0.227, 0.266]
self.transforms = transforms.Compose([
transforms.ToCVImage(),
transforms.Resize((128,256)),
transforms.ToTensor(),
transforms.Normalize(TRAIN_MEAN, TRAIN_STD)
])
def recog(self, img_path):
img = cv2.imread(img_path)
img = self.transforms(img)
img = img.unsqueeze(0)
with torch.no_grad():
self.net.eval()
img_input = img.to(self.device)
outputs = self.net(img_input)
results = []
for output in outputs:
output = torch.softmax(output, 1)
output = np.array(output[0].cpu())
label = np.argmax(output)
score = output[label]
results.append((label, score))
return results
name_dict = {
"gender":['男', '女'],
"age":["老年", "中年", "少年"],
"orientation":['正面', '侧面', '背面'],
"hat":["不戴帽子", "戴帽子"],
"glasses":["不戴眼镜", "戴眼镜"],
"handBag":["没有", "有"],
"shoulderBag":["没有", "有"],
"backBag":["没有", "有"],
"upClothing":["短袖", "长袖"],
"downClothing":["长裤", "短裤", "裙子"]
}
if __name__ == "__main__":
atts = ["gender","age", "orientation", "hat", "glasses",
"handBag", "shoulderBag", "backBag", "upClothing", "downClothing"]
person_attribute = Person_Attribute("./resnest50-50-regular.pth")
img_path = "test.jpg"
results = person_attribute.recog(img_path)
print(results)
img = cv2.imread(img_path)
img = cv2.resize(img, (128,256))
img1 = img*0 +255
img1[:,:,0] *= 255
img1[:,:,2] *= 255
line = ""
labels = [i[0] for i in results]
for att, label in zip(atts, labels):
if label == -1:
continue
line += "%s:%s\n" % (att, name_dict[att][label])
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
img1 = Image.fromarray(img1)
draw = ImageDraw.Draw(img1)
font = ImageFont.truetype("simhei.ttf", 12, encoding="utf-8")
draw.text((0, 0), line, (255, 0, 0), font=font)
img1 = cv2.cvtColor(np.array(img1), cv2.COLOR_RGB2BGR)
img_rst = np.hstack([img, img1])
cv2.imwrite(os.path.basename(img_path), img_rst)测试效果如下：
注：本文所用的训练数据都是公开数据集，如果你需要训练好的模型或者训练数据或者技术交流，请留言或者微信。近期会将代码开源。
