轻松学Pytorch – 年龄与性别预测
轻松学Pytorch – 年龄与性别预测
OpenCV学堂
发布于 2020-06-19 10:33:15
发布于 2020-06-19 10:33:15
大家好，上周太忙，没有更新Pytorch轻松学系列文章，但是我还是会坚定的继续走下去的，所谓有始有终，这个系列我会一直坚持写下去，希望大家继续支持我，积极给我反馈，当然也感谢大家的信任与点赞支持。
本文主要是基于公开数据集，完成了一个人脸的年龄与性别预测网络模型，以及模型训练与导出使用、本篇主要讲述的知识点有以下：
- 如何实现卷积神经网络的多任务不同输出
- 如何同时实现分类跟回归预测
- 基于人脸年龄与性别的公开数据的数据制作
- 使用多任务网络实现推理预测
数据集
本文使用的数据集来自这里
https://susanqq.github.io/UTKFace/我使用的是已经对齐跟剪切之后的人脸数据，超过2W多张的标注数据，标注信息如下：
[age]_[gender]_[race]_[date&time].jpg
文件名称格式就是每张图像的标注信息
- Age表示年龄，范围在0~116岁之间
- Gender表示性别，0表示男性，1表示女性
- Race表示人种，
基于Pytorch的dataset超类，重新完成了我的自定义数据集，代码如下：
class AgeGenderDataset(Dataset):
def __init__(self, root_dir):
self.transform = transforms.Compose([transforms.ToTensor()])
img_files = os.listdir(root_dir)
nums_ = len(img_files)
# age: 0 ~116, 0 :male, 1 :female
self.ages = []
self.genders = []
self.images = []
index = 0
for file_name in img_files:
age_gender_group = file_name.split("_")
age_ = age_gender_group[0]
gender_ = age_gender_group[1]
self.genders.append(np.float32(gender_))
self.ages.append(np.float32(age_)/max_age)
self.images.append(os.path.join(root_dir, file_name))
index += 1
def __len__(self):
return len(self.images)
def num_of_samples(self):
return len(self.images)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
image_path = self.images[idx]
else:
image_path = self.images[idx]
img = cv.imread(image_path) # BGR order
h, w, c = img.shape
# rescale
img = cv.resize(img, (64, 64))
img = (np.float32(img) /255.0 - 0.5) / 0.5
# H, W C to C, H, W
img = img.transpose((2, 0, 1))
sample = {'image': torch.from_numpy(img), 'age': self.ages[idx], 'gender': self.genders[idx]}
return sample网络模型结构
卷积层作为基础模型，在卷积层之后使用最大全局池化，完成降维操作，然后再此基础上分为两路的全链接层，分布预测年龄跟性别分类，模型结构详细信息如下：
MyMulitpleTaskNet(
(cnn_layers): Sequential(
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(5): ReLU()
(6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(8): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU()
(10): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(12): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU()
(14): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(16): Conv2d(128, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU()
(18): BatchNorm2d(196, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(global_max_pooling): AdaptiveMaxPool2d(output_size=(1, 1))
(age_fc_layers): Sequential(
(0): Linear(in_features=196, out_features=25, bias=True)
(1): ReLU()
(2): Linear(in_features=25, out_features=1, bias=True)
(3): Sigmoid()
)
(gender_fc_layers): Sequential(
(0): Linear(in_features=196, out_features=25, bias=True)
(1): ReLU()
(2): Linear(in_features=25, out_features=2, bias=True)
)
)其中年龄是一个数值结果，所以通过回归来预测，这里使用sigmoid，需要注意的是在制作数据集的时候需要把年龄归一化到0~1之间。性别是二分类预测，使用softmax，实现预测。损失函数选择，对于年龄预测使用MSE，对于分类预测使用了交叉熵损失，总的损失函数是二值之和、权重相同
模型训练
模型训练的输入图像格式为BGR、NCHW = Nx3x64x64、每个批次N=16个样本，代码实现如下：
if train_on_gpu:
model.cuda()
ds = AgeGenderDataset("D:/python/pytorch_tutorial/UTKFace/")
num_train_samples = ds.num_of_samples()
bs = 16
dataloader = DataLoader(ds, batch_size=bs, shuffle=True)
# 训练模型的次数
num_epochs = 25
# optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
model.train()
# 损失函数
mse_loss = torch.nn.MSELoss()
cross_loss = torch.nn.CrossEntropyLoss()
index = 0
for epoch in range(num_epochs):
train_loss = 0.0
for i_batch, sample_batched in enumerate(dataloader):
images_batch, age_batch, gender_batch = \
sample_batched['image'], sample_batched['age'], sample_batched['gender']
if train_on_gpu:
images_batch, age_batch, gender_batch = images_batch.cuda(), age_batch.cuda(), gender_batch.cuda()
optimizer.zero_grad()
# forward pass: compute predicted outputs by passing inputs to the model
m_age_out_, m_gender_out_ = model(images_batch)
age_batch = age_batch.view(-1, 1)
gender_batch = gender_batch.long()
# calculate the batch loss
loss = mse_loss(m_age_out_, age_batch) + cross_loss(m_gender_out_, gender_batch)
# backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# perform a single optimization step (parameter update)
optimizer.step()
# update training loss
train_loss += loss.item()
if index % 100 == 0:
print('step: {} \tTraining Loss: {:.6f} '.format(index, loss.item()))
index += 1
# 计算平均损失
train_loss = train_loss / num_train_samples
# 显示训练集与验证集的损失函数
print('Epoch: {} \tTraining Loss: {:.6f} '.format(epoch, train_loss))
# save model
model.eval()
torch.save(model, 'age_gender_model.pt')网络模型结构
基于训练模型，实现人脸年龄与性别预测，人脸检测模型使用OpenCV DNN模块，代码实现如下：
1def video_landmark_demo():
2 cnn_model = torch.load("./age_gender_model.pt")
3 print(cnn_model)
4 # capture = cv.VideoCapture(0)
5 capture = cv.VideoCapture("D:/images/video/example_dsh.mp4")
6
7 # load tensorflow model
8 net = cv.dnn.readNetFromTensorflow(model_bin, config=config_text)
9 while True:
10 ret, frame = capture.read()
11 if ret is not True:
12 break
13 frame = cv.flip(frame, 1)
14 h, w, c = frame.shape
15 blobImage = cv.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0), False, False);
16 net.setInput(blobImage)
17 cvOut = net.forward()
18 # 绘制检测矩形
19 for detection in cvOut[0,0,:,:]:
20 score = float(detection[2])
21 if score > 0.5:
22 left = detection[3]*w
23 top = detection[4]*h
24 right = detection[5]*w
25 bottom = detection[6]*h
26
27 # roi and detect landmark
28 roi = frame[np.int32(top):np.int32(bottom),np.int32(left):np.int32(right),:]
29 rw = right - left
30 rh = bottom - top
31 img = cv.resize(roi, (64, 64))
32 img = (np.float32(img) / 255.0 - 0.5) / 0.5
33 img = img.transpose((2, 0, 1))
34 x_input = torch.from_numpy(img).view(1, 3, 64, 64)
35 age_, gender_ = cnn_model(x_input.cuda())
36 predict_gender = torch.max(gender_, 1)[1].cpu().detach().numpy()[0]
37 gender = "Male"
38 if predict_gender == 1:
39 gender = "Female"
40 predict_age = age_.cpu().detach().numpy()*116.0
41 print(predict_gender, predict_age)
42
43 # 绘制
44 cv.putText(frame, ("gender: %s, age:%d"%(gender, int(predict_age[0][0]))), (int(left), int(top)-15), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 1)
45 cv.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (255, 0, 0), thickness=2)
46 # cv.putText(frame, "score:%.2f"%score, (int(left), int(top)), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
47 c = cv.waitKey(10)
48 if c == 27:
49 break
50 cv.imshow("face detection + landmark", frame)
51
52 cv.waitKey(0)
53 cv.destroyAllWindows()
54
55
56if __name__ == "__main__":
57 video_landmark_demo()运行结果如下：
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原始发表：2020-06-15，如有侵权请联系 cloudcommunity@tencent.com 删除
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