一:环境依赖
我自己的环境在这篇博客上https://developer.aliyun.com/article/1624222
下面是我为了实现可视化需要安装新的环境依赖包
- step1：pip install mxnet-cu100 -i https://pypi.douban.com/simple
- step2：pip install graphviz==0.8.4
并把F:\Anaconda3\envs\cv_car\Lib\site-packages\graphviz添加到电脑环境中 - step3: pip install python-graphviz==0.16
- https://graphviz.org/download/到这里下载Graphviz，我需要的是windows的版本所以我下的是下图这个
我安装在C:\Program Files\Graphviz，然后右键此电脑–高级系统设置–环境变量–在用户变量中添加C:\Program Files\Graphviz\bin\dot.exe，在系统变量中添加下面这些
二:具体代码
这个代码是实现MTCNN的四个级联网络，分别是P-Net、R-Net、O-Net、L-Net
import mxnet as mx
def pnet():
data = mx.sym.Variable('data')
conv1 = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=10, name='conv1')
prelu1 = mx.sym.LeakyReLU(data=conv1, act_type='prelu', name='prelu1')
pool1 = mx.sym.Pooling(data=prelu1, kernel=(2, 2), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool1')
conv2 = mx.sym.Convolution(data=pool1, kernel=(3, 3), num_filter=16, name='conv2')
prelu2 = mx.sym.LeakyReLU(data=conv2, act_type='prelu', name='prelu2')
conv3 = mx.sym.Convolution(data=prelu2, kernel=(3, 3), num_filter=32, name='conv3')
prelu3 = mx.sym.LeakyReLU(data=conv3, act_type='prelu', name='prelu3')
score = mx.sym.Convolution(data=prelu3, kernel=(1, 1), num_filter=2, name='score')
prob = mx.sym.SoftmaxActivation(data=score, mode='channel', name='prob')
bbox_pred = mx.sym.Convolution(data=prelu3, kernel=(1, 1), num_filter=4, name='bbox_pred')
landmark_pred = mx.sym.Convolution(data=prelu3, kernel=(1, 1), num_filter=10, name='landmark_pred')
out = mx.sym.Group([prob, bbox_pred, landmark_pred])
return out
def rnet():
data = mx.sym.Variable('data')
conv1 = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=28, name='conv1')
prelu1 = mx.sym.LeakyReLU(data=conv1, act_type='prelu', name='prelu1')
pool1 = mx.sym.Pooling(data=prelu1, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool1')
conv2 = mx.sym.Convolution(data=pool1, kernel=(3, 3), num_filter=48, name='conv2')
prelu2 = mx.sym.LeakyReLU(data=conv2, act_type='prelu', name='prelu2')
pool2 = mx.sym.Pooling(data=prelu2, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool2')
conv3 = mx.sym.Convolution(data=pool2, kernel=(2, 2), num_filter=64, name='conv3')
prelu3 = mx.sym.LeakyReLU(data=conv3, act_type='prelu', name='prelu3')
fc = mx.sym.FullyConnected(data=prelu3, num_hidden=128, name='fc')
prelu4 = mx.sym.LeakyReLU(data=fc, act_type='prelu', name='prelu4')
score = mx.sym.FullyConnected(data=prelu4, num_hidden=2, name='score')
prob = mx.sym.SoftmaxActivation(data=score, name='prob')
bbox_pred = mx.sym.FullyConnected(data=prelu4, num_hidden=4, name='bbox_pred')
landmark_pred = mx.sym.FullyConnected(data=prelu4, num_hidden=10, name='landmark_pred')
out = mx.sym.Group([prob, bbox_pred, landmark_pred])
return out
def onet():
data = mx.sym.Variable('data')
conv1 = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=32, name='conv1')
prelu1 = mx.sym.LeakyReLU(data=conv1, act_type='prelu', name='prelu1')
pool1 = mx.sym.Pooling(data=prelu1, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool1')
conv2 = mx.sym.Convolution(data=pool1, kernel=(3, 3), num_filter=64, name='conv2')
prelu2 = mx.sym.LeakyReLU(data=conv2, act_type='prelu', name='prelu2')
pool2 = mx.sym.Pooling(data=prelu2, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool2')
conv3 = mx.sym.Convolution(data=pool2, kernel=(3, 3), num_filter=64, name='conv3')
prelu3 = mx.sym.LeakyReLU(data=conv3, act_type='prelu', name='prelu3')
pool3 = mx.sym.Pooling(data=prelu3, kernel=(2, 2), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool3')
conv4 = mx.sym.Convolution(data=pool3, kernel=(2, 2), num_filter=128, name='conv4')
prelu4 = mx.sym.LeakyReLU(data=conv4, act_type='prelu', name='prelu4')
fc = mx.sym.FullyConnected(data=prelu4, num_hidden=256, name='fc')
prelu5 = mx.sym.LeakyReLU(data=fc, act_type='prelu', name='prelu5')
score = mx.sym.FullyConnected(data=prelu5, num_hidden=2, name='score')
prob = mx.sym.SoftmaxActivation(data=score, name='prob')
bbox_pred = mx.sym.FullyConnected(data=prelu5, num_hidden=4, name='bbox_pred')
landmark_pred = mx.sym.FullyConnected(data=prelu5, num_hidden=10, name='landmark_pred')
out = mx.sym.Group([prob, bbox_pred, landmark_pred])
return out
def lnet():
data = mx.sym.Variable('data')
conv1 = mx.sym.Convolution(data=data, kernel=(3, 3), num_filter=50, num_group=5, name='conv1')
prelu1 = mx.sym.LeakyReLU(data=conv1, act_type='prelu', name='prelu1')
pool1 = mx.sym.Pooling(data=prelu1, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool1')
conv2 = mx.sym.Convolution(data=pool1, kernel=(3, 3), num_filter=100, num_group=5, name='conv2')
prelu2 = mx.sym.LeakyReLU(data=conv2, act_type='prelu', name='prelu2')
pool2 = mx.sym.Pooling(data=prelu2, kernel=(3, 3), stride=(2, 2), pool_type='max', \
pooling_convention='full', name='pool2')
conv3 = mx.sym.Convolution(data=pool2, kernel=(2, 2), num_filter=200, num_group=5, name='conv3')
prelu3 = mx.sym.LeakyReLU(data=conv3, act_type='prelu', name='prelu3')
conv4 = mx.sym.Convolution(data=prelu3, kernel=(3, 3), stride=(3, 3), num_filter=100, num_group=5, name='conv4')
prelu4 = mx.sym.LeakyReLU(data=conv4, act_type='prelu', name='prelu4')
conv5 = mx.sym.Convolution(data=prelu4, kernel=(1, 1), num_filter=50, num_group=5, name='conv5')
prelu5 = mx.sym.LeakyReLU(data=conv5, act_type='prelu', name='prelu5')
conv6 = mx.sym.Convolution(data=prelu5, kernel=(1, 1), num_filter=10, num_group=5, name='conv6')
out = mx.sym.Reshape(conv6, shape=(-1, 10))
return out
if __name__ == '__main__':
p = pnet()
r = rnet()
o = onet()
l = lnet()
mx.viz.plot_network(p, shape={'data': (1, 3, 12, 12)}).render('tmp/pnet')
mx.viz.plot_network(r, shape={'data': (1, 3, 24, 24)}).render('tmp/rnet')
mx.viz.plot_network(o, shape={'data': (1, 3, 48, 48)}).render('tmp/onet')
mx.viz.plot_network(l, shape={'data': (1, 15, 24, 24)}).render('tmp/lnet')
AI 代码解读
三:运行结果
P-Net:
R-Net:
O-Net:
L-Net:
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