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Caffe-Python-自定义网络 原

让我们通过一个实际的例子来体验一下如何在windows环境下使用caffe进行自定义网络层的开发。

首先，我们需要设置环境变量$PYTHONPATH。我在Windows开发环境中使用的是Windows PowerShell。如果您还没有编译Windows版本的Caffe，请自行解决。

添加环境变量的代码如下：

$env:PYTHONPATH="F:\caffe-python\python\;F:\caffe-windows\windows\install\python"

这里，F:\caffe-python\python 是我的新Layer的路径，而 F:\caffe-windows\windows\install\python 是我编译后的Caffe的安装路径。

接下来，我们编写自己的自定义Layer，称为TestLayer：

import caffe
import numpy as np

class TestLayer(caffe.Layer):
def setup(self, bottom, top):
if len(bottom) != 1:
raise Exception("Need one input to process.")
def reshape(self, bottom, top):
    print("-----------------1---------------------")
    top[0].reshape(1)

def forward(self, bottom, top):
    top[0].data[...] = bottom[0].data
    print("-----------------2---------------------")

def backward(self, top, propagate_down, bottom):
    bottom[0].diff[...] = top[0].data
    pass
接下来，我们看一个官方提供的示例层，即EuclideanLossLayer，以便进行比较：
import caffe
import numpy as np


class EuclideanLossLayer(caffe.Layer):
def setup(self, bottom, top):
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
    if bottom[0].count != bottom[1].count:
        raise Exception("Inputs must have the same dimension.")
    self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
    top[0].reshape(1)

def forward(self, bottom, top):
    self.diff[...] = bottom[0].data - bottom[1].data
    top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.

def backward(self, top, propagate_down, bottom):
    for i in range(2):
        if not propagate_down[i]:
            continue
        if i == 0:
            sign = 1
        else:
            sign = -1
        bottom[i].diff[...] = sign * self.diff / bottom[i].num
完成Layer的编写后，我们需要定义网络结构：
name: "TEST"
layer {
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mean_file: "examples/cifar10/Release/cifar10/mean.binaryproto"
}
data_param {
source: "examples/cifar10/Release/cifar10/cifar10_train_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "cifar"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mean_file: "examples/cifar10/Release/cifar10/mean.binaryproto"
}
data_param {
source: "examples/cifar10/Release/cifar10/cifar10_test_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "test1"
type: "Python"
bottom: "data"
top: "test1"
python_param {
module: "test_layer"
layer: "Test_Layer"
}
}


可视化后的网络结构如下图所示：




接下来，我们编写solver文件：
net: "F:/caffe-python/python/test_layer.prototxt"
base_lr: 0.001
lr_policy: "fixed"
max_iter: 10
solver_mode: CPU
然后，我们在PowerShell中启动Caffe。首先，切换到Caffe所在的目录，我的目录是这样的：
cd F:\Smart_Classroom\3rdparty\ALLPLATHFORM\caffe-windows\windows\examples\cifar10\Release
然后执行Caffe：
./caffe.exe train --solver=F:/caffe-python/python/test_python_layer_solver.prototxt
执行结果如下图所示：




在前向和后向传播过程中，我们成功调用了两个print语句，标志着我们成功编写了自己的Caffe层。


注意：编写时请严格注意路径，否则可能会出现如下错误：

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