Caffe in Python之定义网络结构和添加自定义网络层

转自：http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html

Define a model in Python

It is also possible to define the net model directly in Python, and save it to a prototxt files. Here are the commands :

from caffe import layers as L
from caffe import params as P

def lenet(lmdb, batch_size):
    # our version of LeNet: a series of linear and simple nonlinear transformations
    n = caffe.NetSpec()
    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,
                             transform_param=dict(scale=1./255), ntop=2)
    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))
    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))
    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))
    n.relu1 = L.ReLU(n.ip1, in_place=True)
    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))
    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
    return n.to_proto()

with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:
    f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))

with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:
    f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))




Create your custom python layer

Let’s create a layer to add a value.

Add a custom python layer to your conv.prototxt file :

layer {
  name: 'MyPythonLayer'
  type: 'Python'
  top: 'output'
  bottom: 'conv'
  python_param {
    module: 'mypythonlayer'
    layer: 'MyLayer'
    param_str: "'num': 21"
  }
}

and create a mypythonlayer.py file that has to to be in the current directory or in the PYTHONPATH :

import caffe
import numpy as np
import yaml

class MyLayer(caffe.Layer):

    def setup(self, bottom, top):
        self.num = yaml.load(self.param_str)["num"]
        print "Parameter num : ", self.num

    def reshape(self, bottom, top):
        pass

    def forward(self, bottom, top):
        top[0].reshape(*bottom[0].shape)
        top[0].data[...] = bottom[0].data + self.num

    def backward(self, top, propagate_down, bottom):
        pass

This layer will simply add a value

net = caffe.Net('conv.prototxt',caffe.TEST)
im = np.array(Image.open('cat_gray.jpg'))
im_input = im[np.newaxis, np.newaxis, :, :]
net.blobs['data'].reshape(*im_input.shape)
net.blobs['data'].data[...] = im_input
net.forward()