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name: GoogleNet on ImageNet

SINGA version: 1.0.1

SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744

parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz

parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d

license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet

---

# 用GoogleNet做图像分类


这个例子中，我们将caffe训练好的GoogleNet转换为SINGA模型以用作图像分类。

## 操作说明

* 下载参数的checkpoint文件到如下目录

        $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
        $ tar xvf bvlc_googlenet.tar.gz

* 运行程序

        # use cpu
        $ python serve.py -C &
        # use gpu
        $ python serve.py &

* 提交图片进行分类

        $ curl -i -F image=@image1.jpg http://localhost:9999/api
        $ curl -i -F image=@image2.jpg http://localhost:9999/api
        $ curl -i -F image=@image3.jpg http://localhost:9999/api

image1.jpg, image2.jpg和image3.jpg应该在执行指令前就已被下载。

## 详细信息

我们首先从[Caffe的checkpoint文件](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel)中提取参数值，并转换为pickle版本。下载checkpoint文件后进入`caffe_root/python`文件夹，运行如下脚本：

    # to be executed within caffe_root/python folder
    import caffe
    import numpy as np
    import cPickle as pickle

    model_def = '../models/bvlc_googlenet/deploy.prototxt'
    weight = 'bvlc_googlenet.caffemodel'  # must be downloaded at first
    net = caffe.Net(model_def, weight, caffe.TEST)

    params = {}
    for layer_name in net.params.keys():
        weights=np.copy(net.params[layer_name][0].data)
        bias=np.copy(net.params[layer_name][1].data)
        params[layer_name+'_weight']=weights
        params[layer_name+'_bias']=bias
        print layer_name, weights.shape, bias.shape

    with open('bvlc_googlenet.pickle', 'wb') as fd:
        pickle.dump(params, fd)

然后我们使用SINGA的FeedForwardNet结构构建GoogleNet。 请注意，我们添加了一个EndPadding层来解决Caffe（下取整）和cuDNN（上取整）之间池化图层舍入策略差异的问题。 只有MaxPooling图层以外的启动块才有此问题。 参考[这里](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html)更多详情。