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

Image Classification using GoogleNet

In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.

Instructions

  • Download the parameter checkpoint file into this folder

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

      # use cpu
      $ python serve.py -C &
      # use gpu
      $ python serve.py &
    
  • Submit images for classification

      $ 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 and image3.jpg should be downloaded before executing the above commands.

Details

We first extract the parameter values from Caffe's checkpoint file into a pickle version After downloading the checkpoint file into caffe_root/python folder, run the following script

# 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)

Then we construct the GoogleNet using SINGA's FeedForwardNet structure. Note that we added a EndPadding layer to resolve the issue from discrepancy of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor). Only the MaxPooling layers outside inception blocks have this problem. Refer to this for more detials.