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| |
| |
| # Image Classification using GoogleNet |
| |
| |
| In this example, we convert GoogleNet trained on Caffe to SINGA for image classification. Tested on [SINGA commit](8c990f7da2de220e8a012c6a8ecc897dc7532744) with [the parameters](https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz). |
| |
| ## 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](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) 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](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html) for more detials. |