Prerequisites

Pre-configuring OBS Storage Data

Create an OBS bucket and ensure that the following folders have been created and required files have been uploaded to the specified paths using the OBS Browser.

The folder name can be in the format of File path in the bucket/File name. You can search for the file download addresses in the specified paths of the specified project in GitHub, as shown in 1 and 2.

1. models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel

   https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet

   

2. models/bvlc_reference_caffenet/deploy.prototxt

   https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet

   

3. python/caffe/imagenet/ilsvrc_2012_mean.npy

   https://github.com/BVLC/caffe/tree/master/python/caffe/imagenet

   

4. outputimg/

   An empty folder outputimg is created to store output files.

5. examples/images/cat.jpg

   https://github.com/BVLC/caffe/blob/master/examples/00-classification.ipynb

   Save the picture of the cat in the link.

6. data/ilsvrc12/*

   https://github.com/BVLC/caffe/tree/master/data/ilsvrc12

   Obtain and execute the get_ilsvrc_aux.sh script. The script downloads a compressed package and decompresses it. After the script is executed, upload all decompressed files to the directory.

7. caffeEx00.py

   # set up Python environment: numpy for numerical routines, and matplotlib for plotting
   import numpy as np
   import matplotlib as mpl
   mpl.use('Agg')
   import matplotlib.pyplot as plt
   # display plots in this notebook
   #%matplotlib inline
   
   # set display defaults
   plt.rcParams['figure.figsize'] = (10, 10)        # large images
   plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels
   plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap
   
   # The caffe module needs to be on the Python path;
   #  we'll add it here explicitly.
   import sys
   caffe_root = '/home/'  # this file should be run from {caffe_root}/examples (otherwise change this line)
   sys.path.insert(0, caffe_root + 'python')
   
   import caffe
   # If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.
   
   import os
   #if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):
   #    print 'CaffeNet found.'
   #else:
   #    print 'Downloading pre-trained CaffeNet model...'
   #    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet
   	
   caffe.set_mode_cpu()
   
   model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'
   model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
   
   net = caffe.Net(model_def,      # defines the structure of the model
                   model_weights,  # contains the trained weights
                   caffe.TEST)     # use test mode (e.g., don't perform dropout)
   
   # load the mean ImageNet image (as distributed with Caffe) for subtraction
   mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
   mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values
   print 'mean-subtracted values:', zip('BGR', mu)
   
   # create transformer for the input called 'data'
   transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
   
   transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension
   transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel
   transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]
   transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR
   
   # set the size of the input (we can skip this if we're happy
   #  with the default; we can also change it later, e.g., for different batch sizes)
   net.blobs['data'].reshape(50,        # batch size
                             3,         # 3-channel (BGR) images
                             227, 227)  # image size is 227x227
   						
   image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')
   transformed_image = transformer.preprocess('data', image)
   plt.imshow(image)
   plt.savefig(caffe_root + 'outputimg/img1.png')
   
   # copy the image data into the memory allocated for the net
   net.blobs['data'].data[...] = transformed_image
   
   ### perform classification
   output = net.forward()
   
   output_prob = output['prob'][0]  # the output probability vector for the first image in the batch
   
   print 'predicted class is:', output_prob.argmax()
   
   # load ImageNet labels
   labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'
   #if not os.path.exists(labels_file):
   #    !../data/ilsvrc12/get_ilsvrc_aux.sh
   
   labels = np.loadtxt(labels_file, str, delimiter='\t')
   
   print 'output label:', labels[output_prob.argmax()]
   
   # sort top five predictions from softmax output
   top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items
   
   print 'probabilities and labels:'
   zip(output_prob[top_inds], labels[top_inds])