# 预置条件
本实践提供在CCE上运行caffe的基础分类例子<https://github.com/BVLC/caffe/blob/master/examples/00-classification.ipynb>的过程。
 #### OBS存储数据预置
创建OBS桶，并确认以下文件夹已创建，文件已上传至指定位置（需要使用OBS Browser工具）。
例如：桶内文件路径/文件名，文件下载地址可至github中指定项目的指定路径下查找，示例如[1]、[2]所示。
1. models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
   <https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet>
   ![](https://support.huaweicloud.com/bestpractice-cce/zh-cn_image_0000001259024219.png "点击放大")
   

2. models/bvlc_reference_caffenet/deploy.prototxt
   <https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet>
   ![](https://support.huaweicloud.com/bestpractice-cce/zh-cn_image_0000001153074739.png "点击放大")
   
3. python/caffe/imagenet/ilsvrc_2012_mean.npy <https://github.com/BVLC/caffe/tree/master/python/caffe/imagenet>
   ![](https://support.huaweicloud.com/bestpractice-cce/zh-cn_image_0000001152871831.png "点击放大")
   
4. outputimg/ 创建一个空文件夹outputimg，以供存放输出文件。
   
5. examples/images/cat.jpg <https://github.com/BVLC/caffe/blob/master/examples/00-classification.ipynb>
   另存链接中里面小猫图片。
   
6. data/ilsvrc12/\* <https://github.com/BVLC/caffe/tree/master/data/ilsvrc12>
   获取get_ilsvrc_aux.sh这个脚本并执行，这个脚本会下载一个压缩包并解压，执行完毕后将解压出来的所有文件上传至目录下。
   
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])
   ```