Windows10上使用Caffe的Python接口进行图像分类例程

本文将会介绍Caffe的Python接口的使用方法。编辑Python可以使用很多种方法，我们采用的是IPython交互式编辑环境。

 

1 Python的安装

如果你的Windows电脑还没有安装Python，请先自行搜索Python的安装方法，例如 http://jupyter.org/install.html，推荐使用Anaconda软件包安装方式，这样就自带IPython/Jupyter环境了。本文使用的是Python2.7。

 

2 Caffe的安装

Windows Caffe的安装请参照之前的一篇文章：

http://blog.csdn.net/zzlyw/article/details/66971669

 

3 详细操作

3.1 设置

（1）首先，设置Python、numpy、和matplotlib。

In [1]:

# set up Python environment: numpy for numerical routines, and matplotlib for plottingimport numpy as npimport matplotlib.pyplot as plt# display plots in this notebookget_ipython().magic(u'matplotlib inline')# set display defaultsplt.rcParams['figure.figsize'] = (10, 10)        # large imagesplt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixelsplt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap

（2）导入caffe

In [2]:

# The caffe module needs to be on the Python path;#  we'll add it here explicitly.import syscaffe_root = 'F:\\Projects\\caffe\\'  # 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.

（3）如果还没有自己训练好的模型，可以下载一个CaffeNet

In [3]:

import osif os.path.isfile(caffe_root + 'models\\bvlc_reference_caffenet\\bvlc_reference_caffenet.caffemodel'):    print 'CaffeNet found.'else:    print 'Downloading pre-trained CaffeNet model...'    get_ipython().system(u'python F:\\Projects\\caffe\\scripts\\download_model_binary.py F:\\Projects\\caffe\\models\\bvlc_reference_caffenet')

Out: 

CaffeNet found.

3.2 导入网络和输入预处理

（1）设置Caffe为CPU模式，从硬盘导入网络。

In [4]:

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)

（2）设置输入预处理。我们使用Caffe的caffe.io.Transformer 来做这件事，它与caffe的其他部分是独立的，所以任何其他自定义的预处理代码都可以使用。

默认的CaffeNet使用图像为BGR格式。它们的灰度范围应该使用[0 , 255]，于是可以使用ImageNet的图像像素均值作为要减去的数值。

Matplotlib会把导入的图像设定为[0, 1]范围的RGB格式，所以需要做一些转换。

In [5]:

# load the mean ImageNet image (as distributed with Caffe) for subtractionmu = 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 valuesprint '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 dimensiontransformer.set_mean('data', mu)            # subtract the dataset-mean value in each channeltransformer.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

Out:

mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]

3.3 CPU分类

（1）设置batch size为50

In [6]:

# 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

（2）导入图像，执行预处理

In [7]:

image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')transformed_image = transformer.preprocess('data', image)plt.imshow(image)

Out:

<matplotlib.image.AxesImage at 0x19ed6ba8>

（3）执行分类

In [8]:

# copy the image data into the memory allocated for the netnet.blobs['data'].data[...] = transformed_image### perform classificationoutput = net.forward()output_prob = output['prob'][0]  # the output probability vector for the first image in the batchprint 'predicted class is:', output_prob.argmax()

Out:

predicted class is: 281

（4）网络给出了一个概率向量，最可能的类别是编号281的类。我们需要找到Image的类别标签。下面的程序是检验有没有sysset_words.txt文件，如果没有则使用脚本从网上下载。由于脚本本来是在Linux shell中运行的，在Windows命令行中执行报错，所以我是先使用别的方法下载了这个文件，放到了该对应的路径下。你可以使用win10自带的Linux内核系统运行shell命令来下载，也可以从网上搜索这个文件。

In [9]:

# load ImageNet labelslabels_file = caffe_root + 'data\\ilsvrc12\\synset_words.txt'if not os.path.exists(labels_file):    get_ipython().system(u'F:\Projects\caffe\data\ilsvrc12\get_ilsvrc_aux.sh')    labels = np.loadtxt(labels_file, str, delimiter='\t')print 'output label:', labels[output_prob.argmax()]

Out:

output label: n02123045 tabby, tabby cat

（5）查看全部分类结果列表

In [10]:

# sort top five predictions from softmax outputtop_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest itemsprint 'probabilities and labels:'zip(output_prob[top_inds], labels[top_inds])

Out: 

probabilities and labels:

[(0.31244686, 'n02123045 tabby, tabby cat'), (0.23796991, 'n02123159 tiger cat'), (0.12387832, 'n02124075 Egyptian cat'), (0.10075155, 'n02119022 red fox, Vulpes vulpes'), (0.070957169, 'n02127052 lynx, catamount')]

3.4 使用GPU模式

（1）先看下CPU模式下分类时间

In [11]:

get_ipython().magic(u'timeit net.forward()')

Out:

1 loop, best of 3: 929 ms per loop

（2）改到GPU模式下看分类时间

In [12]:

caffe.set_device(0)  # if we have multiple GPUs, pick the first onecaffe.set_mode_gpu()net.forward()  # run once before timing to set up memoryget_ipython().magic(u'timeit net.forward()')

Out:

10 loops, best of 3: 51.9 ms per loop

3.5 检查中间输出

网络并非是一个黑盒，让我们看看中间的参数信息。

In [13]:

# for each layer, show the output shapefor layer_name, blob in net.blobs.iteritems():    print layer_name + '\t' + str(blob.data.shape)

Out:

data(50L, 3L, 227L, 227L)conv1(50L, 96L, 55L, 55L)pool1(50L, 96L, 27L, 27L)norm1(50L, 96L, 27L, 27L)conv2(50L, 256L, 27L, 27L)pool2(50L, 256L, 13L, 13L)norm2(50L, 256L, 13L, 13L)conv3(50L, 384L, 13L, 13L)conv4(50L, 384L, 13L, 13L)conv5(50L, 256L, 13L, 13L)pool5(50L, 256L, 6L, 6L)fc6(50L, 4096L)fc7(50L, 4096L)fc8(50L, 1000L)prob(50L, 1000L)

In [14]:

for layer_name, param in net.params.iteritems():    print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)

Out:

conv1(96L, 3L, 11L, 11L) (96L,)conv2(256L, 48L, 5L, 5L) (256L,)conv3(384L, 256L, 3L, 3L) (384L,)conv4(384L, 192L, 3L, 3L) (384L,)conv5(256L, 192L, 3L, 3L) (256L,)fc6(4096L, 9216L) (4096L,)fc7(4096L, 4096L) (4096L,)fc8(1000L, 4096L) (1000L,)

In [15]:

def vis_square(data):    """Take an array of shape (n, height, width) or (n, height, width, 3)       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""        # normalize data for display    data = (data - data.min()) / (data.max() - data.min())        # force the number of filters to be square    n = int(np.ceil(np.sqrt(data.shape[0])))    padding = (((0, n ** 2 - data.shape[0]),               (0, 1), (0, 1))                 # add some space between filters               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)        # tile the filters into an image    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])        plt.imshow(data); plt.axis('off')

In [16]:

# the parameters are a list of [weights, biases]filters = net.params['conv1'][0].datavis_square(filters.transpose(0, 2, 3, 1))

Out:

In [17]:

feat = net.blobs['conv1'].data[0, :36]vis_square(feat)

Out:

In [18]:

feat = net.blobs['pool5'].data[0]vis_square(feat)

Out:

In [19]:

feat = net.blobs['fc6'].data[0]plt.subplot(2, 1, 1)plt.plot(feat.flat)plt.subplot(2, 1, 2)_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

Out:

In [20]:

feat = net.blobs['prob'].data[0]plt.figure(figsize=(15, 3))plt.plot(feat.flat)

Out: 

[<matplotlib.lines.Line2D at 0x4202c358>]

3.6 尝试自己的图像

In [21]:

# download an image#my_image_url = "https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1491715902209&di=82ef5c02c812e21e2e0f44fce2a1d4b6&imgtype=0&src=http%3A%2F%2Fcyjctrip.qiniudn.com%2F56329%2F1374595566800p18064d9kk169p1j291j1l1u31k0lk.jpg"  # paste your URL here# for example:# my_image_url = "https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG"#!wget -O image.jpg $my_image_url# transform it and copy it into the netimage = caffe.io.load_image('C:\\Users\\Bill\\Desktop\\image.jpg')net.blobs['data'].data[...] = transformer.preprocess('data', image)# perform classificationnet.forward()# obtain the output probabilitiesoutput_prob = net.blobs['prob'].data[0]# sort top five predictions from softmax outputtop_inds = output_prob.argsort()[::-1][:5]plt.imshow(image)print 'probabilities and labels:'zip(output_prob[top_inds], labels[top_inds])

Out:

[(0.69523662, 'n02403003 ox'), (0.16318876, 'n02389026 sorrel'), (0.039488554, 'n02087394 Rhodesian ridgeback'), (0.029075578, 'n03967562 plow, plough'), (0.015077997, 'n02422106 hartebeest')]