bvlc_reference_caffenet.caffemodel

#uncoding:utf-8
# set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
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 


# The caffe module needs to be on the Python path;
#  we'll add it here explicitly.
import sys
caffe_root = '/home/sea/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 no


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'
print "定义网络结构："
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)


print "加载平均图："
# 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)

print "初始化转换输入数据格式转换器："
# create transformer for the input called 'data'
transformer = caffe.io.Transformer({
    'data': net.blobs['data'].data.shape})

print "设置输入数据格式转换器参数："
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


print "设置输入数据格式："
# 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

print "加载猫："
image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')
transformed_image = transformer.preprocess('data', image)
plt.imshow(image)
plt.show()


print "将猫加载到内存："
# 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()


print "加载图像集合标签："
# load ImageNet labels
labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'
if not os.path.exists(labels_file):
    print "/data/ilsvrc12/get......sh"
    #!../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 "打印分类结果：概率和标签："
print 'probabilities and labels:'
zip(output_prob[top_inds], labels[top_inds])


#%timeit net.forward()


print "切换到gpu模式："
caffe.set_device(0)  # if we have multiple GPUs, pick the first one
caffe.set_mode_gpu()
net.forward()  # run once before timing to set up memory
#%timeit net.forward()


# for each layer, show the output shape
for layer_name, blob in net.blobs.iteritems():
    print layer_name + '\t' + str(blob.data.shape)
        
for layer_name, param in net.params.iteritems():
    print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)
        
        
print "定义可视化直方图的函数："
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')
#     plt.show()
                                                                       
print "显示：直方图--conv1"
# the parameters are a list of [weights, biases]
filters = net.params['conv1'][0].data
vis_square(filters.transpose(0, 2, 3, 1))
# plt.show()


print "显示：直方图：conv5"
feat = net.blobs['conv1'].data[0, :36]
vis_square(feat)
# plt.show()


print "显示：直方图, pool5"
feat = net.blobs['pool5'].data[0]
vis_square(feat)
# plt.show()


print "显示：hist -fc6 "
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)
# plt.show()


print "显示：t--prob"
t = net.blobs['prob'].data[0]
plt.figure(figsize=(15, 3))
# plt.plot(feat.flat)
# plt.show()



# download an image
#my_image_url = "..."  # 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

print "加载图像"
# transform it and copy it into the net
#image = caffe.io.load_image('/home/sea/shareVm/images/monkey/2.jpg')
image=caffe.io.load_image('/home/sea/Downloads/555eae4532988a6dc175031eed969fc0.jpg')
net.blobs['data'].data[...] = transformer.preprocess('data', image)

# perform classification
net.forward()

# obtain the output probabilities
output_prob = net.blobs['prob'].data[0]
# print "output_prob = ", output_prob


# sort top five predictions from softmax output
top_inds = output_prob.argsort()[::-1][:5]
print "top_inds = ", top_inds

print "显示：图像"
plt.imshow(image)
plt.show()


print "打印分类结果："
print 'probabilities and labels:'
zd = zip(output_prob[top_inds], labels[top_inds])
print "结果：  ",  zd
for e in zd:
    print e

#copy-------------------------------------------------------------------------------

output_prob = output['prob'][0]  # the output probability vector for the first image in the batch
print 'predicted class is:', output_prob.argmax()
indd = output_prob.argmax()
top_inds = indd
print "加载图像集合标签："
print (output_prob[top_inds], labels[top_inds])