tariqul-islam · April 20, 2019 09:03 · tariqul-islam · May 20, 2016 · mortred12306 · Apr 20, 2019
diff --git a/caffe_time_for_caffenet.py b/caffe_time_for_caffenet.py
 #This is a basic benchmark code to see how fast
 #your GPU is compared to your CPU
 #
 #Before you begin:
 #go to caffe root, copy this code there
 #get the bvlc_reference_caffenet using these two commands in caffe root:
 #./scripts/download_model_binary.py models/bvlc_reference_caffenet
 #./data/ilsvrc12/get_ilsvrc_aux.sh
 #


 #import stuff
 import caffe
 import numpy as np
 #import matplotlib.pyplot as plt
 #from PIL import Image

 import timeit



 #load the model
 #model description is in deploy.prototxt
 #weights are in bvlc_reference_caffenet.caffemodel
 net = caffe.Net('models/bvlc_reference_caffenet/deploy.prototxt',
                'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',
                caffe.TEST)

 #load input and configure preprocessing
 #caffe.io.Transformer funciton defines the objec that defines
 #transformation  of an image as a preprocessor
 #object.preprocess processes the image
 transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
 transformer.set_mean('data', np.load('python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))
 transformer.set_transpose('data',(2,0,1))  #(H,W,C) to (C,H,W); channel comes first
 transformer.set_channel_swap('data', (2,1,0)) #image loads in RGB, convert it to BGR or vice versa
 transformer.set_raw_scale('data',255.0)

 #we need to classify one image only
 #see the current shape of the input blob / data blob
 print net.blobs['data'].data.shape

 #it prints (10,3,227,227)
 #10 is number of images per batch.
 #we want to use only one image to test, so we change it to 1
 net.blobs['data'].reshape(1,3,227,227)

 #now we load the image
 im = caffe.io.load_image('examples/images/cat.jpg')
 #now add the image in data
 net.blobs['data'].data[...] = transformer.preprocess('data',im)


 #set CPU as the computing device
 caffe.set_mode_cpu()
 #compute the output
 out = net.forward()

 #now time it in CPU
 t1 = timeit.default_timer()
 out = net.forward()
 t2 = timeit.default_timer()


 #set GPU as the computing device
 caffe.set_device(0)
 caffe.set_mode_gpu()

 #compute the output
 out = net.forward()

 #now time it again in GPU
 t3 = timeit.default_timer()
 out = net.forward()
 t4 = timeit.default_timer()

 print 'CPU Time : ', t2-t1
 print 'GPU Time : ', t4-t3
 print 'GPU is ', (t2-t1)/(t4-t3), ' times faster'

 #print predicted labels
 #labels = np.loadtxt('data/ilsvrc12/synset_words.txt', str, delimiter='\t')
 #top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
 #print labels[top_k]
	#This is a basic benchmark code to see how fast
	#your GPU is compared to your CPU
	#
	#Before you begin:
	#go to caffe root, copy this code there
	#get the bvlc_reference_caffenet using these two commands in caffe root:
	#./scripts/download_model_binary.py models/bvlc_reference_caffenet
	#./data/ilsvrc12/get_ilsvrc_aux.sh
	#


	#import stuff
	import caffe
	import numpy as np
	#import matplotlib.pyplot as plt
	#from PIL import Image

	import timeit



	#load the model
	#model description is in deploy.prototxt
	#weights are in bvlc_reference_caffenet.caffemodel
	net = caffe.Net('models/bvlc_reference_caffenet/deploy.prototxt',
	'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',
	caffe.TEST)

	#load input and configure preprocessing
	#caffe.io.Transformer funciton defines the objec that defines
	#transformation of an image as a preprocessor
	#object.preprocess processes the image
	transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
	transformer.set_mean('data', np.load('python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))
	transformer.set_transpose('data',(2,0,1)) #(H,W,C) to (C,H,W); channel comes first
	transformer.set_channel_swap('data', (2,1,0)) #image loads in RGB, convert it to BGR or vice versa
	transformer.set_raw_scale('data',255.0)

	#we need to classify one image only
	#see the current shape of the input blob / data blob
	print net.blobs['data'].data.shape

	#it prints (10,3,227,227)
	#10 is number of images per batch.
	#we want to use only one image to test, so we change it to 1
	net.blobs['data'].reshape(1,3,227,227)

	#now we load the image
	im = caffe.io.load_image('examples/images/cat.jpg')
	#now add the image in data
	net.blobs['data'].data[...] = transformer.preprocess('data',im)


	#set CPU as the computing device
	caffe.set_mode_cpu()
	#compute the output
	out = net.forward()

	#now time it in CPU
	t1 = timeit.default_timer()
	out = net.forward()
	t2 = timeit.default_timer()


	#set GPU as the computing device
	caffe.set_device(0)
	caffe.set_mode_gpu()

	#compute the output
	out = net.forward()

	#now time it again in GPU
	t3 = timeit.default_timer()
	out = net.forward()
	t4 = timeit.default_timer()

	print 'CPU Time : ', t2-t1
	print 'GPU Time : ', t4-t3
	print 'GPU is ', (t2-t1)/(t4-t3), ' times faster'

	#print predicted labels
	#labels = np.loadtxt('data/ilsvrc12/synset_words.txt', str, delimiter='\t')
	#top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
	#print labels[top_k]