前面隨筆給出了NNIE開發的基本知識，下面幾篇隨筆將著重於Mobilefacenet NNIE開發，實現mobilefacenet.wk的chip版本，並在Hi3559AV100上實現mobilefacenet網路功能，外接USB攝像頭通過MPP平臺輸出至VO HDMI顯示結果。下文是Hi3559AV100 NNIE開發（5）mobilefacenet.wk模擬成功量化及與CNN_convert_bin_and_print_featuremap.py輸出中間層資料對比過程，目前實現PC端對mobilefacenet.wk模擬成功量化，為後續在板載chip上載入mobilefacenet.wk輸出資料進行比較做準備。

1、開發環境

　　作業系統：Windows 10

　　模擬工具： Ruyi Studio 2.0.28

　　開發平臺： Hi3559AV100

　　網路模型： Mobilefacenet

　　框架：Caffe

2、測試前提

　　測試前需要搭建好RuyiStudio開發環境，能夠正確執行工程，並進行了mobilefacenet 網路訓練，生成mobilefacenet.caffemodel，確定好mobilefacenet.prototxt（因為mobilefacenet的所有網路層都是NNIE支援的網路層，所以不需要手動修改、增加或者刪除操作，可以通過marked_prototxt中的mark和check操作檢視是否正確生成對應的網路結構）。

3、mobileface.wk模擬量化引數配置與測試過程

　　在測試前先給出NNIE一般量化流程：

　　（1）需要把其他非caffemodel模型對應轉換到caffemodel模型，因為Hi35xx系列NNIE只支援caffemodel模型；
　　（2）配置模擬量化引數（即配置mobilefacenet.cfg）進行PC模擬量化，獲得中間層輸出結果A（mapper_quant目錄下）；
　　（3）使用RuyiStudio提供的python中間層輸出工具，獲得中間層輸出結果B（data/ouput目錄下）；
　　（4）使用Ruyi Studio的向量對比工具Vector Comparison對A和B進行對比，觀察誤差，使誤差控制在一定範圍（利用CosineSimilarity引數）；

　　建立好工程後，首先配置mobilefacenet.wk檔案，需要注意以下幾點：

　　（1）首先選擇is_simulation為Simulation進行模擬測試，對比結果正確後再進行Inst/Chip生成板上執行的wk檔案。因為mobilefacenet的所有網路層都是NNIE支援的網路層，所以不需要手動修改、增加或者刪除操作，可以通過marked_prototxt中的mark和check操作檢視是否正確生成對應的網路結構；

　　（2）log_level = 3可以輸出所有中間層的結果，在進行模擬對比除錯時應當開啟，方便進行向量對比除錯；

　　（3）batch_num不能取大，取到之後會報錯，目前batch_num = 16；

　　（4）image_list的設定十分關鍵，其決定了你實際輸入給模型的圖片資料是怎麼樣的。其中image_type預設選擇U8，RGB_order表示輸入給網路的RGB影象的RGB三通道順序，norm_type是對影象資料的預處理，這裡我們選擇channel mean_value with data_scale，對輸入影象的資料進行減均值並歸一。設定data_scale為0.0078125，即1/128，pixel_mean.txt如下圖所示。即讓原本[0,255]區間的畫素值轉換到[-1,1]的區間內。下面給出imageList.txt文字內容：

　　（5）mapper_quant中儲存了所有的輸出資訊，Mobileface_func.wk是生成的模擬wk檔案。注意：mapper_quant中儲存的輸出資訊是選擇的image_list檔案的最後一張圖片的輸出（這個非常關鍵，為後面.py輸出中間層結果對比的時候確認是哪張圖片進行向量資料對比）

 　　給出mobileface.cfg的具體配置：（具體.cfg引數設定可以見：Hi3559AV100 NNIE開發（3）RuyiStudio軟體 .wk檔案生成過程-mobilefacenet.cfg的引數配置  https://www.cnblogs.com/iFrank/p/14515089.html）

 　　隨後點選RuyiStudio軟體左上角的make Wk按鈕，跳出下面示意圖，點選OK即可生成mobileface.wk：

4、xx.py輸出中間層資料配置與過程

　　給出CNN_convert_bin_and_print_featuremap.py（RuyiStudio版本為2.0.28）:（見此檔案放置到mobileface工程data目錄下）

  1 #from __future__ import print_function 
  2 import caffe
  3 import pickle
  4 from datetime import datetime
  5 import numpy as np
  6 import struct
  7 import sys, getopt
  8 import cv2, os, re
  9 import pickle as p
 10 import matplotlib.pyplot as pyplot
 11 import ctypes
 12 import codecs
 13 import caffe.proto.caffe_pb2 as caffe_pb2
 14 import google.protobuf as caffe_protobuf
 15 import google.protobuf.text_format
 16 import platform
 17 
 18 supported_layers=[
 19 "Convolution", "Deconvolution", "Pooling", "InnerProduct", "LRN", "BatchNorm", "Scale", "Bias", "Eltwise", "ReLU", "PReLU", "AbsVal", "TanH", "Sigmoid", "BNLL", "ELU", "LSTM", "RNN", "Softmax", "Exp", "Log", "Reshape", "Flattern", "Split", "Slice", "Concat", "SPP", "Power", "Threshold", "MVN", "Parameter", "Reduction", "Proposal",  "Custom", "Input", "Dropout"]
 20 
 21 def isSupportedLayer(layer_type):
 22     for type in supported_layers:
 23         if(layer_type == type):
 24             return True
 25     return False
 26     
 27 
 28 def image_to_array(img_file, shape_c_h_w, output_dir):
 29     result = np.array([])
 30     print("converting begins ...")
 31     resizeimage = cv2.resize(cv2.imread(img_file), (shape_c_h_w[2],shape_c_h_w[1]))
 32     b,g,r = cv2.split(resizeimage )
 33     height, width, channels = resizeimage.shape
 34     length = height*width
 35     #print(channels )
 36     r_arr = np.array(r).reshape(length)
 37     g_arr = np.array(g).reshape(length)
 38     b_arr = np.array(b).reshape(length)
 39     image_arr = np.concatenate((r_arr, g_arr, b_arr))
 40     result = image_arr.reshape((1, length*3))
 41     print("converting finished ...")
 42     file_path = os.path.join(output_dir, "test_input_img_%d_%d_%d.bin"%(channels,height,width))
 43     with open(file_path, mode='wb') as f:
 44          p.dump(result, f)
 45     print("save bin file success")
 46 
 47 def image_to_rgb(img_file,shape_c_h_w, output_dir):
 48     print("converting begins ...")
 49     #image = cv2.imread(img_file)
 50     image = cv2.imdecode(np.fromfile(img_file, dtype=np.uint8), 1)
 51     image = cv2.resize(image, (shape_c_h_w[2],shape_c_h_w[1]))
 52     image = image.astype('uint8')
 53     height = image.shape[0]
 54     width = image.shape[1]
 55     channels = image.shape[2]
 56     file_path = os.path.join(output_dir, "test_input_img_%d_%d_%d.rgb"%(channels,height,width))
 57     fileSave =  open(file_path,'wb')
 58     for step in range(0,height):
 59        for step2 in range (0, width):
 60           fileSave.write(image[step,step2,2])
 61     for step in range(0,height):
 62        for step2 in range (0, width):
 63           fileSave.write(image[step,step2,1])
 64     for step in range(0,height):
 65        for step2 in range (0, width):
 66           fileSave.write(image[step,step2,0])
 67 
 68     fileSave.close()
 69     print("converting finished ...")
 70 
 71 def image_to_bin(img_file,shape_c_h_w, output_dir):
 72     print("converting begins ...")
 73     #image = cv2.imread(img_file)
 74     image = cv2.imdecode(np.fromfile(img_file, dtype=np.uint8), 1)
 75     image = cv2.resize(image, (shape_c_h_w[2],shape_c_h_w[1]))
 76     image = image.astype('uint8')
 77     height = image.shape[0]
 78     width = image.shape[1]
 79     channels = image.shape[2]
 80     file_path = os.path.join(output_dir, "test_input_img_%d_%d_%d.bin"%(channels,height,width))
 81     fileSave =  open(file_path,'wb')
 82     for step in range(0,height):
 83        for step2 in range (0, width):
 84           fileSave.write(image[step,step2,2])
 85     for step in range(0,height):
 86        for step2 in range (0, width):
 87           fileSave.write(image[step,step2,1])
 88     for step in range(0,height):
 89        for step2 in range (0, width):
 90           fileSave.write(image[step,step2,0])
 91 
 92     fileSave.close()
 93     print("converting finished ...")
 94 
 95 def image_to_bgr(img_file,shape_c_h_w, output_dir):
 96     print("converting begins ...")
 97     #image = cv2.imread(img_file)
 98     #print(shape_c_h_w[2])
 99     #print(shape_c_h_w[1])
100     image = cv2.imdecode(np.fromfile(img_file, dtype=np.uint8), -1)
101     image = cv2.resize(image, (shape_c_h_w[2],shape_c_h_w[1]))
102     image = image.astype('uint8')
103     b,g,r = cv2.split(image)
104     height = image.shape[0]
105     width = image.shape[1]
106     channels = image.shape[2]
107     file_path = os.path.join(output_dir, "test_input_img_%d_%d_%d.bgr"%(channels,height,width))
108     fileSave =  open(file_path,'wb')
109     for step in range(0,height):
110        for step2 in range (0, width):
111           fileSave.write(b[step,step2])
112     for step in range(0,height):
113        for step2 in range (0, width):
114           fileSave.write(g[step,step2])
115     for step in range(0,height):
116        for step2 in range (0, width):
117           fileSave.write(r[step,step2])
118 
119     fileSave.close()
120     print("converting finished ...")
121 
122 def bin_to_image(bin_file,shape_c_h_w):
123     #fileReader = open(bin_file,'rb', encoding='utf-8')
124     if(platform.system()=="Linux"):
125         fileReader = open(bin_file,'rb')
126     else:
127         fileReader = open(bin_file.encode('gbk'),'rb')
128     height = shape_c_h_w[1]
129     width = shape_c_h_w[2]
130     channel = shape_c_h_w[0]
131     imageRead = np.zeros((shape_c_h_w[1], shape_c_h_w[2], shape_c_h_w[0]), np.uint8)
132     for step in range(0,height):
133        for step2 in range (0, width):
134           a = struct.unpack("B", fileReader.read(1))
135           imageRead[step,step2,2] = a[0]
136     for step in range(0,height):
137        for step2 in range (0, width):
138           a = struct.unpack("B", fileReader.read(1))
139           imageRead[step,step2,1] = a[0]
140     for step in range(0,height):
141        for step2 in range (0, width):
142           a = struct.unpack("B", fileReader.read(1))
143           imageRead[step,step2,0] = a[0]
144     fileReader.close()
145     return imageRead
146  
147 def isfloat(value):
148     try:
149        float(value)
150        return True
151     except ValueError:
152        return False
153 
154 
155 def get_float_numbers(floatfile):
156     mat = []
157     if(platform.system()=="Linux"):
158         with open(floatfile, 'rb') as input_file:
159             for line in input_file:
160                 line = line.strip()
161                 for number in line.split():
162                     if isfloat(number):
163                         mat.append(float(number))
164     else:
165         with open(floatfile.encode('gbk'), 'rb') as input_file:
166             for line in input_file:
167                 line = line.strip()
168                 for number in line.split():
169                     if isfloat(number):
170                         mat.append(float(number))
171     return mat
172 
173 def isHex(value):
174      try:
175        int(value,16)
176        return True
177      except ValueError:
178        return False
179 
180 def isHex_old(value):
181     strvalue=str(value)
182     length = len(strvalue)
183     if length == 0:
184         return False
185     i = 0
186     while(i < length):
187         if not (strvalue[i] >= 'a' and strvalue[i] <= 'e' or strvalue[i] >= 'A' and strvalue[i] <= 'E' or strvalue[i] >= '0' and strvalue[i] <= '9'):
188             return False
189         i += 1
190     return True
191 
192 def get_hex_numbers(hexfile):
193     mat = []
194     if(platform.system()=="Linux"):
195         with open(hexfile) as input_file:
196             for line in input_file:
197                 line = line.strip()
198                 for number in line.split():
199                     if isHex(number):
200                         mat.append(1.0*ctypes.c_int32(int(number,16)).value/4096)
201     else:
202        with open(hexfile.encode("gbk")) as input_file:
203             for line in input_file:
204                 line = line.strip()
205                 for number in line.split():
206                     if isHex(number):
207                         mat.append(1.0*ctypes.c_int32(int(number,16)).value/4096)
208     return mat 
209 
210 def print_CNNfeaturemap(net, output_dir):
211     params = list(net.blobs.keys())
212     print (params)
213     for pr in params[0:]:
214         print (pr)        
215         res = net.blobs[pr].data[...]
216         pr = pr.replace('/', '_')
217         pr = pr.replace('-', '_')
218         print (res.shape)
219         for index in range(0,res.shape[0]):
220            if len(res.shape) == 4:
221               filename = os.path.join(output_dir, "%s_output%d_%d_%d_%d_caffe.linear.float"%(pr,index,res.shape[1],res.shape[2],res.shape[3]))
222            elif len(res.shape) == 3:
223               filename = os.path.join(output_dir, "%s_output%d_%d_%d_caffe.linear.float"%(pr, index,res.shape[1],res.shape[2]))
224            elif len(res.shape) == 2:
225               filename = os.path.join(output_dir, "%s_output%d_%d_caffe.linear.float"%(pr,index,res.shape[1]))
226            elif len(res.shape) == 1:
227               filename = os.path.join(output_dir, "%s_output%d_caffe.linear.float"%(pr,index))
228            f = open(filename, 'wb') 
229 
230            np.savetxt(f, list(res.reshape(-1, 1)))
231 
232 # save result by layer name
233 def save_result(train_net, net, output_dir):
234     #logging.debug(net_param)
235     max_len = len(train_net.layer)
236 
237     # input data layer
238     index = 0
239     for input in train_net.input:
240         layer_data = net.blobs[input].data[...]
241         layer_name=input.replace("/", "_")
242         layer_name=input.replace("-", "_")
243         shape_str= str(layer_data.shape)
244         shape_str=shape_str[shape_str.find(", ") + 1:].replace("(", "").replace(")", "").replace(" ", "").replace(",", "_")
245         filename = os.path.join(output_dir, "%s_output%d_%s_caffe.linear.float"%(layer_name, index, shape_str))
246         np.savetxt(filename, layer_data.reshape(-1, 1))
247         index = index + 1
248     # other layer
249     i = 0
250     for layer in train_net.layer:
251         index = 0
252         for top in layer.top:
253             # ignore inplace layer
254             if 1 == len(layer.top) and 1 == len(layer.bottom) and layer.top[0] == layer.bottom[0]:
255                 break
256             layer_data = net.blobs[top].data[...]
257             layer_name=layer.name.replace("/", "_")
258             layer_name=layer.name.replace("-", "_")
259             shape_str= str(layer_data.shape)
260             shape_str=shape_str[shape_str.find(", ") + 1:].replace("(", "").replace(")", "").replace(" ", "").replace(",", "_")
261             filename = os.path.join(output_dir, "%s_output%d_%s_caffe.linear.float"%(layer_name, index, shape_str))
262             np.savetxt(filename, layer_data.reshape(-1, 1))
263             index = index + 1
264         # update the process_bar
265         i = i + 1
266         k = i * 100 / max_len
267         process_str = ">" * int(k) + " " * (100 - int(k))
268         sys.stdout.write('\r'+ process_str +'[%s%%]'%(k))
269         sys.stdout.flush()
270     sys.stdout.write("\n")
271     sys.stdout.flush()
272 
273 def main(argv):
274     if len(argv) < 6:
275         print ('CNN_convert_bin_and_print_featuremap.py -m <model_file> -w <weight_file> -i <img_file or bin_file or float_file> -p <"104","117","123" or "ilsvrc_2012_mean.npy">')
276         print ('-m <model_file>:  .prototxt, batch num should be 1')
277         print ('-w <weight_file>: .caffemodel')
278         print ('-i <img_file>:    .JPEG or jpg or png or PNG or bmp or BMP')
279         print ('-i <bin_file>:    test_img_$c_$h_$w.bin')
280         print ('-i <float_file>:  %s_output%d_%d_%d_%d_caffe.linear.float')
281         print ('-n <norm_type>:   0(default): no process, 1: sub img-val and please give the img path in the parameter p, 2: sub channel mean value and please give each channel value in the parameter p in BGR order, 3: dividing 256, 4: sub mean image file and dividing 256, 5: sub channel mean value and dividing 256') 
282         print ('-s <data_scale>:  optional, if not set, 0.003906 is set by default')
283         print ('-p <"104", "117", "123" or "ilsvrc_2012_mean.npy" or "xxx.binaryproto">: -p "104", "117", "123" is sub channel-mean-val, -p "ilsvrc_2012_mean.npy" is sub img-val and need a ilsvrc_2012_mean.npy')
284         print ('-o <output_dir:   optional, if not set, there will be a directory named output created in current dir>')
285         print ('-c <0 or 1> 1, gpu, 0 cpu')
286         print ('any parameter only need one input')
287 
288         sys.exit(2)
289     norm_type = 0
290     data_scale = 0.003906
291     output_dir = 'output/'
292     opts, args = getopt.getopt(argv, "hm:w:i:n:s:p:o:c:")
293     cuda_flag = 0
294     for opt, arg in opts:
295         if opt == '-h':
296             print ('CNN_convert_bin_and_print_featuremap.py -m <model_file> -w <weight_file> -i <img_file or bin_file or float_file> -p <"104","117","123" or "ilsvrc_2012_mean.npy">')
297             print ('-m <model_file>:  .prototxt, batch num should be 1')
298             print ('-w <weight_file>: .caffemodel')
299             print ('-i <img_file>:    .JPEG or jpg or png or PNG or bmp or BMP')
300             print ('-i <bin_file>:    test_img_$c_$h_$w.bin')
301             print ('-i <float_file>:  %s_output%d_%d_%d_%d_caffe.linear.float')
302             print ('-n <norm_type>:   0(default): no process, 1: sub img-val and please give the img path in the parameter p, 2: sub channel mean value and please give each channel value in the parameter p in BGR order, 3: dividing 256, 4: sub mean image file and dividing 256, 5: sub channel mean value and dividing 256')
303             print ('-s <data_scale>:  optional, if not set, 0.003906 is set by default')
304             print ('-p <"104", "117", "123", "ilsvrc_2012_mean.npy" or "xxx.binaryproto">: -p "104", "117", "123" is sub channel-mean-val, -p "ilsvrc_2012_mean.npy" is sub img-val and need a ilsvrc_2012_mean.npy')
305             print ('-o <output_dir:   optional, if not set, there will be a directory named output created in current dir>')
306             print ('-c <0 or 1> 1, gpu, 0 cpu')
307             print ('any parameter only need one input')
308 
309             sys.exit()
310         elif opt == "-m":
311             model_filename = arg
312         elif opt == "-w":
313             weight_filename = arg
314         elif opt == "-i":
315             img_filename = arg
316         elif opt == "-n":
317             norm_type = arg
318         elif opt == "-s":
319             data_scale = arg
320         elif opt == "-o":
321             output_dir = arg
322         elif opt == "-p":
323             meanfile = arg # default is to divide by 255
324             initialway = "sub mean by: " + meanfile
325         elif opt == "-c":
326             cuda_flag = arg
327 
328     if('1' == cuda_flag):
329         caffe.set_mode_gpu()
330         caffe.set_device(0)
331 
332     train_net = caffe_pb2.NetParameter()
333     if(platform.system()=="Linux"):
334         f=open(model_filename, 'rb')
335     else:
336         f=open(model_filename.encode('gbk'), 'rb')
337 
338     train_str = f.read()
339     caffe_protobuf.text_format.Parse(train_str, train_net)
340     f.close()
341     layers = train_net.layer
342  
343     for layer in layers:
344         if(False == isSupportedLayer(layer.type)):
345             print("Layer " + layer.name + " with type " + layer.type + " is not supported, please refer to chapter 3.1.4 and FAQ of \"HiSVP Development Guide.pdf\" to extend caffe!")
346             sys.exit(1)
347     print ('model file is ', model_filename)
348     print ('weight file is ', weight_filename)
349     print ('image file is ', img_filename)
350     print ('image preprocessing method is ', norm_type) # default is no process
351     print ('output dir is ', output_dir)
352     print ('data scale is ', data_scale)
353     if(platform.system()=="Linux"):
354         net = caffe.Net(model_filename, weight_filename, caffe.TEST)
355     else:
356         net = caffe.Net(model_filename.encode('gbk'), weight_filename.encode('gbk'), caffe.TEST)
357     
358     print ('model load success')
359     
360     if norm_type == '1' or norm_type == '4': 
361        if not os.path.isfile(meanfile):
362           print("Please give the mean image file path") 
363           sys.exit(1)
364        if meanfile.endswith('.binaryproto'):
365           meanfileBlob = caffe.proto.caffe_pb2.BlobProto()
366           if(platform.system()=="Linux"):
367               meanfileData = open(meanfile, 'rb').read()
368           else:
369               meanfileData = open(meanfile.encode('gbk'), 'rb').read()
370           meanfileBlob.ParseFromString(meanfileData)
371           arr = np.array(caffe.io.blobproto_to_array(meanfileBlob))
372           out = arr[0]
373           np.save('transMean.npy', out)
374           meanfile = 'transMean.npy'
375 
376     print ('model file is ', model_filename)
377     print ('weight file is ', weight_filename)
378     print ('image file is ', img_filename)
379     print ('image preprocessing method is ', norm_type) # default is no process
380     print ('output dir is ', output_dir)
381     print ('data scale is ', data_scale)
382     
383     if not os.path.isdir(output_dir):
384         os.mkdir(output_dir)
385 
386     if img_filename.endswith('.jpg') or img_filename.endswith('.png') or img_filename.endswith('.jpeg') or img_filename.endswith('.bmp') or img_filename.endswith('.JPEG') or img_filename.endswith('.PNG') or img_filename.endswith('.JPG') or img_filename.endswith('.BMP'):
387        
388        image_to_bin(img_filename, net.blobs['data'].data.shape[1:], output_dir)
389        if net.blobs['data'].data.shape[1]==1:
390           color = False
391        elif net.blobs['data'].data.shape[1]==3:
392           image_to_rgb(img_filename, net.blobs['data'].data.shape[1:], output_dir)
393           image_to_bgr(img_filename, net.blobs['data'].data.shape[1:], output_dir)
394           color = True
395        img = cv2.imdecode(np.fromfile(img_filename, dtype=np.uint8), -1)
396        #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  #add here
397        inputs = img
398     elif img_filename.endswith('.bin'):
399        if(platform.system()=="Linux"):
400           fbin = open(img_filename) 
401        else:
402           fbin = open(img_filename.encode('gbk'))   
403        data = bin_to_image(img_filename,net.blobs['data'].data.shape[1:])
404        inputs = data
405     elif img_filename.endswith('.float'):
406        data = np.asarray(get_float_numbers(img_filename))
407        inputs = data
408        inputs= np.reshape(inputs, net.blobs[list(net.blobs.keys())[0]].data.shape)
409     elif img_filename.endswith('.hex'):
410        data = np.asarray(get_hex_numbers(img_filename))
411        inputs = data
412        inputs= np.reshape(inputs,net.blobs[list(net.blobs.keys())[0]].data.shape)
413     else:
414        print("errors: unknown input file!")
415        sys.exit(1)
416 
417     if len(inputs):
418        transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
419        if net.blobs['data'].data.shape[1]==3:
420           transformer.set_transpose('data', (2,0,1))
421        if norm_type == '1' or norm_type == '4' and os.path.isfile(meanfile): # (sub mean by meanfile):  
422           if net.blobs['data'].data.shape[1]==3:
423               transformer.set_mean('data',np.load(meanfile).mean(1).mean(1))
424           elif net.blobs['data'].data.shape[1]==1:
425               tempMeanValue = np.load(meanfile).mean(1).mean(1)
426               tempa = list(tempMeanValue)
427               inputs = inputs - np.array(list(map(float, [tempa[0]])))
428        elif norm_type == '2' or norm_type == '5':
429           if net.blobs['data'].data.shape[1]==3:
430               lmeanfile=meanfile.split(',')
431               if len(lmeanfile) != 3:
432                   print("Please give the channel mean value in BGR order with 3 values, like 112,113,120") 
433                   sys.exit(1)
434               if not isfloat(lmeanfile[0]) or not isfloat(lmeanfile[1]) or not isfloat(lmeanfile[2]): 
435                   print("Please give the channel mean value in BGR order") 
436                   sys.exit(1)
437               else:
438                   transformer.set_mean('data',np.array(list(map(float,re.findall(r'[-+]?\d*\.\d+|\d+',meanfile)))))
439           elif net.blobs['data'].data.shape[1]==1:
440               lmeanfile=meanfile.split(',')
441               if isfloat(lmeanfile[0]):  # (sub mean by channel)
442                   inputs = inputs - np.array(list(map(float, [lmeanfile[0]])))
443 
444        elif norm_type == '3':
445           inputs = inputs * float(data_scale)
446        if img_filename.endswith('.txt') or img_filename.endswith('.float') or img_filename.endswith('.hex'):
447           print (inputs.shape)
448           data = inputs
449        else:
450           data = np.asarray([transformer.preprocess('data', inputs)])
451        if norm_type == '4' or norm_type == '5':
452           data = data * float(data_scale)
453     
454     data_reshape= np.reshape(data,net.blobs[list(net.blobs.keys())[0]].data.shape)
455     net.blobs[list(net.blobs.keys())[0]].data[...] = data_reshape.astype('float')
456     out = net.forward()
457     save_result(train_net, net, output_dir)
458     #print_CNNfeaturemap(net, output_dir)
459     sys.exit(0)
460 if __name__=='__main__':
461     main(sys.argv[1:])

　　執行結束後會在data資料夾下生成一個output資料夾，其中儲存了中間層輸出結果B，cmder輸入命令:（在mobilefacenet/data目錄下）注意：此處-i引數選擇的圖片為上面生成mobileface_func.wk時imageList.txt的最後一張照片#key#

 python CNN_convert_bin_and_print_featuremap.py -i .\images\10_MariaCallas_28_f.jpg -m mobilefacenet.prototxt -w mobilefacenet.prototxt.caffemodel -s 0.0078125 -n 5 -p "128","128","128"

5、中間層資料比較檔案配置與結果

　　點選Vector Comparision，配置如下：

 　　配置注意事項：

　　（1）Parse Dot File選擇主目錄下的cnn_net_tree.dot，Prototxt File為data目錄下的mobilefacenet.prototxt；

　　（2）Left Folder和Right Folder分別選擇mapper_quant資料夾和data/output資料夾；

　　（3）等待資料載入完成後點選compare即可開始對比；

　　測試結果：通過Vector Comparison進行資料對比，可以發現量化成功了，最終輸出結果為0.991，然後中間有些層偏差比較大，懷疑可能是NNIE的層處理與caffe有所不同，這種誤差可能可以通過merge_batchnorm操作消除，具體如下：

&n