本次实验均在官方提供的docker容器内部进行，使用的模型为tensorflow提供的MobileNet_v2_1.0_224 预训练模型。

环境准备

• docker环境

   # 方法一，从docker hub下载，需要梯子
  sudo docker pull zepan/zhouyi
   方法二，百度云下载镜像文件（压缩包约2.9GB，解压后约5.3GB）
   链接：https://pan.baidu.com/s/1yaKBPDxR_oakdTnqgyn5fg 
   提取码：f8dr 
  gunzip zhouyi_docker.tar.gz
  sudo docker load --input zhouyi_docker.tar
  
  docker run -it zepan/zhouyi /bin/bash
  
  cd ~/demos
  mkdir mobile_v2_1
  cd mobile_v2_1

材料准备

• 模型
  常见预训练模型文件在 github上可以下载：https://aijishu.com/link?targ...

  mkdir model
  mv mobilenet_v2_1.4_224.tgz ./model
  tar -xzvf mobilenet_v2_1.4_224.tgz
  """
  mobilenet_v2_1.4_224.ckpt.index
  ...
  mobilenet_v2_1.4_224_frozen.pb
  """

• 数据集
  docker 容器内部提供了测试数据集(ILSVRC2012)/root/demos/tflite/dataset,生成npy文件的数据集和标注文件，用于校正int8预测结果

  cp -r ../tflite/dataset ./
  vi dataset/preprocess_dataset.py
  
  """
  # mean = [123.68, 116.78, 103.94]
  mean = [127.5, 127.5, 127.5]
  """
  cd dataset
  python preprocess_dataset.py

编译

编写配置文件 pb_mobilenet_v2_run.cfg

"""
[Common]
mode = run

[Parser]
model_name = mobilenet_v2
detection_postprocess =
model_domain = image_classification
input_model = ./model/mobilenet_v2_1_frozen.pb
input = input
input_shape = [1, 224, 224, 3]
output = MobilenetV2/Predictions/Softmax     # 可以通过netron查看输出节点
output_dir = ./

[AutoQuantizationTool]
quantize_method = SYMMETRIC
quant_precision = int8
ops_per_channel = DepthwiseConv
reverse_rgb = False
label_id_offset = 0
dataset_name =
detection_postprocess =
anchor_generator =
log = False
calibration_data = ./dataset/dataset.npy
calibration_label = ./dataset/label.npy
preprocess_mode = normalize

[GBuilder]
inputs=./model/input.bin
simulator=aipu_simulator_z1
outputs=mobilenet_v2_output.bin
profile= True
target=Z1_0701
"""

将测试图片（长臂猿）转成二进制文件

from PIL import Image
from matplotlib import pyplot as plt
import numpy as np

input_height=224
input_width=224
input_channel = 3
mean = [127.5, 127.5, 127.5]
var = 1

img_name = "1.jpg"
img = Image.open(img_name)
plt.imshow(img)

img_w, img_h = img.size
if img_w/img_h > input_width/input_height :
    tmp_h = input_height
    tmp_w = int(input_height/img_h*img_w)
    oft_y = 0
    oft_x = (tmp_w-input_width)/2
else:
    tmp_w = input_width
    tmp_h = int(input_width/img_w*img_h)
    oft_x = 0
    oft_y = (tmp_h-input_height)/2
img1 = img.resize((tmp_w, tmp_h),Image.ANTIALIAS)
plt.imshow(img1)
img2 = img1.crop((oft_x,oft_y,oft_x+input_width,oft_y+input_height))
plt.imshow(img2)

img_arr = (np.array(img2)-mean)/var
img_arr=img_arr.astype(np.int8)

# 保存成仿真需要的bin文件
import struct
data=b''
for y in range(img_arr.shape[1]):
    for x in range(img_arr.shape[0]):
        data += struct.pack('bbb',img_arr[y,x,0],img_arr[y,x,1],img_arr[y,x,2])

fw = open("input.bin", "wb")
fw.write(data)
fw.close()
print("save to input.bin OK")

NN compiler编译 同时推理测试图片结果

aipubuild ./config/pb_mobilenet_v2_run.cfg

返回结果：
"""

[I] [main.cpp : 135] Simulator finished.
Total errors: 0, warnings: 0

"""

解码预测结果-mobilenet_v2_output.bin

编写quant_predict.py

from PIL import Image
import cv2
from matplotlib import pyplot as plt
import matplotlib.patches as patches
import numpy as np
import os
import imagenet_classes as class_name

current_dir = os.getcwd()
label_offset = 1
outputfile = current_dir + '/mobilenet_v2_output.bin'
npyoutput = np.fromfile(outputfile, dtype=np.uint8)
outputclass = npyoutput.argmax()
head5p = npyoutput.argsort()[-5:][::-1]

# labelfile = current_dir + '/output_ref.bin'
# npylabel = np.fromfile(labelfile, dtype=np.int8)
# labelclass = npylabel.argmax()
# head5t = npylabel.argsort()[-5:][::-1]

print("predict first 5 label:")
for i in head5p:
    print("    index %4d, prob %3d, name: %s"%(i, npyoutput[i], class_name.class_names[i-label_offset]))

# print("true first 5 label:")
# for i in head5t:
#     print("    index %4d, prob %3d, name: %s"%(i, npylabel[i], class_name.class_names[i-label_offset]))

# Show input picture
print('Detect picture save to result.jpeg')

input_path = './model/input.bin'
npyinput = np.fromfile(input_path, dtype=np.int8)
image = np.clip(np.round(npyinput)+128, 0, 255).astype(np.uint8)
image = np.reshape(image, (224, 224, 3))
im = Image.fromarray(image)
im.save('result.jpeg')

"""
predict first 5 label:

index  918, prob  27, name: comic book  
index  819, prob  23, name: spotlight, spot  
index  368, prob   9, name: chimpanzee, chimp, Pan troglodytes  
index  372, prob   8, name: patas, hussar monkey, Erythrocebus patas  
index  367, prob   6, name: gorilla, Gorilla gorilla  

Detect picture save to result.jpeg

"""
百度随机抓取的一个猴子图片，在top-5内有三类猴子。
工程文件夹链接：https://pan.baidu.com/s/1X_yoCDPghqBp84qjvwyJZg
提取码：JJJR