基於Intel OpenVINO的搭建及應用，包含分類，目標檢測，及分割，超分辨

本文轉載自查看原文 2019-05-19 11:09 3290 深度學習/ 模型壓縮

PART I: 搭建環境OPENVINO+Tensorflow1.12.0

I: l_openvino_toolkit_p_2019.1.094

第一步常規安裝參考鏈接：https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html

第二步編譯Inference Engine Samples:

cd /PATH/TO/deployment_tools/inference_engine/samples

run ./build_samples.sh

編譯后的生成文件路徑

/root/inference_engine_samples_build/intel64/Release

II. tensorflow編譯

Bazel編譯Tensorflow

參考鏈接：https://blog.csdn.net/chenyuping333/article/details/82108509

Bazel下載連接：https://github.com/bazelbuild/bazel/releases

(bazel-0.18.0-installer-linux-x86_64.sh)

2. Tensorflow下載連接：https://github.com/tensorflow/tensorflow/tags (tensorflow-1.12.0)

Step 1: cd /PATH/TO/tensorflows

Step2: ./configure (全選No)

Step3: 編譯freeze_graph

bazel build tensorflow/python/tools:freeze_graph

可能會遇到的問題：

error:Python.h:No such file or directory

solutions:yum install python34-devel

note:一定將默認python改為python3，缺少numpy文件，yum安裝時報錯，更改對應文件頭部的/usr/bin/python地址為/usr/bin/python2，使用pip3 安裝numpy

錯誤參考鏈接: https://www.jianshu.com/p/db943b0f1627 https://blog.csdn.net/xjmxym/article/details/73610648

https://www.cnblogs.com/toSeek/p/6192481.html

Step4: 編譯transform_graph

bazel build tensorflow/tools/graph_transforms:transform_graph

Step5: 編譯summarize_graph

bazel build tensorflow/tools/graph_transforms:summarize_graph

PART II: OPENVINO for Classification

數據集准備：ImageNet val

分別制作六個文件夾每個文件夾內的圖片數量依次為1,8,16,32,64,96

形式如下

測試模型：VGG-19,resnet-50,resnet-101,resnet-152,inception-v3,inception-v4

參考鏈接: https://github.com/vdevaram/deep_learning_utilities_cpu/blob/master/dldt/run_dldt_tf.sh

StepI: 下載預訓練的模型(6個)

mkdir pretrainedModels && cd pretrainedModels

wget http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz

tar -xvf vgg_19_2016_08_28.tar.gz

wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz

tar -xvf inception_v3_2016_08_28.tar.gz

wget http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz

tar -xvf inception_v4_2016_09_09.tar.gz

wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz

tar -xvf resnet_v1_50_2016_08_28.tar.gz

wget http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz

tar -xvf resnet_v1_101_2016_08_28.tar.gz

wget http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz

tar -xvf resnet_v1_152_2016_08_28.tar.gz

解壓后的形式如下所示

StepII: 生成對應分類預訓練模型的.pb文件

cd /PATH/TO/pretrainedModels

mkdir frozen && mkdir pb

(*不同網絡的對應指令有些許不同)

1． python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=vgg_19 \

--output_file=/PATH/TO/pb/vgg_19.pb \

--labels_offset=1

2. python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=resnet_v1_50 \

--output_file=/PATH/TO/pb/resnet_v1_50.pb \

--labels_offset=1

3. python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=resnet_v1_101 \

--output_file=/PATH/TO/pb/resnet_v1_101.pb \

--labels_offset=1

4. python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=resnet_v1_152 \

--output_file=/PATH/TO/pb/resnet_v1_152.pb \

--labels_offset=1

5. python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=inception_v3 \

--output_file=/PATH/TO/pb/inception_v3.pb

6. python3.6 /PATH/TO/tensorflowModels/research/slim/export_inference_graph.py \

--alsologtostderr \

--model_name=inception_v4 \

--output_file=/PATH/TO/pb/inception_v4.pb

StepIII: 對生成對應分類預訓練模型的.pb文件進行freeze操作

(*若下面指令報錯可以使用python3.6 運行對應的freeze_graph.py文件進行生成)

cd /PATH/TO/TensorflowModels

1. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/vgg_19.pb \

--input_checkpoint=/PATH/TO/vgg_19.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_vgg_19.pb \

--output_node_names=vgg_19/fc8/squeezed

2. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/resnet_v1_50.pb \

--input_checkpoint=/PATH/TO/resnet_v1_50.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_resnet_v1_50.pb \

--output_node_names=resnet_v1_50/predictions/Reshape_1

3. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/resnet_v1_101.pb \

--input_checkpoint=/PATH/TO/resnet_v1_101.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_resnet_v1_101.pb \

--output_node_names=resnet_v1_101/predictions/Reshape_1

4. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/resnet_v1_152.pb \

--input_checkpoint=/PATH/TO/resnet_v1_152.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_resnet_v1_152.pb \

--output_node_names=resnet_v1_152/predictions/Reshape_1

5. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/inception_v3.pb \

--input_checkpoint=/PATH/TO/inception_v3.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_inception_v3.pb \

--output_node_names=InceptionV3/Predictions/Reshape_1

6. bazel-bin/tensorflow/python/tools/freeze_graph \

--input_graph=/PATH/TO/pb/inception_v4.pb \

--input_checkpoint=/PATH/TO/inception_v4.ckpt \

--input_binary=true \

--output_graph=/PATH/TO/frozen/frozen_inception_v4.pb \

--output_node_names=InceptionV4/Logits/Predictions

StepIV: 生成IR文件

cd /PATH/TO /deployment_tools/model_optimizer

1. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_vgg_19.pb \

--data_type FP32 \

--output_dir /PATH/TO/frozen/ \

--reverse_input_channels \

--batch 64

(batch大小可選1,8,16,32,64,96)

2. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_inception_v3.pb \

--data_type FP32 \

--scale 255 \

--reverse_input_channels \

--output_dir /PATH/TO/frozen/ \

--batch 16

3. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_inception_v4.pb \

--data_type FP32 \

--scale 255 \

--reverse_input_channels \

--output_dir /PATH/TO/frozen/ \

--batch 16

4. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_resnet_v1_50.pb \

--data_type FP32 \

--output_dir /PATH/TO/frozen/ \

--reverse_input_channels \

--batch 16

5. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_resnet_v1_101.pb \

--data_type FP32 \

--output_dir /PATH/TO/frozen/ \

--reverse_input_channels \

--batch 16

6. python3.6 mo.py --framework tf \

--input_model /PATH/TO/frozen/frozen_resnet_v1_152.pb \

--data_type FP32 \

--output_dir /PATH/TO/frozen/ \

--reverse_input_channels \

--batch 16

StepV:測試(-ni -niter 迭代100次)

cd /root/inference_engine_samples_build/intel64/Release

./classification_sample \

-i /PATH/TO/frozen/frozen_inception_v3.xml \

-d CPU -ni 100 \

-l /PATH/TO/deployment_tools/inference_engine/samples/intel64/Release/lib/libcpu_extension.so \

-nt 1 \

-i /PATH/TO/val1

./benchmark_app \

-m /PATH/TO /frozen/ frozen_inception_v3.xml \

-d CPU -api async -niter 100 \

-l /PATH/TO/deployment_tools/inference_engine/samples/intel64/Release/lib/libcpu_extension.so -nireq 32 \

-i /PATH/TO/val1

(-nireq單CPU核的數量，通過lscpu指令查看)

(上述輸入圖片的數量與batch的大小相同，對應前面的val數據集，通過更改不同模型對應的.xml文件來測試不同的模型)

PART III: OPENVINO for Object Detection Test

數據集准備：COCO val2017

分別制作六個文件夾每個文件夾內的圖片數量依次為1,8,16,32,64,96

形式如下

Note: Tensorflow Object_Detection API參考：

https://github.com/tensorflow/models/tree/master/research/object_detection

Step I:預訓練模型的下載

mkdir object_detection && cd object_detection && mkdir test_models
下載對應的預訓練的目標檢測模型

模型鏈接：

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

解壓后的文件格式如下圖

Step II: 生成IR文件

參考鏈接：

https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html

（更改SSD,Faster R-CNN,RFCN,Mask R-CNN對應的 .json及pipeline.config文件

對應的.json文件如下

ssd_v2_support.json

faster_rcnn_support.json

rfcn_support.json

mask_rcnn_support.json）

python3.6 mo_tf.py \

--input_model=/PATH/TO/frozen_inference_graph.pb \

--tensorflow_use_custom_operations_config /PATH/TO/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json \

--tensorflow_object_detection_api_pipeline_config /PATH/TO/pipeline.config \

--reverse_input_channels --batch 16

(batch只針對SSD的大小可調,Faster RCNN 及RFCN只能測試batch為1的情形)

StepIII:測試

For SSD/Faster RCNN/RFCN series

(benchmark_app只適用於SSD)

./benchmark_app \

-m /PATH/TO/frozen_inference_graph.xml \

-d CPU -api async -niter 100 \

-l /PATH/TO/intel64/Debug/lib/libcpu_extension.so \

-nireq 32 -i /PATH/TO /val1/

(-nireq代表單CPU的核數，通過lscpu可以查看，val與batch的大小相對應)

(RFCN, SSD 及 Faster R-CNNs, 測試指令都為object_detection_sample_ssd

***Faster R-CNN/RFCN的只適用於batch=1

)

./object_detection_sample_ssd \

-m /PATH/TO/frozen_inference_graph.xml -d CPU -ni 100 \

-l /PATH/TO/intel64/Debug/lib/libcpu_extension.so -i /PATH/TO /val1/

For Mask R-CNN

參考鏈接：https://docs.openvinotoolkit.org/latest/_inference_engine_samples_mask_rcnn_demo_README.html

./mask_rcnn_demo \

-m /PATH/TO/frozen_inference_graph.xml -d CPU -ni 100 \

-l /root/inference_engine_samples_build/intel64/Debug/lib/libcpu_extension.so \

-i /PATH/TO/coco_val/val64/

在Mask R-CNN進行batch 值大於16以上時，會出現錯誤

“segmentation fault”是因為在測試時會生成在當前路徑下生成圖片，隨着batch的增加，內存爆掉。此部分代碼可以注釋，並不影響正常時間的測量。

注釋部分代碼如下圖。

main.cpp 路徑：/opt/intel/openvino_2019.1.0394/deployment_tools/inference_engine/samples/mask_rcnn_demo

此處開始注釋

此處結束注釋

文件修改后重新進行編譯

run ./build_samples.sh

PART IV: OpenVINO for DeepLabV3+

Reference：https://github.com/FionaZZ92/OpenVINO/tree/master/DeeplabV3%2B_MobileNetV2

Tensorflow運行指令(wwen.sh)：

echo 1 > /proc/sys/vm/compact_memory

echo 3 > /proc/sys/vm/drop_caches

echo 100 > /sys/devices/system/cpu/intel_pstate/min_perf_pct

echo 0 > /sys/devices/system/cpu/intel_pstate/no_turbo

echo 0 > /proc/sys/kernel/numa_balancing

cpupower frequency-set -g performance

export KMP_BLOCKTIME=0

export KMP_SETTINGS=1

export KMP_AFFINITY=granularity=fine,compact,1,0

export OMP_NUM_THREADS=16

numactl --physcpubind=0-15,32-47 --membind=0 python3.6 demo_multi.py --input_folder ./img --output_folder ./output --logdir ./model > node_2_1.log 2>&1 &

numactl --physcpubind=16-31,48-63 --membind=1 python3.6 demo_multi.py --input_folder ./img --output_folder ./output --logdir ./model > node_2_2.log 2>&1 &

Step1: IR文件的生成

python3.6 mo_tf.py --input_model /home/gsj/deeplab/research/deeplab/model/frozen_inference_graph.pb --data_type FP32 --output_dir /home/gsj/super-resolution/tf_estimator_barebone/models/ --input 0:xception_65/Pad --output aspp0/Relu,aspp1_pointwise/Relu,aspp2_pointwise/Relu,aspp3_pointwise/Relu,ResizeBilinear_1 --input_shape [1,1953,2593,3](根據下一步inference過程，預處理圖片對應輸出尺寸進行調整，如下圖)

Step2: Inference(infer_IE_TF.py位於intel64/Release下)

echo 1 > /proc/sys/vm/compact_memory

echo 3 > /proc/sys/vm/drop_caches

echo 100 > /sys/devices/system/cpu/intel_pstate/min_perf_pct

echo 0 > /sys/devices/system/cpu/intel_pstate/no_turbo

echo 0 > /proc/sys/kernel/numa_balancing

cpupower frequency-set -g performance

export KMP_BLOCKTIME=0

export KMP_SETTINGS=1

export KMP_AFFINITY=granularity=fine,compact,1,0

export OMP_NUM_THREADS=16

python3.6 infer_IE_TF.py -m /home/gsj/super-resolution/tf_estimator_barebone/models/frozen_inference_graph.xml -i 1.jpg -l lib/libcpu_extension .so

#infer_IE_TF.py代碼
#author:fourmi_gsj
from __future__ import print_function
import sys
import os 
from argparse import ArgumentParser
import numpy as np
import cv2
import time 

import tensorflow as tf
from tensorflow.python.platform import gfile

from openvino.inference_engine import IENetwork,IEPlugin



def build_argparser():
    parser = ArgumentParser()
    parser.add_argument("-m", "--model" ,help="Path to an .xml file with a trained model.",required=True,type=str)
    parser.add_argument("-i", "--input", help="Path to a folder with images or path to an image files", required=True,
                        type=str)
    parser.add_argument("-l", "--cpu_extension",
                        help="MKLDNN (CPU)-targeted custom layers.Absolute path to a shared library with the kernels "
                             "impl.", type=str, default=None)
    parser.add_argument("-pp", "--plugin_dir", help="Path to a plugin folder", type=str, default=None)
    parser.add_argument("-d", "--device",
                        help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample "
                             "will look for a suitable plugin for device specified (CPU by default)", default="CPU",
                        type=str)
    parser.add_argument("-nt", "--number_top", help="Number of top results", default=10, type=int)
    parser.add_argument("-pc", "--performance", help="Enables per-layer performance report", action='store_true')

    return parser


def resoze_for_concat(a0,a1,a2,a3,RB):
    iimg_ir = []
    '''
    print('a0:',a0.shape)
    print('a1:',a1.shape)
    print('a2:',a2.shape)
    print('a3:',a3.shape)
    print('RB:',RB.shape)
    resize_aspp0 =np.float32(np.zeros((1,256,123,163)))
    resize_ResizeBilinear_1=np.float32(np.zeros((1,256,123,163)))
    resize_aspp1 =np.float32(np.zeros((1,256,123,163)))
    resize_aspp2 =np.float32(np.zeros((1,256,123,163)))
    resize_aspp3 =np.float32(np.zeros((1,256,123,163)))
    for i in range(256):
        resize_aspp0[0,i]=cv2.resize(a0[0,i],(163,123), interpolation=cv2.INTER_LINEAR)
        resize_ResizeBilinear_1[0,i]=cv2.resize(RB[0,i],(163,123), interpolation=cv2.INTER_LINEAR)
        resize_aspp1[0,i]=cv2.resize(a1[0,i],(163,123), interpolation=cv2.INTER_LINEAR)
        resize_aspp2[0,i]=cv2.resize(a2[0,i],(163,123), interpolation=cv2.INTER_LINEAR)
        resize_aspp3[0,i]=cv2.resize(a3[0,i],(163,123), interpolation=cv2.INTER_LINEAR)
    ResizeBilinear_1=resize_ResizeBilinear_1.transpose((0,2,3,1))
    aspp0=resize_aspp0.transpose((0,2,3,1))
    aspp1=resize_aspp1.transpose((0,2,3,1))
    aspp2=resize_aspp2.transpose((0,2,3,1))
    aspp3=resize_aspp3.transpose((0,2,3,1))
    '''
    ResizeBilinear_1=RB.transpose((0,2,3,1))
    aspp0=a0.transpose((0,2,3,1))
    aspp1=a1.transpose((0,2,3,1))
    aspp2=a2.transpose((0,2,3,1))
    aspp3=a3.transpose((0,2,3,1))
    '''
    print(aspp0.shape)
    print(aspp1.shape)
    print(aspp2.shape)
    print(aspp3.shape)
    print(ResizeBilinear_1.shape)
    '''
    iimg_ir.append(ResizeBilinear_1)
    iimg_ir.append(aspp0)
    iimg_ir.append(aspp1)
    iimg_ir.append(aspp2)
    iimg_ir.append(aspp3)
    return iimg_ir



class _model_preprocess():
    def __init__(self):
        graph = tf.Graph()
        f_handle = gfile.FastGFile("/home/gsj/deeplab/research/deeplab/model/frozen_inference_graph.pb",'rb')
        graph_def = tf.GraphDef.FromString(f_handle.read())
        with graph.as_default():
            tf.import_graph_def(graph_def,name='')
        self.sess = tf.Session(graph=graph)

    def _pre_process(self,image):
        seg_map = self.sess.run('sub_7:0',feed_dict={'ImageTensor:0':[image]})
        #print('The shape of the seg_map is :',seg_map.shape)
        return seg_map

class _model_postprocess():
    def __init__(self):
        graph = tf.Graph()
        f_handle = gfile.FastGFile("/home/gsj/deeplab/research/deeplab/model/frozen_inference_graph.pb",'rb')
        graph_def = tf.GraphDef.FromString(f_handle.read())
        
        with graph.as_default():            
            new_input0=tf.placeholder(tf.float32,shape=(1,123,163,256),name='new_input0')
            new_input1=tf.placeholder(tf.float32,shape=(1,123,163,256),name='new_input1')
            new_input2=tf.placeholder(tf.float32,shape=(1,123,163,256),name='new_input2')
            new_input3=tf.placeholder(tf.float32,shape=(1,123,163,256),name='new_input3')
            new_input4=tf.placeholder(tf.float32,shape=(1,123,163,256),name='new_input4')

            tf.import_graph_def(graph_def,input_map={'ResizeBilinear_1:0':new_input0,'aspp0/Relu:0':new_input1,'aspp1_pointwise/Relu:0':new_input2,'aspp2_pointwise/Relu:0':new_input3,'aspp3_pointwise/Relu:0':new_input4},name='')
            
        self.sess = tf.Session(graph=graph)
    def _post_process(self,image_ir,image):
        seg_map = self.sess.run('SemanticPredictions:0', feed_dict={'ImageTensor:0': [image], 'new_input0:0': image_ir[0],
                                    'new_input1:0': image_ir[1],'new_input2:0': image_ir[2],'new_input3:0': image_ir[3],
                                    'new_input4:0': image_ir[4]})
        return seg_map


_pre = _model_preprocess()
_post = _model_postprocess()



def main_IE_infer():
    args = build_argparser().parse_args()
    model_xml = args.model
    model_bin = os.path.splitext(model_xml)[0] + ".bin"
    image = cv2.imread(args.input)
    print("The size of the orig image is:",image.shape[0],image.shape[1])
    
    h_input_size=1360 #the height of the output
    w_input_size=1020 #the width of the output
    
    h_ratio = 1.0 * h_input_size / image.shape[0] 
    w_ratio = 1.0 * w_input_size / image.shape[1] 
    shrink_size = (int(w_ratio * image.shape[1]),int(h_ratio*image.shape[0]))
    image = cv2.resize(image,shrink_size, interpolation=cv2.INTER_LINEAR)
    print("The shape of the resized Image is:",image.shape)
        
    # Plugin initialization for specified device and load extensions library if specified
    plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
    if args.cpu_extension and 'CPU' in args.device:
        plugin.add_cpu_extension(args.cpu_extension)
    if args.performance:
        plugin.set_config({"PERF_COUNT": "YES"})

    # Read IR
    net = IENetwork.from_ir(model=model_xml, weights=model_bin)
    #print("the output Info of the net is :",net.outputs)

    input_blob = next(iter(net.inputs))
    print('input_blob is :',input_blob)
    exec_net = plugin.load(network=net)

    img_ir = []      
    for itr in range(1):
        now = time.time()
        image_ = _pre._pre_process(image)
        image_ = image_.transpose((0,3,1,2))
        #print("the shape of the Front Net'output:",image_.shape)
        
        res =exec_net.infer(inputs={input_blob:image_})
        #print(res.keys())
        aspp0 = res['aspp0/Relu']
        aspp1 = res['aspp1_pointwise/Relu']
        aspp2 = res['aspp2_pointwise/Relu']
        aspp3 = res['aspp3_pointwise/Relu']
        ResizeBilinear_1=res['ResizeBilinear_1']

        img_ir = resoze_for_concat(aspp0,aspp1,aspp2,aspp3,ResizeBilinear_1)
        result = _post._post_process(img_ir,image)[0]
    print('time cost:',time.time()-now)
        #print(result)
    result[result!=0]=255
        
    cv2.imwrite('./result_deeplabv3.jpg', result)
    del net
    del exec_net
    del plugin
    
if __name__=='__main__':
    sys.exit(main_IE_infer() or 0)

PART V: OpenVINO for Super Resolution

Step 1: tensorflow進行測試

Github: https://github.com/ychfan/tf_estimator_barebone

運行inference程序，查找該模型的輸出節點，查找到的節點名稱為”clip_by_value”

指令執行路徑：/home/gsj/super-resolution/tf_estimator_barebone/

運行相關指令如下：

export KMP_BLOCKTIME=1

export KMP_AFFINITY=granularity=fine,compact,1,0

export OMP_NUM_THREADS=16

numactl -C 0-15,32-47 -m 0 python3.6 -m datasets.div2k --mode wdsr --model-dir /home/gsj/super-resolution/tf_estimator_barebone/models/ --input-dir /home/gsj/super-resolution/tf_estimator_barebone/data/DIV2K_valid_HR/ --output-dir ../output

Step 2:

將文件夾models下的模型相關文件(saved_model.pb, variabels文件夾)進行處理，freeze saved_model.pb文件，生成pruned_saved_model_or_whatever.pb文件

指令執行路徑：/home/gsj/super-resolution/tf_inference_demo/tensorflow-1.12.0

相關指令：

bazel-bin/tensorflow/python/tools/freeze_graph --in_graph=/home/gsj/super-resolution/tf_estimator_barebone/models/saved_model.pb --output_graph=/home/gsj/super-resolution/tf_estimator_barebone/models/pruned_saved_model_or_whatever.pb --input_saved_model_dir=/home/gsj/super-resolution/tf_estimator_barebone/models --input_checkpoint=/home/gsj/super-resolution/tf_estimator_barebone/models/variables --output_node_names="clip_by_value" --input_binary=true

Step3:

對生成的pruned_saved_model_or_whatever.pb文件進一步進行壓縮變換操作（將模型中的節點操作”Mul”進行常量值替換）生成transform.pb文件，執行時指定輸入inputs為”input_tensor”，即模型的輸入節點名稱。

指令執行路徑：/home/gsj/super-resolution/tf_inference_demo/tensorflow-1.12.0

相關指令：

bazel-bin/tensorflow/tools/graph_transforms/transform_graph --in_graph=/home/gsj/super-resolution/tf_estimator_barebone/models/pruned_saved_model_or_whatever.pb --out_graph=/home/gsj/super-resolution/tf_estimator_barebone/models/transform.pb --inputs=input_tensor --outputs=clip_by_value --transforms='fold_constants'

Step4:

生成IR文件(transform.bin,transform.xml,transform.mapping)

文件生成路徑：/home/gsj/super-resolution/tf_estimator_barebone/models

指令執行路徑: /opt/intel/computer_vision_sdk_2018.5.455/deployment_tools/model_optimizer

相關指令：(PS：input_shape的大小根據待測試的圖片大小確定)

python3.6 mo_tf.py --input_model /home/gsj/super-resolution/tf_estimator_barebone/models/transform.pb --input_shape [1,1080,1920,3] --data_type FP32 --output_dir /home/gsj/super-resolution/tf_estimator_barebone/models/ --scale_values [255.0] --input input_tensor

Step5:修改生成的transform.xml文件

修改兩處位置：將以下幾處黃色區域即輸入節點的名稱”input_tensor”更改為0

開頭處：

結尾處：

Step 6:執行測試

指令執行路徑：

/root/inference_engine_samples_build/intel64/Release

相關指令：

./super_resolution_demo -i /home/gsj/super-resolution/tf_estimator_barebone/va1/0896_1920_1080.png -m /home/gsj/super-resolution/tf_estimator_barebone/models/transform.xml

Step7:OPENVINO圖片生成位置

/root/inference_engine_samples_build/intel64/Release/sr_1.png

Step8:tensorflow圖片生成位置

/home/gsj/super-resolution/output

-----------------------------------------------我是華麗的分割線--------------------------------------------------------

原生tensorflow測試

Tensorflow 下載地址：https://pypi.org/project/tensorflow/1.12.0/#files

參考前面step1

優化tensorflow測試

卸載舊版本tensorflow
在當前路徑執行：

export PATH=/home/build_gcc72/bin:$PATH

export LD_LIBRARY_PATH=/home/build_gcc72/lib64:$LD_LIBRARY_PATH

查看是否為優化的tensorflow, 執行

python3.6 -c "import tensorflow; print(tensorflow.pywrap_tensorflow.IsMklEnabled())"

返回true則代表加載MKL

2. 同step1進行測試

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