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Ascend/ModelZoo-TensorFlow

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README

InceptionV2 Inference for Tensorflow

This repository provides a script and recipe to Inference of the InceptionV2 model.

Notice

This sample only provides reference for you to learn the Ascend software stack and is not for commercial purposes.

Before starting, please pay attention to the following adaptation conditions. If they do not match, may leading in failure.

Conditions Need
CANN Version >=5.0.3
Chip Platform Ascend310/Ascend310P3
3rd Party Requirements Please follow the 'requirements.txt'

Quick Start Guide

1. Clone the respository

git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL

2. Download and preprocess the dataset

  1. Download the ImageNet2012 Validation dataset by yourself. You can get the validation pictures(50000 JPEGS and a ILSVRC2012val-label-index.txt)

  2. Put JPEGS to 'scripts/ILSVRC2012val' and label text to 'scripts/'

  3. Images Preprocess:

cd scripts
mkdir input_bins
python3 inception_preprocessing.py ./ILSVRC2012val/ ./input_bins/

The jpegs pictures will be preprocessed to bin fils.

3. Offline Inference

Convert pb to om.

  • configure the env

    export install_path=/usr/local/Ascend
    export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
    export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
    export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
    export ASCEND_OPP_PATH=${install_path}/opp
    
  • convert pb to om

    pb download link

    atc --model=inceptionv2_tf.pb --framework=3 --output=inceptionv2_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="input:1,224,224,3" --insert_op_conf=inceptionv2_aipp.cfg --enable_small_channel=1 --log=info
    
  • Build the program

    bash build.sh
    
  • Run the program:

    cd scripts
    bash benchmark_tf.sh
    

Performance

Result

Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.

Inference accuracy results

model data Top1/Top5
offline Inference 50000 images 74.0 %/ 91.8%
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