Resnet50v1 Inference for Tensorflow

This repository provides a script and recipe to Inference of the Resnet50v1.5 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.

Quick Start Guide

1. Clone the respository

git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Resnet50v1_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 resnet50v1_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=resnet50v1_tf.pb --framework=3 --output=resnet50v1_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="input:1,224,224,3" --insert_op_conf=resnet50v1_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

Reference

[1] https://github.com/tensorflow/models/tree/master/research/slim