This document has instructions for running ResNet50 v1.5 FP32 inference using Intel-optimized TensorFlow.
Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.
Set the DATASET_DIR
to point to the TF records directory when running ResNet50 v1.5.
Script name | Description |
---|---|
fp32_online_inference.sh |
Runs online inference (batch_size=1). |
fp32_batch_inference.sh |
Runs batch inference (batch_size=128). |
fp32_accuracy.sh |
Measures the model accuracy (batch_size=100). |
multi_instance_batch_inference.sh |
Uses numactl to run batch inference (batch_size=128) with one instance per socket for 1500 steps and 50 warmup steps. If no DATASET_DIR is set, synthetic data is used. Waits for all instances to complete, then prints a summarized throughput value. |
multi_instance_online_inference.sh |
Uses numactl to run online inference (batch_size=1) using four cores per instance for 1500 steps and 50 warmup steps. If no DATASET_DIR is set, synthetic data is used. Waits for all instances to complete, then prints a summarized throughput value. |
Setup your environment using the instructions below, depending on if you are using AI Kit:
Setup using AI Kit on Linux | Setup without AI Kit on Linux | Setup without AI Kit on Windows |
---|---|---|
To run using AI Kit on Linux you will need:
|
To run without AI Kit on Linux you will need:
|
To run without AI Kit on Windows you will need:
|
After finishing the setup above, download the pretrained model and set the
PRETRAINED_MODEL
environment var to the path to the frozen graph.
If you run on Windows, please use a browser to download the pretrained model using the link below.
For Linux, run:
wget https://zenodo.org/record/2535873/files/resnet50_v1.pb
export PRETRAINED_MODEL=$(pwd)/resnet50_v1.pb
Set environment variables for the path to your DATASET_DIR
for ImageNet and an OUTPUT_DIR
where log files will be written. Navigate to your model zoo directory and then
run a quickstart script on either Linux or Windows.
# cd to your model zoo directory
cd models
export PRETRAINED_MODEL=<path to the frozen graph downloaded above>
export DATASET_DIR=<path to the ImageNet TF records>
export OUTPUT_DIR=<directory where log files and checkpoints will be written>
./quickstart/image_recognition/tensorflow/resnet50v1_5/inference/cpu/fp32/<script name>.sh
Using cmd.exe
, run:
# cd to your model zoo directory
cd models
set PRETRAINED_MODEL=<path to the frozen graph downloaded above>
set DATASET_DIR=<path to the ImageNet TF records>
set OUTPUT_DIR=<directory where log files will be written>
bash quickstart\image_recognition\tensorflow\resnet50v1_5\inference\cpu\fp32\<script name>.sh
Note: You may use
cygpath
to convert the Windows paths to Unix paths before setting the environment variables. As an example, if the dataset location on Windows isD:\user\ImageNet
, convert the Windows path to Unix as shown:cygpath D:\user\ImageNet /d/user/ImageNet
Then, set the
DATASET_DIR
environment variableset DATASET_DIR=/d/user/ImageNet
.
- To run more advanced use cases, see the instructions here
for calling the
launch_benchmark.py
script directly. - To run the model using docker, please see the oneContainer
workload container:
https://software.intel.com/content/www/us/en/develop/articles/containers/resnet50v1-5-fp32-inference-tensorflow-container.html.