Pull Command
docker pull intel/image-recognition:pytorch-1.5.0-rc3-resnet50-bfloat16-inference
Description
This document has instructions for running ResNet50* bfloat16 inference using Intel® Extension for PyTorch*.
Datasets
The ImageNet validation dataset is used when testing accuracy. The inference scripts use synthetic data, so no dataset is needed.
Download and extract the ImageNet2012 dataset from http://www.image-net.org/, then move validation images to labeled subfolders, using the valprep.sh shell script
The accuracy script looks for a folder named val, so after running the data prep script, your folder structure should look something like this:
imagenet
└── val
├── ILSVRC2012_img_val.tar
├── n01440764
│ ├── ILSVRC2012_val_00000293.JPEG
│ ├── ILSVRC2012_val_00002138.JPEG
│ ├── ILSVRC2012_val_00003014.JPEG
│ ├── ILSVRC2012_val_00006697.JPEG
│ └── ...
└── ...
The folder that contains the val directory should be set as the DATASET_DIR when running accuracy (for example: export DATASET_DIR=/home//imagenet).
Quick Start Scripts
| Script name | Description |
|---|---|
bf16_online_inference |
Runs online inference using synthetic data (batch_size=1). |
bf16_batch_inference |
Runs batch inference using synthetic data (batch_size=128). |
bf16_accuracy |
Measures the model accuracy (batch_size=128). |
Docker*
The model container includes the scripts and libraries needed to run ResNet50* bfloat16 inference.
To run the accuracy test, you will need mount a volume and set the DATASET_DIR environment variable to point to the prepped ImageNet validation dataset. The accuracy script also downloads the pretrained model at runtime, so provide proxy environment variables, if necessary.
DATASET_DIR=<path to the dataset folder>
docker run \
--env DATASET_DIR=${DATASET_DIR} \
--env http_proxy=${http_proxy} \
--env https_proxy=${https_proxy} \
--volume ${DATASET_DIR}:${DATASET_DIR} \
--privileged --init -t \
intel/image-recognition:pytorch-1.5.0-rc3-resnet50-bfloat16-inference \
/bin/bash quickstart/bf16_accuracy.sh
Synthetic data is used when running batch or online inference, so no dataset mount is needed.
docker run \
--privileged --init -t \
intel/image-recognition:pytorch-1.5.0-rc3-resnet50-bfloat16-inference \
/bin/bash quickstart/<script name>.sh
Documentation and Sources
Get Started
Docker* Repository
Main GitHub*
Readme
Release Notes
Get Started Guide
Code Sources
Dockerfile
Report Issue
License Agreement
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