This document has instructions for running UNet FP32 inference using Intel Optimized TensorFlow.
| Script name | Description |
|---|---|
| fp32_inference.sh | Runs inference with a batch size of 1 using a pretrained model |
Setup your environment using the instructions below, depending on if you are using AI Tools:
| Setup using AI Tools | Setup without AI Tools |
|---|---|
|
AI Tools does not currently support TF 1.15.2 models |
To run without AI Tools you will need:
|
Running UNet also requires a clone of the
tf_unet repository with PR #276
to get cpu optimizations. Set the TF_UNET_DIR env var to the path of your clone.
git clone https://github.com/jakeret/tf_unet.git
cd tf_unet/
git fetch origin pull/276/head:cpu_optimized
git checkout cpu_optimized
export TF_UNET_DIR=$(pwd)
cd ..
Download and extract the pretrained model and set the path to the
PRETRAINED_MODEL env var.
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/unet_fp32_pretrained_model.tar.gz
tar -xvf unet_fp32_pretrained_model.tar.gz
export PRETRAINED_MODEL=$(pwd)/unet_trained
After your environment is setup, set an environment variable to
an OUTPUT_DIR where log files will be written. Ensure that you already have
the TF_UNET_DIR and PRETRAINED_MODEL paths set from the previous commands.
Once the environment variables are all set, you can run a
quickstart script.
# cd to your AI Reference Models directory
cd models
export OUTPUT_DIR=<path to the directory where log files will be written>
export TF_UNET_DIR=<path to the TF UNet directory tf_unet>
export PRETRAINED_MODEL=<path to the pretrained model>
# For a custom batch size, set env var `BATCH_SIZE` or it will run with a default value.
export BATCH_SIZE=<customized batch size value>
./quickstart/image_segmentation/tensorflow/unet/inference/cpu/fp32/fp32_inference.sh
- To run more advanced use cases, see the instructions here
for calling the
launch_benchmark.pyscript directly. - To run the model using docker, please see the oneContainer
workload container:
https://www.intel.com/content/www/us/en/developer/articles/containers/unet-fp32-inference-tensorflow-container.html.