README.md

UNet

This document has instructions for how to run UNet for the following modes/precisions:

• FP32 inference

FP32 Inference Instructions

1. Clone this intelai/models repository:

   $ git clone git@github.com:IntelAI/models.git
   

   This repository includes launch scripts for running Unet.

2. Download and extract the pretrained model:

   $ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/unet_fp32_pretrained_model.tar.gz
   $ tar -xvf unet_fp32_pretrained_model.tar.gz
   

3. Clone the tf_unet repository, and then get PR #202 to get cpu optimizations:

   $ git clone git@github.com:jakeret/tf_unet.git
   
   $ cd tf_unet/
   
   $ git fetch origin pull/202/head:cpu_optimized
   From github.com:jakeret/tf_unet
    * [new ref]         refs/pull/202/head -> cpu_optimized
   
   $ git checkout cpu_optimized
   Switched to branch 'cpu_optimized'
   

4. Navigate to the benchmarks directory in your local clone of the intelai/models repo from step 1. The launch_benchmark.py script in the benchmarks directory is used for starting a model run in a optimized TensorFlow docker container. It has arguments to specify which model, framework, mode, precision, and docker image to use, along with the checkpoint files that were downloaded in step 2 and the path to the UNet model repository that you cloned in step 3.

   UNet can be run to test batch and online inference using the following command with your checkpoint and model-source-dir paths:

   python launch_benchmark.py \
       --model-name unet \
       --precision fp32 \
       --mode inference \
       --framework tensorflow \
       --benchmark-only \
       --batch-size 1 \
       --socket-id 0 \
       --docker-image gcr.io/deeplearning-platform-release/tf-cpu.1-14 \
       --checkpoint /home/<user>/unet_trained \
       --model-source-dir /home/<user>/tf_unet \
       -- checkpoint_name=model.cpkt
   

   Note that the --verbose or --output-dir flag can be added to the above command to get additional debug output or change the default output location.

5. The log file is saved to the value of --output-dir.

   Below is an example of what the log file tail:

   Time spent per BATCH: 1.1043 ms
   Total samples/sec: 905.5344 samples/s
   Ran inference with batch size 1
   Log location outside container: {--output-dir value}/benchmark_unet_inference_fp32_20190201_205601.log