U-Net FP32 Inference TensorFlow* Model Package
Published: 10/23/2020
Last Updated: 06/15/2022
Download Command
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/unet-fp32-inference.tar.gz
Description
This document has instructionsto run a U-Net FP32 inference using Intel® Optimization for TensorFlow*.
Quick Start Scripts
Script name | Description |
---|---|
fp32_inference | Runs inference with a batch size of 1 using a pretrained model |
Bare Metal
To run on bare metal, the following prerequisites must be installed in your environment:
-
Python* 3
-
numactl
-
numpy==1.16.1
-
Pillow==5.3.0
-
matplotlib
-
click
-
Clone the tf_unet repository, and then get PR #276 to get cpu optimizations:
git clone https://github.com/jakeret/tf_unet.git
cd tf_unet/
git fetch origin pull/276/head:cpu_optimized
git checkout cpu_optimized
After installing the prerequisites, download and untar the model package. Set environment variables for the path to your TF_UNET_DIR
and an OUTPUT_DIR
where log files will be written, then run a quickstart script.
TF_UNET_DIR=<tensorflow-wavenet directory>
OUTPUT_DIR=<directory where log files will be written>
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/unet-fp32-inference.tar.gz
tar -xzf unet-fp32-inference.tar.gz
cd unet_trained
quickstart/fp32_inference.sh
Documentation and Sources
Get Started
Main GitHub*
Readme
Release Notes
Get Started Guide
Code Sources
Report Issue
License Agreement
LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.
Related Containers and Solutions
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.