tonyreina

import numpy as np
import tensorflow as tf
from tensorflow.python.client import timeline
import json

def create_model():
    # Just a simple CNN
    # Alternatively, just
    # model = tf.keras.models.load_model("mymodel.hdf5")

tonyreina

## TO BUILD CONTAINER:
## Make sure you have downloaded the Linux version of OpenVINO: https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-linux
## Place the downloaded OpenVINO installer in the same directory as this Dockerfile.
## docker build -t 3d_unet_decathlon --build-arg HTTP_PROXY=${HTTP_PROXY} --build-arg HTTPS_PROXY=${HTTPS_PROXY} --build-arg NO_PROXY=${NO_PROXY} --build-arg http_proxy=${http_proxy} --build-arg https_proxy=${https_proxy} --build-arg no_proxy=${no_proxy} .
## TO RUN BUILT CONTAINER:
## For CPU - `docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp.X11-unix  -it 3d_unet_decathlon`
FROM ubuntu:16.04

ENV APP_DIR /app
ADD . ${APP_DIR}

tonyreina

There are several options here. For Keras, just simply load the Keras model and use sess = keras.get_session() to link the TF session variable to the Keras model (model.output and model.input are the output and input ops)

# Option 1: Saved TensorFlow model
tf.saved_model.simple_save(session, export_dir, inputs={"x":x, "y":y}, outputs={"z": z})
mo_tf.py -saved_model_dir export_dir

# Option 2: Saved TensorFlow checkpoint
saver = tf.train.Saver()
saver.save(sess, "./model.ckpt")

tonyreina

source /opt/intel/openvino/bin/setupvars.sh
alias downloader='python3 ${INTEL_OPENVINO_DIR}/deployment_tools/model_downloader/downloader.py'
alias mo='${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/mo.py'

tonyreina

FROM intelaipg/openvino-model-server

# Create this working directory in container
# All commands after this are relative to WORKDIR
WORKDIR /usr/openvino

# Copy everything from local machine directory into container
COPY . .

# Install h5py within container

tonyreina

import nibabel as nib
import numpy as np

data = np.ones((32, 32, 15, 100), dtype=np.int16) # dummy data in numpy matrix
img = nib.Nifti1Image(data, np.eye(4))  # Save axis for data (just identity)

img.header.get_xyzt_units()
img.to_filename(os.path.join('build','test4d.nii.gz'))  # Save as NiBabel file

tonyreina

#Prints FPS from from the logs emitted from tf_cnn_bench_6nets scripts.
#Usage: ./print_fps_tf_cnn_bench_6nets.sh 96
echo -e "\n Net BZ FPS \n"

num_cores=`grep -c ^processor /proc/cpuinfo`   # Get number of cores

for network in googlenet inception3 resnet50 resnet152 vgg16 ; do
  for bz in 1 32 64 96 128; do
    fps=$(grep  "total images/sec:"  net_${network}_bz_${bz}_numcores_${num_cores}.log | cut -d ":" -f2 | xargs)
    echo "$network $bz $fps"

tonyreina

#!/bin/bash
#sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'
date > start_benchmark.txt

# Get number of sockets (set interthread to num sockets)
inter=`grep -i "physical id" /proc/cpuinfo | sort -u | wc -l`
# Get number of physical cores (set intra to num physical cores)
num_cores=`grep -c ^processor /proc/cpuinfo`   # Get number of cores
for network in googlenet inception3 resnet50 resnet152 vgg16 ; do
  for bz in 1; do