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#!/usr/bin/python

from __future__ import division

import os

from random import randint

import numpy as np

try:

from PIL import Image

except ImportError as err:

raise ImportError("""ERROR: Failed to import module ({})

Please make sure you have Pillow installed.

For installation instructions, see:

http://pillow.readthedocs.io/en/stable/installation.html""".format(err))

try:

import pycuda.driver as cuda

import pycuda.gpuarray as gpuarray

import pycuda.autoinit

import argparse

except ImportError as err:

raise ImportError("""ERROR: Failed to import module ({})

Please make sure you have pycuda and the example dependencies installed.

https://wiki.tiker.net/PyCuda/Installation/Linux

pip(3) install tensorrt[examples]""".format(err))

try:

import tensorrt as trt

from tensorrt import parsers

except ImportError as err:

raise ImportError("""ERROR: Failed to import module ({})

Please make sure you have the TensorRT Library installed

and accessible in your LD_LIBRARY_PATH""".format(err))

class Profiler(trt.infer.Profiler):

print("Time over all layers: {:4.3f}".format(totalTime / self.timing_iterations))

BATCH_SIZE = 4

TIMING_INTERATIONS = 1000

G_PROFILER = Profiler(TIMING_INTERATIONS)

G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.INFO)

INPUT_LAYERS = ["image"]

OUTPUT_LAYERS = ["net_output"]

OUTPUT_SIZE = 57

PARSER = argparse.ArgumentParser(description="Example of how to create a Caffe based TensorRT Engine and profile inference")

PARSER.add_argument('datadir', help='Path to Python TensorRT data directory (realpath)')

DATA_DIR = PARSER.parse_args().datadir

MODEL_PROTOTXT = DATA_DIR + "pose_deploy.prototxt"

CAFFEMODEL = DATA_DIR + "pose_iter_584000.caffemodel"

# INPUT_LAYERS = ["data"]

# OUTPUT_LAYERS = ["prob"]

# MODEL_PROTOTXT = DATA_DIR + "googlenet.prototxt"

# CAFFEMODEL = DATA_DIR + "googlenet.caffemodel"

#Run inference on device

def time_inference(engine, batch_size):

return

def main():

return

if __name__ == "__main__":

main()