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inference.py
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#!/usr/bin/env python3
"""
Copyright (c) 2018 Intel Corporation.
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
import os
import sys
import logging as log
import numpy as np
from openvino.inference_engine import IENetwork, IECore
class Network:
"""
Load and configure inference plugins for the specified target devices
and performs synchronous and asynchronous modes for the specified infer requests.
"""
def __init__(self):
### Initialize any class variables desired ###
self.plugin = None
self.network = None
self.input_blob = None
self.input_blob_name = None
self.output_blob = None
self.exec_network = None
self.infer_request = None
self.output_name = None
self.output_info = None
self.feed_dict = None
def load_model(self, model, device="CPU", cpu_extension=None):
### Load the model ###
model_xml = model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
### init inference engine plugin
self.plugin = IECore()
### Add any necessary extensions ###
log.info("attempting to add CPU extension!")
log.info(cpu_extension)
if cpu_extension and "CPU" in device:
plugin_dir = '/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64'
self.plugin.add_extension(cpu_extension, device)
#plugin = IEPlugin(device=‘CPU', plugin_dirs=plugin_dir)
#cpu_extension_path = '/home/temp/intel/openvino/deployment_tools/inference_engine/lib/intel64/libMKLDNNPlugin.so'
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
self.network = IENetwork(model=model_xml, weights=model_bin)
### Check for supported layers ###
if "CPU" in device:
supported_layers = self.plugin.query_network(self.network, "CPU")
not_supported_layers = [l for l in self.network.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(args.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
versions = self.plugin.get_versions(device)
log.info("{}{}".format(" "*8, device))
log.info("{}MKLDNNPlugin version ......... {}.{}".format(" "*8, versions[device].major, versions[device].minor))
log.info("{}Build ........... {}".format(" "*8, versions[device].build_number))
### Return the loaded inference plugin ###
self.network = IENetwork(model=model_xml, weights=model_bin)
self.exec_network = self.plugin.load_network(self.network, device)
## grab the input layer ##
self.input_blob = self.network.inputs
##next(iter(self.network.inputs))
img_info_input_blob = None
self.feed_dict = {}
for blob_name in self.network.inputs:
log.info("blob_name: " + blob_name)
if len(self.network.inputs[blob_name].shape) == 4:
self.input_blob_name = blob_name
elif len(self.network.inputs[blob_name].shape) == 2:
img_info_input_blob = blob_name
else:
raise RuntimeError("Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported"
.format(len(net.inputs[blob_name].shape), blob_name))
self.output_blob = next(iter(self.network.outputs))
if img_info_input_blob:
log.info("set input info")
n, c, h, w = self.get_input_shape()
self.feed_dict[img_info_input_blob] = [h, w, 1]
self.output_name, self.output_info = "", self.network.outputs[next(iter(self.network.outputs.keys()))]
for output_key in self.network.outputs:
if self.network.layers[output_key].type == "DetectionOutput":
self.output_name, self.output_info = output_key, self.network.outputs[output_key]
if self.output_name == "":
log.error("Can't find a DetectionOutput layer in the topology")
log.info("load model done")
### Note: You may need to update the function parameters. ###
log.info('Preparing output blobs')
output_name, output_info = "", self.network.outputs[next(iter(self.network.outputs.keys()))]
for output_key in self.network.outputs:
log.info("iteration: %s", output_key)
if self.network.layers[output_key].type == "DetectionOutput":
output_name, output_info = output_key, self.network.outputs[output_key]
if output_name == "":
log.error("Can't find a DetectionOutput layer in the topology")
output_dims = output_info.shape
if len(output_dims) != 4:
log.error("Incorrect output dimensions for SSD model")
max_proposal_count, object_size = output_dims[2], output_dims[3]
log.info("Max Proposal Count is: %s", max_proposal_count)
log.info("Output Object Size: %s, %s", output_dims[2], output_dims[3])
if object_size != 7:
log.error("Output item should have 7 as a last dimension")
output_info.precision = "FP32"
return
def get_input_blob(self):
##return self.network.inputs[self.input_blob]
##input_blob = None
##return input_blob
##self.network.inputs[input_blob].shape
return self.input_blob
def get_input_blob_name(self):
##return self.network.inputs[self.input_blob]
##input_blob = None
##return input_blob
##self.network.inputs[input_blob].shape
return self.input_blob_name
def get_input_shape(self):
### Return the shape of the input layer ###
return self.network.inputs[self.input_blob_name].shape
def get_output_name(self):
return self.output_name
def get_output_info(self):
return self.output_info
def exec_net(self, image):
### Start an asynchronous request ###
### Return any necessary information ###
##log.info("Start Async Inference")
self.input_blob[self.input_blob_name] = image
self.feed_dict[self.input_blob_name] = image
self.exec_network.start_async(request_id=0, inputs=self.feed_dict)
return
def wait(self):
### Wait for the request to be complete. ###
### Return any necessary information ###
### Note: You may need to update the function parameters. ###
status = self.exec_network.requests[0].wait(-1)
return status
def get_output(self):
### Extract and return the output results
### Note: You may need to update the function parameters. ###
res = self.exec_network.requests[0].outputs[self.output_blob]
return res