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oeqa/runtime/cases/dldt: Enable inference engine and model optimizer tests
Add sanity tests for inference engine:
- test inference engine c/cpp shared library
- test inference engine python api
- test inference engine cpu, gpu, myriad plugin
Add sanity tests for model optimizer
- test model optmizer can generate ir
Licenses:
- classification_sample.py
license: Apache 2.0
source: <install_root>/deployment_tools/inference_engine/samples/*
Signed-off-by: Yeoh Ee Peng <ee.peng.yeoh@intel.com>
Signed-off-by: Anuj Mittal <anuj.mittal@intel.com>
This commit is contained in:
Yeoh Ee Peng
Anuj Mittal
parent
f39ad91524
commit
1e514838df
101
lib/oeqa/runtime/cases/dldt_inference_engine.py
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101
lib/oeqa/runtime/cases/dldt_inference_engine.py
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from oeqa.runtime.case import OERuntimeTestCase
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from oeqa.runtime.decorator.package import OEHasPackage
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from oeqa.core.decorator.depends import OETestDepends
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from oeqa.runtime.miutils.targets.oeqatarget import OEQATarget
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from oeqa.runtime.miutils.tests.squeezenet_model_download_test import SqueezenetModelDownloadTest
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from oeqa.runtime.miutils.tests.dldt_model_optimizer_test import DldtModelOptimizerTest
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from oeqa.runtime.miutils.tests.dldt_inference_engine_test import DldtInferenceEngineTest
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from oeqa.runtime.miutils.dldtutils import get_testdata_config
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class DldtInferenceEngine(OERuntimeTestCase):
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@classmethod
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def setUpClass(cls):
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cls.sqn_download = SqueezenetModelDownloadTest(OEQATarget(cls.tc.target), '/tmp/ie/md')
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cls.sqn_download.setup()
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cls.dldt_mo = DldtModelOptimizerTest(OEQATarget(cls.tc.target), '/tmp/ie/ir')
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cls.dldt_mo.setup()
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cls.dldt_ie = DldtInferenceEngineTest(OEQATarget(cls.tc.target), '/tmp/ie/inputs')
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cls.dldt_ie.setup()
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cls.ir_files_dir = cls.dldt_mo.work_dir
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@classmethod
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def tearDownClass(cls):
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cls.dldt_ie.tear_down()
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cls.dldt_mo.tear_down()
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cls.sqn_download.tear_down()
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@OEHasPackage(['dldt-model-optimizer'])
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@OEHasPackage(['wget'])
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def test_dldt_ie_can_create_ir_and_download_input(self):
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proxy_port = get_testdata_config(self.tc.td, 'DLDT_PIP_PROXY')
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if not proxy_port:
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self.skipTest('Need to configure bitbake configuration (DLDT_PIP_PROXY="proxy.server:port").')
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(status, output) = self.sqn_download.test_can_download_squeezenet_model(proxy_port)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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(status, output) = self.sqn_download.test_can_download_squeezenet_prototxt(proxy_port)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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mo_exe_dir = get_testdata_config(self.tc.td, 'DLDT_MO_EXE_DIR')
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if not mo_exe_dir:
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self.skipTest('Need to configure bitbake configuration (DLDT_MO_EXE_DIR="directory_to_mo.py").')
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mo_files_dir = self.sqn_download.work_dir
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(status, output) = self.dldt_mo.test_dldt_mo_can_create_ir(mo_exe_dir, mo_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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(status, output) = self.dldt_ie.test_can_download_input_file(proxy_port)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-samples'])
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def test_dldt_ie_classification_with_cpu(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_with_device('CPU', self.ir_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-samples'])
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@OEHasPackage(['intel-compute-runtime'])
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@OEHasPackage(['opencl-icd-loader'])
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def test_dldt_ie_classification_with_gpu(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_with_device('GPU', self.ir_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-samples'])
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@OEHasPackage(['dldt-inference-engine-vpu-firmware'])
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def test_dldt_ie_classification_with_myriad(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_with_device('MYRIAD', self.ir_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-python3'])
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@OEHasPackage(['python3-opencv'])
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@OEHasPackage(['python3-numpy'])
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def test_dldt_ie_classification_python_api_with_cpu(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device('CPU', self.ir_files_dir, 'libcpu_extension.so')
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-python3'])
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@OEHasPackage(['intel-compute-runtime'])
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@OEHasPackage(['opencl-icd-loader'])
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@OEHasPackage(['python3-opencv'])
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@OEHasPackage(['python3-numpy'])
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def test_dldt_ie_classification_python_api_with_gpu(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device('GPU', self.ir_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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@OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
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@OEHasPackage(['dldt-inference-engine'])
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@OEHasPackage(['dldt-inference-engine-python3'])
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@OEHasPackage(['dldt-inference-engine-vpu-firmware'])
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@OEHasPackage(['python3-opencv'])
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@OEHasPackage(['python3-numpy'])
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def test_dldt_ie_classification_python_api_with_myriad(self):
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(status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device('MYRIAD', self.ir_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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38
lib/oeqa/runtime/cases/dldt_model_optimizer.py
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38
lib/oeqa/runtime/cases/dldt_model_optimizer.py
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@ -0,0 +1,38 @@
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from oeqa.runtime.case import OERuntimeTestCase
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from oeqa.runtime.decorator.package import OEHasPackage
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from oeqa.runtime.miutils.targets.oeqatarget import OEQATarget
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from oeqa.runtime.miutils.tests.squeezenet_model_download_test import SqueezenetModelDownloadTest
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from oeqa.runtime.miutils.tests.dldt_model_optimizer_test import DldtModelOptimizerTest
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from oeqa.runtime.miutils.dldtutils import get_testdata_config
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class DldtModelOptimizer(OERuntimeTestCase):
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@classmethod
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def setUpClass(cls):
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cls.sqn_download = SqueezenetModelDownloadTest(OEQATarget(cls.tc.target), '/tmp/mo/md')
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cls.sqn_download.setup()
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cls.dldt_mo = DldtModelOptimizerTest(OEQATarget(cls.tc.target), '/tmp/mo/ir')
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cls.dldt_mo.setup()
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@classmethod
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def tearDownClass(cls):
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cls.dldt_mo.tear_down()
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cls.sqn_download.tear_down()
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@OEHasPackage(['dldt-model-optimizer'])
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@OEHasPackage(['wget'])
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def test_dldt_mo_can_create_ir(self):
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proxy_port = get_testdata_config(self.tc.td, 'DLDT_PIP_PROXY')
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if not proxy_port:
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self.skipTest('Need to configure bitbake configuration (DLDT_PIP_PROXY="proxy.server:port").')
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(status, output) = self.sqn_download.test_can_download_squeezenet_model(proxy_port)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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(status, output) = self.sqn_download.test_can_download_squeezenet_prototxt(proxy_port)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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mo_exe_dir = get_testdata_config(self.tc.td, 'DLDT_MO_EXE_DIR')
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if not mo_exe_dir:
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self.skipTest('Need to configure bitbake configuration (DLDT_MO_EXE_DIR="directory_to_mo.py").')
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mo_files_dir = self.sqn_download.work_dir
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(status, output) = self.dldt_mo.test_dldt_mo_can_create_ir(mo_exe_dir, mo_files_dir)
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self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
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#!/usr/bin/env python3
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"""
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Copyright (C) 2018-2019 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from __future__ import print_function
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import sys
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import os
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from argparse import ArgumentParser, SUPPRESS
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import cv2
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import numpy as np
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import logging as log
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from time import time
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from openvino.inference_engine import IENetwork, IECore
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def build_argparser():
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parser = ArgumentParser(add_help=False)
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args = parser.add_argument_group('Options')
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args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
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args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True,
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type=str)
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args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files",
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required=True,
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type=str, nargs="+")
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args.add_argument("-l", "--cpu_extension",
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help="Optional. Required for CPU custom layers. "
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"MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the"
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" kernels implementations.", type=str, default=None)
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args.add_argument("-d", "--device",
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help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: is "
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"acceptable. The sample will look for a suitable plugin for device specified. Default "
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"value is CPU",
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default="CPU", type=str)
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args.add_argument("--labels", help="Optional. Path to a labels mapping file", default=None, type=str)
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args.add_argument("-nt", "--number_top", help="Optional. Number of top results", default=10, type=int)
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return parser
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def main():
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log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
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args = build_argparser().parse_args()
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model_xml = args.model
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model_bin = os.path.splitext(model_xml)[0] + ".bin"
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# Plugin initialization for specified device and load extensions library if specified
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log.info("Creating Inference Engine")
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ie = IECore()
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if args.cpu_extension and 'CPU' in args.device:
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ie.add_extension(args.cpu_extension, "CPU")
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# Read IR
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log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
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net = IENetwork(model=model_xml, weights=model_bin)
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if "CPU" in args.device:
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supported_layers = ie.query_network(net, "CPU")
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not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
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if len(not_supported_layers) != 0:
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log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
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format(args.device, ', '.join(not_supported_layers)))
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log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
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"or --cpu_extension command line argument")
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sys.exit(1)
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assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
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assert len(net.outputs) == 1, "Sample supports only single output topologies"
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log.info("Preparing input blobs")
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input_blob = next(iter(net.inputs))
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out_blob = next(iter(net.outputs))
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net.batch_size = len(args.input)
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# Read and pre-process input images
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n, c, h, w = net.inputs[input_blob].shape
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images = np.ndarray(shape=(n, c, h, w))
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for i in range(n):
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image = cv2.imread(args.input[i])
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if image.shape[:-1] != (h, w):
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log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w)))
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image = cv2.resize(image, (w, h))
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image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
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images[i] = image
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log.info("Batch size is {}".format(n))
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# Loading model to the plugin
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log.info("Loading model to the plugin")
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exec_net = ie.load_network(network=net, device_name=args.device)
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# Start sync inference
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log.info("Starting inference in synchronous mode")
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res = exec_net.infer(inputs={input_blob: images})
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# Processing output blob
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log.info("Processing output blob")
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res = res[out_blob]
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log.info("Top {} results: ".format(args.number_top))
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if args.labels:
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with open(args.labels, 'r') as f:
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labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f]
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else:
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labels_map = None
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classid_str = "classid"
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probability_str = "probability"
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for i, probs in enumerate(res):
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probs = np.squeeze(probs)
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top_ind = np.argsort(probs)[-args.number_top:][::-1]
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print("Image {}\n".format(args.input[i]))
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print(classid_str, probability_str)
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print("{} {}".format('-' * len(classid_str), '-' * len(probability_str)))
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for id in top_ind:
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det_label = labels_map[id] if labels_map else "{}".format(id)
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label_length = len(det_label)
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space_num_before = (len(classid_str) - label_length) // 2
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space_num_after = len(classid_str) - (space_num_before + label_length) + 2
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space_num_before_prob = (len(probability_str) - len(str(probs[id]))) // 2
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print("{}{}{}{}{:.7f}".format(' ' * space_num_before, det_label,
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' ' * space_num_after, ' ' * space_num_before_prob,
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probs[id]))
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print("\n")
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log.info("This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n")
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if __name__ == '__main__':
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sys.exit(main() or 0)
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3
lib/oeqa/runtime/miutils/dldtutils.py
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3
lib/oeqa/runtime/miutils/dldtutils.py
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@ -0,0 +1,3 @@
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def get_testdata_config(testdata, config):
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return testdata.get(config)
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11
lib/oeqa/runtime/miutils/targets/oeqatarget.py
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11
lib/oeqa/runtime/miutils/targets/oeqatarget.py
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@ -0,0 +1,11 @@
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class OEQATarget(object):
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def __init__(self, target):
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self.target = target
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def run(self, cmd):
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return self.target.run(cmd)
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def copy_to(self, source, destination_dir):
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self.target.copyTo(source, destination_dir)
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48
lib/oeqa/runtime/miutils/tests/dldt_inference_engine_test.py
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48
lib/oeqa/runtime/miutils/tests/dldt_inference_engine_test.py
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@ -0,0 +1,48 @@
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import os
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script_path = os.path.dirname(os.path.realpath(__file__))
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files_path = os.path.join(script_path, '../../files/')
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class DldtInferenceEngineTest(object):
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ie_input_files = {'ie_python_sample': 'classification_sample.py',
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'input': 'chicky_512.png',
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'input_download': 'https://raw.githubusercontent.com/opencv/opencv/master/samples/data/chicky_512.png',
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'model': 'squeezenet_v1.1.xml'}
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def __init__(self, target, work_dir):
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self.target = target
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self.work_dir = work_dir
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def setup(self):
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self.target.run('mkdir -p %s' % self.work_dir)
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self.target.copy_to(os.path.join(files_path, 'dldt-inference-engine', self.ie_input_files['ie_python_sample']),
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self.work_dir)
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def tear_down(self):
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self.target.run('rm -rf %s' % self.work_dir)
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def test_can_download_input_file(self, proxy_port):
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return self.target.run('cd %s; wget %s -e https_proxy=%s' %
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(self.work_dir,
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self.ie_input_files['input_download'],
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proxy_port))
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def test_dldt_ie_classification_with_device(self, device, ir_files_dir):
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return self.target.run('classification_sample_async -d %s -i %s -m %s' %
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(device,
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os.path.join(self.work_dir, self.ie_input_files['input']),
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os.path.join(ir_files_dir, self.ie_input_files['model'])))
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|
||||
def test_dldt_ie_classification_python_api_with_device(self, device, ir_files_dir, extension=''):
|
||||
if extension:
|
||||
return self.target.run('python3 %s -d %s -i %s -m %s -l %s' %
|
||||
(os.path.join(self.work_dir, self.ie_input_files['ie_python_sample']),
|
||||
device,
|
||||
os.path.join(self.work_dir, self.ie_input_files['input']),
|
||||
os.path.join(ir_files_dir, self.ie_input_files['model']),
|
||||
extension))
|
||||
else:
|
||||
return self.target.run('python3 %s -d %s -i %s -m %s' %
|
||||
(os.path.join(self.work_dir, self.ie_input_files['ie_python_sample']),
|
||||
device,
|
||||
os.path.join(self.work_dir, self.ie_input_files['input']),
|
||||
os.path.join(ir_files_dir, self.ie_input_files['model'])))
|
||||
23
lib/oeqa/runtime/miutils/tests/dldt_model_optimizer_test.py
Normal file
23
lib/oeqa/runtime/miutils/tests/dldt_model_optimizer_test.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
import os
|
||||
|
||||
class DldtModelOptimizerTest(object):
|
||||
mo_input_files = {'model': 'squeezenet_v1.1.caffemodel',
|
||||
'prototxt': 'deploy.prototxt'}
|
||||
mo_exe = 'mo.py'
|
||||
|
||||
def __init__(self, target, work_dir):
|
||||
self.target = target
|
||||
self.work_dir = work_dir
|
||||
|
||||
def setup(self):
|
||||
self.target.run('mkdir -p %s' % self.work_dir)
|
||||
|
||||
def tear_down(self):
|
||||
self.target.run('rm -rf %s' % self.work_dir)
|
||||
|
||||
def test_dldt_mo_can_create_ir(self, mo_exe_dir, mo_files_dir):
|
||||
return self.target.run('python3 %s --input_model %s --input_proto %s --output_dir %s --data_type FP16' %
|
||||
(os.path.join(mo_exe_dir, self.mo_exe),
|
||||
os.path.join(mo_files_dir, self.mo_input_files['model']),
|
||||
os.path.join(mo_files_dir, self.mo_input_files['prototxt']),
|
||||
self.work_dir))
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
class SqueezenetModelDownloadTest(object):
|
||||
download_files = {'squeezenet1.1.prototxt': 'https://raw.githubusercontent.com/DeepScale/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/deploy.prototxt',
|
||||
'squeezenet1.1.caffemodel': 'https://github.com/DeepScale/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel'}
|
||||
|
||||
def __init__(self, target, work_dir):
|
||||
self.target = target
|
||||
self.work_dir = work_dir
|
||||
|
||||
def setup(self):
|
||||
self.target.run('mkdir -p %s' % self.work_dir)
|
||||
|
||||
def tear_down(self):
|
||||
self.target.run('rm -rf %s' % self.work_dir)
|
||||
|
||||
def test_can_download_squeezenet_model(self, proxy_port):
|
||||
return self.target.run('cd %s; wget %s -e https_proxy=%s' %
|
||||
(self.work_dir,
|
||||
self.download_files['squeezenet1.1.caffemodel'],
|
||||
proxy_port))
|
||||
|
||||
def test_can_download_squeezenet_prototxt(self, proxy_port):
|
||||
return self.target.run('cd %s; wget %s -e https_proxy=%s' %
|
||||
(self.work_dir,
|
||||
self.download_files['squeezenet1.1.prototxt'],
|
||||
proxy_port))
|
||||
Loading…
Reference in New Issue
Block a user