openvino-inference-engine : Remove openvino related recipes and tests

* Remove all openvino related recipes, tests and other data from meta-intel
  layer as a new layer (meta-oepnvino) specific to openvino has been created.

* Update openvino documentation

meta-openvino layer URL:
https://github.com/intel/meta-openvino

Signed-off-by: Yogesh Tyagi <yogesh.tyagi@intel.com>
Signed-off-by: Anuj Mittal <anuj.mittal@intel.com>

7 files changed, 0 insertions, 389 deletions

diff --git a/lib/oeqa/runtime/cases/dldt_inference_engine.py b/lib/oeqa/runtime/cases/dldt_inference_engine.py
deleted file mode 100644
index fb35d52f..00000000
--- a/lib/oeqa/runtime/cases/dldt_inference_engine.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from oeqa.runtime.case import OERuntimeTestCase
-from oeqa.runtime.decorator.package import OEHasPackage
-from oeqa.core.decorator.depends import OETestDepends
-from oeqa.runtime.miutils.targets.oeqatarget import OEQATarget
-from oeqa.runtime.miutils.tests.squeezenet_model_download_test import SqueezenetModelDownloadTest
-from oeqa.runtime.miutils.tests.dldt_model_optimizer_test import DldtModelOptimizerTest
-from oeqa.runtime.miutils.tests.dldt_inference_engine_test import DldtInferenceEngineTest
-from oeqa.runtime.miutils.dldtutils import get_testdata_config
-
-class DldtInferenceEngine(OERuntimeTestCase):
-
-    @classmethod
-    def setUpClass(cls):
-        cls.sqn_download = SqueezenetModelDownloadTest(OEQATarget(cls.tc.target), '/tmp/ie/md')
-        cls.sqn_download.setup()
-        cls.dldt_mo = DldtModelOptimizerTest(OEQATarget(cls.tc.target), '/tmp/ie/ir')
-        cls.dldt_mo.setup()
-        cls.dldt_ie = DldtInferenceEngineTest(OEQATarget(cls.tc.target), '/tmp/ie/inputs')
-        cls.dldt_ie.setup()
-        cls.ir_files_dir = cls.dldt_mo.work_dir
-
-    @classmethod
-    def tearDownClass(cls):
-        cls.dldt_ie.tear_down()
-        cls.dldt_mo.tear_down()
-        cls.sqn_download.tear_down()
-
-    @OEHasPackage(['dldt-model-optimizer'])
-    @OEHasPackage(['wget'])
-    def test_dldt_ie_can_create_ir_and_download_input(self):
-        proxy_port = get_testdata_config(self.tc.td, 'DLDT_PIP_PROXY')
-        if not proxy_port:
-            self.skipTest('Need to configure bitbake configuration (DLDT_PIP_PROXY="proxy.server:port").')
-        (status, output) = self.sqn_download.test_can_download_squeezenet_model(proxy_port)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-        (status, output) = self.sqn_download.test_can_download_squeezenet_prototxt(proxy_port)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-        mo_exe_dir = get_testdata_config(self.tc.td, 'DLDT_MO_EXE_DIR')
-        if not mo_exe_dir:
-            self.skipTest('Need to configure bitbake configuration (DLDT_MO_EXE_DIR="directory_to_mo.py").')
-        mo_files_dir = self.sqn_download.work_dir
-        (status, output) = self.dldt_mo.test_dldt_mo_can_create_ir(mo_exe_dir, mo_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-        (status, output) = self.dldt_ie.test_can_download_input_file(proxy_port)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-samples'])
-    def test_dldt_ie_classification_with_cpu(self):
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_with_device('CPU', self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-samples'])
-    @OEHasPackage(['intel-compute-runtime'])
-    @OEHasPackage(['ocl-icd'])
-    def test_dldt_ie_classification_with_gpu(self):
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_with_device('GPU', self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-samples'])
-    @OEHasPackage(['dldt-inference-engine-vpu-firmware'])
-    def test_dldt_ie_classification_with_myriad(self):
-        device = 'MYRIAD'
-        (status, output) = self.dldt_ie.test_check_if_openvino_device_available(device)
-        if not status:
-            self.skipTest('OpenVINO %s device not available on target machine(availalbe devices: %s)' % (device, output))
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_with_device(device, self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-python3'])
-    @OEHasPackage(['python3-opencv'])
-    @OEHasPackage(['python3-numpy'])
-    def test_dldt_ie_classification_python_api_with_cpu(self):
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device('CPU', self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-python3'])
-    @OEHasPackage(['intel-compute-runtime'])
-    @OEHasPackage(['ocl-icd'])
-    @OEHasPackage(['python3-opencv'])
-    @OEHasPackage(['python3-numpy'])
-    def test_dldt_ie_classification_python_api_with_gpu(self):
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device('GPU', self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-    @OETestDepends(['dldt_inference_engine.DldtInferenceEngine.test_dldt_ie_can_create_ir_and_download_input'])
-    @OEHasPackage(['dldt-inference-engine'])
-    @OEHasPackage(['dldt-inference-engine-python3'])
-    @OEHasPackage(['dldt-inference-engine-vpu-firmware'])
-    @OEHasPackage(['python3-opencv'])
-    @OEHasPackage(['python3-numpy'])
-    def test_dldt_ie_classification_python_api_with_myriad(self):
-        device = 'MYRIAD'
-        (status, output) = self.dldt_ie.test_check_if_openvino_device_available(device)
-        if not status:
-            self.skipTest('OpenVINO %s device not available on target machine(availalbe devices: %s)' % (device, output))
-        (status, output) = self.dldt_ie.test_dldt_ie_classification_python_api_with_device(device, self.ir_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
diff --git a/lib/oeqa/runtime/cases/dldt_model_optimizer.py b/lib/oeqa/runtime/cases/dldt_model_optimizer.py
deleted file mode 100644
index 736ea661..00000000
--- a/lib/oeqa/runtime/cases/dldt_model_optimizer.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from oeqa.runtime.case import OERuntimeTestCase
-from oeqa.runtime.decorator.package import OEHasPackage
-from oeqa.runtime.miutils.targets.oeqatarget import OEQATarget
-from oeqa.runtime.miutils.tests.squeezenet_model_download_test import SqueezenetModelDownloadTest
-from oeqa.runtime.miutils.tests.dldt_model_optimizer_test import DldtModelOptimizerTest
-from oeqa.runtime.miutils.dldtutils import get_testdata_config
-
-class DldtModelOptimizer(OERuntimeTestCase):
-
-    @classmethod
-    def setUpClass(cls):
-        cls.sqn_download = SqueezenetModelDownloadTest(OEQATarget(cls.tc.target), '/tmp/mo/md')
-        cls.sqn_download.setup()
-        cls.dldt_mo = DldtModelOptimizerTest(OEQATarget(cls.tc.target), '/tmp/mo/ir')
-        cls.dldt_mo.setup()
-
-    @classmethod
-    def tearDownClass(cls):
-        cls.dldt_mo.tear_down()
-        cls.sqn_download.tear_down()
-
-    @OEHasPackage(['dldt-model-optimizer'])
-    @OEHasPackage(['wget'])
-    def test_dldt_mo_can_create_ir(self):
-        proxy_port = get_testdata_config(self.tc.td, 'DLDT_PIP_PROXY')
-        if not proxy_port:
-            self.skipTest('Need to configure bitbake configuration (DLDT_PIP_PROXY="proxy.server:port").')
-        (status, output) = self.sqn_download.test_can_download_squeezenet_model(proxy_port)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-        (status, output) = self.sqn_download.test_can_download_squeezenet_prototxt(proxy_port)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
-
-        mo_exe_dir = get_testdata_config(self.tc.td, 'DLDT_MO_EXE_DIR')
-        if not mo_exe_dir:
-            self.skipTest('Need to configure bitbake configuration (DLDT_MO_EXE_DIR="directory_to_mo.py").')
-        mo_files_dir = self.sqn_download.work_dir
-        (status, output) = self.dldt_mo.test_dldt_mo_can_create_ir(mo_exe_dir, mo_files_dir)
-        self.assertEqual(status, 0, msg='status and output: %s and %s' % (status, output))
diff --git a/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py b/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py
deleted file mode 100644
index 1906e9fe..00000000
--- a/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-"""
- Copyright (C) 2018-2019 Intel Corporation
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
-      http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-"""
-from __future__ import print_function
-import sys
-import os
-from argparse import ArgumentParser, SUPPRESS
-import cv2
-import numpy as np
-import logging as log
-from time import time
-from openvino.inference_engine import IENetwork, IECore
-
-
-def build_argparser():
-    parser = ArgumentParser(add_help=False)
-    args = parser.add_argument_group('Options')
-    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
-    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True,
-                      type=str)
-    args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files",
-                      required=True,
-                      type=str, nargs="+")
-    args.add_argument("-l", "--cpu_extension",
-                      help="Optional. Required for CPU custom layers. "
-                           "MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the"
-                           " kernels implementations.", type=str, default=None)
-    args.add_argument("-d", "--device",
-                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: is "
-                           "acceptable. The sample will look for a suitable plugin for device specified. Default "
-                           "value is CPU",
-                      default="CPU", type=str)
-    args.add_argument("--labels", help="Optional. Path to a labels mapping file", default=None, type=str)
-    args.add_argument("-nt", "--number_top", help="Optional. Number of top results", default=10, type=int)
-
-    return parser
-
-
-def main():
-    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
-    args = build_argparser().parse_args()
-    model_xml = args.model
-    model_bin = os.path.splitext(model_xml)[0] + ".bin"
-
-    # Plugin initialization for specified device and load extensions library if specified
-    log.info("Creating Inference Engine")
-    ie = IECore()
-    if args.cpu_extension and 'CPU' in args.device:
-        ie.add_extension(args.cpu_extension, "CPU")
-    # Read IR
-    log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
-    net = IENetwork(model=model_xml, weights=model_bin)
-
-    if "CPU" in args.device:
-        supported_layers = ie.query_network(net, "CPU")
-        not_supported_layers = [l for l in net.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)
-
-    assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
-    assert len(net.outputs) == 1, "Sample supports only single output topologies"
-
-    log.info("Preparing input blobs")
-    input_blob = next(iter(net.inputs))
-    out_blob = next(iter(net.outputs))
-    net.batch_size = len(args.input)
-
-    # Read and pre-process input images
-    n, c, h, w = net.inputs[input_blob].shape
-    images = np.ndarray(shape=(n, c, h, w))
-    for i in range(n):
-        image = cv2.imread(args.input[i])
-        if image.shape[:-1] != (h, w):
-            log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w)))
-            image = cv2.resize(image, (w, h))
-        image = image.transpose((2, 0, 1))  # Change data layout from HWC to CHW
-        images[i] = image
-    log.info("Batch size is {}".format(n))
-
-    # Loading model to the plugin
-    log.info("Loading model to the plugin")
-    exec_net = ie.load_network(network=net, device_name=args.device)
-
-    # Start sync inference
-    log.info("Starting inference in synchronous mode")
-    res = exec_net.infer(inputs={input_blob: images})
-
-    # Processing output blob
-    log.info("Processing output blob")
-    res = res[out_blob]
-    log.info("Top {} results: ".format(args.number_top))
-    if args.labels:
-        with open(args.labels, 'r') as f:
-            labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f]
-    else:
-        labels_map = None
-    classid_str = "classid"
-    probability_str = "probability"
-    for i, probs in enumerate(res):
-        probs = np.squeeze(probs)
-        top_ind = np.argsort(probs)[-args.number_top:][::-1]
-        print("Image {}\n".format(args.input[i]))
-        print(classid_str, probability_str)
-        print("{} {}".format('-' * len(classid_str), '-' * len(probability_str)))
-        for id in top_ind:
-            det_label = labels_map[id] if labels_map else "{}".format(id)
-            label_length = len(det_label)
-            space_num_before = (len(classid_str) - label_length) // 2
-            space_num_after = len(classid_str) - (space_num_before + label_length) + 2
-            space_num_before_prob = (len(probability_str) - len(str(probs[id]))) // 2
-            print("{}{}{}{}{:.7f}".format(' ' * space_num_before, det_label,
-                                          ' ' * space_num_after, ' ' * space_num_before_prob,
-                                          probs[id]))
-        print("\n")
-    log.info("This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n")
-
-if __name__ == '__main__':
-    sys.exit(main() or 0)
diff --git a/lib/oeqa/runtime/miutils/dldtutils.py b/lib/oeqa/runtime/miutils/dldtutils.py
deleted file mode 100644
index 45bf2e12..00000000
--- a/lib/oeqa/runtime/miutils/dldtutils.py
+++ /dev/null
@@ -1,3 +0,0 @@
-
-def get_testdata_config(testdata, config):
-    return testdata.get(config)
diff --git a/lib/oeqa/runtime/miutils/tests/dldt_inference_engine_test.py b/lib/oeqa/runtime/miutils/tests/dldt_inference_engine_test.py
deleted file mode 100644
index 31bfb539..00000000
--- a/lib/oeqa/runtime/miutils/tests/dldt_inference_engine_test.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-script_path = os.path.dirname(os.path.realpath(__file__))
-files_path = os.path.join(script_path, '../../files/')
-
-class DldtInferenceEngineTest(object):
-    ie_input_files = {'ie_python_sample': 'classification_sample.py',
-                      'input': 'chicky_512.png',
-                      'input_download': 'https://raw.githubusercontent.com/opencv/opencv/master/samples/data/chicky_512.png',
-                      'model': 'squeezenet_v1.1.xml'}
-
-    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)
-        self.target.copy_to(os.path.join(files_path, 'dldt-inference-engine', self.ie_input_files['ie_python_sample']),
-                            self.work_dir)
-        python_cmd = 'from openvino.inference_engine import IENetwork, IECore; ie = IECore(); print(ie.available_devices)'
-        __, output = self.target.run('python3 -c "%s"' % python_cmd)
-        self.available_devices = output
-
-    def tear_down(self):
-        self.target.run('rm -rf %s' % self.work_dir)
-
-    def test_check_if_openvino_device_available(self, device):
-        if device not in self.available_devices:
-            return False, self.available_devices
-        return True, self.available_devices
-
-    def test_can_download_input_file(self, proxy_port):
-        return self.target.run('cd %s; wget %s -e https_proxy=%s' %
-                               (self.work_dir,
-                                self.ie_input_files['input_download'],
-                                proxy_port))
-
-    def test_dldt_ie_classification_with_device(self, device, ir_files_dir):
-        return self.target.run('classification_sample_async -d %s -i %s -m %s' %
-                               (device,
-                                os.path.join(self.work_dir, self.ie_input_files['input']),
-                                os.path.join(ir_files_dir, self.ie_input_files['model'])))
-
-    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'])))
diff --git a/lib/oeqa/runtime/miutils/tests/dldt_model_optimizer_test.py b/lib/oeqa/runtime/miutils/tests/dldt_model_optimizer_test.py
deleted file mode 100644
index 7d3db15b..00000000
--- a/lib/oeqa/runtime/miutils/tests/dldt_model_optimizer_test.py
+++ /dev/null
@@ -1,23 +0,0 @@
-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))
diff --git a/lib/oeqa/runtime/miutils/tests/squeezenet_model_download_test.py b/lib/oeqa/runtime/miutils/tests/squeezenet_model_download_test.py
deleted file mode 100644
index a3e46a0a..00000000
--- a/lib/oeqa/runtime/miutils/tests/squeezenet_model_download_test.py
+++ /dev/null
@@ -1,25 +0,0 @@
-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))