Merge pull request openvinotoolkit#1185 from opencv/release

Merge OpenVINO toolkit 2020.3 content into master

Author: Roman Donchenko

README.md

@@ -1,5 +1,5 @@
 # [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Open Model Zoo repository
-[![Stable release](https://img.shields.io/badge/version-2020.2-green.svg)](https://github.com/opencv/open_model_zoo/releases/tag/2020.2)
+[![Stable release](https://img.shields.io/badge/version-2020.3-green.svg)](https://github.com/opencv/open_model_zoo/releases/tag/2020.3)
 [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/open_model_zoo/community)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 

demos/common/monitors/cpu_monitor.cpp

@@ -52,8 +52,12 @@ class CpuMonitor::PerformanceCounter {
         for (std::size_t i = 0; i < coreTimeCounters.size(); ++i) {
             status = PdhGetFormattedCounterValue(coreTimeCounters[i], PDH_FMT_DOUBLE, NULL,
                 &displayValue);
-            if (ERROR_SUCCESS != status) {
-                throw std::system_error(status, std::system_category(), "PdhGetFormattedCounterValue() failed");
+            switch (status) {
+                case ERROR_SUCCESS: break;
+                // PdhGetFormattedCounterValue() can sometimes return PDH_CALC_NEGATIVE_DENOMINATOR for some reason
+                case PDH_CALC_NEGATIVE_DENOMINATOR: return {};
+                default:
+                    throw std::system_error(status, std::system_category(), "PdhGetFormattedCounterValue() failed");
             }
             if (PDH_CSTATUS_VALID_DATA != displayValue.CStatus && PDH_CSTATUS_NEW_DATA != displayValue.CStatus) {
                 throw std::runtime_error("Error in counter data");
@@ -125,6 +129,7 @@ class CpuMonitor::PerformanceCounter {
                 cpuLoad[i] = 1.0
                     - idleDiff / clockTicks / std::chrono::duration_cast<Sec>(timePoint - prevTimePoint).count();
             }
+            prevTimePoint = timePoint;
             return cpuLoad;
         }
         return {};

demos/object_detection_demo_ssd_async/main.cpp

@@ -188,14 +188,8 @@ int main(int argc, char *argv[]) {
                     break;
                 }
             }
-        } else {
-            const CNNLayerPtr outputLayer = cnnNetwork.getLayerByName(outputName.c_str());
-            if (outputLayer->type != "DetectionOutput") {
-                throw std::logic_error("Object Detection network output layer(" + outputLayer->name +
-                                       ") should be DetectionOutput, but was " +  outputLayer->type);
-            }
-
-            num_classes = outputLayer->GetParamAsInt("num_classes");
+        } else if (!labels.empty()) {
+            throw std::logic_error("Class labels are not supported with IR version older than 10");
         }
 
         if (static_cast<int>(labels.size()) != num_classes) {

demos/python_demos/3d_segmentation_demo/3d_segmentation_demo.py

@@ -28,7 +28,7 @@
 from scipy.ndimage import interpolation
 from sys import stdout
 
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 
 
 logging.basicConfig(format="[ %(levelname)s ] %(message)s", level=logging.INFO, stream=stdout)
@@ -269,11 +269,7 @@ def main():
     logger.info("Plugin version is {}".format(version_str))
 
     # --------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ---------------------
-
-    xml_filename = os.path.abspath(args.path_to_model)
-    bin_filename = os.path.abspath(os.path.splitext(xml_filename)[0] + '.bin')
-
-    ie_network = IENetwork(xml_filename, bin_filename)
+    ie_network = ie.read_network(args.path_to_model, os.path.splitext(args.path_to_model)[0] + '.bin')
 
     input_info = ie_network.inputs
     if len(input_info) == 0:

demos/python_demos/action_recognition/action_recognition_demo/models.py

@@ -19,7 +19,6 @@
 
 import cv2
 import numpy as np
-from openvino.inference_engine import IENetwork
 
 
 def center_crop(frame, crop_size):
@@ -87,7 +86,7 @@ def __init__(self, model_xml, model_bin, ie_core, target_device, num_requests, b
 
         # Read IR
         print("Reading IR...")
-        self.net = IENetwork(model_xml, model_bin)
+        self.net = ie_core.read_network(model_xml, model_bin)
         self.net.batch_size = batch_size
         assert len(self.net.inputs.keys()) == 1, "One input is expected"
         assert len(self.net.outputs) == 1, "One output is expected"

demos/python_demos/asl_recognition_demo/asl_recognition_demo/common.py

@@ -14,7 +14,7 @@
  limitations under the License.
 """
 
-from openvino.inference_engine import IENetwork, IECore  # pylint: disable=no-name-in-module
+from openvino.inference_engine import IECore  # pylint: disable=no-name-in-module
 
 
 def load_ie_core(device, cpu_extension=None):
@@ -34,9 +34,7 @@ def __init__(self, model_path, device, ie_core, num_requests, output_shape=None)
         """Constructor"""
         if model_path.endswith((".xml", ".bin")):
             model_path = model_path[:-4]
-        model_xml = model_path + ".xml"
-        model_bin = model_path + ".bin"
-        self.net = IENetwork(model=model_xml, weights=model_bin)
+        self.net = ie_core.read_network(model_path + ".xml", model_path + ".bin")
         assert len(self.net.inputs.keys()) == 1, "One input is expected"
 
         supported_layers = ie_core.query_network(self.net, device)

demos/python_demos/colorization_demo/colorization_demo.py

@@ -15,7 +15,7 @@
  limitations under the License.
 """
 
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 import cv2 as cv
 import numpy as np
 import os
@@ -48,18 +48,17 @@ def build_arg():
 
 if __name__ == '__main__':
     args = build_arg().parse_args()
-    model_path = os.path.splitext(args.model)[0]
-    weights_bin = model_path + ".bin"
     coeffs = args.coeffs
 
     # mean is stored in the source caffe model and passed to IR
     log.basicConfig(format="[ %(levelname)s ] %(message)s",
                     level=log.INFO if not args.verbose else log.DEBUG, stream=sys.stdout)
 
     log.debug("Load network")
-    load_net = IENetwork(model=args.model, weights=weights_bin)
+    ie = IECore()
+    load_net = ie.read_network(args.model, os.path.splitext(args.model)[0] + ".bin")
     load_net.batch_size = 1
-    exec_net = IECore().load_network(network=load_net, device_name=args.device)
+    exec_net = ie.load_network(network=load_net, device_name=args.device)
 
     assert len(load_net.inputs) == 1, "Expected number of inputs is equal 1"
     input_blob = next(iter(load_net.inputs))

demos/python_demos/face_recognition_demo/face_recognition_demo.py

@@ -24,7 +24,6 @@
 import cv2
 import numpy as np
 
-from openvino.inference_engine import IENetwork
 from ie_module import InferenceContext
 from landmarks_detector import LandmarksDetector
 from face_detector import FaceDetector
@@ -167,14 +166,13 @@ def __init__(self, args):
 
     def load_model(self, model_path):
         model_path = osp.abspath(model_path)
-        model_description_path = model_path
         model_weights_path = osp.splitext(model_path)[0] + ".bin"
-        log.info("Loading the model from '%s'" % (model_description_path))
-        assert osp.isfile(model_description_path), \
-            "Model description is not found at '%s'" % (model_description_path)
+        log.info("Loading the model from '%s'" % (model_path))
+        assert osp.isfile(model_path), \
+            "Model description is not found at '%s'" % (model_path)
         assert osp.isfile(model_weights_path), \
             "Model weights are not found at '%s'" % (model_weights_path)
-        model = IENetwork(model_description_path, model_weights_path)
+        model = self.context.ie_core.read_network(model_path, model_weights_path)
         log.info("Model is loaded")
         return model
 
@@ -242,7 +240,7 @@ def __init__(self, args):
         self.print_perf_stats = args.perf_stats
 
         self.frame_time = 0
-        self.frame_start_time = 0
+        self.frame_start_time = time.perf_counter()
         self.fps = 0
         self.frame_num = 0
         self.frame_count = -1
@@ -254,8 +252,8 @@ def __init__(self, args):
         self.frame_timeout = 0 if args.timelapse else 1
 
     def update_fps(self):
-        now = time.time()
-        self.frame_time = now - self.frame_start_time
+        now = time.perf_counter()
+        self.frame_time = max(now - self.frame_start_time, sys.float_info.epsilon)
         self.fps = 1.0 / self.frame_time
         self.frame_start_time = now
 

demos/python_demos/handwritten_japanese_recognition_demo/handwritten_japanese_recognition_demo.py

@@ -21,7 +21,7 @@
 import cv2
 import numpy as np
 
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 from utils.codec import CTCCodec
 
 
@@ -66,14 +66,12 @@ def preprocess_input(image_name, height, width):
 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
     ie = IECore()
     # Read IR
-    log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
-    net = IENetwork(model=model_xml, weights=model_bin)
+    log.info("Loading network")
+    net = ie.read_network(args.model, os.path.splitext(args.model)[0] + ".bin")
 
     assert len(net.inputs) == 1, "Demo supports only single input topologies"
     assert len(net.outputs) == 1, "Demo supports only single output topologies"

demos/python_demos/human_pose_estimation_3d_demo/modules/inference_engine.py

@@ -16,24 +16,24 @@
 
 import numpy as np
 
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 
 
 class InferenceEngine:
     def __init__(self, net_model_xml_path, device, stride):
         self.device = device
         self.stride = stride
 
-        net_model_bin_path = os.path.splitext(net_model_xml_path)[0] + '.bin'
-        self.net = IENetwork(model=net_model_xml_path, weights=net_model_bin_path)
+        self.ie = IECore()
+
+        self.net = self.ie.read_network(net_model_xml_path, os.path.splitext(net_model_xml_path)[0] + '.bin')
         required_input_key = {'data'}
         assert required_input_key == set(self.net.inputs.keys()), \
             'Demo supports only topologies with the following input key: {}'.format(', '.join(required_input_key))
         required_output_keys = {'features', 'heatmaps', 'pafs'}
         assert required_output_keys.issubset(self.net.outputs.keys()), \
             'Demo supports only topologies with the following output keys: {}'.format(', '.join(required_output_keys))
 
-        self.ie = IECore()
         self.exec_net = self.ie.load_network(network=self.net, num_requests=1, device_name=device)
 
     def infer(self, img):

demos/python_demos/image_inpainting_demo/inpainting.py

@@ -15,11 +15,10 @@
 import cv2
 import numpy as np
 
-from openvino.inference_engine import IENetwork, IECore
 
 class ImageInpainting(object):
     def __init__(self, ie, model_path, parts, max_brush_width, max_length, max_vertex, device='CPU'):
-        model = IENetwork(model=model_path, weights=os.path.splitext(model_path)[0] + '.bin')
+        model = ie.read_network(model_path, os.path.splitext(model_path)[0] + '.bin')
 
         assert len(model.inputs) == 2, "Expected 2 input blob"
         assert len(model.outputs) == 1, "Expected 1 output blobs"

demos/python_demos/image_retrieval_demo/image_retrieval_demo/image_retrieval.py

@@ -22,7 +22,7 @@
 
 from image_retrieval_demo.common import from_list, crop_resize
 
-from openvino.inference_engine import IENetwork, IECore # pylint: disable=no-name-in-module
+from openvino.inference_engine import IECore # pylint: disable=no-name-in-module
 
 
 class IEModel(): # pylint: disable=too-few-public-methods
@@ -34,7 +34,7 @@ def __init__(self, model_path, device, cpu_extension):
             ie.add_extension(cpu_extension, 'CPU')
 
         path = '.'.join(model_path.split('.')[:-1])
-        self.net = IENetwork(model=path + '.xml', weights=path + '.bin')
+        self.net = ie.read_network(path + '.xml', path + '.bin')
         self.output_name = list(self.net.outputs.keys())[0]
         self.exec_net = ie.load_network(network=self.net, device_name=device)
 

demos/python_demos/instance_segmentation_demo/instance_segmentation_demo.py

@@ -25,7 +25,7 @@
 
 import cv2
 import numpy as np
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 
 from instance_segmentation_demo.tracker import StaticIOUTracker
 from instance_segmentation_demo.visualizer import Visualizer
@@ -121,17 +121,14 @@ 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)
+    log.info('Loading network')
+    net = ie.read_network(args.model, os.path.splitext(args.model)[0] + '.bin')
 
     if 'CPU' in args.device:
         supported_layers = ie.query_network(net, 'CPU')

demos/python_demos/monodepth_demo/monodepth_demo.py

@@ -5,7 +5,7 @@
 import cv2
 import numpy as np
 import logging as log
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 import matplotlib.pyplot as plt
 
 
@@ -30,17 +30,13 @@ def main():
     log.basicConfig(format="[ %(levelname)s ] %(message)s",
                     level=log.INFO, stream=sys.stdout)
 
-    # load and prepare net
-    model_xml = args.model
-    model_bin = os.path.splitext(model_xml)[0] + ".bin"
-
     log.info("creating inference engine")
     ie = IECore()
     if args.cpu_extension and "CPU" in args.device:
         ie.add_extension(args.cpu_extension, "CPU")
 
-    log.info("loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
-    net = IENetwork(model=model_xml, weights=model_bin)
+    log.info("Loading network")
+    net = ie.read_network(args.model, os.path.splitext(args.model)[0] + ".bin")
 
     if "CPU" in args.device:
         supported_layers = ie.query_network(net, "CPU")

demos/python_demos/multi_camera_multi_person_tracking/multi_camera_multi_person_tracking.py

@@ -129,7 +129,7 @@ def run(params, config, capture, detector, reid):
             key = check_pressed_keys(key)
             if key == 27:
                 break
-        start = time.time()
+        start = time.perf_counter()
         try:
             frames = thread_body.frames_queue.get_nowait()
         except queue.Empty:
@@ -152,7 +152,7 @@ def run(params, config, capture, detector, reid):
         tracker.process(prev_frames, all_detections, all_masks)
         tracked_objects = tracker.get_tracked_objects()
 
-        latency = time.time() - start
+        latency = max(time.perf_counter() - start, sys.float_info.epsilon)
         avg_latency.update(latency)
         fps = round(1. / latency, 1)
 

demos/python_demos/multi_camera_multi_person_tracking/utils/ie_tools.py

@@ -16,7 +16,6 @@
 
 import logging as log
 import numpy as np
-from openvino.inference_engine import IENetwork, IECore # pylint: disable=import-error,E0611
 import cv2 as cv
 
 
@@ -61,15 +60,14 @@ def get_input_shape(self):
 
 def load_ie_model(ie, model_xml, device, plugin_dir, cpu_extension='', num_reqs=1):
     """Loads a model in the Inference Engine format"""
-    model_bin = os.path.splitext(model_xml)[0] + ".bin"
     # Plugin initialization for specified device and load extensions library if specified
     log.info("Initializing Inference Engine plugin for %s ", device)
 
     if cpu_extension and 'CPU' in device:
         ie.add_extension(cpu_extension, 'CPU')
     # Read IR
-    log.info("Loading network files:\n\t%s\n\t%s", model_xml, model_bin)
-    net = IENetwork(model=model_xml, weights=model_bin)
+    log.info("Loading network")
+    net = ie.read_network(model_xml, os.path.splitext(model_xml)[0] + ".bin")
 
     if "CPU" in device:
         supported_layers = ie.query_network(net, "CPU")

demos/python_demos/object_detection_demo_centernet/detector.py

@@ -19,11 +19,10 @@
 import numpy as np
 from numpy.lib.stride_tricks import as_strided
 
-from openvino.inference_engine import IENetwork, IECore
 
 class Detector(object):
     def __init__(self, ie, model_path, threshold=0.3, device='CPU'):
-        model = IENetwork(model=model_path, weights=os.path.splitext(model_path)[0] + '.bin')
+        model = ie.read_network(model_path, os.path.splitext(model_path)[0] + '.bin')
 
         assert len(model.inputs) == 1, "Expected 1 input blob"
         assert len(model.outputs) == 3, "Expected 3 output blobs"