Merge branch 'openvinotoolkit:master' into master

Author: ieliz

CONTRIBUTING.md

@@ -214,7 +214,7 @@ This example uses validation configuration file for [DenseNet-121](models/public
 models:
   - name: densenet-121-tf
     launchers:
-      - framework: dlsdk
+      - framework: openvino
         adapter: classification
 
     datasets:

ci/dependencies.yml

@@ -1,6 +1,6 @@
 opencv_linux: '20220210_0636-4.5.5_043'
 opencv_windows: '20220210_0636-4.5.5_043'
-openvino_linux: '2022.1.0.577'
-openvino_windows: '2022.1.0.577'
-wheel_linux: '2022.1.0.dev20220209-6562'
-wheel_windows: '2022.1.0.dev20220209-6562'
+openvino_linux: '2022.1.0.582'
+openvino_windows: '2022.1.0.582'
+wheel_linux: '2022.1.0.dev20220211-6610'
+wheel_windows: '2022.1.0.dev20220211-6610'

data/dataset_definitions.yml

@@ -1,4 +1,19 @@
 datasets:
+  - name: HumanMattingClips120
+    annotation_conversion:
+      converter: background_matting_sequential
+      images_dir: HumanMattingClips120
+      masks_dir: HumanMattingClips120
+      backgrounds_dir: HumanMattingClips120
+      image_prefix: com/
+      mask_prefix: fgr/
+      background_prefix: bgr/
+      with_background: True
+      with_alpha: True
+    annotation: human_matting_120.pickle
+    dataset_meta: human_matting_120_meta.json
+    data_source: HumanMattingClips120
+
   - name: ms_coco_mask_rcnn
     annotation_conversion:
       converter: mscoco_mask_rcnn

demos/3d_segmentation_demo/python/3d_segmentation_demo.py

@@ -96,18 +96,12 @@ def parse_arguments():
     args.add_argument('-d', '--target_device', type=str, required=False, default="CPU",
                         help="Optional. Specify a target device to infer on: CPU, GPU. "
                              "Use \"-d HETERO:<comma separated devices list>\" format to specify HETERO plugin.")
-    args.add_argument('-l', '--path_to_extension', type=str, required=False, default=None,
-                        help="Required for CPU custom layers. "
-                             "Absolute path to a shared library with the kernels implementations.")
     args.add_argument("-nii", "--output_nifti", help="Show output inference results as raw values", default=False,
                         action="store_true")
     args.add_argument('-nthreads', '--number_threads', type=int, required=False, default=None,
                         help="Optional. Number of threads to use for inference on CPU (including HETERO cases).")
     args.add_argument('-s', '--shape', nargs='*', type=int, required=False, default=None,
                         help="Optional. Specify shape for a network")
-    args.add_argument('-c', '--path_to_cldnn_config', type=str, required=False,
-                        help="Required for GPU custom kernels. "
-                             "Absolute path to an .xml file with the kernels description.")
     args.add_argument('-ms', '--mri_sequence', type=mri_sequence, metavar='N1,N2,N3,N4', default=(0, 1, 2, 3),
                       help='Optional. Transfer MRI-sequence from dataset order to the network order.')
     args.add_argument("--full_intensities_range", required=False, default=False, action="store_true",
@@ -250,14 +244,9 @@ def main():
     core = Core()
 
     if 'CPU' in args.target_device:
-        if args.path_to_extension:
-            core.add_extension(args.path_to_extension, "CPU")
         if args.number_threads is not None:
-            core.set_config({'CPU_THREADS_NUM': str(args.number_threads)}, "CPU")
-    elif 'GPU' in args.target_device:
-        if args.path_to_cldnn_config:
-            core.set_config({'CONFIG_FILE': args.path_to_cldnn_config}, "GPU")
-    else:
+            core.set_property("CPU", {'CPU_THREADS_NUM': str(args.number_threads)})
+    elif 'GPU' not in args.target_device:
         raise AttributeError("Device {} do not support of 3D convolution. "
                              "Please use CPU, GPU or HETERO:*CPU*, HETERO:*GPU*")
 
@@ -278,6 +267,7 @@ def main():
     n, c, d, h, w = model.inputs[0].shape
 
     compiled_model = core.compile_model(model, args.target_device)
+    output_tensor = compiled_model.outputs[0]
     infer_request = compiled_model.create_infer_request()
     log.info('The model {} is loaded to {}'.format(args.path_to_model, args.target_device))
 
@@ -308,8 +298,8 @@ def main():
         original_data = data_crop
         original_size = original_data.shape[-3:]
 
-    result = infer_request.infer({input_tensor_name: data_crop})
-    result = next(iter(result.values()))
+    input_data = {input_tensor_name: data_crop}
+    result = infer_request.infer(input_data)[output_tensor]
     batch, channels, out_d, out_h, out_w = result.shape
 
     list_img = []

demos/action_recognition_demo/python/action_recognition_demo.py

@@ -89,7 +89,7 @@ def main():
 
     if 'MYRIAD' in args.device:
         myriad_config = {'MYRIAD_ENABLE_HW_ACCELERATION': 'YES'}
-        core.set_config(myriad_config, 'MYRIAD')
+        core.set_property('MYRIAD', myriad_config)
 
     decoder_target_device = 'CPU'
     if args.device != 'CPU':

demos/action_recognition_demo/python/action_recognition_demo/models.py

@@ -93,12 +93,13 @@ def __init__(self, model_path, core, target_device, num_requests, model_type):
             log.error("Demo supports only models with 1 output")
             sys.exit(1)
 
-        self.compiled_model = core.compile_model(self.model, target_device)
+        compiled_model = core.compile_model(self.model, target_device)
+        self.output_tensor = compiled_model.outputs[0]
         self.input_name = self.model.inputs[0].get_any_name()
         self.input_shape = self.model.inputs[0].shape
 
         self.num_requests = num_requests
-        self.infer_requests = [self.compiled_model.create_infer_request() for _ in range(self.num_requests)]
+        self.infer_requests = [compiled_model.create_infer_request() for _ in range(self.num_requests)]
         log.info('The {} model {} is loaded to {}'.format(model_type, model_path, target_device))
 
     def async_infer(self, frame, req_id):
@@ -107,7 +108,7 @@ def async_infer(self, frame, req_id):
 
     def wait_request(self, req_id):
         self.infer_requests[req_id].wait()
-        return next(iter(self.infer_requests[req_id].results.values()))
+        return self.infer_requests[req_id].results[self.output_tensor]
 
 
 class DummyDecoder:

demos/background_subtraction_demo/cpp_gapi/main.cpp

@@ -122,15 +122,15 @@ int main(int argc, char *argv[]) {
         });
 
         /** Configure network **/
-        auto config = ConfigFactory::getUserConfig(FLAGS_d, "", "", FLAGS_nireq,
+        auto config = ConfigFactory::getUserConfig(FLAGS_d, FLAGS_nireq,
                                                    FLAGS_nstreams, FLAGS_nthreads);
         const auto net = cv::gapi::ie::Params<cv::gapi::Generic> {
             model->getName(),
             FLAGS_m,                           // path to topology IR
             fileNameNoExt(FLAGS_m) + ".bin",   // path to weights
             FLAGS_d                            // device specifier
         }.cfgNumRequests(config.maxAsyncRequests)
-         .pluginConfig(config.execNetworkConfig);
+         .pluginConfig(config.getLegacyConfig());
         slog::info << "The background matting model " << FLAGS_m << " is loaded to " << FLAGS_d << " device." << slog::endl;
 
         auto kernels = cv::gapi::combine(custom::kernels(),

demos/background_subtraction_demo/python/README.md

@@ -78,7 +78,7 @@ omz_converter --list models.lst
 * instance-segmentation-person-????
 * yolact-resnet50-fpn-pytorch
 * background-matting-mobilenetv2
-* robust-video-matting
+* robust-video-matting-mobilenetv3
 
 > **NOTE**: Refer to the tables [Intel's Pre-Trained Models Device Support](../../../models/intel/device_support.md) and [Public Pre-Trained Models Device Support](../../../models/public/device_support.md) for the details on models inference support at different devices.
 

demos/background_subtraction_demo/python/background_subtraction_demo.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
- Copyright (C) 2021 Intel Corporation
+ Copyright (C) 2021-2022 Intel Corporation
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
@@ -68,6 +68,10 @@ def build_argparser():
                            'same shape as an input image.')
     args.add_argument('--blur_bgr', default=0, type=int,
                       help='Optional. Background blur strength (by default with value 0 is not applied).')
+    args.add_argument('--layout', type=str, default=None,
+                      help='Optional. Model inputs layouts. '
+                           'Format "[<layout>]" or "<input1>[<layout1>],<input2>[<layout2>]" in case of more than one input.'
+                           'To define layout you should use only capital letters')
 
     infer_args = parser.add_argument_group('Inference options')
     infer_args.add_argument('-nireq', '--num_infer_requests', help='Optional. Number of infer requests.',
@@ -211,7 +215,7 @@ def main():
     if args.adapter == 'openvino':
         plugin_config = get_user_config(args.device, args.num_streams, args.num_threads)
         model_adapter = OpenvinoAdapter(create_core(), args.model, device=args.device, plugin_config=plugin_config,
-                                        max_num_requests=args.num_infer_requests)
+                                        max_num_requests=args.num_infer_requests, model_parameters = {'input_layouts': args.layout})
     elif args.adapter == 'ovms':
         model_adapter = OVMSAdapter(args.model)
 

demos/background_subtraction_demo/python/models.lst

@@ -1,5 +1,5 @@
 # This file can be used with the --list option of the model downloader.
 instance-segmentation-person-????
 yolact-resnet50-fpn-pytorch
-# TODO: background-matting-mobilenetv2
-# TODO: robust-video-matting
+background-matting-mobilenetv2
+robust-video-matting-mobilenetv3