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Merge branch 'openvinotoolkit:master' into master
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‎ci/dependencies.yml

Lines changed: 6 additions & 6 deletions
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@@ -1,6 +1,6 @@
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opencv_linux: '2022.1.0.dev20220131'
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opencv_windows: '2022.1.0.dev20220131'
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openvino_linux: '2022.1.0.dev20220131'
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openvino_windows: '2022.1.0.dev20220131'
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wheel_linux: '2022.1.0.dev20220131-6386'
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wheel_windows: '2022.1.0.dev20220131-6386'
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opencv_linux: '20220210_0636-4.5.5_043'
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opencv_windows: '20220210_0636-4.5.5_043'
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openvino_linux: '2022.1.0.577'
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openvino_windows: '2022.1.0.577'
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wheel_linux: '2022.1.0.dev20220209-6562'
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wheel_windows: '2022.1.0.dev20220209-6562'

‎demos/classification_benchmark_demo/cpp/README.md

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@@ -45,6 +45,7 @@ omz_converter --list models.lst
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* googlenet-v1
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* googlenet-v1-tf
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* googlenet-v2
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* googlenet-v2-tf
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* googlenet-v3
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* googlenet-v3-pytorch
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* googlenet-v4-tf
@@ -59,6 +60,8 @@ omz_converter --list models.lst
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* mobilenet-v2-1.0-224
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* mobilenet-v2-1.4-224
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* mobilenet-v2-pytorch
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* mobilenet-v3-large-1.0-224-tf
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* mobilenet-v3-small-1.0-224-tf
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* nfnet-f0
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* octave-resnet-26-0.25
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* regnetx-3.2gf
@@ -67,6 +70,7 @@ omz_converter --list models.lst
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* repvgg-b3
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* resnest-50-pytorch
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* resnet-18-pytorch
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* resnet-34-pytorch
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* resnet-50-pytorch
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* resnet-50-tf
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* resnet18-xnor-binary-onnx-0001

‎demos/classification_benchmark_demo/cpp/models.lst

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@@ -9,6 +9,7 @@ efficientnet-b0-pytorch
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googlenet-v1
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googlenet-v1-tf
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googlenet-v2
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googlenet-v2-tf
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googlenet-v3
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googlenet-v3-pytorch
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googlenet-v4-tf
@@ -23,6 +24,8 @@ mobilenet-v2
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mobilenet-v2-1.0-224
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mobilenet-v2-1.4-224
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mobilenet-v2-pytorch
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mobilenet-v3-large-1.0-224-tf
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mobilenet-v3-small-1.0-224-tf
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nfnet-f0
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octave-resnet-26-0.25
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regnetx-3.2gf
@@ -31,6 +34,7 @@ repvgg-b1
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repvgg-b3
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resnest-50-pytorch
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resnet-18-pytorch
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resnet-34-pytorch
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resnet-50-pytorch
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resnet-50-tf
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resnet18-xnor-binary-onnx-0001

‎demos/classification_demo/python/README.md

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@@ -43,6 +43,7 @@ omz_converter --list models.lst
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* googlenet-v1
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* googlenet-v1-tf
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* googlenet-v2
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* googlenet-v2-tf
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* googlenet-v3
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* googlenet-v3-pytorch
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* googlenet-v4-tf
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* mobilenet-v2-1.0-224
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* mobilenet-v2-1.4-224
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* mobilenet-v2-pytorch
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* mobilenet-v3-large-1.0-224-tf
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* mobilenet-v3-small-1.0-224-tf
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* nfnet-f0
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* octave-resnet-26-0.25
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* regnetx-3.2gf
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* repvgg-b3
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* resnest-50-pytorch
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* resnet-18-pytorch
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* resnet-34-pytorch
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* resnet-50-pytorch
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* resnet-50-tf
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* resnet18-xnor-binary-onnx-0001

‎demos/classification_demo/python/models.lst

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@@ -11,6 +11,7 @@ efficientnet-v2-s
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googlenet-v1
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googlenet-v1-tf
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googlenet-v2
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googlenet-v2-tf
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googlenet-v3
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googlenet-v3-pytorch
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googlenet-v4-tf
@@ -25,6 +26,8 @@ mobilenet-v2
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mobilenet-v2-1.0-224
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mobilenet-v2-1.4-224
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mobilenet-v2-pytorch
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mobilenet-v3-large-1.0-224-tf
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mobilenet-v3-small-1.0-224-tf
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nfnet-f0
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octave-resnet-26-0.25
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regnetx-3.2gf
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repvgg-b3
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resnest-50-pytorch
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resnet-18-pytorch
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resnet-34-pytorch
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resnet-50-pytorch
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resnet-50-tf
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resnet18-xnor-binary-onnx-0001

‎demos/common/cpp/utils/include/utils/common.hpp

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@@ -296,11 +296,11 @@ void logCompiledModelInfo(
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for (const auto& device : devices) {
297297
try {
298298
slog::info << "\tDevice: " << device << slog::endl;
299-
std::string nstreams = compiledModel.get_property(device + "_THROUGHPUT_STREAMS").as<std::string>();
299+
int32_t nstreams = compiledModel.get_property(ov::streams::num);
300300
slog::info << "\t\tNumber of streams: " << nstreams << slog::endl;
301301
if (device == "CPU") {
302-
std::string nthreads = compiledModel.get_property("CPU_THREADS_NUM").as<std::string>();
303-
slog::info << "\t\tNumber of threads: " << (nthreads == "0" ? "AUTO" : nthreads) << slog::endl;
302+
int32_t nthreads = compiledModel.get_property(ov::inference_num_threads);
303+
slog::info << "\t\tNumber of threads: " << (nthreads == 0 ? "AUTO" : std::to_string(nthreads)) << slog::endl;
304304
}
305305
}
306306
catch (const ov::Exception&) {}

‎demos/instance_segmentation_demo/python/README.md

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### Supported Models
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* instance-segmentation-person-0007
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* instance-segmentation-security-0002
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* instance-segmentation-security-0091
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* instance-segmentation-security-0228

‎demos/instance_segmentation_demo/python/models.lst

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# This file can be used with the --list option of the model downloader.
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instance-segmentation-person-0007
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instance-segmentation-security-????
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yolact-resnet50-fpn-pytorch

‎demos/interactive_face_detection_demo/cpp/detectors.cpp

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@@ -207,7 +207,7 @@ void AntispoofingClassifier::enqueue(const cv::Mat& face) {
207207
}
208208
ov::Tensor batch = request.get_input_tensor();
209209
batch.set_shape(inShape);
210-
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces, inShape[1], inShape[2], inShape[3]}});
210+
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces + 1, inShape[1], inShape[2], inShape[3]}});
211211
enquedFaces++;
212212
}
213213

@@ -259,7 +259,7 @@ void AgeGenderDetection::enqueue(const cv::Mat &face) {
259259
}
260260
ov::Tensor batch = request.get_input_tensor();
261261
batch.set_shape(inShape);
262-
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces, inShape[1], inShape[2], inShape[3]}});
262+
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces + 1, inShape[1], inShape[2], inShape[3]}});
263263
enquedFaces++;
264264
}
265265

@@ -317,7 +317,7 @@ void HeadPoseDetection::enqueue(const cv::Mat &face) {
317317
}
318318
ov::Tensor batch = request.get_input_tensor();
319319
batch.set_shape(inShape);
320-
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces, inShape[1], inShape[2], inShape[3]}});
320+
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces + 1, inShape[1], inShape[2], inShape[3]}});
321321
enquedFaces++;
322322
}
323323

@@ -374,7 +374,7 @@ void EmotionsDetection::enqueue(const cv::Mat &face) {
374374
}
375375
ov::Tensor batch = request.get_input_tensor();
376376
batch.set_shape(inShape);
377-
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces, inShape[1], inShape[2], inShape[3]}});
377+
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces + 1, inShape[1], inShape[2], inShape[3]}});
378378
enquedFaces++;
379379
}
380380

@@ -452,7 +452,7 @@ void FacialLandmarksDetection::enqueue(const cv::Mat &face) {
452452
}
453453
ov::Tensor batch = request.get_input_tensor();
454454
batch.set_shape(inShape);
455-
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces, inShape[1], inShape[2], inShape[3]}});
455+
resize2tensor(face, ov::Tensor{batch, {enquedFaces, 0, 0, 0}, {enquedFaces + 1, inShape[1], inShape[2], inShape[3]}});
456456
enquedFaces++;
457457
}
458458

‎demos/machine_translation_demo/python/README.md

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* machine-translation-nar-en-de-0002
3232
* machine-translation-nar-en-ru-0001
3333
* machine-translation-nar-ru-en-0001
34+
* machine-translation-nar-en-ru-0002
35+
* machine-translation-nar-ru-en-0002
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3537
> **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.
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