From 7664e6d090534d629f40991bdc10c20748d586a2 Mon Sep 17 00:00:00 2001
From: Alexander Alekhin <alexander.a.alekhin@gmail.com>
Date: Wed, 17 Mar 2021 20:29:52 +0000
Subject: [PATCH 1/4] ml: use OpenCV license header for logistic regression
---
modules/ml/src/lr.cpp | 63 +++++------------------------
modules/ml/test/test_lr.cpp | 10 +++--
samples/cpp/logistic_regression.cpp | 59 +--------------------------
3 files changed, 17 insertions(+), 115 deletions(-)
diff --git a/modules/ml/src/lr.cpp b/modules/ml/src/lr.cpp
index ad7b8079a2..ed4fb4c720 100644
--- a/modules/ml/src/lr.cpp
+++ b/modules/ml/src/lr.cpp
@@ -1,57 +1,12 @@
-///////////////////////////////////////////////////////////////////////////////////////
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
-
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// #
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// # Logistic Regression ALGORITHM
-
-
-// License Agreement
-// For Open Source Computer Vision Library
-
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-
-// * Redistributions of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-
-// * Redistributions in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-
-// * The name of the copyright holders may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
+
+//
+// This is a implementation of the Logistic Regression algorithm
+//
#include "precomp.hpp"
diff --git a/modules/ml/test/test_lr.cpp b/modules/ml/test/test_lr.cpp
index d68266cc6f..ec77fcbdda 100644
--- a/modules/ml/test/test_lr.cpp
+++ b/modules/ml/test/test_lr.cpp
@@ -1,11 +1,13 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
+//
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
+//
+// Test data uses subset of data from the popular Iris Dataset (1936):
+// - http://archive.ics.uci.edu/ml/datasets/Iris
+// - https://en.wikipedia.org/wiki/Iris_flower_data_set
//
#include "test_precomp.hpp"
diff --git a/samples/cpp/logistic_regression.cpp b/samples/cpp/logistic_regression.cpp
index 4338b61f7b..1bc2bf9711 100644
--- a/samples/cpp/logistic_regression.cpp
+++ b/samples/cpp/logistic_regression.cpp
@@ -1,60 +1,5 @@
-/*//////////////////////////////////////////////////////////////////////////////////////
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
-//
-
-// contains a subset of data from the popular Iris Dataset (taken from
-// "http://archive.ics.uci.edu/ml/datasets/Iris")
-
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// #
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// # Logistic Regression ALGORITHM
-
-// License Agreement
-// For Open Source Computer Vision Library
-
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-
-// * Redistributions of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-
-// * Redistributions in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-
-// * The name of the copyright holders may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.*/
+// Logistic Regression sample
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
#include <iostream>
From f680505086ad6ff16b694017bafd3a0e5f96b840 Mon Sep 17 00:00:00 2001
From: Alexander Alekhin <alexander.a.alekhin@gmail.com>
Date: Thu, 18 Mar 2021 21:30:12 +0000
Subject: [PATCH 2/4] features2d(mser): chi_table.h notes
---
modules/features2d/include/opencv2/features2d.hpp | 3 +--
modules/features2d/src/mser.cpp | 2 +-
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/modules/features2d/include/opencv2/features2d.hpp b/modules/features2d/include/opencv2/features2d.hpp
index 293b5beff0..18ec360a1d 100644
--- a/modules/features2d/include/opencv2/features2d.hpp
+++ b/modules/features2d/include/opencv2/features2d.hpp
@@ -468,8 +468,7 @@ article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
-than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
-code which is distributed under GPL.
+than grey image method ( 3~4 times )
- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
*/
diff --git a/modules/features2d/src/mser.cpp b/modules/features2d/src/mser.cpp
index a37b4ea482..4fe07bd6eb 100644
--- a/modules/features2d/src/mser.cpp
+++ b/modules/features2d/src/mser.cpp
@@ -35,7 +35,7 @@
* it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
* 3. the color image algorithm is taken from: Maximally Stable Colour Regions for Recognition and Match;
* it should be much slower than gray image method ( 3~4 times );
- * the chi_table.h file is taken directly from paper's source code which is distributed under GPL.
+ * the chi_table.h file is taken directly from paper's source code which is distributed under permissive BSD-like license: http://users.isy.liu.se/cvl/perfo/software/chi_table.h
* 4. though the name is *contours*, the result actually is a list of point set.
*/
From c0dd82fb537f6defa0b4ef7d9b7ae021101bb433 Mon Sep 17 00:00:00 2001
From: Liubov Batanina <piccione-mail@yandex.ru>
Date: Sat, 20 Mar 2021 14:20:02 +0300
Subject: [PATCH 3/4] Merge pull request #19632 from l-bat:lb/ie_arm_target
Added OpenVINO ARM target
* Added IE ARM target
* Added OpenVINO ARM target
* Delete ARM target
* Detect ARM platform
* Changed device name in ArmPlugin
* Change ARM detection
---
.../opencv2/dnn/utils/inference_engine.hpp | 7 ++++
modules/dnn/src/dnn.cpp | 8 +++--
modules/dnn/src/layers/batch_norm_layer.cpp | 4 +++
modules/dnn/src/layers/convolution_layer.cpp | 13 ++++---
modules/dnn/src/layers/elementwise_layers.cpp | 8 +++--
.../dnn/src/layers/normalize_bbox_layer.cpp | 23 ++++++------
modules/dnn/src/layers/padding_layer.cpp | 9 +++--
modules/dnn/src/layers/permute_layer.cpp | 4 +++
modules/dnn/src/layers/pooling_layer.cpp | 4 ++-
modules/dnn/src/layers/region_layer.cpp | 6 ++--
modules/dnn/src/layers/scale_layer.cpp | 6 +++-
modules/dnn/src/op_inf_engine.cpp | 35 +++++++++++++++++++
modules/dnn/src/op_inf_engine.hpp | 2 ++
modules/dnn/test/test_common.hpp | 1 +
modules/dnn/test/test_onnx_importer.cpp | 6 ++++
15 files changed, 107 insertions(+), 29 deletions(-)
diff --git a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
index cda2f9bfad..4a7e9e0786 100644
--- a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
+++ b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
@@ -49,6 +49,8 @@ CV_EXPORTS_W void resetMyriadDevice();
#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 "Myriad2"
/// Intel(R) Neural Compute Stick 2, NCS2 (USB 03e7:2485), MyriadX (https://software.intel.com/ru-ru/neural-compute-stick)
#define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X "MyriadX"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE "ARM_COMPUTE"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86 "X86"
/** @brief Returns Inference Engine VPU type.
@@ -57,6 +59,11 @@ CV_EXPORTS_W void resetMyriadDevice();
*/
CV_EXPORTS_W cv::String getInferenceEngineVPUType();
+/** @brief Returns Inference Engine CPU type.
+ *
+ * Specify OpenVINO plugin: CPU or ARM.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineCPUType();
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace
diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index 34222b9547..45be6eb97c 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -1286,17 +1286,19 @@ struct Net::Impl : public detail::NetImplBase
CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
preferableTarget == DNN_TARGET_CPU ||
preferableTarget == DNN_TARGET_OPENCL);
+#ifdef HAVE_INF_ENGINE
if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
CV_Assert(
- preferableTarget == DNN_TARGET_CPU ||
+ (preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) ||
preferableTarget == DNN_TARGET_OPENCL ||
preferableTarget == DNN_TARGET_OPENCL_FP16 ||
preferableTarget == DNN_TARGET_MYRIAD ||
preferableTarget == DNN_TARGET_FPGA
);
}
+#endif
if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
{
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
@@ -1972,8 +1974,8 @@ struct Net::Impl : public detail::NetImplBase
return;
}
- bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
- BackendRegistry::checkIETarget(DNN_TARGET_CPU);
+ bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU ||
+ BackendRegistry::checkIETarget(DNN_TARGET_CPU));
// Build Inference Engine networks from sets of layers that support this
// backend. Split a whole model on several Inference Engine networks if
diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp
index 2624d3c53e..27c3db6c44 100644
--- a/modules/dnn/src/layers/batch_norm_layer.cpp
+++ b/modules/dnn/src/layers/batch_norm_layer.cpp
@@ -382,7 +382,11 @@ public:
shape[1] = weights_.total();
auto weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), weights_.data);
auto bias = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), bias_.data);
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
+ auto scale_node = std::make_shared<ngraph::op::v1::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#else
auto scale_node = std::make_shared<ngraph::op::v0::Multiply>(ieInpNode, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#endif
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(scale_node, bias, ngraph::op::AutoBroadcastType::NUMPY);
return Ptr<BackendNode>(new InfEngineNgraphNode(scale_shift));
}
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index ef1dc8f29a..eeb9f73f5d 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -273,10 +273,13 @@ public:
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
- if (ksize == 1)
+ bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
+ if (isArmTarget && blobs.empty())
return false;
+ if (ksize == 1)
+ return isArmTarget;
if (ksize == 3)
- return preferableTarget == DNN_TARGET_CPU;
+ return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableTarget != DNN_TARGET_MYRIAD) && blobs.empty())
return false;
return (preferableTarget != DNN_TARGET_MYRIAD || dilation.width == dilation.height);
@@ -578,7 +581,7 @@ public:
CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
std::vector<size_t> dims = ieInpNode->get_shape();
- CV_Assert(dims.size() == 4 || dims.size() == 5);
+ CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, "");
std::shared_ptr<ngraph::Node> ieWeights = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
if (nodes.size() > 1)
CV_Assert(ieWeights); // dynamic_cast should not fail
@@ -616,7 +619,7 @@ public:
else
{
auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
- ngraph::Shape{kernel_shape.size()}, kernel_shape.data());
+ ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
ieWeights = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
}
@@ -651,7 +654,7 @@ public:
if (nodes.size() == 3)
{
auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
- ngraph::Shape{shape.size()}, shape.data());
+ ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
}
else
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index d47e08886c..e6cf714bff 100644
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -1164,11 +1164,15 @@ struct PowerFunctor : public BaseFunctor
ngraph::Shape{1}, &scale);
auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
ngraph::Shape{1}, &shift);
- auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
- ngraph::Shape{1}, &power);
auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
+
+ if (power == 1)
+ return scale_shift;
+
+ auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
+ ngraph::Shape{1}, &power);
return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
}
#endif // HAVE_DNN_NGRAPH
diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp
index 5def78f221..cdaa87bde5 100644
--- a/modules/dnn/src/layers/normalize_bbox_layer.cpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp
@@ -324,8 +324,8 @@ public:
if (!acrossSpatial) {
axes_data.push_back(1);
} else {
- axes_data.resize(ieInpNode->get_shape().size());
- std::iota(axes_data.begin(), axes_data.end(), 0);
+ axes_data.resize(ieInpNode->get_shape().size() - 1);
+ std::iota(axes_data.begin(), axes_data.end(), 1);
}
auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data);
auto norm = std::make_shared<ngraph::op::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
@@ -334,19 +334,18 @@ public:
std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
shape[0] = blobs.empty() ? 1 : batch;
shape[1] = numChannels;
- std::shared_ptr<ngraph::op::Constant> weight;
- if (blobs.empty())
+ if (!blobs.empty())
{
- std::vector<float> ones(numChannels, 1);
- weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), ones.data());
- }
- else
- {
- weight = std::make_shared<ngraph::op::Constant>(
+ auto weight = std::make_shared<ngraph::op::Constant>(
ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
+ auto mul = std::make_shared<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#else
+ auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#endif
+ return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
}
- auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
- return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
+ return Ptr<BackendNode>(new InfEngineNgraphNode(norm));
}
#endif // HAVE_DNN_NGRAPH
diff --git a/modules/dnn/src/layers/padding_layer.cpp b/modules/dnn/src/layers/padding_layer.cpp
index b6e1874be0..af3dacdd7a 100644
--- a/modules/dnn/src/layers/padding_layer.cpp
+++ b/modules/dnn/src/layers/padding_layer.cpp
@@ -97,9 +97,12 @@ public:
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
- return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
- (preferableTarget != DNN_TARGET_MYRIAD ||
- (dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
+ {
+ if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && preferableTarget == DNN_TARGET_MYRIAD)
+ return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0;
+
+ return (dstRanges.size() <= 4 || !isArmComputePlugin());
+ }
#endif
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);
diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp
index e3129556ba..cb05e4ca52 100644
--- a/modules/dnn/src/layers/permute_layer.cpp
+++ b/modules/dnn/src/layers/permute_layer.cpp
@@ -105,6 +105,10 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
+#ifdef HAVE_INF_ENGINE
+ if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU)
+ return _order.size() <= 4 || !isArmComputePlugin();
+#endif
return backendId == DNN_BACKEND_OPENCV ||
((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
}
diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp
index ac25bf4dae..e79aa367d3 100644
--- a/modules/dnn/src/layers/pooling_layer.cpp
+++ b/modules/dnn/src/layers/pooling_layer.cpp
@@ -205,7 +205,9 @@ public:
#endif
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
- return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1;
+#ifdef HAVE_DNN_NGRAPH
+ return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
+#endif
}
else if (backendId == DNN_BACKEND_OPENCV)
{
diff --git a/modules/dnn/src/layers/region_layer.cpp b/modules/dnn/src/layers/region_layer.cpp
index c0ba4b2ccf..4a8cb724d6 100644
--- a/modules/dnn/src/layers/region_layer.cpp
+++ b/modules/dnn/src/layers/region_layer.cpp
@@ -393,8 +393,10 @@ public:
std::vector<int64_t> mask(anchors, 1);
region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
+ auto tr_shape = tr_input->get_shape();
auto shape_as_inp = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
- ngraph::Shape{tr_input->get_shape().size()}, tr_input->get_shape().data());
+ ngraph::Shape{tr_shape.size()},
+ std::vector<int64_t>(tr_shape.begin(), tr_shape.end()));
region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
@@ -540,7 +542,7 @@ public:
result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
if (b > 1)
{
- std::vector<size_t> sizes = {(size_t)b, result->get_shape()[0] / b, result->get_shape()[1]};
+ std::vector<int64_t> sizes{b, static_cast<int64_t>(result->get_shape()[0]) / b, static_cast<int64_t>(result->get_shape()[1])};
auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
}
diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp
index 058140235b..e8a01672ad 100644
--- a/modules/dnn/src/layers/scale_layer.cpp
+++ b/modules/dnn/src/layers/scale_layer.cpp
@@ -249,7 +249,11 @@ public:
auto weight = blobs.empty() ? ieInpNode1 :
std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
- node = std::make_shared<ngraph::op::v0::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
+ node = std::make_shared<ngraph::op::v1::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#else
+ node = std::make_shared<ngraph::op::v0::Multiply>(node, weight, ngraph::op::AutoBroadcastType::NUMPY);
+#endif
}
if (hasBias || !hasWeights)
{
diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp
index 43fb5999d9..41783d10b5 100644
--- a/modules/dnn/src/op_inf_engine.cpp
+++ b/modules/dnn/src/op_inf_engine.cpp
@@ -651,6 +651,22 @@ InferenceEngine::Core& getCore(const std::string& id)
}
#endif
+static bool detectArmPlugin_()
+{
+ InferenceEngine::Core& ie = getCore("CPU");
+ const std::vector<std::string> devices = ie.GetAvailableDevices();
+ for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
+ {
+ if (i->find("CPU") != std::string::npos)
+ {
+ const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
+ CV_LOG_INFO(NULL, "CPU plugin: " << name);
+ return name.find("arm_compute::NEON") != std::string::npos;
+ }
+ }
+ return false;
+}
+
#if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
static bool detectMyriadX_()
{
@@ -1162,6 +1178,12 @@ bool isMyriadX()
return myriadX;
}
+bool isArmComputePlugin()
+{
+ static bool armPlugin = getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE;
+ return armPlugin;
+}
+
static std::string getInferenceEngineVPUType_()
{
static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_DNN_IE_VPU_TYPE", "");
@@ -1199,6 +1221,14 @@ cv::String getInferenceEngineVPUType()
return vpu_type;
}
+cv::String getInferenceEngineCPUType()
+{
+ static cv::String cpu_type = detectArmPlugin_() ?
+ CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE :
+ CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86;
+ return cpu_type;
+}
+
#else // HAVE_INF_ENGINE
cv::String getInferenceEngineBackendType()
@@ -1214,6 +1244,11 @@ cv::String getInferenceEngineVPUType()
{
CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
}
+
+cv::String getInferenceEngineCPUType()
+{
+ CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
+}
#endif // HAVE_INF_ENGINE
diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
index f29af3e0b1..25844710b9 100644
--- a/modules/dnn/src/op_inf_engine.hpp
+++ b/modules/dnn/src/op_inf_engine.hpp
@@ -254,6 +254,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
bool isMyriadX();
+bool isArmComputePlugin();
+
CV__DNN_EXPERIMENTAL_NS_END
InferenceEngine::Core& getCore(const std::string& id);
diff --git a/modules/dnn/test/test_common.hpp b/modules/dnn/test/test_common.hpp
index aa7e49537b..e98fbf4f91 100644
--- a/modules/dnn/test/test_common.hpp
+++ b/modules/dnn/test/test_common.hpp
@@ -35,6 +35,7 @@
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 "dnn_skip_ie_myriad2"
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X "dnn_skip_ie_myriadx"
#define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
+#define CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU "dnn_skip_ie_arm_cpu"
#ifdef HAVE_INF_ENGINE
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index eb63aa085a..ad28cad61a 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -144,6 +144,10 @@ TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU &&
+ getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
@@ -717,6 +721,8 @@ TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+ if (target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
String basename = "conv1d_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
From a97f6f8058f3d136805344a8960331574879dbd7 Mon Sep 17 00:00:00 2001
From: Alexander Alekhin <alexander.a.alekhin@gmail.com>
Date: Sat, 20 Mar 2021 12:38:12 +0000
Subject: [PATCH 4/4] js: support setLogLevel() / getLogLevel() calls
---
.../include/opencv2/core/bindings_utils.hpp | 24 +++++++++++++++++--
modules/js/generator/CMakeLists.txt | 4 +++-
platforms/js/opencv_js.config.py | 21 ++++++++++------
3 files changed, 39 insertions(+), 10 deletions(-)
diff --git a/modules/core/include/opencv2/core/bindings_utils.hpp b/modules/core/include/opencv2/core/bindings_utils.hpp
index 179d60323f..98a4a2b785 100644
--- a/modules/core/include/opencv2/core/bindings_utils.hpp
+++ b/modules/core/include/opencv2/core/bindings_utils.hpp
@@ -7,6 +7,7 @@
#include <opencv2/core/async.hpp>
#include <opencv2/core/detail/async_promise.hpp>
+#include <opencv2/core/utils/logger.hpp>
#include <stdexcept>
@@ -144,7 +145,26 @@ AsyncArray testAsyncException()
return p.getArrayResult();
}
-//! @}
-}} // namespace
+//! @} // core_utils
+} // namespace cv::utils
+
+//! @cond IGNORED
+
+CV_WRAP static inline
+int setLogLevel(int level)
+{
+ // NB: Binding generators doesn't work with enums properly yet, so we define separate overload here
+ return cv::utils::logging::setLogLevel((cv::utils::logging::LogLevel)level);
+}
+
+CV_WRAP static inline
+int getLogLevel()
+{
+ return cv::utils::logging::getLogLevel();
+}
+
+//! @endcond IGNORED
+
+} // namespaces cv / utils
#endif // OPENCV_CORE_BINDINGS_UTILS_HPP
diff --git a/modules/js/generator/CMakeLists.txt b/modules/js/generator/CMakeLists.txt
index 75c8a03545..7a53429651 100644
--- a/modules/js/generator/CMakeLists.txt
+++ b/modules/js/generator/CMakeLists.txt
@@ -26,7 +26,9 @@ ocv_list_filterout(opencv_hdrs "modules/cuda.*")
ocv_list_filterout(opencv_hdrs "modules/cudev")
ocv_list_filterout(opencv_hdrs "modules/core/.*/hal/")
ocv_list_filterout(opencv_hdrs "modules/.*/detection_based_tracker.hpp") # Conditional compilation
-ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/.*")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/*.private.*")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/instrumentation.hpp")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/trace*")
ocv_update_file("${CMAKE_CURRENT_BINARY_DIR}/headers.txt" "${opencv_hdrs}")
diff --git a/platforms/js/opencv_js.config.py b/platforms/js/opencv_js.config.py
index 86551ae4d9..3e09805cd3 100644
--- a/platforms/js/opencv_js.config.py
+++ b/platforms/js/opencv_js.config.py
@@ -1,10 +1,17 @@
-core = {'': ['absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',\
- 'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen', \
- 'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude', \
- 'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize', \
- 'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed', \
- 'solve', 'solvePoly', 'split', 'sqrt', 'subtract', 'trace', 'transform', 'transpose', 'vconcat'],
- 'Algorithm': []}
+# Classes and methods whitelist
+
+core = {
+ '': [
+ 'absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',
+ 'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen',
+ 'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude',
+ 'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize',
+ 'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed',
+ 'solve', 'solvePoly', 'split', 'sqrt', 'subtract', 'trace', 'transform', 'transpose', 'vconcat',
+ 'setLogLevel', 'getLogLevel',
+ ],
+ 'Algorithm': [],
+}
imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP', 'HoughCircles', 'Scharr','Sobel', \
'adaptiveThreshold','approxPolyDP','arcLength','bilateralFilter','blur','boundingRect','boxFilter',\