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Merge remote-tracking branch 'upstream/master'

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This commit is contained in:
Steve Yoo 2021-08-09 09:01:44 +09:00
commit 89ba7c1bf1
1169 changed files with 29869 additions and 12697 deletions
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2
.ci/azure/ci_utils/onnxruntime/version
View File

@ -1 +1 @@
rel-1.7.1
rel-1.8.1

58
.ci/azure/linux.yml
View File

@ -16,13 +16,12 @@ jobs:
timeoutInMinutes: 90
pool:
name: LIN_VMSS_VENV_F16S_WU2
name: LIN_VMSS_VENV_F16S_U20_WU2
variables:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 16
BUILD_TYPE: Release
REPO_DIR: $(Build.Repository.LocalPath)
OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)/../openvino_contrib
@ -43,6 +42,7 @@ jobs:
echo Python info ; which python ; python --version
echo Java info ; which java ; java -version
echo gcc info ; which gcc ; gcc --version
echo cmake info ; which cmake ; cmake --version
lsb_release
env
cat /proc/cpuinfo
@ -74,27 +74,26 @@ jobs:
submodules: recursive
path: openvino_contrib
- checkout: testdata
clean: true
lfs: true
path: testdata
- script: |
sudo apt --assume-yes install libusb-1.0-0-dev
# For opencv-python: setuptools and upgrade
sudo apt-get install python3-setuptools patchelf
set -e
$(REPO_DIR)/install_build_dependencies.sh
# Move jdk into contrib
sudo apt --assume-yes install openjdk-11-jdk
# For opencv-python: python3-setuptools and pip upgrade
python3 -m pip install --upgrade pip
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/requirements.txt
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
# For running Python API tests
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/src/requirements-dev.txt
# For running nGraph unit tests dependent on Python frameworks
python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test.txt
# For running PaddlePaddle frontend unit tests
python3 -m pip install -r $(REPO_DIR)/ngraph/test/frontend/paddlepaddle/requirements_dev.txt
# For running ONNX frontend unit tests
python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test_onnx.txt
# For MO unit tests
python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements.txt
python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements_dev.txt
# Speed up build
wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
wget https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip
unzip ninja-linux.zip
sudo cp -v ninja /usr/local/bin/
# Speed up tests
@ -102,6 +101,11 @@ jobs:
workingDirectory: $(WORK_DIR)
displayName: 'Install dependencies'
- checkout: testdata
clean: true
lfs: true
path: testdata
- task: CMake@1
inputs:
# CMake must get Python 3.x version by default
@ -110,13 +114,14 @@ jobs:
-DVERBOSE_BUILD=ON
-DCMAKE_BUILD_TYPE=$(BUILD_TYPE)
-DENABLE_PYTHON=ON
-DPYTHON_EXECUTABLE=/usr/bin/python3.6
-DPYTHON_EXECUTABLE=/usr/bin/python3.8
-DENABLE_WHEEL=ON
-DENABLE_TESTS=ON
-DNGRAPH_ONNX_IMPORT_ENABLE=ON
-DNGRAPH_ONNX_FRONTEND_ENABLE=ON
-DENABLE_FASTER_BUILD=ON
-DENABLE_STRICT_DEPENDENCIES=OFF
-DENABLE_REQUIREMENTS_INSTALL=OFF
-DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules
$(REPO_DIR)
workingDirectory: $(BUILD_DIR)
@ -139,8 +144,10 @@ jobs:
displayName: 'List install files'
- script: |
set -e
mkdir $(INSTALL_DIR)/opencv/
cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -DCOMPONENT=tests -P cmake_install.cmake && cp -R $(REPO_DIR)/inference-engine/temp/opencv_4.5.2_ubuntu18/opencv/* $(INSTALL_DIR)/opencv/
cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -DCOMPONENT=tests -P cmake_install.cmake
cp -R $(REPO_DIR)/inference-engine/temp/opencv_4.5.2_ubuntu20/opencv/* $(INSTALL_DIR)/opencv/
workingDirectory: $(BUILD_DIR)
displayName: 'Install tests'
@ -155,17 +162,31 @@ jobs:
workingDirectory: $(BUILD_SAMPLES_DIR)
displayName: 'Build c samples'
- script: rm -fr $(BUILD_DIR)
displayName: 'Clean build dir'
continueOnError: false
# Skip test_onnx/test_zoo_models and test_onnx/test_backend due to long execution time
- script: . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_TEST_DIR)/pyngraph --junitxml=TEST-Pyngraph.xml --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_zoo_models.py --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_backend.py
displayName: 'nGraph Python Bindings Tests'
continueOnError: false
- script: |
export MO_ROOT=$(INSTALL_DIR)/deployment_tools/model_optimizer
. $(SETUPVARS) -pyver 3.6 && python3 -m pytest -s $(INSTALL_DIR)/deployment_tools/model_optimizer/unit_tests --junitxml=TEST-ModelOptimizer.xml
. $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_DIR)/deployment_tools/model_optimizer/unit_tests --junitxml=TEST-ModelOptimizer.xml
displayName: 'Model Optimizer UT'
continueOnError: false
- script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
workingDirectory: $(INSTALL_TEST_DIR)
displayName: 'nGraph UT'
continueOnError: false
# . $(SETUPVARS) && python3 $(WORK_DIR)/gtest-parallel/gtest_parallel.py $(INSTALL_TEST_DIR)/InferenceEngineUnitTests --workers=$(WORKERS_NUMBER) --dump_json_test_results=InferenceEngineUnitTests.json --gtest_filter=*smoke* -- --gtest_print_time=1
- script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/paddlepaddle_tests --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-PaddlePaddle.xml
displayName: 'PaddlePaddle Frontend UT'
continueOnError: false
# . $(SETUPVARS) && python3 $(WORK_DIR)/gtest-parallel/gtest_parallel.py $(INSTALL_TEST_DIR)/InferenceEngineUnitTests --workers=16 --dump_json_test_results=InferenceEngineUnitTests.json --gtest_filter=*smoke* -- --gtest_print_time=1
- script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/InferenceEngineUnitTests --gtest_print_time=1 --gtest_output=xml:TEST-InferenceEngineUnitTests.xml
displayName: 'IE UT old'
continueOnError: false
@ -213,10 +234,9 @@ jobs:
export DATA_PATH=$(MODELS_PATH)
export MODELS_PATH=$(MODELS_PATH)
cd $(REPO_DIR)/inference-engine/ie_bridges/python/tests
. $(SETUPVARS) -pyver 3.6 && pytest pytest --junitxml=TEST-PythonAPI.xml
. $(SETUPVARS) -pyver 3.8 && python3 -m pytest --junitxml=TEST-PythonAPI.xml
displayName: 'Python API Tests'
continueOnError: false
enabled: false
- task: PublishTestResults@2
condition: always()

16
.ci/azure/linux_conditional_compilation.yml
View File

@ -4,20 +4,18 @@ jobs:
timeoutInMinutes: 90
pool:
name: LIN_VMSS_VENV_F16S_WU2
name: LIN_VMSS_VENV_F16S_U20_WU2
variables:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 16
BUILD_TYPE: Release
REPO_DIR: $(Build.Repository.LocalPath)
OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)/../openvino_contrib
MODELS_PATH: $(REPO_DIR)/../testdata
WORK_DIR: $(Pipeline.Workspace)/_w
BUILD_DIR: $(WORK_DIR)/build
BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
INSTALL_DIR: $(WORK_DIR)/install_pkg
SETUPVARS: $(INSTALL_DIR)/bin/setupvars.sh
@ -30,6 +28,7 @@ jobs:
echo Python info ; which python ; python --version
echo Java info ; which java ; java -version
echo gcc info ; which gcc ; gcc --version
echo cmake info ; which cmake ; cmake --version
lsb_release
env
cat /proc/cpuinfo
@ -53,10 +52,11 @@ jobs:
path: openvino
- script: |
sudo apt --assume-yes install libusb-1.0-0-dev
set -e
$(REPO_DIR)/install_build_dependencies.sh
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/requirements.txt
# Speed up build
wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
wget https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip
unzip ninja-linux.zip
sudo cp -v ninja /usr/local/bin/
workingDirectory: $(WORK_DIR)
@ -76,12 +76,14 @@ jobs:
- script: ninja
workingDirectory: $(BUILD_DIR)
displayName: 'Build'
displayName: 'Build LinCC'
- script: ls -alR $(REPO_DIR)/bin/
displayName: 'List files'
displayName: 'List bin files'
- script: cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -P cmake_install.cmake
workingDirectory: $(BUILD_DIR)
displayName: 'Install'
- script: ls -alR $(INSTALL_DIR)
displayName: 'List install files'

19
.ci/azure/linux_ngraph_onnx.yml
View File

@ -20,13 +20,12 @@ jobs:
timeoutInMinutes: 90
pool:
name: LIN_VMSS_VENV_ONNX_WU2
name: LIN_VMSS_VENV_ONNX_U20_WU2
variables:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 8
REPO_DIR: $(Build.Repository.LocalPath)
WORK_DIR: $(Pipeline.Workspace)/_w
MODELS_DIR: /mount/cinfsshare/onnxtestdata
@ -43,6 +42,7 @@ jobs:
echo Python info ; which python ; python --version
echo Java info ; which java ; java -version
echo gcc info ; which gcc ; gcc --version
echo cmake info ; which cmake ; cmake --version
lsb_release
env
cat /proc/cpuinfo
@ -68,16 +68,23 @@ jobs:
submodules: recursive
path: openvino
- script: docker build --tag=openvino-onnx-ci-image --file=.ci/openvino-onnx/Dockerfile --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg PROTOBUF_LITE=$(PROTOBUF_LITE) .
- script: |
set -e
sudo apt --assume-yes install git-lfs uidmap
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
workingDirectory: $(WORK_DIR)
displayName: 'Install dependencies'
- script: sudo docker build --tag=openvino-onnx-ci-image --file=.ci/openvino-onnx/Dockerfile --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg PROTOBUF_LITE=$(PROTOBUF_LITE) .
displayName: 'Docker build $(BUILD_TYPE) protobuf-lite: $(PROTOBUF_LITE)'
- script: ngraph/python/tests/test_onnx/model_zoo_preprocess.sh -d $(TMP_DIR) -o -s "$(ONNX_MODEL_ZOO_SHA)"
displayName: 'Get models'
condition: ne(variables['BUILD_TYPE'], 'Debug')
- script: sudo fallocate -l 48G /swapfile ; sudo mkswap /swapfile ; sudo swapon /swapfile ; df ; free -h
- script: sudo fallocate -l 64G /swapfile ; sudo mkswap /swapfile ; sudo swapon /swapfile ; df ; free -h
displayName: 'Create swap'
- script: |
docker run --name openvino-onnx-ci-container --volume $(TMP_DIR)/model_zoo/onnx_model_zoo_$(ONNX_MODEL_ZOO_SHA):/root/.onnx/model_zoo/onnx_model_zoo --volume $(MODELS_DIR)/msft:/root/.onnx/model_zoo/MSFT openvino-onnx-ci-image /bin/bash -c "$(TOX_COMMAND)"
- script: sudo docker run --name openvino-onnx-ci-container --volume $(TMP_DIR)/model_zoo/onnx_model_zoo_$(ONNX_MODEL_ZOO_SHA):/root/.onnx/model_zoo/onnx_model_zoo --volume $(MODELS_DIR)/msft:/root/.onnx/model_zoo/MSFT openvino-onnx-ci-image /bin/bash -c "$(TOX_COMMAND)"
displayName: 'Docker run $(BUILD_TYPE) protobuf-lite: $(PROTOBUF_LITE)'

23
.ci/azure/linux_onnxruntime.yml
View File

@ -3,23 +3,23 @@ jobs:
timeoutInMinutes: 90
pool:
name: LIN_VMSS_VENV_ONNX_WU2
name: LIN_VMSS_VENV_ONNX_U20_WU2
variables:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 8
BUILD_TYPE: Release
REPO_DIR: $(Build.Repository.LocalPath)
ONNXRUNTIME_REPO_DIR: $(REPO_DIR)/../onnxruntime
WORK_DIR: $(Pipeline.Workspace)/_w
MODELS_DIR: /mount/cinfsshare/onnxtestdata
TMP_DIR: /mnt/tmp
INSTALL_DIR: $(WORK_DIR)/install_pkg
INSTALL_DIR: $(WORK_DIR)/install_pkg/openvino
BUILD_DIR: $(WORK_DIR)/build
ONNXRUNTIME_UTILS: $(REPO_DIR)/.ci/azure/ci_utils/onnxruntime
ONNXRUNTIME_BUILD_DIR: $(ONNXRUNTIME_REPO_DIR)/build
steps:
- script: |
curl -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/instance?api-version=2019-06-01"
@ -29,6 +29,7 @@ jobs:
echo Python info ; which python ; python --version
echo Java info ; which java ; java -version
echo gcc info ; which gcc ; gcc --version
echo cmake info ; which cmake ; cmake --version
lsb_release
env
cat /proc/cpuinfo
@ -60,15 +61,14 @@ jobs:
displayName: 'Clone onnxruntime'
- script: |
sudo apt --assume-yes install libusb-1.0-0-dev
# For opencv-python: setuptools and upgrade
sudo apt-get install python3-setuptools
set -e
$(REPO_DIR)/install_build_dependencies.sh
python3 -m pip install --upgrade pip
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/requirements.txt
# For running Python API tests
python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/src/requirements-dev.txt
# Speed up build
wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
wget https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip
unzip ninja-linux.zip
sudo cp -v ninja /usr/local/bin/
# Speed up tests
@ -83,7 +83,7 @@ jobs:
-GNinja
-DCMAKE_BUILD_TYPE=$(BUILD_TYPE)
-DENABLE_PYTHON=ON
-DPYTHON_EXECUTABLE=/usr/bin/python3.6
-DPYTHON_EXECUTABLE=/usr/bin/python3.8
-DENABLE_VPU=OFF
-DENABLE_GNA=OFF
-DENABLE_OPENCV=OFF
@ -102,10 +102,10 @@ jobs:
- script: ninja
workingDirectory: $(BUILD_DIR)
displayName: 'Build Lin'
displayName: 'Build Lin ONNX'
- script: ls -alR $(REPO_DIR)/bin/
displayName: 'List files'
displayName: 'List bin files'
- script: cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -P cmake_install.cmake
workingDirectory: $(BUILD_DIR)
@ -113,10 +113,9 @@ jobs:
- script: |
source $(INSTALL_DIR)/bin/setupvars.sh
echo "2021.2" > $(INSTALL_DIR)/deployment_tools/inference_engine/version.txt
CXXFLAGS="-Wno-error=deprecated-declarations" ./build.sh --config RelWithDebInfo --use_openvino CPU_FP32 --build_shared_lib --parallel --skip_tests --build_dir $(ONNXRUNTIME_BUILD_DIR)
workingDirectory: $(ONNXRUNTIME_REPO_DIR)
displayName: 'Build ONNX Runtime'
displayName: 'Build Lin ONNX Runtime'
- script: |
source $(INSTALL_DIR)/bin/setupvars.sh

5
.ci/azure/mac.yml
View File

@ -22,7 +22,6 @@ jobs:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 3
BUILD_TYPE: Release
REPO_DIR: $(Build.Repository.LocalPath)
OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)/../openvino_contrib
@ -76,6 +75,7 @@ jobs:
- script: |
brew install cython
brew install automake
python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test_onnx.txt
# Speed up build
brew install ninja
# Speed up tests
@ -87,7 +87,7 @@ jobs:
export PATH="/usr/local/opt/cython/bin:$PATH"
export CC=gcc
export CXX=g++
cmake -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules $(REPO_DIR)
cmake -GNinja -DVERBOSE_BUILD=ON -DENABLE_REQUIREMENTS_INSTALL=OFF -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules $(REPO_DIR)
workingDirectory: $(BUILD_DIR)
displayName: 'CMake'
@ -103,6 +103,7 @@ jobs:
displayName: 'Install'
- script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid:IE_CPU/GRUSequenceOp.onnx_model_gru* --gtest_output=xml:TEST-NGraphUT.xml
workingDirectory: $(BIN_DIR)
displayName: 'nGraph UT'
continueOnError: false

26
.ci/azure/windows.yml
View File

@ -22,7 +22,6 @@ jobs:
system.debug: true
VSTS_HTTP_RETRY: 5
VSTS_HTTP_TIMEOUT: 200
WORKERS_NUMBER: 8
BUILD_TYPE: Release
REPO_DIR: $(Build.Repository.LocalPath)
OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)\..\openvino_contrib
@ -42,6 +41,7 @@ jobs:
- script: |
powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
where python3
python3 --version
where python
python --version
where java
@ -83,7 +83,18 @@ jobs:
path: testdata
- script: |
certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-win.zip ninja-win.zip
python -m pip install --upgrade pip
rem For running Python API tests
python -m pip install -r $(REPO_DIR)\inference-engine\ie_bridges\python\src\requirements-dev.txt
rem For running PaddlePaddle frontend unit tests
python -m pip install -r $(REPO_DIR)\ngraph\test\frontend\paddlepaddle\requirements_dev.txt
rem For running ONNX frontend unit tests
python -m pip install -r $(REPO_DIR)\ngraph\test\requirements_test_onnx.txt
rem For MO unit tests
python -m pip install -r $(REPO_DIR)\model-optimizer\requirements.txt
python -m pip install -r $(REPO_DIR)\model-optimizer\requirements_dev.txt
rem Speed up build
certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip ninja-win.zip
powershell -command "Expand-Archive -Force ninja-win.zip"
git clone https://github.com/google/gtest-parallel.git
workingDirectory: $(WORK_DIR)
@ -91,7 +102,7 @@ jobs:
- script: |
set PATH=$(WORK_DIR)\ninja-win;%PATH%
call "$(MSVS_VARS_PATH)" && cmake -GNinja -DENABLE_FASTER_BUILD=ON -DENABLE_TEMPLATE_PLUGIN=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
call "$(MSVS_VARS_PATH)" && cmake -GNinja -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DENABLE_TEMPLATE_PLUGIN=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
workingDirectory: $(BUILD_DIR)
displayName: 'CMake'
@ -129,10 +140,19 @@ jobs:
workingDirectory: $(BUILD_SAMPLES_DIR)
displayName: 'Build c samples'
- script: rd /Q /S $(BUILD_DIR)
displayName: 'Clean build dir'
continueOnError: false
- script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
workingDirectory: $(INSTALL_TEST_DIR)
displayName: 'nGraph UT'
continueOnError: false
- script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddlepaddle_tests --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-PaddlePaddle.xml
displayName: 'PaddlePaddle Frontend UT'
continueOnError: false
- script: |
set PATH=$(IB_DIR);%PATH%
call $(SETUPVARS) && "$(IB_TESTCONSOLE)" $(INSTALL_TEST_DIR)\InferenceEngineUnitTests.exe --gtest_output=xml:TEST-InferenceEngineUnitTests-IB.xml

28
.github/workflows/code_style.yml vendored
View File

@ -28,7 +28,7 @@ jobs:
cmake -DENABLE_PYTHON=ON -DENABLE_TESTS=ON -DENABLE_PROFILING_ITT=ON -DSELECTIVE_BUILD=COLLECT ..
- name: Check code style
run: cmake --build build --target clang_format_check_all
run: cmake --build build --target clang_format_check_all -j8
- name: Create code style diff
if: failure()
@ -64,5 +64,29 @@ jobs:
cmake ..
- name: ShellCheck
run: make ie_shellcheck
run: cmake --build . --target ie_shellcheck -j8
working-directory: build
NamingConventionCheck:
runs-on: ubuntu-18.04
steps:
- uses: actions/checkout@v2
with:
submodules: recursive
- name: Install Clang dependency
run: sudo apt --assume-yes install libusb-1.0-0-dev libclang-9-dev
- name: Install Python-based dependencies
run: |
python3 -m pip install pyyaml clang==9.0
- name: CMake
run: |
mkdir build
cd build
cmake ..
- name: Naming convention check
run: cmake --build . --target ncc_all -j8
working-directory: build

5
.gitmodules vendored
View File

@ -40,7 +40,7 @@
ignore = dirty
[submodule "thirdparty/onnx"]
path = thirdparty/onnx/onnx
url = https://github.com/openvinotoolkit/onnx.git
url = https://github.com/onnx/onnx.git
[submodule "thirdparty/protobuf"]
path = thirdparty/protobuf/protobuf
url = https://github.com/protocolbuffers/protobuf.git
@ -50,3 +50,6 @@
[submodule "thirdparty/ittapi/ittapi"]
path = thirdparty/ittapi/ittapi
url = https://github.com/intel/ittapi.git
[submodule "ncc"]
path = cmake/developer_package/ncc_naming_style/ncc
url = https://github.com/nithinn/ncc.git

3
CMakeLists.txt
View File

@ -77,6 +77,9 @@ endfunction()
ie_cpack_add_component(ngraph REQUIRED)
ie_cpack_add_component(ngraph_dev REQUIRED DEPENDS ngraph)
# add target with processed tests model zoo
include(cmake/test_model_zoo.cmake)
add_subdirectory(thirdparty)
add_subdirectory(openvino)
add_subdirectory(ngraph)

10
CODEOWNERS
View File

@ -30,13 +30,13 @@ azure-pipelines.yml @openvinotoolkit/openvino-admins
# IE GPU:
/inference-engine/src/cldnn_engine/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
/inference-engine/include/gpu/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
/inference-engine/include/cldnn/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
/inference-engine/src/inference_engine/include/gpu/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
/inference-engine/src/inference_engine/include/cldnn/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
/inference-engine/thirdparty/clDNN/ @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
# IE VPU:
/inference-engine/src/vpu/ @openvinotoolkit/openvino-ie-vpu-maintainers
/inference-engine/include/vpu/ @openvinotoolkit/openvino-ie-vpu-maintainers
/inference-engine/src/inference_engine/include/vpu/ @openvinotoolkit/openvino-ie-vpu-maintainers
/inference-engine/thirdparty/movidius/ @openvinotoolkit/openvino-ie-vpu-maintainers
/inference-engine/tests_deprecated/unit/engines/vpu/ @openvinotoolkit/openvino-ie-vpu-maintainers @openvinotoolkit/openvino-ie-tests-maintainers
/inference-engine/tests_deprecated/functional/vpu/ @openvinotoolkit/openvino-ie-vpu-maintainers @openvinotoolkit/openvino-ie-tests-maintainers
@ -49,11 +49,11 @@ azure-pipelines.yml @openvinotoolkit/openvino-admins
# IE GNA:
/inference-engine/src/gna_plugin/ @openvinotoolkit/openvino-ie-gna-maintainers
/inference-engine/include/gna/ @openvinotoolkit/openvino-ie-gna-maintainers
/inference-engine/src/inference_engine/include/gna/ @openvinotoolkit/openvino-ie-gna-maintainers
# IE MULTI:
/inference-engine/src/multi_device/ @openvinotoolkit/openvino-ie-multi-maintainers
/inference-engine/include/multi-device/ @openvinotoolkit/openvino-ie-multi-maintainers
/inference-engine/src/inference_engine/include/multi-device/ @openvinotoolkit/openvino-ie-multi-maintainers
# IE Tests:
/inference-engine/tests/ @openvinotoolkit/openvino-ie-tests-maintainers

22
cmake/dependencies.cmake
View File

@ -7,10 +7,6 @@ cmake_policy(SET CMP0054 NEW)
# TODO: fix it
set_temp_directory(TEMP "${IE_MAIN_SOURCE_DIR}")
if(CMAKE_CROSSCOMPILING)
set(CMAKE_STAGING_PREFIX "${TEMP}")
endif()
if(ENABLE_SAME_BRANCH_FOR_MODELS)
branchName(MODELS_BRANCH)
else()
@ -315,25 +311,25 @@ if(ENABLE_SPEECH_DEMO)
if(DEFINED IE_PATH_TO_DEPS)
if(WIN32 AND X86_64)
RESOLVE_DEPENDENCY(SPEECH_LIBS_AND_DEMOS
ARCHIVE_WIN "speech_demo_1.0.0.774_windows.zip"
ARCHIVE_WIN "speech_demo_1.0.0.780_windows.zip"
VERSION_REGEX ".*_([0-9]+.[0-9]+.[0-9]+.[0-9]+).*"
TARGET_PATH "${TEMP}/speech_demo_1.0.0.774"
SHA256 "67b25170be5e89a4f0e90e8b39623b60c9a15b965c30329385e295fcd2edc856")
TARGET_PATH "${TEMP}/speech_demo_1.0.0.780"
SHA256 "957bd274a1f6dc1d83a46879c7ef3b3b06f17d11af85cc45c18919051d145abd")
debug_message(STATUS "speech_libs_and_demos=" ${SPEECH_LIBS_AND_DEMOS})
elseif(LINUX AND X86_64)
if(LINUX_OS_NAME STREQUAL "CentOS 7" OR CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.9")
RESOLVE_DEPENDENCY(SPEECH_LIBS_AND_DEMOS
ARCHIVE_LIN "speech_demo_1.0.0.774_centos.tgz"
ARCHIVE_LIN "speech_demo_1.0.0.780_centos.tgz"
VERSION_REGEX ".*_([0-9]+.[0-9]+.[0-9]+.[0-9]+).*"
TARGET_PATH "${TEMP}/speech_demo_1.0.0.774"
SHA256 "5ec3b7be9ae05376aefae5bd5fd4a39b12c274e82817fd3218120b8e8fc8ff5a")
TARGET_PATH "${TEMP}/speech_demo_1.0.0.780"
SHA256 "6d8d1111d0e662fe71d71cd3debad2995f6fb6fe5df3b92196dae06ff7abdf44")
debug_message(STATUS "speech_libs_and_demos=" ${SPEECH_LIBS_AND_DEMOS})
else()
RESOLVE_DEPENDENCY(SPEECH_LIBS_AND_DEMOS
ARCHIVE_LIN "speech_demo_1.0.0.774_linux.tgz"
ARCHIVE_LIN "speech_demo_1.0.0.780_linux.tgz"
VERSION_REGEX ".*_([0-9]+.[0-9]+.[0-9]+.[0-9]+).*"
TARGET_PATH "${TEMP}/speech_demo_1.0.0.774"
SHA256 "f0bbd0a6218b0365e7cfb1f860b34e4ace7e0d47dd60b369cdea8a480329810f")
TARGET_PATH "${TEMP}/speech_demo_1.0.0.780"
SHA256 "0ec6f1e47c00d781dc918af5d3055ab474ff47b9978dd6fe2add73e3339b0763")
debug_message(STATUS "speech_libs_and_demos=" ${SPEECH_LIBS_AND_DEMOS})
endif()
else()

35
cmake/developer_package/IEDevScriptsConfig.cmake
View File

@ -132,7 +132,7 @@ set(IE_DEBUG_POSTFIX_WIN "d")
set(IE_RELEASE_POSTFIX_WIN "")
set(IE_DEBUG_POSTFIX_LIN "")
set(IE_RELEASE_POSTFIX_LIN "")
set(IE_DEBUG_POSTFIX_MAC "d")
set(IE_DEBUG_POSTFIX_MAC "")
set(IE_RELEASE_POSTFIX_MAC "")
if(WIN32)
@ -187,8 +187,8 @@ set_property(GLOBAL PROPERTY USE_FOLDERS ON)
# Enable CMAKE_<LANG>_COMPILER_ID AppleClang
set(CMAKE_POLICY_DEFAULT_CMP0025 NEW)
set(CMAKE_WARN_DEPRECATED OFF)
set(CMAKE_WARN_ON_ABSOLUTE_INSTALL_DESTINATION ON)
set(CMAKE_WARN_DEPRECATED OFF CACHE BOOL "Don't warn about obsolete cmake versions in 3rdparty")
set(CMAKE_WARN_ON_ABSOLUTE_INSTALL_DESTINATION ON CACHE BOOL "Warn about absolute paths in destination")
# LTO
@ -251,27 +251,48 @@ endfunction()
# check python package
function(ie_check_pip_package name message_type)
function(ie_check_pip_package full_name message_type)
find_package(PythonInterp 3 REQUIRED)
get_filename_component(PYTHON_EXEC_DIR ${PYTHON_EXECUTABLE} DIRECTORY)
# extract version if any
if(full_name MATCHES "^([a-z_]+)[~=<>!]*(.*)$")
set(name ${CMAKE_MATCH_1})
set(req_version ${CMAKE_MATCH_2})
else()
set(name ${full_name})
endif()
execute_process(
COMMAND ${PYTHON_EXECUTABLE} -m pip show ${name}
WORKING_DIRECTORY ${PYTHON_EXEC_DIR}
RESULT_VARIABLE PIP_EXIT_CODE
OUTPUT_QUIET
)
OUTPUT_VARIABLE output)
if(NOT PIP_EXIT_CODE EQUAL 0)
set(${name}_FOUND OFF PARENT_SCOPE)
message(${message_type} "${name} package is not installed. Please use \"${PYTHON_EXECUTABLE} -m pip install ${name}\".")
message(${message_type} "${name} package is not installed. Please use \"${PYTHON_EXECUTABLE} -m pip install ${full_name}\".")
else()
if(req_version)
string(REGEX MATCH "Version: ([0-9]+\.?[0-9]*\.?[0-9]*)\n" installed_version "${output}")
if(installed_version)
set(installed_version "${CMAKE_MATCH_1}")
endif()
message(${message_type} "${name} package is installed, but may have different version (${installed_version}). "
"Please use \"${PYTHON_EXECUTABLE} -m pip install ${full_name}\".")
else()
set(${name}_FOUND ON PARENT_SCOPE)
endif()
endif()
endfunction()
# Code style utils
include(cpplint/cpplint)
include(clang_format/clang_format)
include(ncc_naming_style/ncc_naming_style)
# Restore state
set(CMAKE_MODULE_PATH ${OLD_CMAKE_MODULE_PATH})

2
cmake/developer_package/clang_format/clang_format.cmake
View File

@ -4,7 +4,7 @@
if(ENABLE_CLANG_FORMAT)
set(CLANG_FORMAT_FILENAME clang-format-9 clang-format)
find_program(CLANG_FORMAT NAMES ${CLANG_FORMAT_FILENAME} PATHS ENV PATH)
find_host_program(CLANG_FORMAT NAMES ${CLANG_FORMAT_FILENAME} PATHS ENV PATH)
if(CLANG_FORMAT)
execute_process(COMMAND ${CLANG_FORMAT} ${CMAKE_CURRENT_SOURCE_DIR} ARGS --version OUTPUT_VARIABLE CLANG_VERSION)
if(NOT CLANG_VERSION OR CLANG_VERSION STREQUAL "")

38
cmake/developer_package/compile_flags/os_flags.cmake
View File

@ -68,13 +68,13 @@ function(ie_sse42_optimization_flags flags)
if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
# No such option for MSVC 2019
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
set(${flags} /arch:SSE4.2 /QxSSE4.2 PARENT_SCOPE)
set(${flags} /QxSSE4.2 PARENT_SCOPE)
else()
message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}")
endif()
else()
if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
set(${flags} -msse4.2 -xSSE4.2 PARENT_SCOPE)
set(${flags} -xSSE4.2 PARENT_SCOPE)
else()
set(${flags} -msse4.2 PARENT_SCOPE)
endif()
@ -95,7 +95,7 @@ function(ie_avx2_optimization_flags flags)
endif()
else()
if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
set(${flags} -march=core-avx2 -xCORE-AVX2 -mtune=core-avx2 PARENT_SCOPE)
set(${flags} -xCORE-AVX2 PARENT_SCOPE)
else()
set(${flags} -mavx2 -mfma PARENT_SCOPE)
endif()
@ -152,6 +152,24 @@ function(ie_arm_neon_optimization_flags flags)
endif()
endfunction()
#
# Disables all warnings for 3rd party targets
#
function(ov_disable_all_warnings)
foreach(target IN LISTS ARGN)
if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
target_compile_options(${target} PRIVATE /WX-)
elseif(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG)
target_compile_options(${target} PRIVATE -w)
elseif(UNIX AND CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
# 193: zero used for undefined preprocessing identifier "XXX"
# 1011: missing return statement at end of non-void function "XXX"
# 2415: variable "xxx" of static storage duration was declared but never referenced
target_compile_options(${target} PRIVATE -diag-disable=warn,193,1011,2415)
endif()
endforeach()
endfunction()
#
# Enables Link Time Optimization compilation
#
@ -286,15 +304,13 @@ else()
ie_add_compiler_flags(-Wreturn-type)
ie_add_compiler_flags(-Wunused-variable)
# Disable noisy warnings
if (CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
ie_add_compiler_flags(-Wswitch)
elseif(UNIX)
ie_add_compiler_flags(-Wuninitialized -Winit-self)
if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
ie_add_compiler_flags(-Wno-error=switch
-Winconsistent-missing-override)
ie_add_compiler_flags(-Winconsistent-missing-override
-Wstring-plus-int)
else()
ie_add_compiler_flags(-Wmaybe-uninitialized)
check_cxx_compiler_flag("-Wsuggest-override" SUGGEST_OVERRIDE_SUPPORTED)
@ -304,10 +320,11 @@ else()
endif()
endif()
# Disable noisy warnings
if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
ie_add_compiler_flags(-diag-disable=remark)
# noisy warnings from Intel Compiler 19.1.1.217 20200306
ie_add_compiler_flags(-diag-disable=2196)
# 177: function "XXX" was declared but never referenced
ie_add_compiler_flags(-diag-disable=remark,177,2196)
endif()
# Linker flags
@ -315,7 +332,6 @@ else()
if(APPLE)
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -Wl,-dead_strip")
set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} -Wl,-dead_strip")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,-dead_strip")
elseif(LINUX)
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -Wl,--gc-sections -Wl,--exclude-libs,ALL")
set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} -Wl,--gc-sections -Wl,--exclude-libs,ALL")

15
cmake/developer_package/compile_flags/sanitizer.cmake
View File

@ -18,6 +18,8 @@ if (ENABLE_UB_SANITIZER)
# TODO: Remove -fno-sanitize=null as thirdparty/ocl/clhpp_headers UBSAN compatibility resolved:
# https://github.com/KhronosGroup/OpenCL-CLHPP/issues/17
set(SANITIZER_COMPILER_FLAGS "${SANITIZER_COMPILER_FLAGS} -fsanitize=undefined -fno-sanitize=null")
# TODO: Remove -Wno-maybe-uninitialized after CVS-61143 fix
set(SANITIZER_COMPILER_FLAGS "${SANITIZER_COMPILER_FLAGS} -Wno-maybe-uninitialized")
check_cxx_compiler_flag("-fsanitize-recover=undefined" SANITIZE_RECOVER_UNDEFINED_SUPPORTED)
if (SANITIZE_RECOVER_UNDEFINED_SUPPORTED)
set(SANITIZER_COMPILER_FLAGS "${SANITIZER_COMPILER_FLAGS} -fsanitize-recover=undefined")
@ -33,18 +35,19 @@ endif()
# common sanitizer options
if (DEFINED SANITIZER_COMPILER_FLAGS)
# ensure sumbols are present
# ensure symbols are present
set(SANITIZER_COMPILER_FLAGS "${SANITIZER_COMPILER_FLAGS} -g -fno-omit-frame-pointer")
if(NOT OV_COMPILER_IS_CLANG)
# GPU plugin tests compilation is slow with -fvar-tracking-assignments on GCC.
# Clang has no var-tracking-assignments.
set(SANITIZER_COMPILER_FLAGS "${SANITIZER_COMPILER_FLAGS} -fno-var-tracking-assignments")
endif()
# prevent unloading libraries at runtime, so sanitizer can resolve their symbols
set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -Wl,-z,nodelete")
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=gold")
elseif(OV_COMPILER_IS_CLANG AND NOT WIN32)
if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
if(OV_COMPILER_IS_CLANG AND NOT WIN32 AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld")
endif()
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SANITIZER_COMPILER_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SANITIZER_COMPILER_FLAGS}")

6
cmake/developer_package/features.cmake
View File

@ -44,16 +44,14 @@ ie_option (BUILD_SHARED_LIBS "Build as a shared library" ON)
ie_dependent_option (ENABLE_FASTER_BUILD "Enable build features (PCH, UNITY) to speed up build time" OFF "CMAKE_VERSION VERSION_GREATER_EQUAL 3.16" OFF)
if(NOT DEFINED ENABLE_CPPLINT)
ie_dependent_option (ENABLE_CPPLINT "Enable cpplint checks during the build" ON "UNIX;NOT ANDROID" OFF)
endif()
if(NOT DEFINED ENABLE_CPPLINT_REPORT)
ie_dependent_option (ENABLE_CPPLINT_REPORT "Build cpplint report instead of failing the build" OFF "ENABLE_CPPLINT" OFF)
endif()
ie_dependent_option (ENABLE_CLANG_FORMAT "Enable clang-format checks during the build" ON "UNIX;NOT ANDROID" OFF)
ie_dependent_option (ENABLE_NCC_STYLE "Enable ncc style check" ON "UNIX;NOT ANDROID" OFF)
ie_option (VERBOSE_BUILD "shows extra information about build" OFF)
ie_option (ENABLE_UNSAFE_LOCATIONS "skip check for MD5 for dependency" OFF)

1
cmake/developer_package/ncc_naming_style/ncc Submodule

@ -0,0 +1 @@
Subproject commit d7d83049708eaa18ea6796adf0eeef85b28ebc1f

137
cmake/developer_package/ncc_naming_style/ncc_naming_style.cmake Normal file
View File

@ -0,0 +1,137 @@
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
if(NOT COMMAND ie_check_pip_package)
message(FATAL_ERROR "ncc_naming_style.cmake must be included after ie_check_pip_package")
endif()
set(ncc_style_dir "${IEDevScripts_DIR}/ncc_naming_style")
set(ncc_style_bin_dir "${CMAKE_CURRENT_BINARY_DIR}/ncc_naming_style")
# try to find_package(Clang QUIET)
# ClangConfig.cmake contains bug that if libclang-XX-dev is not
# installed, then find_package fails with errors even in QUIET mode
configure_file("${ncc_style_dir}/try_find_clang.cmake"
"${ncc_style_bin_dir}/source/CMakeLists.txt" COPYONLY)
execute_process(
COMMAND
"${CMAKE_COMMAND}" -S "${ncc_style_bin_dir}/source"
-B "${ncc_style_bin_dir}/build"
RESULT_VARIABLE clang_find_result
OUTPUT_VARIABLE output
ERROR_VARIABLE output)
if(NOT clang_find_result EQUAL "0")
message(WARNING "Please, install libclang-[N]-dev package (required for ncc naming style check)")
set(ENABLE_NCC_STYLE OFF)
endif()
# Since we were able to find_package(Clang) in a separate process
# let's try to find in current process
if(ENABLE_NCC_STYLE)
find_host_package(Clang QUIET)
if(Clang_FOUND AND TARGET libclang)
get_target_property(libclang_location libclang LOCATION)
set(ncc_wrapper_py "${ncc_style_bin_dir}/ncc_wrapper.py")
configure_file("${ncc_style_dir}/ncc_wrapper.py.in" ${ncc_wrapper_py} @ONLY)
message(STATUS "Found libclang: ${libclang_location}")
else()
message(WARNING "libclang is not found (required for ncc naming style check)")
set(ENABLE_NCC_STYLE OFF)
endif()
endif()
# find python3
find_package(PythonInterp 3 QUIET)
if(NOT PYTHONINTERP_FOUND)
message(WARNING "Python3 interpreter was not found (required for ncc naming style check)")
set(ENABLE_NCC_STYLE OFF)
endif()
# check python requirements_dev.txt
set(req_file "${ncc_style_dir}/requirements_dev.txt")
file(STRINGS ${req_file} req_lines)
foreach(req IN LISTS req_lines)
ie_check_pip_package(${req} STATUS)
endforeach()
set(ncc_script_dir "${ncc_style_dir}/ncc/")
set(ncc_script_py "${ncc_style_dir}/ncc/ncc.py")
if(NOT EXISTS ${ncc_script_py})
message(WARNING "ncc.py is not downloaded via submodule")
set(ENABLE_NCC_STYLE OFF)
endif()
# create high-level target
if(ENABLE_NCC_STYLE AND NOT TARGET ncc_all)
add_custom_target(ncc_all ALL)
set_target_properties(ncc_all PROPERTIES FOLDER ncc_naming_style)
endif()
#
# ov_ncc_naming_style(FOR_TARGET target_name
# INCLUDE_DIRECTORY dir
# [ADDITIONAL_INCLUDE_DIRECTORIES dir1 dir2 ..])
#
# FOR_TARGET - name of the target
# INCLUDE_DIRECTORY - directory to check headers from
# ADDITIONAL_INCLUDE_DIRECTORIES - additional include directories used in checked headers
#
function(ov_ncc_naming_style)
if(NOT ENABLE_NCC_STYLE)
return()
endif()
cmake_parse_arguments(NCC_STYLE ""
"FOR_TARGET;INCLUDE_DIRECTORY" "ADDITIONAL_INCLUDE_DIRECTORIES" ${ARGN})
file(GLOB_RECURSE headers
RELATIVE "${NCC_STYLE_INCLUDE_DIRECTORY}"
"${NCC_STYLE_INCLUDE_DIRECTORY}/*.hpp")
set(new_pythonpath "${ncc_script_dir}:$ENV{PYTHOPATH}")
list(APPEND ADDITIONAL_INCLUDE_DIRECTORIES "${NCC_STYLE_INCLUDE_DIRECTORY}")
foreach(header IN LISTS headers)
set(output_file "${ncc_style_bin_dir}/${header}.ncc_style")
set(full_header_path "${NCC_STYLE_INCLUDE_DIRECTORY}/${header}")
add_custom_command(
OUTPUT
${output_file}
COMMAND
"${CMAKE_COMMAND}" -E env PYTHONPATH=${new_pythonpath}
"${CMAKE_COMMAND}"
-D "PYTHON_EXECUTABLE=${PYTHON_EXECUTABLE}"
-D "NCC_PY_SCRIPT=${ncc_wrapper_py}"
-D "INPUT_FILE=${full_header_path}"
-D "OUTPUT_FILE=${output_file}"
-D "STYLE_FILE=${ncc_style_dir}/openvino.style"
-D "ADDITIONAL_INCLUDE_DIRECTORIES=${ADDITIONAL_INCLUDE_DIRECTORIES}"
-P "${ncc_style_dir}/ncc_run.cmake"
DEPENDS
"${full_header_path}"
"${ncc_style_dir}/openvino.style"
"${ncc_script_py}"
"${ncc_wrapper_py}"
"${ncc_style_dir}/ncc_run.cmake"
COMMENT
"[ncc naming style] ${header}"
VERBATIM)
list(APPEND output_files ${output_file})
endforeach()
set(ncc_target ${NCC_STYLE_FOR_TARGET}_ncc_check)
add_custom_target(${ncc_target}
DEPENDS ${output_files}
COMMENT "[ncc naming style] ${NCC_STYLE_FOR_TARGET}")
add_dependencies(${NCC_STYLE_FOR_TARGET} ${ncc_target})
add_dependencies(ncc_all ${ncc_target})
endfunction()

31
cmake/developer_package/ncc_naming_style/ncc_run.cmake Normal file
View File

@ -0,0 +1,31 @@
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
foreach(var NCC_PY_SCRIPT PYTHON_EXECUTABLE OUTPUT_FILE
INPUT_FILE ADDITIONAL_INCLUDE_DIRECTORIES STYLE_FILE)
if(NOT DEFINED ${var})
message(FATAL_ERROR "${var} is not defined for ncc_run.cmake")
endif()
endforeach()
file(REMOVE "${OUTPUT_FILE}")
execute_process(
COMMAND
"${PYTHON_EXECUTABLE}"
"${NCC_PY_SCRIPT}"
--path ${INPUT_FILE}
--style ${STYLE_FILE}
--include ${ADDITIONAL_INCLUDE_DIRECTORIES}
RESULT_VARIABLE result
OUTPUT_VARIABLE output
ERROR_VARIABLE output)
file(WRITE "${OUTPUT_FILE}" "${output}")
if(NOT result EQUAL "0")
# Display the output to console (to parse it form IDE)
message("${output}")
message(FATAL_ERROR "[ncc naming style] Naming style check failed for ${INPUT_FILE}")
endif()

52
cmake/developer_package/ncc_naming_style/ncc_wrapper.py.in Normal file
View File

@ -0,0 +1,52 @@
#!/usr/bin/python3
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import logging
import os
import sys
from clang.cindex import Config
from ncc import Options, RulesDb, do_validate, Validator
if __name__ == "__main__":
# set path to speicific clang library location
Config.set_library_file('@libclang_location@')
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s',
filename='log.txt', filemode='w')
""" Parse all command line arguments and validate """
op = Options()
op.parse_cmd_line()
if op.args.path is None:
sys.exit(1)
""" Creating the rules database """
rules_db = RulesDb(op._style_file)
""" Check the source code against the configured rules """
errors = 0
for path in op.args.path:
if os.path.isfile(path):
if do_validate(op, path):
v = Validator(rules_db, path, op)
errors += v.validate()
elif os.path.isdir(path):
for (root, subdirs, files) in os.walk(path):
for filename in files:
path = root + '/' + filename
if do_validate(op, path):
v = Validator(rules_db, path, op)
errors += v.validate()
if not op.args.recurse:
break
else:
sys.stderr.write("File '{}' not found!\n".format(path))
if errors:
print("Total number of errors = {}".format(errors))
sys.exit(1)

129
cmake/developer_package/ncc_naming_style/openvino.style Normal file
View File

@ -0,0 +1,129 @@
# custom OpenVINO values
CppMethod: '^(operator\W+|[a-z_\d]+|signaling_NaN|quiet_NaN)$'
# TODO: remove stopwatch|unsupported_op
ClassName: '^([A-Z][\w]+|b?float16|numeric_limits|ngraph_error|stopwatch|unsupported_op)$'
# TODO: remove oi_pair
StructName: '^([A-Z][\w]+|element_type_traits|hash|oi_pair)$'
FunctionName: '^(operator\W+|[a-z_\d]+)$'
Namespace: '^[a-z\d_]+$'
NamespaceAlias: '^[a-z\d_]+$'
UnionName: '[A-Z][\w]+$'
TemplateTemplateParameter: '[A-Z][\w]+'
NamespaceReference: '^[a-z\d_]+$'
TemplateNonTypeParameter: '^\w*$'
ClassTemplate: '^([A-Z][\w]+|element_type_traits)$'
TemplateTypeParameter: '^\w*$'
ParameterName: '^\w*$'
FunctionTemplate: '^(operator.+|\w+)$'
TypeAliasName: '^\w+$'
VariableReference: '^\w+$'
# TODO: align
EnumConstantName: '^.*$'
EnumName: '^.*$'
UsingDeclaration: '^.*$'
TypedefName: '^.*$'
# not needed values
ClassTemplatePartialSpecialization: 'XXXX'
ConversionFunction: '^.*$'
UsingDirective: 'XXXX'
ClassAccessSpecifier: '^.*$' # looks like can be fixed
TypeReference: '^.*$' # looks like can be fixed
CxxBaseSpecifier: '^.*$' # looks like can be fixed
TemplateReference: '^.*$'
MemberReference: '^.*$'
LabelReference: 'XXXX'
OverloadedDeclarationReference: '^.*$'
InvalidFile: 'XXXX'
NoDeclarationFound: 'XXXX'
NotImplemented: 'XXXX'
InvalidCode: 'XXXX'
UnexposedExpression: '^.*$'
DeclarationReferenceExpression: '^.*$'
MemberReferenceExpression: '^.*$'
CallExpression: '^.*$'
BlockExpression: 'XXXX'
IntegerLiteral: '^.*$'
FloatingLiteral: '^.*$'
ImaginaryLiteral: 'XXXX'
StringLiteral: '^.*$'
CharacterLiteral: '^.*$'
ParenExpression: '^.*$'
UnaryOperator: '^.*$'
ArraySubscriptExpression: '^.*$'
BinaryOperator: '^.*$'
CompoundAssignmentOperator: '^.*$'
ConditionalOperator: '^.*$'
CstyleCastExpression: '^.*$'
CompoundLiteralExpression: 'XXXX'
InitListExpression: '^.*$'
AddrLabelExpression: 'XXXX'
StatementExpression: 'XXXX'
GenericSelectionExpression: 'XXXX'
GnuNullExpression: 'XXXX'
CxxStaticCastExpression: '^.*$'
CxxDynamicCastExpression: 'XXXX'
CxxReinterpretCastExpression: '^.*$'
CxxConstCastExpression: 'XXXX'
CxxFunctionalCastExpression: '^.*$'
CxxTypeidExpression: 'XXXX'
CxxBoolLiteralExpression: '^.*$'
CxxNullPointerLiteralExpression: '^.*$'
CxxThisExpression: '^.*$'
CxxThrowExpression: '^.*$'
CxxNewExpression: '^.*$'
CxxDeleteExpression: 'XXXX'
CxxUnaryExpression: '^.*$'
PackExpansionExpression: '^.*$'
SizeOfPackExpression: '^.*$'
LambdaExpression: '^.*$'
ObjectBoolLiteralExpression: 'XXXX'
ObjectSelfExpression: 'XXXX'
UnexposedStatement: 'XXXX'
LabelStatement: 'XXXX'
CompoundStatement: '^.*$'
CaseStatement: '^.*$'
DefaultStatement: '^.*$'
IfStatement: '^.*$'
SwitchStatement: '^.*$'
WhileStatement: '^.*$'
DoStatement: '^.*$'
ForStatement: '^.*$'
GotoStatement: 'XXXX'
IndirectGotoStatement: 'XXXX'
ContinueStatement: '^.*$'
BreakStatement: '^.*$'
ReturnStatement: '^.*$'
AsmStatement: 'XXXX'
CxxCatchStatement: 'XXXX'
CxxTryStatement: 'XXXX'
CxxForRangeStatement: '^.*$'
MsAsmStatement: 'XXXX'
NullStatement: 'XXXX'
DeclarationStatement: '^.*$'
TranslationUnit: 'XXXX'
UnexposedAttribute: '^.*$'
CxxFinalAttribute: 'XXXX'
CxxOverrideAttribute: '^.*$'
AnnotateAttribute: 'XXXX'
AsmLabelAttribute: 'XXXX'
PackedAttribute: 'XXXX'
PureAttribute: 'XXXX'
ConstAttribute: 'XXXX'
NoduplicateAttribute: 'XXXX'
PreprocessingDirective: 'XXXX'
MacroDefinition: 'XXXX'
MacroInstantiation: 'XXXX'
InclusionDirective: 'XXXX'
VariableName:
ScopePrefix:
Global: ''
Static: ''
ClassMember: ''
DataTypePrefix:
String: ''
Integer: ''
Bool: ''
Pointer: ''
Pattern: '^.*$'

2
cmake/developer_package/ncc_naming_style/requirements_dev.txt Normal file
View File

@ -0,0 +1,2 @@
clang==9.0
pyyaml

8
cmake/developer_package/ncc_naming_style/try_find_clang.cmake Normal file
View File

@ -0,0 +1,8 @@
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
cmake_minimum_required(VERSION 3.13)
project(try_find_clang)
find_package(Clang QUIET)

2
cmake/developer_package/version.cmake
View File

@ -42,7 +42,7 @@ macro(ie_parse_ci_build_number)
return()
endif()
set(ie_version_hpp "${OpenVINO_SOURCE_DIR}/inference-engine/include/ie_version.hpp")
set(ie_version_hpp "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/ie/ie_version.hpp")
if(NOT EXISTS ${ie_version_hpp})
message(FATAL_ERROR "File ie_version.hpp with IE_VERSION definitions is not found")
endif()

7
cmake/features.cmake
View File

@ -98,7 +98,7 @@ ie_dependent_option (ENABLE_FUNCTIONAL_TESTS "functional tests" ON "ENABLE_TESTS
ie_dependent_option (ENABLE_SAMPLES "console samples are part of inference engine package" ON "NOT MINGW" OFF)
ie_dependent_option (ENABLE_SPEECH_DEMO "enable speech demo integration" ON "NOT APPLE;NOT ANDROID;X86 OR X86_64" OFF)
ie_dependent_option (ENABLE_SPEECH_DEMO "enable speech demo integration" ON "NOT APPLE;NOT ANDROID;X86_64" OFF)
ie_option (ENABLE_OPENCV "enables OpenCV" ON)
@ -125,14 +125,15 @@ endif()
ie_dependent_option(NGRAPH_ONNX_IMPORT_ENABLE "Enable ONNX importer" ON "protoc_available" OFF)
ie_dependent_option(NGRAPH_ONNX_FRONTEND_ENABLE "Enable ONNX FrontEnd" OFF "NGRAPH_ONNX_IMPORT_ENABLE" OFF)
ie_dependent_option(NGRAPH_PDPD_FRONTEND_ENABLE "Enable PaddlePaddle FrontEnd" ON "protoc_available" OFF)
ie_dependent_option(NGRAPH_USE_PROTOBUF_LITE "Compiles and links with protobuf-lite" OFF
"NGRAPH_ONNX_IMPORT_ENABLE OR NGRAPH_PDPD_FRONTEND_ENABLE" OFF)
ie_dependent_option(NGRAPH_USE_PROTOBUF_LITE "Compiles and links with protobuf-lite" ON
"NGRAPH_ONNX_IMPORT_ENABLE" OFF)
ie_dependent_option(NGRAPH_USE_SYSTEM_PROTOBUF "Use system protobuf" OFF
"NGRAPH_ONNX_IMPORT_ENABLE OR NGRAPH_PDPD_FRONTEND_ENABLE" OFF)
ie_dependent_option(NGRAPH_UNIT_TEST_ENABLE "Enables ngraph unit tests" ON "ENABLE_TESTS;NOT ANDROID" OFF)
ie_dependent_option(NGRAPH_UNIT_TEST_BACKENDS_ENABLE "Control the building of unit tests using backends" ON
"NGRAPH_UNIT_TEST_ENABLE" OFF)
option(NGRAPH_DEBUG_ENABLE "Enable output for NGRAPH_DEBUG statements" OFF)
option(ENABLE_REQUIREMENTS_INSTALL "Dynamic dependencies install" ON)
# WA for ngraph python build on Windows debug
list(REMOVE_ITEM IE_OPTIONS NGRAPH_UNIT_TEST_ENABLE NGRAPH_UNIT_TEST_BACKENDS_ENABLE)

131
cmake/test_model_zoo.cmake Normal file
View File

@ -0,0 +1,131 @@
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
function(ov_model_convert SRC DST OUT)
set(onnx_gen_script ${OpenVINO_SOURCE_DIR}/ngraph/test/models/onnx/onnx_prototxt_converter.py)
file(GLOB_RECURSE prototxt_models RELATIVE "${SRC}" "${SRC}/*.prototxt")
file(GLOB_RECURSE xml_models RELATIVE "${SRC}" "${SRC}/*.xml")
file(GLOB_RECURSE bin_models RELATIVE "${SRC}" "${SRC}/*.bin")
file(GLOB_RECURSE onnx_models RELATIVE "${SRC}" "${SRC}/*.onnx")
file(GLOB_RECURSE data_models RELATIVE "${SRC}" "${SRC}/*.data")
foreach(in_file IN LISTS prototxt_models xml_models bin_models onnx_models data_models)
get_filename_component(ext "${in_file}" EXT)
get_filename_component(rel_dir "${in_file}" DIRECTORY)
get_filename_component(name_we "${in_file}" NAME_WE)
set(model_source_dir "${SRC}/${rel_dir}")
if(NOT NGRAPH_ONNX_IMPORT_ENABLE AND ext MATCHES "^\\.(onnx|prototxt)$")
# don't copy / process ONNX / prototxt files
continue()
endif()
if(ext STREQUAL ".prototxt")
# convert model
set(rel_out_name "${name_we}.onnx")
if(rel_dir)
set(rel_out_name "${rel_dir}/${rel_out_name}")
endif()
else()
# copy as is
set(rel_out_name "${in_file}")
endif()
set(full_out_name "${DST}/${rel_out_name}")
file(MAKE_DIRECTORY "${DST}/${rel_dir}")
if(ext STREQUAL ".prototxt")
# convert .prototxt models to .onnx binary
add_custom_command(OUTPUT ${full_out_name}
COMMAND ${PYTHON_EXECUTABLE} ${onnx_gen_script}
"${SRC}/${in_file}" ${full_out_name}
DEPENDS ${onnx_gen_script} "${SRC}/${in_file}"
COMMENT "Generate ${rel_out_name}"
WORKING_DIRECTORY "${model_source_dir}")
else()
add_custom_command(OUTPUT ${full_out_name}
COMMAND "${CMAKE_COMMAND}" -E copy_if_different
"${SRC}/${in_file}" ${full_out_name}
DEPENDS ${onnx_gen_script} "${SRC}/${in_file}"
COMMENT "Copy ${rel_out_name}"
WORKING_DIRECTORY "${model_source_dir}")
endif()
list(APPEND files "${full_out_name}")
endforeach()
set(${OUT} ${files} PARENT_SCOPE)
endfunction()
ov_model_convert("${CMAKE_CURRENT_SOURCE_DIR}/ngraph/test"
"${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/ngraph"
onnx_out_files)
set(rel_path "inference-engine/tests/functional/inference_engine/onnx_reader")
ov_model_convert("${OpenVINO_SOURCE_DIR}/${rel_path}"
"${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/onnx_reader"
ie_onnx_out_files)
set(rel_path "inference-engine/tests/functional/inference_engine/ir_serialization")
ov_model_convert("${OpenVINO_SOURCE_DIR}/${rel_path}"
"${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/ir_serialization"
ie_serialize_out_files)
set(rel_path "inference-engine/tests/unit/frontends/onnx_import/models")
ov_model_convert("${OpenVINO_SOURCE_DIR}/${rel_path}"
"${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/onnx_import"
ie_onnx_import_out_files)
if(ENABLE_TESTS)
if(NGRAPH_ONNX_IMPORT_ENABLE AND ENABLE_REQUIREMENTS_INSTALL)
find_package(PythonInterp 3 REQUIRED)
get_filename_component(PYTHON_EXEC_DIR ${PYTHON_EXECUTABLE} DIRECTORY)
execute_process(COMMAND "${PYTHON_EXECUTABLE}" -m pip --version
WORKING_DIRECTORY ${PYTHON_EXEC_DIR}
RESULT_VARIABLE pip3_exit_code
OUTPUT_VARIABLE pip3_version)
if(NOT pip3_exit_code EQUAL 0)
message(FATAL_ERROR "Failed to extract pip module version")
endif()
if(pip3_version MATCHES ".* ([0-9]+)+\.([0-9]+)([\.0-9 ]).*")
set(pip3_version ${CMAKE_MATCH_1}.${CMAKE_MATCH_2})
else()
message(FATAL_ERROR "Failed to parse ${pip3_version}")
endif()
message(STATUS "pip version is ${pip3_version}")
set(args --quiet)
if(pip3_version VERSION_GREATER 20.2.2)
list(APPEND args --use-feature=2020-resolver)
endif()
set(reqs "${OpenVINO_SOURCE_DIR}/ngraph/test/requirements_test_onnx.txt")
add_custom_target(test_pip_prerequsites ALL
"${PYTHON_EXECUTABLE}" -m pip install ${args} -r ${reqs}
COMMENT "Install requirements_test.txt"
VERBATIM
SOURCES ${reqs})
endif()
add_custom_target(test_model_zoo DEPENDS ${onnx_out_files}
${ie_onnx_out_files}
${ie_serialize_out_files}
${ie_onnx_import_out_files})
if(TARGET test_pip_prerequsites)
add_dependencies(test_model_zoo test_pip_prerequsites)
endif()
if (NGRAPH_PDPD_FRONTEND_ENABLE)
add_dependencies(test_model_zoo paddlepaddle_test_models)
endif()
install(DIRECTORY "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo"
DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
set(TEST_MODEL_ZOO "./test_model_zoo" CACHE PATH "Path to test model zoo")
endif()

64
docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
View File

@ -519,3 +519,67 @@ Standard ONNX\* operators:
| Upsample | No |
| Where | No |
| Xor | No |
## PaddlePaddle\* Supported Operators
Standard PaddlePaddle(paddlepaddle>=2.1)\* Operators:
| Operator Name in PaddlePaddle\*| Limitations|
| :----------| :----------|
| adpative_pool2d | 'NHWC' data_layout is not supported |
| arg_max | 'int32' output data_type is not supported |
| assign_value | No |
| batch_norm | No |
| bilinear_interp | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
| bilinear_interp_v2 | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
| bmm | No |
| cast | No |
| clip | No |
| concat | No |
| conv2d | 'NHWC' data_layout is not supported |
| depthwise_conv2d | 'NHWC' data_layout is not supported |
| deformable_conv | No |
| elementwise_add | No |
| elementwise_div | No |
| elementwise_max | No |
| elementwise_min | No |
| elementwise_mul | No |
| elementwise_pow | No |
| elementwise_sub | No |
| equal | No |
| expand_v2 | No |
| fill_constant_batch_size_like | No |
| fill_constant | No |
| flatten_contiguous_range | No |
| greater_equal | No |
| hard_sigmoid | No |
| hard_swish | No |
| leaky_relu | No |
| log | No |
| logical_not | No |
| matmul | No |
| matrix_nms | Only supports IE CPU plugin with 'number of selected boxes' static shape(eg: min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)) |
| max_pool2d_with_index | No |
| mul | No |
| multiclass_nms | Only supports IE CPU plugin with 'number of selected boxes' static shape(eg: min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)) |
| nearest_interp | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
| nearest_interp_v2 | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
| pad3d | 'Circular' mode is not supported |
| pow | No |
| pool2d | 'NHWC' data_layout is not supported |
| range | No |
| relu | No |
| relu6 | No |
| reshape2 | No |
| rnn | 'SimpleRNN' and 'GRU' modes are not supported |
| scale | No |
| shape | No |
| slice | No |
| softmax | No |
| sigmoid | No |
| split | No |
| squeeze2 | No |
| transpose2 | No |
| unsqueeze2 | No |
| yolo_box | No |

7
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md
View File

@ -1,6 +1,7 @@
# Converting TensorFlow* Object Detection API Models {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models}
> **NOTES**:
> * Starting with the 2022.1 release, the Model Optimizer can convert the TensorFlow\* Object Detection API Faster and Mask RCNNs topologies differently. By default, the Model Optimizer adds operation "Proposal" to the generated IR. This operation needs an additional input to the model with name "image_info" which should be fed with several values describing the pre-processing applied to the input image (refer to the [Proposal](../../../../ops/detection/Proposal_4.md) operation specification for more information). However, this input is redundant for the models trained and inferred with equal size images. Model Optimizer can generate IR for such models and insert operation [DetectionOutput](../../../../ops/detection/DetectionOutput_1.md) instead of `Proposal`. The `DetectionOutput` operation does not require additional model input "image_info" and moreover, for some models the produced inference results are closer to the original TensorFlow\* model. In order to trigger new behaviour the attribute "operation_to_add" in the corresponding JSON transformation configuration file should be set to value "DetectionOutput" instead of default one "Proposal".
> * Starting with the 2021.1 release, the Model Optimizer converts the TensorFlow\* Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
> * To generate IRs for SSD topologies, the Model Optimizer creates a number of `PriorBoxClustered` layers instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the Inference Engine using dedicated Inference Engine API. The reshaping is supported for all SSD topologies except FPNs which contain hardcoded shapes for some operations preventing from changing topology input shape.
@ -29,14 +30,16 @@ To convert a TensorFlow\* Object Detection API model, go to the `<INSTALL_DIR>/d
* `faster_rcnn_support_api_v1.13.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.13.X
* `faster_rcnn_support_api_v1.14.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.14.0 up to 1.14.X inclusively
* `faster_rcnn_support_api_v1.15.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.15.0 up to 2.0
* `faster_rcnn_support_api_v2.0.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 or higher
* `faster_rcnn_support_api_v2.0.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 up to 2.3.X inclusively
* `faster_rcnn_support_api_v2.4.json` --- for Faster R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.4 or higher
* `mask_rcnn_support.json` --- for Mask R-CNN topologies from the TF 1.X models zoo trained with TensorFlow\* version 1.9.0 or lower.
* `mask_rcnn_support_api_v1.7.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.7.0 up to 1.9.X inclusively
* `mask_rcnn_support_api_v1.11.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.11.0 up to 1.12.X inclusively
* `mask_rcnn_support_api_v1.13.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.13.0 up to 1.13.X inclusively
* `mask_rcnn_support_api_v1.14.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.14.0 up to 1.14.X inclusively
* `mask_rcnn_support_api_v1.15.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 1.15.0 up to 2.0
* `mask_rcnn_support_api_v2.0.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 or higher
* `mask_rcnn_support_api_v2.0.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.0 up to 2.3.X inclusively
* `mask_rcnn_support_api_v2.4.json` --- for Mask R-CNN topologies trained using the TensorFlow\* Object Detection API version 2.4 or higher
* `rfcn_support.json` --- for RFCN topology from the models zoo trained with TensorFlow\* version up to 1.9.X inclusively
* `rfcn_support_api_v1.10.json` --- for RFCN topology from the models zoo frozen with TensorFlow\* version 1.10.0 up to 1.12.X inclusively
* `rfcn_support_api_v1.13.json` --- for RFCN topology from the models zoo frozen with TensorFlow\* version 1.13.X

10
docs/doxygen/doxygen-ignore.txt
View File

@ -16,8 +16,8 @@ openvino/docs/optimization_guide/dldt_optimization_guide.md
openvino/docs/IE_DG/ShapeInference.md
build/docs/openvino_docs.xml
openvino/docs/install_guides/installing-openvino-linux-ivad-vpu.md
inference-engine/include/ie_parallel.hpp
inference-engine/include/ie_plugin_config.hpp
inference-engine/include/vpu/myriad_config.hpp
inference-engine/include/vpu/vpu_config.hpp
inference-engine/include/vpu/vpu_plugin_config.hpp
inference-engine/src/inference_engine/include/ie/ie_parallel.hpp
inference-engine/src/inference_engine/include/ie/ie_plugin_config.hpp
inference-engine/src/inference_engine/include/ie/vpu/myriad_config.hpp
inference-engine/src/inference_engine/include/ie/vpu/vpu_config.hpp
inference-engine/src/inference_engine/include/ie/vpu/vpu_plugin_config.hpp

2
docs/doxygen/ie_docs.config
View File

@ -824,7 +824,7 @@ WARN_LOGFILE = "@DOCS_BUILD_DIR@/ie_docs.log"
# Note: If this tag is empty the current directory is searched.
INPUT = "@DOCS_BUILD_DIR@" \
"@IE_SOURCE_DIR@/include"
"@IE_SOURCE_DIR@/src/inference_engine/include"
# This tag can be used to specify the character encoding of the source files
# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses

18
docs/ops/arithmetic/Erf_1.md
View File

@ -4,34 +4,32 @@
**Category**: Arithmetic unary operation
**Short description**: *Erf* calculates the Gauss error function element-wise with given tensor.
**Short description**: *Erf* performs element-wise Gauss error function (erf) on a given input tensor.
**Detailed Description**
For each element from the input tensor calculates corresponding element in the output tensor with the following formula:
*Erf* performs element-wise erf operation on a given input tensor, based on the following mathematical formula:
\f[
erf(x) = \pi^{-1} \int_{-x}^{x} e^{-t^2} dt
\f]
**Attributes**:
No attributes available.
**Attributes**: *Erf* operation has no attributes.
**Inputs**
* **1**: A tensor of type *T*. **Required.**
* **1**: A tensor of type *T* and arbitrary shape. **Required.**
**Outputs**
* **1**: The result of element-wise operation. A tensor of type *T*.
* **1**: The result of element-wise *Erf* function applied to the input tensor. A tensor of type *T* and the same shape as the input tensor.
**Types**
* *T*: any supported floating-point type.
* *T*: any supported numeric type.
**Examples**
*Example 1*
**Example**
```xml
<layer ... type="Erf">

45
docs/ops/arithmetic/Tan_1.md
View File

@ -6,32 +6,39 @@
**Short description**: *Tan* performs element-wise tangent operation with given tensor.
**Attributes**:
No attributes available.
**Inputs**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise tan operation. A tensor of type *T*.
**Types**
* *T*: any numeric type.
*Tan* does the following with the input tensor *a*:
**Detailed description**: Operation takes one input tensor and performs the element-wise tangent function on a given input tensor, based on the following mathematical formula:
\f[
a_{i} = tan(a_{i})
\f]
**Examples**
*Example 1*
input = [0.0, 0.25, -0.25, 0.5, -0.5]
output = [0.0, 0.25534192, -0.25534192, 0.54630249, -0.54630249]
*Example 2*
input = [-2, -1, 0, 1, 2]
output = [2, -2, 0, 2, -2]
**Attributes**: *tan* operation has no attributes.
**Inputs**
* **1**: A tensor of type *T* and arbitrary shape, measured in radians. **Required.**
**Outputs**
* **1**: The result of element-wise *tan* applied to the input tensor. A tensor of type *T* and same shape as the input tensor.
**Types**
* *T*: any supported numeric type.
**Examples**
```xml
<layer ... type="Tan">
<input>

59
docs/ops/comparison/LessEqual_1.md
View File

@ -4,32 +4,7 @@
**Category**: Comparison binary operation
**Short description**: *LessEqual* performs element-wise comparison operation with two given tensors applying multi-directional broadcast rules.
**Attributes**:
* *auto_broadcast*
* **Description**: specifies rules used for auto-broadcasting of input tensors.
* **Range of values**:
* *none* - no auto-broadcasting is allowed, all input shapes should match
* *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
* **Type**: string
* **Default value**: "numpy"
* **Required**: *no*
**Inputs**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise comparison operation. A tensor of type boolean.
**Types**
* *T*: arbitrary supported type.
**Short description**: *LessEqual* performs element-wise comparison operation with two given tensors applying broadcast rules specified in the *auto_broadcast* attribute.
**Detailed description**
Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
@ -40,12 +15,39 @@ After broadcasting *LessEqual* does the following with the input tensors *a* and
o_{i} = a_{i} <= b_{i}
\f]
**Attributes**:
* *auto_broadcast*
* **Description**: specifies rules used for auto-broadcasting of input tensors.
* **Range of values**:
* *none* - no auto-broadcasting is allowed, all input shapes should match,
* *numpy* - numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md),
* *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md).
* **Type**: string
* **Default value**: "numpy"
* **Required**: *no*
**Inputs**
* **1**: A tensor of type *T* and arbitrary shape. **Required.**
* **2**: A tensor of type *T* and arbitrary shape. **Required.**
**Outputs**
* **1**: The result of element-wise comparison operation applied to the input tensors. A tensor of type **boolean** and shape equal to broadcasted shape of two inputs.
**Types**
* *T*: arbitrary supported type.
**Examples**
*Example 1*
*Example 1: no broadcast*
```xml
<layer ... type="LessEqual">
<data auto_broadcast="none"/>
<input>
<port id="0">
<dim>256</dim>
@ -65,9 +67,10 @@ o_{i} = a_{i} <= b_{i}
</layer>
```
*Example 2: broadcast*
*Example 2: numpy broadcast*
```xml
<layer ... type="LessEqual">
<data auto_broadcast="numpy"/>
<input>
<port id="0">
<dim>8</dim>

24
docs/ops/comparison/Less_1.md
View File

@ -6,6 +6,16 @@
**Short description**: *Less* performs element-wise comparison operation with two given tensors applying multi-directional broadcast rules.
**Detailed description**
Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
After broadcasting *Less* does the following with the input tensors *a* and *b*:
\f[
o_{i} = a_{i} < b_{i}
\f]
**Attributes**:
* *auto_broadcast*
@ -13,8 +23,9 @@
* **Description**: specifies rules used for auto-broadcasting of input tensors.
* **Range of values**:
* *none* - no auto-broadcasting is allowed, all input shapes should match
* *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
* **Type**: string
* *numpy* - numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md)
* *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md)
* **Type**: `string`
* **Default value**: "numpy"
* **Required**: *no*
@ -31,15 +42,6 @@
* *T*: arbitrary supported type.
**Detailed description**
Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
After broadcasting *Less* does the following with the input tensors *a* and *b*:
\f[
o_{i} = a_{i} < b_{i}
\f]
**Examples**
*Example 1*

27
docs/ops/logical/LogicalAnd_1.md
View File

@ -6,39 +6,40 @@
**Short description**: *LogicalAnd* performs element-wise logical AND operation with two given tensors applying multi-directional broadcast rules.
**Detailed description**: Before performing logical operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
After broadcasting *LogicalAnd* does the following with the input tensors *a* and *b*:
\f[
o_{i} = a_{i} \wedge b_{i}
\f]
**Attributes**:
* *auto_broadcast*
* **Description**: specifies rules used for auto-broadcasting of input tensors.
* **Range of values**:
* *none* - no auto-broadcasting is allowed, all input shapes should match
* *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
* *none* - no auto-broadcasting is allowed, all input shapes must match,
* *numpy* - numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md),
* *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md).
* **Type**: string
* **Default value**: "numpy"
* **Required**: *no*
**Inputs**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
* **1**: A tensor of type *T* and arbitrary shape. **Required.**
* **2**: A tensor of type *T* and arbitrary shape. **Required.**
**Outputs**
* **1**: The result of element-wise logical AND operation. A tensor of type boolean.
* **1**: The result of element-wise *LogicalAnd* operation. A tensor of type boolean.
**Types**
* *T*: boolean type.
**Detailed description**
Before performing logical operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
After broadcasting *LogicalAnd* does the following with the input tensors *a* and *b*:
\f[
o_{i} = a_{i} and b_{i}
\f]
**Examples**

1
docs/template_extension/cpu_kernel.cpp
View File

@ -102,6 +102,7 @@ InferenceEngine::StatusCode OpImplementation::init(InferenceEngine::LayerConfig&
IE_THROW() << "Operation supports only FP32 precisions!";
}
} catch (InferenceEngine::Exception& ex) {
error = ex.what();
if (resp) {
strncpy(resp->msg, error.c_str(), sizeof(resp->msg) - 1);
resp->msg[sizeof(resp->msg) - 1] = 0;

1
docs/template_extension/fft_kernel.cpp
View File

@ -66,6 +66,7 @@ InferenceEngine::StatusCode FFTImpl::init(InferenceEngine::LayerConfig& config,
IE_THROW() << "Operation supports only FP32 precisions!";
}
} catch (InferenceEngine::Exception& ex) {
error = ex.what();
if (resp) {
strncpy(resp->msg, error.c_str(), sizeof(resp->msg) - 1);
resp->msg[sizeof(resp->msg) - 1] = 0;

63
docs/template_plugin/tests/functional/op_reference/comparison.hpp Normal file
View File

@ -0,0 +1,63 @@
// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <vector>
#include "base_reference_test.hpp"
#include "ngraph_functions/builders.hpp"
namespace reference_tests {
namespace ComparisonOpsRefTestDefinitions {
struct RefComparisonParams {
ngraph::helpers::ComparisonTypes compType;
Tensor input1;
Tensor input2;
Tensor expected;
};
struct Builder : ParamsBuilder<RefComparisonParams> {
REFERENCE_TESTS_ADD_SET_PARAM(Builder, compType);
REFERENCE_TESTS_ADD_SET_PARAM(Builder, input1);
REFERENCE_TESTS_ADD_SET_PARAM(Builder, input2);
REFERENCE_TESTS_ADD_SET_PARAM(Builder, expected);
};
class ReferenceComparisonLayerTest : public testing::TestWithParam<RefComparisonParams>, public CommonReferenceTest {
public:
void SetUp() override {
const auto& params = GetParam();
function = CreateFunction(params.compType, params.input1.shape, params.input2.shape, params.input1.type, params.expected.type);
inputData = {params.input1.data, params.input2.data};
refOutData = {params.expected.data};
}
static std::string getTestCaseName(const testing::TestParamInfo<RefComparisonParams>& obj) {
const auto& param = obj.param;
std::ostringstream result;
result << "comparisonType=" << param.compType << "_";
result << "inpt_shape1=" << param.input1.shape << "_";
result << "inpt_shape2=" << param.input2.shape << "_";
result << "iType=" << param.input1.type << "_";
result << "oType=" << param.expected.type;
return result.str();
}
private:
static std::shared_ptr<ngraph::Function> CreateFunction(ngraph::helpers::ComparisonTypes comp_op_type, const ngraph::PartialShape& input_shape1,
const ngraph::PartialShape& input_shape2, const ngraph::element::Type& input_type,
const ngraph::element::Type& expected_output_type) {
const auto in = std::make_shared<ngraph::op::Parameter>(input_type, input_shape1);
const auto in2 = std::make_shared<ngraph::op::Parameter>(input_type, input_shape2);
const auto comp = ngraph::builder::makeComparison(in, in2, comp_op_type);
return std::make_shared<ngraph::Function>(ngraph::NodeVector {comp}, ngraph::ParameterVector {in, in2});
}
};
} // namespace ComparisonOpsRefTestDefinitions
} // namespace reference_tests

15
docs/template_plugin/tests/functional/op_reference/conversion.cpp Normal file
View File

@ -0,0 +1,15 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "conversion.hpp"
namespace reference_tests {
namespace ConversionOpsRefTestDefinitions {
namespace {
TEST_P(ReferenceConversionLayerTest, CompareWithHardcodedRefs) {
Exec();
}
} // namespace
} // namespace ConversionOpsRefTestDefinitions
} // namespace reference_tests

67
docs/template_plugin/tests/functional/op_reference/conversion.hpp Normal file
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@ -0,0 +1,67 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <vector>
#include "base_reference_test.hpp"
#include "ngraph_functions/builders.hpp"
namespace reference_tests {
namespace ConversionOpsRefTestDefinitions {
static std::map<ngraph::helpers::ConversionTypes, std::string> conversionNames = {
{ngraph::helpers::ConversionTypes::CONVERT, "Convert"},
{ngraph::helpers::ConversionTypes::CONVERT_LIKE, "ConvertLike"}
};
struct ConvertParams {
template <class IT, class OT>
ConvertParams(ngraph::helpers::ConversionTypes convType, const ngraph::PartialShape& shape, const ngraph::element::Type& iType,
const ngraph::element::Type& oType, const std::vector<IT>& iValues, const std::vector<OT>& oValues, size_t iSize = 0, size_t oSize = 0)
: conversionType(convType), pshape(shape), inType(iType), outType(oType), inputData(CreateBlob(iType, iValues, iSize)),
refData(CreateBlob(oType, oValues, oSize)) {}
ngraph::helpers::ConversionTypes conversionType;
ngraph::PartialShape pshape;
ngraph::element::Type inType;
ngraph::element::Type outType;
InferenceEngine::Blob::Ptr inputData;
InferenceEngine::Blob::Ptr refData;
};
class ReferenceConversionLayerTest : public testing::TestWithParam<ConvertParams>, public CommonReferenceTest {
public:
void SetUp() override {
const auto& params = GetParam();
function = CreateFunction(params.pshape, params.inType, params.outType, params.conversionType);
inputData = {params.inputData};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<ConvertParams>& obj) {
const auto& param = obj.param;
std::ostringstream result;
result << "convertionType=" << conversionNames[param.conversionType] << "_";
result << "shape=" << param.pshape << "_";
result << "iType=" << param.inType << "_";
result << "oType=" << param.outType;
return result.str();
}
private:
static std::shared_ptr<ngraph::Function> CreateFunction(const ngraph::PartialShape& input_shape, const ngraph::element::Type& input_type,
const ngraph::element::Type& expected_output_type,
const ngraph::helpers::ConversionTypes& conversion_type) {
const auto in = std::make_shared<ngraph::op::Parameter>(input_type, input_shape);
const auto convert = ngraph::builder::makeConversion(in, expected_output_type, conversion_type);
return std::make_shared<ngraph::Function>(ngraph::NodeVector {convert}, ngraph::ParameterVector {in});
}
};
} // namespace ConversionOpsRefTestDefinitions
} // namespace reference_tests

534
docs/template_plugin/tests/functional/op_reference/convert.cpp
View File

@ -10,433 +10,403 @@
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <tuple>
#include "base_reference_test.hpp"
#include "conversion.hpp"
using namespace reference_tests;
using namespace ngraph;
using namespace InferenceEngine;
using ConversionTypes = ngraph::helpers::ConversionTypes;
struct ConvertParams {
template <class IT, class OT>
ConvertParams(const ngraph::PartialShape& shape, const ngraph::element::Type& iType, const ngraph::element::Type& oType, const std::vector<IT>& iValues,
const std::vector<OT>& oValues, size_t iSize = 0, size_t oSize = 0)
: pshape(shape), inType(iType), outType(oType), inputData(CreateBlob(iType, iValues, iSize)), refData(CreateBlob(oType, oValues, oSize)) {}
ngraph::PartialShape pshape;
ngraph::element::Type inType;
ngraph::element::Type outType;
InferenceEngine::Blob::Ptr inputData;
InferenceEngine::Blob::Ptr refData;
};
class ReferenceConvertLayerTest : public testing::TestWithParam<ConvertParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
function = CreateFunction(params.pshape, params.inType, params.outType);
inputData = {params.inputData};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<ConvertParams>& obj) {
auto param = obj.param;
std::ostringstream result;
result << "shape=" << param.pshape << "_";
result << "iType=" << param.inType << "_";
result << "oType=" << param.outType;
return result.str();
}
private:
static std::shared_ptr<Function> CreateFunction(const PartialShape& input_shape, const element::Type& input_type,
const element::Type& expected_output_type) {
const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
const auto convert = std::make_shared<op::Convert>(in, expected_output_type);
return std::make_shared<Function>(NodeVector {convert}, ParameterVector {in});
}
};
TEST_P(ReferenceConvertLayerTest, CompareWithHardcodedRefs) {
Exec();
}
namespace reference_tests {
namespace ConversionOpsRefTestDefinitions {
namespace {
INSTANTIATE_TEST_SUITE_P(
smoke_Convert_With_Hardcoded_Refs, ReferenceConvertLayerTest,
smoke_Conversion_With_Hardcoded_Refs, ReferenceConversionLayerTest,
::testing::Values(
// destination boolean
ConvertParams(ngraph::PartialShape {2, 3}, ngraph::element::u8, ngraph::element::boolean,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 3}, ngraph::element::u8, ngraph::element::boolean,
std::vector<uint8_t> {0, 12, 23, 0, std::numeric_limits<uint8_t>::lowest(), std::numeric_limits<uint8_t>::max()},
std::vector<char> {0, 1, 1, 0, 0, 1}),
ConvertParams(ngraph::PartialShape {2, 3}, ngraph::element::i32, ngraph::element::boolean,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 3}, ngraph::element::i32, ngraph::element::boolean,
std::vector<int32_t> {0, -12, 23, 0, std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()},
std::vector<char> {0, 1, 1, 0, 1, 1}),
ConvertParams(ngraph::PartialShape {3, 3}, ngraph::element::f32, ngraph::element::boolean,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {3, 3}, ngraph::element::f32, ngraph::element::boolean,
std::vector<float> {0.f, 1.5745f, 0.12352f, 0.f, std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max(),
std::numeric_limits<float>::min(), std::numeric_limits<float>::infinity(), -std::numeric_limits<float>::infinity()},
std::vector<char> {0, 1, 1, 0, 1, 1, 1, 1, 1}),
// destination bf16
ConvertParams(ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::bf16,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::bf16,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<bfloat16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::bf16,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::bf16,
std::vector<uint8_t> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142},
std::vector<bfloat16> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}),
// destination f16
ConvertParams(ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f16,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f16,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::f16, std::vector<uint8_t> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142},
std::vector<float16> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::f16,
std::vector<uint8_t> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}, std::vector<float16> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}),
// destination f32
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u1, ngraph::element::f32, std::vector<uint8_t> {0xA0},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u1, ngraph::element::f32, std::vector<uint8_t> {0xA0},
std::vector<float> {1.0f, 0.0f, 1.0f, 0.0f}, 4),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u4, ngraph::element::f32, std::vector<uint8_t> {0xFB, 0x0A},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u4, ngraph::element::f32, std::vector<uint8_t> {0xFB, 0x0A},
std::vector<float> {15.0f, 11.0f, 0.0f, 10.0f}, 4),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u8, ngraph::element::f32, std::vector<uint8_t> {255, 128, 32, 0},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u8, ngraph::element::f32, std::vector<uint8_t> {255, 128, 32, 0},
std::vector<float> {255.0f, 128.0f, 32.0f, 0.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u16, ngraph::element::f32, std::vector<uint16_t> {64000, 32000, 128, 0},
std::vector<float> {64000.0f, 32000.0f, 128.0f, 0.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u32, ngraph::element::f32, std::vector<uint32_t> {4000000, 2000000, 128, 0},
std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::u64, ngraph::element::f32, std::vector<uint64_t> {4000000, 2000000, 128, 0},
std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::i4, ngraph::element::f32, std::vector<uint8_t> {0xFE, 0xF2},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u16, ngraph::element::f32,
std::vector<uint16_t> {64000, 32000, 128, 0}, std::vector<float> {64000.0f, 32000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u32, ngraph::element::f32,
std::vector<uint32_t> {4000000, 2000000, 128, 0}, std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::u64, ngraph::element::f32,
std::vector<uint64_t> {4000000, 2000000, 128, 0}, std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::i4, ngraph::element::f32, std::vector<uint8_t> {0xFE, 0xF2},
std::vector<float> {-1.0f, -2.0f, -1.0f, 2.0f}, 4),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::i8, ngraph::element::f32, std::vector<int8_t> {-127, -0, 0, 127},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::i8, ngraph::element::f32, std::vector<int8_t> {-127, -0, 0, 127},
std::vector<float> {-127.0f, -0.0f, 0.0f, 127.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::i16, ngraph::element::f32, std::vector<int16_t> {-32000, -0, 0, 32000},
std::vector<float> {-32000.0f, -0.0f, 0.0f, 32000.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::i32, ngraph::element::f32, std::vector<int32_t> {-64000, -0, 0, 64000},
std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ngraph::PartialShape {2, 2}, ngraph::element::i64, ngraph::element::f32, std::vector<int64_t> {-64000, -0, 0, 64000},
std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::bf16, ngraph::element::f32,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::i16, ngraph::element::f32,
std::vector<int16_t> {-32000, -0, 0, 32000}, std::vector<float> {-32000.0f, -0.0f, 0.0f, 32000.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::i32, ngraph::element::f32,
std::vector<int32_t> {-64000, -0, 0, 64000}, std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {2, 2}, ngraph::element::i64, ngraph::element::f32,
std::vector<int64_t> {-64000, -0, 0, 64000}, std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::bf16, ngraph::element::f32,
std::vector<bfloat16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f16, ngraph::element::f32,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f16, ngraph::element::f32,
std::vector<float16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f32,
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f32,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
// destination i4
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::i4, std::vector<uint8_t> {0xA0}, std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i4, std::vector<uint8_t> {0x12, 0x03}, std::vector<uint8_t> {0x12, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i4, std::vector<uint8_t> {1, 2, 0, 3}, std::vector<uint8_t> {0x12, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i4, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::i4, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i4, std::vector<uint8_t> {0x12, 0x03},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i4, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i4, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i4, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i4, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i4, std::vector<uint8_t> {0xFE, 0x03}, std::vector<uint8_t> {0xFE, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i4, std::vector<int8_t> {-1, -2, 2, 3}, std::vector<uint8_t> {0xFE, 0x23},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i4, std::vector<ngraph::float16> {-1, -2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i4, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i4, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i4, std::vector<uint8_t> {0xFE, 0x03},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i4, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i4, std::vector<float> {-1, -2, 2, 3}, std::vector<uint8_t> {0xFE, 0x23},
4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i4, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i4,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i4,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i4, std::vector<float> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
// destination i8
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i8, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i8, std::vector<uint8_t> {0x81},
std::vector<int8_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43}, std::vector<int8_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i8, std::vector<uint8_t> {1, 2, 0, 3}, std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i8, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43},
std::vector<int8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i8, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i8, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i8, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i8, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i8, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43}, std::vector<int8_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i8, std::vector<int8_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i8, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43},
std::vector<int8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i8, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i8, std::vector<int16_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i8, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i8, std::vector<int32_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i8, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i8, std::vector<int64_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i8, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i8, std::vector<ngraph::float16> {-1, -2, 0, 3},
std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i8, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i8, std::vector<float> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i8,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i8,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i8, std::vector<float> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
// destination i16
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i16, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i16, std::vector<uint8_t> {0x81},
std::vector<int16_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43}, std::vector<int16_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i16, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43},
std::vector<int16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i16, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i16, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i16, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i16, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i16, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i16, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i16, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43}, std::vector<int16_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i16, std::vector<int8_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43},
std::vector<int16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i16, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i16, std::vector<int16_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i16, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i16, std::vector<int32_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i16, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i16, std::vector<int64_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i16, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i16, std::vector<ngraph::float16> {-1, -2, 0, 3},
std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i16, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i16, std::vector<float> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i16,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i16,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i16, std::vector<float> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
// destination i32
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i32, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i32, std::vector<uint8_t> {0x81},
std::vector<int32_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43}, std::vector<int32_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i32, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43},
std::vector<int32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i32, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i32, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i32, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i32, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i32, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i32, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i32, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43}, std::vector<int32_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i32, std::vector<int8_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43},
std::vector<int32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i32, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i32, std::vector<int16_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i32, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i32, std::vector<int32_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i32, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i32, std::vector<int64_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i32, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i32, std::vector<ngraph::float16> {-1, -2, 0, 3},
std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i32, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i32, std::vector<float> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i32,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i32,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i32, std::vector<float> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
// destination i64
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i64, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i64, std::vector<uint8_t> {0x81},
std::vector<int64_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43}, std::vector<int64_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i64, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43},
std::vector<int64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i64, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i64, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i64, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i64, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i64, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i64, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i64, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43}, std::vector<int64_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i64, std::vector<int8_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43},
std::vector<int64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i64, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i64, std::vector<int16_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i64, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i64, std::vector<int32_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i64, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i64, std::vector<int64_t> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i64, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i64, std::vector<ngraph::float16> {-1, -2, 0, 3},
std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i64, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i64, std::vector<float> {-1, -2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i64,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i64,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i64, std::vector<float> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
// destination u1
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u1, std::vector<uint8_t> {0xA0}, std::vector<uint8_t> {0xA0}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u4, ngraph::element::u1, std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00},
std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u8, ngraph::element::u1, std::vector<uint8_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u16, ngraph::element::u1, std::vector<uint16_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u32, ngraph::element::u1, std::vector<uint32_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u64, ngraph::element::u1, std::vector<uint64_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::i4, ngraph::element::u1, std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00},
std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::i8, ngraph::element::u1, std::vector<int8_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::i16, ngraph::element::u1, std::vector<int16_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::i32, ngraph::element::u1, std::vector<int32_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::i64, ngraph::element::u1, std::vector<int64_t> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::f16, ngraph::element::u1, std::vector<ngraph::float16> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::bf16, ngraph::element::u1, std::vector<ngraph::bfloat16> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ngraph::PartialShape {8}, ngraph::element::f32, ngraph::element::u1, std::vector<float> {1, 0, 1, 0, 0, 0, 0, 1},
std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u1, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0xA0}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u4, ngraph::element::u1,
std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00}, std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u8, ngraph::element::u1,
std::vector<uint8_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u16, ngraph::element::u1,
std::vector<uint16_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u32, ngraph::element::u1,
std::vector<uint32_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u64, ngraph::element::u1,
std::vector<uint64_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::i4, ngraph::element::u1,
std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00}, std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::i8, ngraph::element::u1,
std::vector<int8_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::i16, ngraph::element::u1,
std::vector<int16_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::i32, ngraph::element::u1,
std::vector<int32_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::i64, ngraph::element::u1,
std::vector<int64_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::f16, ngraph::element::u1,
std::vector<ngraph::float16> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::bf16, ngraph::element::u1,
std::vector<ngraph::bfloat16> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::f32, ngraph::element::u1,
std::vector<float> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
// destination u4
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::u4, std::vector<uint8_t> {0xA0}, std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u4, std::vector<uint8_t> {0x12, 0x03}, std::vector<uint8_t> {0x12, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u4, std::vector<uint8_t> {1, 2, 0, 3}, std::vector<uint8_t> {0x12, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u4, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::u4, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u4, std::vector<uint8_t> {0x12, 0x03},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u4, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u4, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u4, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u4, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u4, std::vector<uint8_t> {0xFE, 0x03}, std::vector<uint8_t> {0xFE, 0x03},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u4, std::vector<int8_t> {-1, -2, 2, 3}, std::vector<uint8_t> {0xFE, 0x23},
4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u4, std::vector<ngraph::float16> {-1, -2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u4, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u4, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u4, std::vector<uint8_t> {0xFE, 0x03},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u4, std::vector<ngraph::bfloat16> {-1, -2, 0, 3},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u4, std::vector<float> {-1, -2, 2, 3}, std::vector<uint8_t> {0xFE, 0x23},
4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u4, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u4,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u4,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u4, std::vector<float> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
// destination u8
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u8, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u8, std::vector<uint8_t> {0x81},
std::vector<uint8_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43}, std::vector<uint8_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u8, std::vector<uint8_t> {1, 2, 0, 3}, std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u8, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u8, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u8, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u8, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u8, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u8, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43}, std::vector<uint8_t> {2, 1, 4, 3},
4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u8, std::vector<int8_t> {1, 2, 2, 3}, std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u8, std::vector<int16_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u8, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u8, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u8, std::vector<int32_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u8, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u8, std::vector<int64_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u8, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u8, std::vector<ngraph::float16> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u8, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u8, std::vector<ngraph::float16> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u8, std::vector<ngraph::bfloat16> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u8, std::vector<float> {1, 2, 2, 3}, std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u8,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u8, std::vector<float> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
// destination u16
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u16, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u16, std::vector<uint8_t> {0x81},
std::vector<uint16_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint16_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u16, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u16, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u16, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u16, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u16, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u16, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u16, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u16, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint16_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u16, std::vector<int8_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u16, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u16, std::vector<int16_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u16, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u16, std::vector<int32_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u16, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u16, std::vector<int64_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u16, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u16, std::vector<ngraph::float16> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u16, std::vector<ngraph::float16> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u16, std::vector<ngraph::bfloat16> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u16, std::vector<float> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u16,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u16, std::vector<float> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
// destination u32
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u32, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u32, std::vector<uint8_t> {0x81},
std::vector<uint32_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint32_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u32, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u32, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u32, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u32, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u32, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u32, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u32, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u32, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint32_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u32, std::vector<int8_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u32, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u32, std::vector<int16_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u32, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u32, std::vector<int32_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u32, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u32, std::vector<int64_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u32, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u32, std::vector<ngraph::float16> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u32, std::vector<ngraph::float16> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u32, std::vector<ngraph::bfloat16> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u32, std::vector<float> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u32,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u32, std::vector<float> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
// destination u64
ConvertParams(ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u64, std::vector<uint8_t> {0x81},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u64, std::vector<uint8_t> {0x81},
std::vector<uint64_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint64_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u64, std::vector<uint8_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u64, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u64, std::vector<uint16_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u64, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u64, std::vector<uint32_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u64, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u64, std::vector<uint64_t> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u64, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint64_t> {2, 1, 4, 3}, 4),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u64, std::vector<int8_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u64, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u64, std::vector<int16_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u64, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u64, std::vector<int32_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u64, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u64, std::vector<int64_t> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u64, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u64, std::vector<ngraph::float16> {1, 2, 0, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u64, std::vector<ngraph::float16> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u64, std::vector<ngraph::bfloat16> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u64, std::vector<float> {1, 2, 2, 3},
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u64,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u64, std::vector<float> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3})),
ReferenceConvertLayerTest::getTestCaseName);
ReferenceConversionLayerTest::getTestCaseName);
} // namespace
} // namespace ConversionOpsRefTestDefinitions
} // namespace reference_tests

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// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <vector>
#include "conversion.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using ConversionTypes = ngraph::helpers::ConversionTypes;
namespace reference_tests {
namespace ConversionOpsRefTestDefinitions {
namespace {
INSTANTIATE_TEST_SUITE_P(
smoke_Conversion_With_Hardcoded_Refs, ReferenceConversionLayerTest,
::testing::Values(
// destination boolean
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 3}, ngraph::element::u8, ngraph::element::boolean,
std::vector<uint8_t> {0, 12, 23, 0, std::numeric_limits<uint8_t>::lowest(), std::numeric_limits<uint8_t>::max()},
std::vector<char> {0, 1, 1, 0, 0, 1}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 3}, ngraph::element::i32, ngraph::element::boolean,
std::vector<int32_t> {0, -12, 23, 0, std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()},
std::vector<char> {0, 1, 1, 0, 1, 1}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {3, 3}, ngraph::element::f32, ngraph::element::boolean,
std::vector<float> {0.f, 1.5745f, 0.12352f, 0.f, std::numeric_limits<float>::lowest(), std::numeric_limits<float>::max(),
std::numeric_limits<float>::min(), std::numeric_limits<float>::infinity(), -std::numeric_limits<float>::infinity()},
std::vector<char> {0, 1, 1, 0, 1, 1, 1, 1, 1}),
// destination bf16
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::bf16,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<bfloat16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::bf16,
std::vector<uint8_t> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142},
std::vector<bfloat16> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}),
// destination f16
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f16,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {11}, ngraph::element::u8, ngraph::element::f16,
std::vector<uint8_t> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142},
std::vector<float16> {0, 10, 15, 20, 43, 56, 78, 99, 102, 130, 142}),
// destination f32
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u1, ngraph::element::f32,
std::vector<uint8_t> {0xA0}, std::vector<float> {1.0f, 0.0f, 1.0f, 0.0f}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u4, ngraph::element::f32,
std::vector<uint8_t> {0xFB, 0x0A}, std::vector<float> {15.0f, 11.0f, 0.0f, 10.0f}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u8, ngraph::element::f32,
std::vector<uint8_t> {255, 128, 32, 0}, std::vector<float> {255.0f, 128.0f, 32.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u16, ngraph::element::f32,
std::vector<uint16_t> {64000, 32000, 128, 0}, std::vector<float> {64000.0f, 32000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u32, ngraph::element::f32,
std::vector<uint32_t> {4000000, 2000000, 128, 0}, std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::u64, ngraph::element::f32,
std::vector<uint64_t> {4000000, 2000000, 128, 0}, std::vector<float> {4000000.0f, 2000000.0f, 128.0f, 0.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::i4, ngraph::element::f32,
std::vector<uint8_t> {0xFE, 0xF2}, std::vector<float> {-1.0f, -2.0f, -1.0f, 2.0f}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::i8, ngraph::element::f32,
std::vector<int8_t> {-127, -0, 0, 127}, std::vector<float> {-127.0f, -0.0f, 0.0f, 127.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::i16, ngraph::element::f32,
std::vector<int16_t> {-32000, -0, 0, 32000}, std::vector<float> {-32000.0f, -0.0f, 0.0f, 32000.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::i32, ngraph::element::f32,
std::vector<int32_t> {-64000, -0, 0, 64000}, std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {2, 2}, ngraph::element::i64, ngraph::element::f32,
std::vector<int64_t> {-64000, -0, 0, 64000}, std::vector<float> {-64000.0f, -0.0f, 0.0f, 64000.0f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::bf16, ngraph::element::f32,
std::vector<bfloat16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f16, ngraph::element::f32,
std::vector<float16> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {1, 1, 3, 5}, ngraph::element::f32, ngraph::element::f32,
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f},
std::vector<float> {0.5f, 1.5f, 0.5f, 2.5f, 1.5f, 0.5f, 3.5f, 2.5f, 0.5f, 0.5f, 2.5f, 0.5f, 0.5f, 0.5f, 1.5f}),
// destination i4
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::i4, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i4, std::vector<uint8_t> {0x12, 0x03},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i4, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i4, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i4, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i4, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i4, std::vector<uint8_t> {0xFE, 0x03},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i4, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i4,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i4,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i4, std::vector<float> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
// destination i8
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i8, std::vector<uint8_t> {0x81},
std::vector<int8_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43},
std::vector<int8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i8, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i8, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i8, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i8, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i8, std::vector<uint8_t> {0x21, 0x43},
std::vector<int8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i8, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i8, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i8, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i8, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i8,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i8,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int8_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i8, std::vector<float> {-1, -2, 2, 3},
std::vector<int8_t> {-1, -2, 2, 3}),
// destination i16
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i16, std::vector<uint8_t> {0x81},
std::vector<int16_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43},
std::vector<int16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i16, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i16, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i16, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i16, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i16, std::vector<uint8_t> {0x21, 0x43},
std::vector<int16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i16, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i16, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i16, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i16, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i16,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i16,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int16_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i16, std::vector<float> {-1, -2, 2, 3},
std::vector<int16_t> {-1, -2, 2, 3}),
// destination i32
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i32, std::vector<uint8_t> {0x81},
std::vector<int32_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43},
std::vector<int32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i32, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i32, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i32, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i32, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i32, std::vector<uint8_t> {0x21, 0x43},
std::vector<int32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i32, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i32, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i32, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i32, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i32,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i32,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int32_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i32, std::vector<float> {-1, -2, 2, 3},
std::vector<int32_t> {-1, -2, 2, 3}),
// destination i64
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::i64, std::vector<uint8_t> {0x81},
std::vector<int64_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43},
std::vector<int64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::i64, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::i64, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::i64, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::i64, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<int64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::i64, std::vector<uint8_t> {0x21, 0x43},
std::vector<int64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::i64, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::i64, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::i64, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::i64, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::i64,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::i64,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<int64_t> {-1, -2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::i64, std::vector<float> {-1, -2, 2, 3},
std::vector<int64_t> {-1, -2, 2, 3}),
// destination u1
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u1, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0xA0}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u4, ngraph::element::u1,
std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00}, std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u8, ngraph::element::u1,
std::vector<uint8_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u16, ngraph::element::u1,
std::vector<uint16_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u32, ngraph::element::u1,
std::vector<uint32_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u64, ngraph::element::u1,
std::vector<uint64_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::i4, ngraph::element::u1,
std::vector<uint8_t> {0x10, 0x01, 0x00, 0x00}, std::vector<uint8_t> {0x90}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::i8, ngraph::element::u1,
std::vector<int8_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::i16, ngraph::element::u1,
std::vector<int16_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::i32, ngraph::element::u1,
std::vector<int32_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::i64, ngraph::element::u1,
std::vector<int64_t> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::f16, ngraph::element::u1,
std::vector<ngraph::float16> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::bf16, ngraph::element::u1,
std::vector<ngraph::bfloat16> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::f32, ngraph::element::u1,
std::vector<float> {1, 0, 1, 0, 0, 0, 0, 1}, std::vector<uint8_t> {0xA1}, 8, 8),
// destination u4
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u1, ngraph::element::u4, std::vector<uint8_t> {0xA0},
std::vector<uint8_t> {0x10, 0x10}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u4, std::vector<uint8_t> {0x12, 0x03},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u4, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u4, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u4, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u4, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {0x12, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u4, std::vector<uint8_t> {0xFE, 0x03},
std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u4, std::vector<int8_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u4, std::vector<int16_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u4, std::vector<int32_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u4, std::vector<int64_t> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u4,
std::vector<ngraph::float16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u4,
std::vector<ngraph::bfloat16> {-1, -2, 0, 3}, std::vector<uint8_t> {0xFE, 0x03}, 4, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u4, std::vector<float> {-1, -2, 2, 3},
std::vector<uint8_t> {0xFE, 0x23}, 4, 4),
// destination u8
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u8, std::vector<uint8_t> {0x81},
std::vector<uint8_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u8, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u8, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u8, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u8, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u8, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint8_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u8, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u8, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u8, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u8, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u8,
std::vector<ngraph::float16> {1, 2, 0, 3}, std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u8,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint8_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u8, std::vector<float> {1, 2, 2, 3},
std::vector<uint8_t> {1, 2, 2, 3}),
// destination u16
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u16, std::vector<uint8_t> {0x81},
std::vector<uint16_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u16, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u16, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u16, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u16, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u16, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint16_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u16, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u16, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u16, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u16, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u16,
std::vector<ngraph::float16> {1, 2, 0, 3}, std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u16,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint16_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u16, std::vector<float> {1, 2, 2, 3},
std::vector<uint16_t> {1, 2, 2, 3}),
// destination u32
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u32, std::vector<uint8_t> {0x81},
std::vector<uint32_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u32, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u32, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u32, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u32, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u32, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint32_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u32, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u32, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u32, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u32, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u32,
std::vector<ngraph::float16> {1, 2, 0, 3}, std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u32,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint32_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u32, std::vector<float> {1, 2, 2, 3},
std::vector<uint32_t> {1, 2, 2, 3}),
// destination u64
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {8}, ngraph::element::u1, ngraph::element::u64, std::vector<uint8_t> {0x81},
std::vector<uint64_t> {1, 0, 0, 0, 0, 0, 0, 1}, 8),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u8, ngraph::element::u64, std::vector<uint8_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u16, ngraph::element::u64, std::vector<uint16_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u32, ngraph::element::u64, std::vector<uint32_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::u64, ngraph::element::u64, std::vector<uint64_t> {1, 2, 0, 3},
std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i4, ngraph::element::u64, std::vector<uint8_t> {0x21, 0x43},
std::vector<uint64_t> {2, 1, 4, 3}, 4),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i8, ngraph::element::u64, std::vector<int8_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i16, ngraph::element::u64, std::vector<int16_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i32, ngraph::element::u64, std::vector<int32_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::i64, ngraph::element::u64, std::vector<int64_t> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f16, ngraph::element::u64,
std::vector<ngraph::float16> {1, 2, 0, 3}, std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::bf16, ngraph::element::u64,
std::vector<ngraph::bfloat16> {1, 2, 0, 3}, std::vector<uint64_t> {1, 2, 0, 3}),
ConvertParams(ConversionTypes::CONVERT_LIKE, ngraph::PartialShape {4}, ngraph::element::f32, ngraph::element::u64, std::vector<float> {1, 2, 2, 3},
std::vector<uint64_t> {1, 2, 2, 3})),
ReferenceConversionLayerTest::getTestCaseName);
} // namespace
} // namespace ConversionOpsRefTestDefinitions
} // namespace reference_tests

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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include "comparison.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using ComparisonTypes = ngraph::helpers::ComparisonTypes;
namespace reference_tests {
namespace ComparisonOpsRefTestDefinitions {
namespace {
TEST_P(ReferenceComparisonLayerTest, EqualCompareWithHardcodedRefs) {
Exec();
}
template <element::Type_t IN_ET>
std::vector<RefComparisonParams> generateComparisonParams(const element::Type& type) {
using T = typename element_type_traits<IN_ET>::value_type;
std::vector<RefComparisonParams> compParams {
// 1D // 2D // 3D // 4D
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.input2({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.expected({{2, 2}, element::boolean, std::vector<char> {1, 1, 1, 1}}),
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{2, 3}, type, std::vector<T> {0, 6, 45, 1, 21, 21}})
.input2({{2, 3}, type, std::vector<T> {1, 18, 23, 1, 19, 21}})
.expected({{2, 3}, element::boolean, std::vector<char> {0, 0, 0, 1, 0, 1}}),
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{1}, type, std::vector<T> {53}})
.input2({{1}, type, std::vector<T> {53}})
.expected({{1}, element::boolean, std::vector<char> {1}}),
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 1, 5, 11, 8}})
.input2({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 10, 5, 11, 8}})
.expected({{2, 4}, element::boolean, std::vector<char> {1, 1, 1, 1, 0, 1, 1, 1}}),
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{3, 1, 2}, type, std::vector<T> {2, 1, 4, 1, 3, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{3, 2, 2}, element::boolean, std::vector<char> {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}}),
Builder {}
.compType(ComparisonTypes::EQUAL)
.input1({{2, 1, 2, 1}, type, std::vector<T> {2, 1, 4, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{2, 1, 2, 1}, element::boolean, std::vector<char> {0, 1, 0, 1}})};
return compParams;
}
std::vector<RefComparisonParams> generateComparisonCombinedParams() {
const std::vector<std::vector<RefComparisonParams>> compTypeParams {
generateComparisonParams<element::Type_t::f32>(element::f32),
generateComparisonParams<element::Type_t::f16>(element::f16),
generateComparisonParams<element::Type_t::i32>(element::i32),
generateComparisonParams<element::Type_t::u32>(element::u32),
generateComparisonParams<element::Type_t::u8>(element::boolean)};
std::vector<RefComparisonParams> combinedParams;
for (const auto& params : compTypeParams) {
combinedParams.insert(combinedParams.end(), params.begin(), params.end());
}
return combinedParams;
}
INSTANTIATE_TEST_SUITE_P(smoke_Comparison_With_Hardcoded_Refs, ReferenceComparisonLayerTest, ::testing::ValuesIn(generateComparisonCombinedParams()),
ReferenceComparisonLayerTest::getTestCaseName);
} // namespace
} // namespace ComparisonOpsRefTestDefinitions
} // namespace reference_tests

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// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <limits>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <tuple>
#include "base_reference_test.hpp"
using namespace reference_tests;
using namespace ngraph;
using namespace InferenceEngine;
struct ErfParams {
template <class IT>
ErfParams(const ngraph::PartialShape& shape, const ngraph::element::Type& iType, const std::vector<IT>& iValues)
: pshape(shape), inType(iType), outType(iType), inputData(CreateBlob(iType, iValues)) {
std::vector<IT> oValues;
std::vector<double> output;
for (auto element : iValues)
output.push_back(static_cast<double>(element));
std::transform(output.begin(), output.end(), output.begin(), [](double input) -> double {
return std::erf(input);
});
if (std::is_integral<IT>()) {
std::transform(output.begin(), output.end(), output.begin(), [](double input) -> double {
return std::round(input);
});
}
for (auto element : output)
oValues.push_back(static_cast<IT>(element));
refData = CreateBlob(outType, oValues);
}
ngraph::PartialShape pshape;
ngraph::element::Type inType;
ngraph::element::Type outType;
InferenceEngine::Blob::Ptr inputData;
InferenceEngine::Blob::Ptr refData;
};
class ReferenceErfLayerTest : public testing::TestWithParam<ErfParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
function = CreateFunction(params.pshape, params.inType, params.outType);
inputData = {params.inputData};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<ErfParams>& obj) {
auto param = obj.param;
std::ostringstream result;
result << "shape=" << param.pshape << "_";
result << "iType=" << param.inType << "_";
result << "oType=" << param.outType;
return result.str();
}
private:
static std::shared_ptr<Function> CreateFunction(const PartialShape& input_shape, const element::Type& input_type,
const element::Type& expected_output_type) {
const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
const auto erf = std::make_shared<op::Erf>(in);
return std::make_shared<Function>(NodeVector {erf}, ParameterVector {in});
}
};
TEST_P(ReferenceErfLayerTest, CompareWithRefs) {
Exec();
}
INSTANTIATE_TEST_SUITE_P(
smoke_Erf_With_Hardcoded_Refs, ReferenceErfLayerTest,
::testing::Values(ErfParams(ngraph::PartialShape {2, 5}, ngraph::element::f32,
std::vector<float> {-INFINITY, -4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, INFINITY}),
ErfParams(ngraph::PartialShape {2, 5}, ngraph::element::f16,
std::vector<float16> {-INFINITY, -4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f, INFINITY}),
ErfParams(ngraph::PartialShape {2, 3}, ngraph::element::i32,
std::vector<int32_t> {std::numeric_limits<int32_t>::min(), -2, -1, 1, 2, std::numeric_limits<int32_t>::max()}),
ErfParams(ngraph::PartialShape {2, 3}, ngraph::element::u32,
std::vector<uint32_t> {std::numeric_limits<uint32_t>::min(), 0, 1, 2, 3, std::numeric_limits<uint32_t>::max()}),
ErfParams(ngraph::PartialShape {2, 3}, ngraph::element::i64,
std::vector<int64_t> {std::numeric_limits<int64_t>::min(), -2, -1, 1, 2, std::numeric_limits<int64_t>::max()}),
ErfParams(ngraph::PartialShape {2, 3}, ngraph::element::u64,
std::vector<uint64_t> {std::numeric_limits<uint64_t>::min(), 0, 1, 2, 3, std::numeric_limits<uint64_t>::max()})),
ReferenceErfLayerTest::getTestCaseName);

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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include "comparison.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using ComparisonTypes = ngraph::helpers::ComparisonTypes;
namespace reference_tests {
namespace ComparisonOpsRefTestDefinitions {
namespace {
TEST_P(ReferenceComparisonLayerTest, LessCompareWithHardcodedRefs) {
Exec();
}
template <element::Type_t IN_ET>
std::vector<RefComparisonParams> generateComparisonParams(const element::Type& type) {
using T = typename element_type_traits<IN_ET>::value_type;
std::vector<RefComparisonParams> compParams {
// 1D // 2D // 3D // 4D
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.input2({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.expected({{2, 2}, element::boolean, std::vector<char> {0, 0, 0, 0}}),
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{2, 3}, type, std::vector<T> {0, 6, 45, 1, 21, 21}})
.input2({{2, 3}, type, std::vector<T> {1, 18, 23, 1, 19, 21}})
.expected({{2, 3}, element::boolean, std::vector<char> {1, 1, 0, 0, 0, 0}}),
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{1}, type, std::vector<T> {53}})
.input2({{1}, type, std::vector<T> {53}})
.expected({{1}, element::boolean, std::vector<char> {0}}),
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 1, 5, 11, 8}})
.input2({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 10, 5, 11, 8}})
.expected({{2, 4}, element::boolean, std::vector<char> {0, 0, 0, 0, 1, 0, 0, 0}}),
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{3, 1, 2}, type, std::vector<T> {2, 1, 4, 1, 3, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{3, 2, 2}, element::boolean, std::vector<char> {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}),
Builder {}
.compType(ComparisonTypes::LESS)
.input1({{2, 1, 2, 1}, type, std::vector<T> {2, 1, 4, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{2, 1, 2, 1}, element::boolean, std::vector<char> {0, 0, 0, 0}})};
return compParams;
}
std::vector<RefComparisonParams> generateComparisonCombinedParams() {
const std::vector<std::vector<RefComparisonParams>> compTypeParams {
generateComparisonParams<element::Type_t::f32>(element::f32),
generateComparisonParams<element::Type_t::f16>(element::f16),
generateComparisonParams<element::Type_t::i32>(element::i32),
generateComparisonParams<element::Type_t::u32>(element::u32),
generateComparisonParams<element::Type_t::u8>(element::boolean)};
std::vector<RefComparisonParams> combinedParams;
for (const auto& params : compTypeParams) {
combinedParams.insert(combinedParams.end(), params.begin(), params.end());
}
return combinedParams;
}
} // namespace
INSTANTIATE_TEST_SUITE_P(smoke_Comparison_With_Hardcoded_Refs, ReferenceComparisonLayerTest, ::testing::ValuesIn(generateComparisonCombinedParams()),
ReferenceComparisonLayerTest::getTestCaseName);
} // namespace ComparisonOpsRefTestDefinitions
} // namespace reference_tests

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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include "comparison.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using ComparisonTypes = ngraph::helpers::ComparisonTypes;
namespace reference_tests {
namespace ComparisonOpsRefTestDefinitions {
namespace {
TEST_P(ReferenceComparisonLayerTest, LessEqualCompareWithHardcodedRefs) {
Exec();
}
template <element::Type_t IN_ET>
std::vector<RefComparisonParams> generateComparisonParams(const element::Type& type) {
using T = typename element_type_traits<IN_ET>::value_type;
std::vector<RefComparisonParams> compParams {
// 1D // 2D // 3D // 4D
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.input2({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
.expected({{2, 2}, element::boolean, std::vector<char> {1, 1, 1, 1}}),
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{2, 3}, type, std::vector<T> {0, 6, 45, 1, 21, 21}})
.input2({{2, 3}, type, std::vector<T> {1, 18, 23, 1, 19, 21}})
.expected({{2, 3}, element::boolean, std::vector<char> {1, 1, 0, 1, 0, 1}}),
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{1}, type, std::vector<T> {53}})
.input2({{1}, type, std::vector<T> {53}})
.expected({{1}, element::boolean, std::vector<char> {1}}),
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 1, 5, 11, 8}})
.input2({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 10, 5, 11, 8}})
.expected({{2, 4}, element::boolean, std::vector<char> {1, 1, 1, 1, 1, 1, 1, 1}}),
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{3, 1, 2}, type, std::vector<T> {2, 1, 4, 1, 3, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{3, 2, 2}, element::boolean, std::vector<char> {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}}),
Builder {}
.compType(ComparisonTypes::LESS_EQUAL)
.input1({{2, 1, 2, 1}, type, std::vector<T> {2, 1, 4, 1}})
.input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
.expected({{2, 1, 2, 1}, element::boolean, std::vector<char> {0, 1, 0, 1}})};
return compParams;
}
std::vector<RefComparisonParams> generateComparisonCombinedParams() {
const std::vector<std::vector<RefComparisonParams>> compTypeParams {
generateComparisonParams<element::Type_t::f32>(element::f32),
generateComparisonParams<element::Type_t::f16>(element::f16),
generateComparisonParams<element::Type_t::i32>(element::i32),
generateComparisonParams<element::Type_t::u32>(element::u32),
generateComparisonParams<element::Type_t::u8>(element::boolean)};
std::vector<RefComparisonParams> combinedParams;
for (const auto& params : compTypeParams) {
combinedParams.insert(combinedParams.end(), params.begin(), params.end());
}
return combinedParams;
}
} // namespace
INSTANTIATE_TEST_SUITE_P(smoke_Comparison_With_Hardcoded_Refs, ReferenceComparisonLayerTest, ::testing::ValuesIn(generateComparisonCombinedParams()),
ReferenceComparisonLayerTest::getTestCaseName);
} // namespace ComparisonOpsRefTestDefinitions
} // namespace reference_tests

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docs/template_plugin/tests/functional/op_reference/logical_and.cpp Normal file
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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <tuple>
#include "base_reference_test.hpp"
using namespace reference_tests;
using namespace ngraph;
using namespace InferenceEngine;
struct LogicalAndParams {
template <class IT, class OT>
LogicalAndParams(const ngraph::PartialShape& input_shape1, const ngraph::PartialShape& input_shape2 ,
const std::vector<IT>& iValues1, const std::vector<IT>& iValues2, const std::vector<OT>& oValues)
: pshape1(input_shape1), pshape2(input_shape2), inType(ngraph::element::boolean), outType(ngraph::element::boolean),
inputData1(CreateBlob(ngraph::element::boolean, iValues1)), inputData2(CreateBlob(ngraph::element::boolean, iValues2)),
refData(CreateBlob(ngraph::element::boolean, oValues)) {}
ngraph::PartialShape pshape1;
ngraph::PartialShape pshape2;
ngraph::element::Type inType;
ngraph::element::Type outType;
InferenceEngine::Blob::Ptr inputData1;
InferenceEngine::Blob::Ptr inputData2;
InferenceEngine::Blob::Ptr refData;
};
class ReferenceLogicalAndLayerTest : public testing::TestWithParam<LogicalAndParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
function = CreateFunction(params.pshape1, params.pshape2, params.inType);
inputData = {params.inputData1, params.inputData2};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<LogicalAndParams>& obj) {
auto param = obj.param;
std::ostringstream result;
result << "input_shape1=" << param.pshape1 << "_";
result << "input_shape2=" << param.pshape2 << "_";
result << "iType=" << param.inType << "_";
result << "oType=" << param.outType;
return result.str();
}
private:
static std::shared_ptr<Function> CreateFunction(const PartialShape& input_shape1,
const PartialShape& input_shape2, const element::Type& input_type) {
const auto in = std::make_shared<op::Parameter>(input_type, input_shape1);
const auto in2 = std::make_shared<op::Parameter>(input_type, input_shape2);
const auto logical_and = std::make_shared<op::v1::LogicalAnd>(in, in2);
return std::make_shared<Function>(NodeVector {logical_and}, ParameterVector {in, in2});
}
};
TEST_P(ReferenceLogicalAndLayerTest, CompareWithHardcodedRefs) {
Exec();
}
INSTANTIATE_TEST_SUITE_P(
smoke_LogicalAnd_With_Hardcoded_Refs, ReferenceLogicalAndLayerTest,
::testing::Values(
LogicalAndParams(ngraph::PartialShape {2, 2}, ngraph::PartialShape {2, 2},
std::vector<char> {true, false, true, false},
std::vector<char> {false, true, true, false},
std::vector<char> {false, false, true, false}),
LogicalAndParams(ngraph::PartialShape {2, 1, 2, 1}, ngraph::PartialShape {1, 1, 2, 1},
std::vector<char> {true, false, true, false},
std::vector<char> {true, false},
std::vector<char> {true, false, true, false}),
LogicalAndParams(ngraph::PartialShape {3, 4}, ngraph::PartialShape {3, 4},
std::vector<char> {true, true, true, true, true, false, true, false, false, true, true, true},
std::vector<char> {true, true, true, true, true, false, true, false, false, true, true, false},
std::vector<char> {true, true, true, true, true, false, true, false, false, true, true, false})),
ReferenceLogicalAndLayerTest::getTestCaseName);

226
docs/template_plugin/tests/functional/op_reference/roi_pooling.cpp Normal file
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@ -0,0 +1,226 @@
// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <tuple>
#include "base_reference_test.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using namespace reference_tests;
struct ROIPoolingParams {
template <class T>
ROIPoolingParams(const size_t iH, const size_t iW, const size_t ch, const size_t rois,
const size_t oH, const size_t oW, const float sS, const std::string mode,
const ngraph::element::Type& type, const std::vector<T>& inputValues,
const std::vector<T>& proposalValues, const std::vector<T>& outputValues)
: inputH(iH), inputW(iW), channelCount(ch), roiCount(rois), outputH(oH), outputW(oW), spatialScale(sS),
poolingMode(mode), dataType(type), featureMap(CreateBlob(type, inputValues)),
proposal(CreateBlob(type, proposalValues)), refData(CreateBlob(type, outputValues)) {}
size_t inputH;
size_t inputW;
size_t channelCount;
size_t roiCount;
size_t outputH;
size_t outputW;
float spatialScale;
std::string poolingMode;
ngraph::element::Type dataType;
InferenceEngine::Blob::Ptr featureMap;
InferenceEngine::Blob::Ptr proposal;
InferenceEngine::Blob::Ptr refData;
public:
template<class T>
inline static std::vector<T> increasinglyFilledBlob(size_t size) {
std::vector<T> inputValues;
T one = 1;
for (size_t i = 0; i < size; i++) {
inputValues.push_back(one * i / 10);
}
return inputValues;
}
template<class T>
inline static std::vector<T> equallyFilledBlob(size_t size, T value) {
std::vector<T> inputValues;
for (size_t i = 0; i < size; i++) {
inputValues.push_back(value);
}
return inputValues;
}
};
class ReferenceRoiPoolingLayerTest : public testing::TestWithParam<ROIPoolingParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
function = CreateFunction(params.inputH, params.inputW, params.channelCount, params.roiCount,
params.outputH, params.outputW, params.spatialScale, params.poolingMode, params.dataType);
inputData = {params.featureMap, params.proposal};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<ROIPoolingParams>& obj) {
auto param = obj.param;
std::ostringstream result;
result << "IS=" << param.inputH << "," << param.inputW << "_";
result << "OS=" << param.outputH << "," << param.outputW << "_";
result << "Ch=" << param.channelCount << "_";
result << "Rois=" << param.roiCount << "_";
result << "Ss=" << param.spatialScale << "_";
result << "Mode=" << param.poolingMode << "_";
result << "Prec=" << param.dataType << "_";
result << std::to_string(obj.index);
return result.str();
}
private:
static std::shared_ptr<Function> CreateFunction(const size_t i_h, const size_t i_w, const size_t ch, const size_t roi_count,
const size_t o_h, const size_t o_w, const float spat_scale, const std::string mode,
const ngraph::element::Type& type) {
Shape feat_map_shape{1, ch, i_h, i_w};
Shape rois_shape{roi_count, 5};
Shape pooled_shape{o_h, o_w};
Shape output_shape{roi_count, ch, o_h, o_w};
const auto feat_map = std::make_shared<op::Parameter>(type, feat_map_shape);
const auto rois = std::make_shared<op::Parameter>(type, rois_shape);
const auto roi_pooling = std::make_shared<op::v0::ROIPooling>(feat_map, rois, pooled_shape, spat_scale, mode);
return std::make_shared<Function>(roi_pooling, ParameterVector{feat_map, rois});
}
};
TEST_P(ReferenceRoiPoolingLayerTest, CompareWithHardcodedRefs) {
Exec();
}
INSTANTIATE_TEST_SUITE_P(
smoke_ROIPooling_With_Hardcoded_Refs, ReferenceRoiPoolingLayerTest,
::testing::Values(
// fp32
// roi_pooling_1x1_max
ROIPoolingParams(6, 6, // iH, iW
3, 3, // channels, rois
1, 1, // oH, oW
1.f, "max", // scale, mode
element::f32, ROIPoolingParams::increasinglyFilledBlob<float>(3 * 6 * 6),
std::vector<float> {0, 1, 1, 2, 3, 0, 1, 1, 2, 3, 0, 1, 1, 2, 3},
std::vector<float> {2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f}),
// roi_pooling_2x2_max
ROIPoolingParams(6, 6, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "max", // scale, mode
element::f32, ROIPoolingParams::increasinglyFilledBlob<float>(1 * 6 * 6),
std::vector<float> {0, 1, 1, 3, 3, 0, 1, 2, 2, 4, 0, 0, 1, 4, 5},
std::vector<float> {1.4f, 1.5f, 2.0f, 2.1f, 1.9f, 2.0f, 2.5f, 2.6f, 2.0f, 2.2f, 3.2f, 3.4f}),
// roi_pooling_1x1_bilinear
ROIPoolingParams(6, 6, // iH, iW
3, 2, // channels, rois
1, 1, // oH, oW
1.f, "bilinear", // scale, mode
element::f32, ROIPoolingParams::increasinglyFilledBlob<float>(3 * 6 * 6),
std::vector<float> {0, 0.2, 0.2, 0.4, 0.4, 0, 0.2, 0.2, 0.6, 0.6},
std::vector<float> {1.05f, 4.65f, 8.25f, 1.4f, 5.0f, 8.6f}),
// roi_pooling_2x2_bilinear
ROIPoolingParams(8, 8, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "bilinear", // scale, mode
element::f32, ROIPoolingParams::increasinglyFilledBlob<float>(1 * 8 * 8),
std::vector<float> {0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f},
std::vector<float> {1.225f, 1.645f, 4.585f, 5.005f,
1.225f, 1.645f, 4.585f, 5.005f,
1.225f, 1.645f, 4.585f, 5.005f}),
// roi_pooling_2x2_bilinear_border_proposal
ROIPoolingParams(50, 50, // iH, iW
1, 1, // channels, rois
4, 4, // oH, oW
1.f, "bilinear", // scale, mode
element::f32, ROIPoolingParams::equallyFilledBlob<float>(1 * 50 * 50, 1),
std::vector<float> {0.f, 0.f, 0.248046786f, 0.471333951f, 1.f},
std::vector<float>(16, 1.f)),
// bf16
// roi_pooling_1x1_max
ROIPoolingParams(6, 6, // iH, iW
3, 3, // channels, rois
1, 1, // oH, oW
1.f, "max", // scale, mode
element::bf16, ROIPoolingParams::increasinglyFilledBlob<bfloat16>(3 * 6 * 6),
std::vector<bfloat16> {0, 1, 1, 2, 3, 0, 1, 1, 2, 3, 0, 1, 1, 2, 3},
std::vector<bfloat16> {2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f}),
// roi_pooling_2x2_max
ROIPoolingParams(6, 6, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "max", // scale, mode
element::bf16, ROIPoolingParams::increasinglyFilledBlob<bfloat16>(1 * 6 * 6),
std::vector<bfloat16> {0, 1, 1, 3, 3, 0, 1, 2, 2, 4, 0, 0, 1, 4, 5},
std::vector<bfloat16> {1.4f, 1.5f, 2.0f, 2.1f, 1.9f, 2.0f, 2.5f, 2.6f, 2.0f, 2.2f, 3.2f, 3.4f}),
// roi_pooling_1x1_bilinear
ROIPoolingParams(6, 6, // iH, iW
3, 2, // channels, rois
1, 1, // oH, oW
1.f, "bilinear", // scale, mode
element::bf16, ROIPoolingParams::increasinglyFilledBlob<bfloat16>(3 * 6 * 6),
std::vector<bfloat16> {0, 0.2, 0.2, 0.4, 0.4, 0, 0.2, 0.2, 0.6, 0.6},
std::vector<bfloat16> {1.05f, 4.65f, 8.25f, 1.4f, 5.0f, 8.6f}),
// roi_pooling_2x2_bilinear
ROIPoolingParams(8, 8, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "bilinear", // scale, mode
element::bf16, ROIPoolingParams::increasinglyFilledBlob<bfloat16>(1 * 8 * 8),
std::vector<bfloat16> {0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f},
std::vector<bfloat16> {1.225f, 1.645f, 4.585f, 4.937f,
1.225f, 1.645f, 4.585f, 4.937f,
1.225f, 1.645f, 4.585f, 4.937f}),
// fp16
// roi_pooling_1x1_max
ROIPoolingParams(6, 6, // iH, iW
3, 3, // channels, rois
1, 1, // oH, oW
1.f, "max", // scale, mode
element::f16, ROIPoolingParams::increasinglyFilledBlob<float16>(3 * 6 * 6),
std::vector<float16> {0, 1, 1, 2, 3, 0, 1, 1, 2, 3, 0, 1, 1, 2, 3},
std::vector<float16> {2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f, 2.0f, 5.6f, 9.2f}),
// roi_pooling_2x2_max
ROIPoolingParams(6, 6, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "max", // scale, mode
element::f16, ROIPoolingParams::increasinglyFilledBlob<float16>(1 * 6 * 6),
std::vector<float16> {0, 1, 1, 3, 3, 0, 1, 2, 2, 4, 0, 0, 1, 4, 5},
std::vector<float16> {1.4f, 1.5f, 2.0f, 2.1f, 1.9f, 2.0f, 2.5f, 2.6f, 2.0f, 2.2f, 3.2f, 3.4f}),
// roi_pooling_1x1_bilinear
ROIPoolingParams(6, 6, // iH, iW
3, 2, // channels, rois
1, 1, // oH, oW
1.f, "bilinear", // scale, mode
element::f16, ROIPoolingParams::increasinglyFilledBlob<float16>(3 * 6 * 6),
std::vector<float16> {0, 0.2, 0.2, 0.4, 0.4, 0, 0.2, 0.2, 0.6, 0.6},
std::vector<float16> {1.05f, 4.65f, 8.25f, 1.4f, 5.0f, 8.6f}),
// roi_pooling_2x2_bilinear
ROIPoolingParams(8, 8, // iH, iW
1, 3, // channels, rois
2, 2, // oH, oW
1.f, "bilinear", // scale, mode
element::f16, ROIPoolingParams::increasinglyFilledBlob<float16>(1 * 8 * 8),
std::vector<float16> {0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f,
0.f, 0.15f, 0.2f, 0.75f, 0.8f},
std::vector<float16> {1.225f, 1.645f, 4.585f, 5.005f,
1.225f, 1.645f, 4.585f, 5.005f,
1.225f, 1.645f, 4.585f, 5.005f})),
ReferenceRoiPoolingLayerTest::getTestCaseName);

85
docs/template_plugin/tests/functional/op_reference/tan.cpp Normal file
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@ -0,0 +1,85 @@
// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <ie_core.hpp>
#include <ie_ngraph_utils.hpp>
#include <ngraph/ngraph.hpp>
#include <shared_test_classes/base/layer_test_utils.hpp>
#include <tuple>
#include "base_reference_test.hpp"
using namespace ngraph;
using namespace InferenceEngine;
using namespace reference_tests;
namespace {
struct TanParams {
template <class IT>
TanParams(const ngraph::PartialShape& shape, const ngraph::element::Type& iType, const std::vector<IT>& iValues,
const std::vector<IT>& oValues)
:pshape(shape), inType(iType), outType(iType), inputData(CreateBlob(iType, iValues)), refData(CreateBlob(iType, oValues)) {}
ngraph::PartialShape pshape;
ngraph::element::Type inType;
ngraph::element::Type outType;
InferenceEngine::Blob::Ptr inputData;
InferenceEngine::Blob::Ptr refData;
};
class ReferenceTanLayerTest : public testing::TestWithParam<TanParams>, public CommonReferenceTest {
public:
void SetUp() override {
auto params = GetParam();
function = CreateFunction(params.pshape, params.inType);
inputData = {params.inputData};
refOutData = {params.refData};
}
static std::string getTestCaseName(const testing::TestParamInfo<TanParams>& obj) {
auto param = obj.param;
std::ostringstream result;
result << "shape=" << param.pshape << "_";
result << "iType=" << param.inType << "_";
result << "oType=" << param.outType;
return result.str();
}
private:
static std::shared_ptr<Function> CreateFunction(const PartialShape& input_shape, const element::Type& input_type) {
const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
const auto tan = std::make_shared<op::Tan>(in);
return std::make_shared<Function>(tan, ParameterVector {in});
}
};
TEST_P(ReferenceTanLayerTest, CompareWithHardcodedRefs) {
Exec();
}
std::vector<TanParams> generateTanCombinedParams() {
std::vector<TanParams> combinedParams {
TanParams(ngraph::PartialShape {5}, ngraph::element::i32, std::vector<int32_t> {-2, -1, 0, 1, 2},
std::vector<int32_t> {2, -2, 0, 2, -2}),
TanParams(ngraph::PartialShape {5}, ngraph::element::i64, std::vector<int64_t> {-2, -1, 0, 1, 2},
std::vector<int64_t> {2, -2, 0, 2, -2}),
TanParams(ngraph::PartialShape {5}, ngraph::element::u32, std::vector<uint32_t> {1, 2, 3, 4, 5},
std::vector<uint32_t> {2, 0xFFFFFFFF - 1, 0, 1, 0xFFFFFFFF - 2}),
TanParams(ngraph::PartialShape {5}, ngraph::element::u64, std::vector<uint64_t> {1, 2, 3, 4, 5},
std::vector<uint64_t> {2, 0xFFFFFFFFFFFFFFFF - 1, 0, 1, 0xFFFFFFFFFFFFFFFF - 2}),
TanParams(ngraph::PartialShape {11}, ngraph::element::f32, std::vector<float> {0.f, 0.25f,
-0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f},
std::vector<float> {0.00000000f, 0.25534192f, -0.25534192f, 0.54630249f, -0.54630249f,
1.55740772f, -1.55740772f, -2.18503986f, 2.18503986f, 1.15782128f, -1.15782128f}),
TanParams(ngraph::PartialShape {11}, ngraph::element::f16, std::vector<float16> {0.f, 0.25f,
-0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f},
std::vector<float16> {0.00000000f, 0.25534192f, -0.25534192f, 0.54630249f, -0.54630249f,
1.55740772f, -1.55740772f, -2.18503986f, 2.18503986f, 1.15782128f, -1.15782128f})
};
return combinedParams;
}
INSTANTIATE_TEST_SUITE_P(smoke_TAN_With_Hardcoded_Refs, ReferenceTanLayerTest, ::testing::ValuesIn(generateTanCombinedParams()),
ReferenceTanLayerTest::getTestCaseName);
} // namespace

3
inference-engine/cmake/ie_parallel.cmake
View File

@ -29,6 +29,7 @@ function(set_ie_threading_interface_for TARGET_NAME)
set(TBB_IMPORTED_TARGETS ${TBB_IMPORTED_TARGETS} PARENT_SCOPE)
set(TBB_VERSION ${TBB_VERSION} PARENT_SCOPE)
if (NOT TBB_FOUND)
set(THREADING "SEQ" PARENT_SCOPE)
ext_message(WARNING "TBB was not found by the configured TBB_DIR/TBBROOT path.\
SEQ method will be used.")
endif ()
@ -95,6 +96,7 @@ function(set_ie_threading_interface_for TARGET_NAME)
set(IE_THREAD_DEFINE "IE_THREAD_TBB")
ie_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} ${TBB_IMPORTED_TARGETS})
else ()
set(THREADING "SEQ" PARENT_SCOPE)
ext_message(WARNING "TBB was not found by the configured TBB_DIR path.\
SEQ method will be used for ${TARGET_NAME}")
endif ()
@ -133,6 +135,7 @@ function(set_ie_threading_interface_for TARGET_NAME)
if (NOT OMP_LIBRARIES_RELEASE)
ext_message(WARNING "Intel OpenMP not found. Intel OpenMP support will be disabled. ${IE_THREAD_DEFINE} is defined")
set(THREADING "SEQ" PARENT_SCOPE)
else ()
set(IE_THREAD_DEFINE "IE_THREAD_OMP")

4
inference-engine/ie_bridges/c/src/CMakeLists.txt
View File

@ -14,7 +14,7 @@ add_library(${TARGET_NAME} SHARED ${HEADERS} ${SOURCES})
target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
target_include_directories(${TARGET_NAME} PUBLIC
$<INSTALL_INTERFACE:${IE_CPACK_IE_DIR}/include>
$<INSTALL_INTERFACE:${IE_CPACK_IE_DIR}/include/ie>
$<BUILD_INTERFACE:${InferenceEngine_C_API_SOURCE_DIR}/include>)
add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
@ -40,5 +40,5 @@ install(TARGETS ${TARGET_NAME} EXPORT InferenceEngineTargets
LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core_c)
install(DIRECTORY ${InferenceEngine_C_API_SOURCE_DIR}/include/
DESTINATION ${IE_CPACK_IE_DIR}/include
DESTINATION ${IE_CPACK_IE_DIR}/include/ie
COMPONENT core_c_dev)

7
inference-engine/ie_bridges/python/CMakeLists.txt
View File

@ -58,6 +58,13 @@ else()
endif()
endif()
function(ov_python_disable_intel_warnings target)
if(UNIX AND CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
# 1292: unknown attribute "fallthrough"
target_compile_options(${target} PRIVATE -diag-disable=1292)
endif()
endfunction()
set (PYTHON_BRIDGE_SRC_ROOT ${CMAKE_CURRENT_SOURCE_DIR})
add_subdirectory (src/openvino/inference_engine)
add_subdirectory (src/openvino/offline_transformations)

4
inference-engine/ie_bridges/python/src/openvino/inference_engine/CMakeLists.txt
View File

@ -20,13 +20,15 @@ set_source_files_properties(${PYX_SOURCES} PROPERTIES CYTHON_IS_CXX ON)
# create target
cython_add_module(${TARGET_NAME} ${SOURCES})
set(INSTALLED_TARGETS ${TARGET_NAME})
ov_python_disable_intel_warnings(${TARGET_NAME})
set(INSTALLED_TARGETS ${TARGET_NAME})
list(REMOVE_ITEM PYX_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/ie_api.pyx")
foreach(PYX_FILE IN LISTS PYX_SOURCES)
get_filename_component(PYX_NAME "${PYX_FILE}" NAME_WE)
cython_add_module(${PYX_NAME} ${PYX_FILE})
ov_python_disable_intel_warnings(${PYX_NAME})
add_dependencies(${TARGET_NAME} ${PYX_NAME})
target_include_directories(${PYX_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}")
target_link_libraries(${PYX_NAME} PRIVATE ${InferenceEngine_LIBRARIES})

193
inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx
View File

@ -284,7 +284,9 @@ cdef class IECore:
# If the parameter is not specified, the default configuration is handled automatically.
# @return Instance of IECore class
def __cinit__(self, xml_config_file: str = ""):
self.impl = C.IECore(xml_config_file.encode())
cdef string c_xml_config_file = xml_config_file.encode()
with nogil:
self.impl = C.IECore(c_xml_config_file)
## Get a `namedtuple` object with versions of the plugin specified
# @param device_name: Name of the the registered plugin
@ -326,12 +328,15 @@ cdef class IECore:
cdef string weights_
cdef string model_
cdef IENetwork net = IENetwork()
cdef size_t bin_size
if init_from_buffer:
model_ = bytes(model)
net.impl = self.impl.readNetwork(model_, weights, len(weights))
bin_buffer = <uint8_t*> weights
bin_size = len(weights)
with nogil:
net.impl = self.impl.readNetwork(model_, bin_buffer, bin_size)
else:
weights_ = "".encode()
model = os.fspath(model)
if not os.path.isfile(model):
raise Exception(f"Path to the model {model} doesn't exist or it's a directory")
@ -342,7 +347,7 @@ cdef class IECore:
if not os.path.isfile(weights):
raise Exception(f"Path to the weights {weights} doesn't exist or it's a directory")
weights_ = weights.encode()
with nogil:
net.impl = self.impl.readNetwork(model_, weights_)
return net
@ -367,16 +372,22 @@ cdef class IECore:
cpdef ExecutableNetwork load_network(self, network: [IENetwork, str], str device_name, config=None, int num_requests=1):
cdef ExecutableNetwork exec_net = ExecutableNetwork()
cdef map[string, string] c_config
cdef string c_device_name
cdef string c_network_path
if num_requests < 0:
raise ValueError(f"Incorrect number of requests specified: {num_requests}. Expected positive integer number "
"or zero for auto detection")
if config:
c_config = dict_to_c_map(config)
exec_net.ie_core_impl = self.impl
c_device_name = device_name.encode()
if isinstance(network, str):
exec_net.impl = move(self.impl.loadNetworkFromFile((<str>network).encode(), device_name.encode(), c_config, num_requests))
c_network_path = network.encode()
with nogil:
exec_net.impl = move(self.impl.loadNetworkFromFile(c_network_path, c_device_name, c_config, num_requests))
else:
exec_net.impl = move(self.impl.loadNetwork((<IENetwork>network).impl, device_name.encode(), c_config, num_requests))
with nogil:
exec_net.impl = move(self.impl.loadNetwork((<IENetwork>network).impl, c_device_name, c_config, num_requests))
return exec_net
## Creates an executable network from a previously exported network
@ -534,7 +545,9 @@ cdef class IECore:
# If there are more than one device of a specific type, they all are listed followed by a dot and a number.
@property
def available_devices(self):
cdef vector[string] c_devices = self.impl.getAvailableDevices()
cdef vector[string] c_devices
with nogil:
c_devices = self.impl.getAvailableDevices()
return [d.decode() for d in c_devices]
## This structure stores info about pre-processing of network inputs (scale, mean image, ...)
@ -897,15 +910,19 @@ cdef class ExecutableNetwork:
## A tuple of `InferRequest` instances
@property
def requests(self):
cdef size_t c_infer_requests_size
with nogil:
c_infer_requests_size = deref(self.impl).infer_requests.size()
if len(self._infer_requests) == 0:
for i in range(deref(self.impl).infer_requests.size()):
for i in range(c_infer_requests_size):
infer_request = InferRequest()
with nogil:
infer_request.impl = &(deref(self.impl).infer_requests[i])
infer_request._inputs_list = list(self.input_info.keys())
infer_request._outputs_list = list(self.outputs.keys())
self._infer_requests.append(infer_request)
if len(self._infer_requests) != deref(self.impl).infer_requests.size():
if len(self._infer_requests) != c_infer_requests_size:
raise Exception("Mismatch of infer requests number!")
return self._infer_requests
@ -923,26 +940,6 @@ cdef class ExecutableNetwork:
inputs[in_.first.decode()] = input_info_ptr
return inputs
## \note The property is deprecated. Please use the input_info property
# to get the map of inputs
#
## A dictionary that maps input layer names to DataPtr objects
@property
def inputs(self):
warnings.warn("'inputs' property of ExecutableNetwork class is deprecated. "
"To access DataPtrs user need to use 'input_data' property "
"of InputInfoCPtr objects which can be accessed by 'input_info' property.",
DeprecationWarning)
cdef map[string, C.DataPtr] c_inputs = deref(self.impl).getInputs()
inputs = {}
cdef DataPtr data_ptr
for in_ in c_inputs:
data_ptr = DataPtr()
data_ptr._ptr = in_.second
data_ptr._ptr_plugin = deref(self.impl).getPluginLink()
inputs[in_.first.decode()] = data_ptr
return inputs
## A dictionary that maps output layer names to CDataPtr objects
@property
def outputs(self):
@ -1022,16 +1019,26 @@ cdef class ExecutableNetwork:
# If not specified, `timeout` value is set to -1 by default.
# @return Request status code: OK or RESULT_NOT_READY
cpdef wait(self, num_requests=None, timeout=None):
cdef int status_code
cdef int64_t c_timeout
cdef int c_num_requests
if num_requests is None:
num_requests = len(self.requests)
c_num_requests = <int> num_requests
if timeout is None:
timeout = WaitMode.RESULT_READY
return deref(self.impl).wait(<int> num_requests, <int64_t> timeout)
c_timeout = <int64_t> timeout
with nogil:
status_code = deref(self.impl).wait(c_num_requests, c_timeout)
return status_code
## Get idle request ID
# @return Request index
cpdef get_idle_request_id(self):
return deref(self.impl).getIdleRequestId()
cdef int request_id
with nogil:
request_id = deref(self.impl).getIdleRequestId()
return request_id
ctypedef extern void (*cb_type)(void*, int) with gil
@ -1177,7 +1184,7 @@ cdef class InferRequest:
cpdef infer(self, inputs=None):
if inputs is not None:
self._fill_inputs(inputs)
with nogil:
deref(self.impl).infer()
## Starts asynchronous inference of the infer request and fill outputs array
@ -1197,6 +1204,7 @@ cdef class InferRequest:
self._fill_inputs(inputs)
if self._py_callback_used:
self._py_callback_called.clear()
with nogil:
deref(self.impl).infer_async()
## Waits for the result to become available. Blocks until specified timeout elapses or the result
@ -1213,9 +1221,14 @@ cdef class InferRequest:
#
# Usage example: See `async_infer()` method of the the `InferRequest` class.
cpdef wait(self, timeout=None):
cdef int status
cdef int64_t c_timeout
cdef int c_wait_mode
if self._py_callback_used:
# check request status to avoid blocking for idle requests
status = deref(self.impl).wait(WaitMode.STATUS_ONLY)
c_wait_mode = WaitMode.STATUS_ONLY
with nogil:
status = deref(self.impl).wait(c_wait_mode)
if status != StatusCode.RESULT_NOT_READY:
return status
if not self._py_callback_called.is_set():
@ -1230,8 +1243,10 @@ cdef class InferRequest:
if timeout is None:
timeout = WaitMode.RESULT_READY
return deref(self.impl).wait(<int64_t> timeout)
c_timeout = <int64_t> timeout
with nogil:
status = deref(self.impl).wait(c_timeout)
return status
## Queries performance measures per layer to get feedback of what is the most time consuming layer.
#
@ -1268,27 +1283,6 @@ cdef class InferRequest:
"cpu_time": info.cpu_time, "execution_index": info.execution_index}
return profile
## A dictionary that maps input layer names to `numpy.ndarray`
# objects of proper shape with input data for the layer
@property
def inputs(self):
warnings.warn("'inputs' property of InferRequest is deprecated. Please instead use 'input_blobs' property.",
DeprecationWarning)
inputs = {}
for input in self._inputs_list:
inputs[input] = self._get_blob_buffer(input.encode()).to_numpy()
return inputs
## A dictionary that maps output layer names to `numpy.ndarray` objects with output data of the layer
@property
def outputs(self):
warnings.warn("'outputs' property of InferRequest is deprecated. Please instead use 'output_blobs' property.",
DeprecationWarning)
outputs = {}
for output in self._outputs_list:
outputs[output] = self._get_blob_buffer(output.encode()).to_numpy()
return deepcopy(outputs)
## Current infer request inference time in milliseconds
@property
def latency(self):
@ -1333,68 +1327,25 @@ cdef class InferRequest:
cdef class IENetwork:
## Class constructor
#
# \note Reading networks using IENetwork constructor is deprecated.
# Please, use IECore.read_network() method instead.
# @param model: A PyCapsule containing smart pointer to nGraph function.
#
# @param model: A `.xml` file of the IR or PyCapsule containing smart pointer to nGraph function.
# In case of passing a `.xml` file attribute value can be a string path or bytes with file content
# depending on `init_from_buffer` attribute value
# .
# @param weights: A `.bin` file of the IR. Depending on `init_from_buffer` value, can be a string path or
# bytes with file content.
# @param init_from_buffer: Defines the way of how `model` and `weights` attributes are interpreted.
# If `False`, attributes are interpreted as strings with paths to .xml and .bin files
# of IR. If `True`, they are interpreted as Python `bytes` object with .xml and .bin files content.
# Ignored in case of `IENetwork` object initialization from nGraph function.
# @return Instance of IENetwork class
#
# Usage example:\n
# Initializing `IENetwork` object from IR files:
# ```python
# net = IENetwork(model=path_to_xml_file, weights=path_to_bin_file)
# func = Function([relu], [param], 'test')
# caps = Function.to_capsule(func)
# net = IENetwork(caps)
# ```
#
# Initializing `IENetwork` object bytes with content of IR files:
# ```python
# with open(path_to_bin_file, 'rb') as f:
# bin = f.read()
# with open(path_to_xml_file, 'rb') as f:
# xml = f.read()
# net = IENetwork(model=xml, weights=bin, init_from_buffer=True)
# ```
def __cinit__(self, model: [str, bytes] = "", weights: [str, bytes] = "", init_from_buffer: bool = False):
def __cinit__(self, model = None):
# Try to create Inference Engine network from capsule
if model.__class__.__name__ == 'PyCapsule' and weights == '' and init_from_buffer is False:
if model is not None:
with nogil:
self.impl = C.IENetwork(model)
return
cdef char*xml_buffer = <char*> malloc(len(model)+1)
cdef uint8_t*bin_buffer = <uint8_t *> malloc(len(weights))
cdef string model_
cdef string weights_
if init_from_buffer:
warnings.warn("Reading network using constructor is deprecated. "
"Please, use IECore.read_network() method instead", DeprecationWarning)
memcpy(xml_buffer, <char*> model, len(model))
memcpy(bin_buffer, <uint8_t *> weights, len(weights))
xml_buffer[len(model)] = b'\0'
self.impl = C.IENetwork()
self.impl.load_from_buffer(xml_buffer, len(model), bin_buffer, len(weights))
else:
if model and weights:
warnings.warn("Reading network using constructor is deprecated. "
"Please, use IECore.read_network() method instead", DeprecationWarning)
if not os.path.isfile(model):
raise Exception(f"Path to the model {model} doesn't exist or it's a directory")
if not os.path.isfile(weights):
raise Exception(f"Path to the weights {weights} doesn't exist or it's a directory")
model_ = model.encode()
weights_ = weights.encode()
self.impl = C.IENetwork(model_, weights_)
else:
with nogil:
self.impl = C.IENetwork()
free(bin_buffer)
free(xml_buffer)
## Name of the loaded network
@property
@ -1405,7 +1356,9 @@ cdef class IENetwork:
## A dictionary that maps input layer names to InputInfoPtr objects.
@property
def input_info(self):
cdef map[string, C.InputInfo.Ptr] c_inputs = self.impl.getInputsInfo()
cdef map[string, C.InputInfo.Ptr] c_inputs
with nogil:
c_inputs = self.impl.getInputsInfo()
inputs = {}
cdef InputInfoPtr input_info_ptr
for input in c_inputs:
@ -1415,30 +1368,12 @@ cdef class IENetwork:
inputs[input.first.decode()] = input_info_ptr
return inputs
## \note The property is deprecated. Please use the input_info property
# to get the map of inputs
#
## A dictionary that maps input layer names to DataPtr objects
@property
def inputs(self):
warnings.warn("'inputs' property of IENetwork class is deprecated. "
"To access DataPtrs user need to use 'input_data' property "
"of InputInfoPtr objects which can be accessed by 'input_info' property.",
DeprecationWarning)
cdef map[string, C.DataPtr] c_inputs = self.impl.getInputs()
inputs = {}
cdef DataPtr data_ptr
for input in c_inputs:
data_ptr = DataPtr()
data_ptr._ptr_network = &self.impl
data_ptr._ptr = input.second
inputs[input.first.decode()] = data_ptr
return inputs
## A dictionary that maps output layer names to DataPtr objects
@property
def outputs(self):
cdef map[string, C.DataPtr] c_outputs = self.impl.getOutputs()
cdef map[string, C.DataPtr] c_outputs
with nogil:
c_outputs = self.impl.getOutputs()
outputs = {}
cdef DataPtr data_ptr
for output in c_outputs:

36
inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api_impl.cpp
View File

@ -200,14 +200,6 @@ InferenceEnginePython::IENetwork InferenceEnginePython::read_network(std::string
return InferenceEnginePython::IENetwork(std::make_shared<InferenceEngine::CNNNetwork>(net));
}
InferenceEnginePython::IENetwork::IENetwork(const std::string& model, const std::string& weights) {
InferenceEngine::Core reader;
auto net = reader.ReadNetwork(model, weights);
actual = std::make_shared<InferenceEngine::CNNNetwork>(net);
name = actual->getName();
batch_size = actual->getBatchSize();
}
InferenceEnginePython::IENetwork::IENetwork(const std::shared_ptr<InferenceEngine::CNNNetwork>& cnn_network): actual(cnn_network) {
if (actual == nullptr)
IE_THROW() << "IENetwork was not initialized.";
@ -228,16 +220,6 @@ InferenceEnginePython::IENetwork::IENetwork(PyObject* network) {
batch_size = actual->getBatchSize();
}
void InferenceEnginePython::IENetwork::load_from_buffer(const char* xml, size_t xml_size, uint8_t* bin, size_t bin_size) {
InferenceEngine::Core reader;
InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, {bin_size}, InferenceEngine::Layout::C);
auto weights_blob = InferenceEngine::make_shared_blob<uint8_t>(tensorDesc, bin, bin_size);
auto net = reader.ReadNetwork(std::string(xml, xml + xml_size), weights_blob);
name = net.getName();
actual = std::make_shared<InferenceEngine::CNNNetwork>(net);
batch_size = actual->getBatchSize();
}
void InferenceEnginePython::IENetwork::serialize(const std::string& path_to_xml, const std::string& path_to_bin) {
actual->serialize(path_to_xml, path_to_bin);
}
@ -275,15 +257,6 @@ const std::map<std::string, InferenceEngine::InputInfo::Ptr> InferenceEnginePyth
return inputs;
}
const std::map<std::string, InferenceEngine::DataPtr> InferenceEnginePython::IENetwork::getInputs() {
std::map<std::string, InferenceEngine::DataPtr> inputs;
const InferenceEngine::InputsDataMap& inputsInfo = actual->getInputsInfo();
for (auto& in : inputsInfo) {
inputs[in.first] = in.second->getInputData();
}
return inputs;
}
const std::map<std::string, InferenceEngine::DataPtr> InferenceEnginePython::IENetwork::getOutputs() {
std::map<std::string, InferenceEngine::DataPtr> outputs;
const InferenceEngine::OutputsDataMap& outputsInfo = actual->getOutputsInfo();
@ -338,15 +311,6 @@ void InferenceEnginePython::IEExecNetwork::exportNetwork(const std::string& mode
actual->Export(model_file);
}
std::map<std::string, InferenceEngine::DataPtr> InferenceEnginePython::IEExecNetwork::getInputs() {
InferenceEngine::ConstInputsDataMap inputsDataMap = actual->GetInputsInfo();
std::map<std::string, InferenceEngine::DataPtr> pyInputs;
for (const auto& item : inputsDataMap) {
pyInputs[item.first] = item.second->getInputData();
}
return pyInputs;
}
std::map<std::string, InferenceEngine::InputInfo::CPtr> InferenceEnginePython::IEExecNetwork::getInputsInfo() {
InferenceEngine::ConstInputsDataMap inputsDataMap = actual->GetInputsInfo();
std::map<std::string, InferenceEngine::InputInfo::CPtr> pyInputs;

7
inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api_impl.hpp
View File

@ -60,18 +60,12 @@ struct IENetwork {
const std::map<std::string, InferenceEngine::InputInfo::Ptr> getInputsInfo();
const std::map<std::string, InferenceEngine::DataPtr> getInputs();
const std::map<std::string, InferenceEngine::DataPtr> getOutputs();
void reshape(const std::map<std::string, std::vector<size_t>>& input_shapes);
void serialize(const std::string& path_to_xml, const std::string& path_to_bin);
void load_from_buffer(const char* xml, size_t xml_size, uint8_t* bin, size_t bin_size);
IENetwork(const std::string& model, const std::string& weights);
IENetwork(const std::shared_ptr<InferenceEngine::CNNNetwork>& cnn_network);
IENetwork(PyObject* network);
@ -146,7 +140,6 @@ struct IEExecNetwork {
void exportNetwork(const std::string& model_file);
std::map<std::string, InferenceEngine::InputInfo::CPtr> getInputsInfo();
std::map<std::string, InferenceEngine::DataPtr> getInputs();
std::map<std::string, InferenceEngine::CDataPtr> getOutputs();
PyObject* getMetric(const std::string& metric_name);

36
inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api_impl_defs.pxd
View File

@ -154,27 +154,24 @@ cdef extern from "ie_api_impl.hpp" namespace "InferenceEnginePython":
cdef cppclass IEExecNetwork:
vector[InferRequestWrap] infer_requests
IENetwork GetExecGraphInfo() except +
map[string, DataPtr] getInputs() except +
map[string, CDataPtr] getOutputs() except +
map[string, InputInfo.CPtr] getInputsInfo()
void exportNetwork(const string & model_file) except +
object getMetric(const string & metric_name) except +
object getConfig(const string & metric_name) except +
int wait(int num_requests, int64_t timeout)
int getIdleRequestId()
int wait(int num_requests, int64_t timeout) nogil
int getIdleRequestId() nogil
shared_ptr[CExecutableNetwork] getPluginLink() except +
cdef cppclass IENetwork:
IENetwork() except +
IENetwork(object) except +
IENetwork(const string &, const string &) except +
IENetwork() nogil except +
IENetwork(object) nogil except +
string name
size_t batch_size
string precision
map[string, vector[size_t]] inputs
const map[string, InputInfo.Ptr] getInputsInfo() except +
const map[string, DataPtr] getInputs() except +
map[string, DataPtr] getOutputs() except +
const map[string, InputInfo.Ptr] getInputsInfo() nogil except +
map[string, DataPtr] getOutputs() nogil except +
void addOutput(string &, size_t) except +
void setAffinity(map[string, string] & types_affinity_map, map[string, string] & layers_affinity_map) except +
void setBatch(size_t size) except +
@ -182,7 +179,6 @@ cdef extern from "ie_api_impl.hpp" namespace "InferenceEnginePython":
void setLayerParams(map[string, map[string, string]] params_map) except +
void serialize(const string& path_to_xml, const string& path_to_bin) except +
void reshape(map[string, vector[size_t]] input_shapes) except +
void load_from_buffer(const char*xml, size_t xml_size, uint8_t*bin, size_t bin_size) except +
object getFunction() except +
void convertToOldRepresentation() except +
string getOVNameForTensor(const string &) except +
@ -195,23 +191,23 @@ cdef extern from "ie_api_impl.hpp" namespace "InferenceEnginePython":
void setBlob(const string &blob_name, const CBlob.Ptr &blob_ptr, CPreProcessInfo& info) except +
const CPreProcessInfo& getPreProcess(const string& blob_name) except +
map[string, ProfileInfo] getPerformanceCounts() except +
void infer() except +
void infer_async() except +
int wait(int64_t timeout) except +
void infer() nogil except +
void infer_async() nogil except +
int wait(int64_t timeout) nogil except +
void setBatch(int size) except +
void setCyCallback(void (*)(void*, int), void *) except +
vector[CVariableState] queryState() except +
cdef cppclass IECore:
IECore() except +
IECore(const string & xml_config_file) except +
IECore() nogil except +
IECore(const string & xml_config_file) nogil except +
map[string, Version] getVersions(const string & deviceName) except +
IENetwork readNetwork(const string& modelPath, const string& binPath) except +
IENetwork readNetwork(const string& modelPath,uint8_t*bin, size_t bin_size) except +
IENetwork readNetwork(const string& modelPath, const string& binPath) nogil except +
IENetwork readNetwork(const string& modelPath,uint8_t*bin, size_t bin_size) nogil except +
unique_ptr[IEExecNetwork] loadNetwork(IENetwork network, const string deviceName,
const map[string, string] & config, int num_requests) except +
const map[string, string] & config, int num_requests) nogil except +
unique_ptr[IEExecNetwork] loadNetworkFromFile(const string & modelPath, const string & deviceName,
const map[string, string] & config, int num_requests) except +
const map[string, string] & config, int num_requests) nogil except +
unique_ptr[IEExecNetwork] importNetwork(const string & modelFIle, const string & deviceName,
const map[string, string] & config, int num_requests) except +
map[string, string] queryNetwork(IENetwork network, const string deviceName,
@ -221,7 +217,7 @@ cdef extern from "ie_api_impl.hpp" namespace "InferenceEnginePython":
void unregisterPlugin(const string & deviceName) except +
void registerPlugins(const string & xmlConfigFile) except +
void addExtension(const string & ext_lib_path, const string & deviceName) except +
vector[string] getAvailableDevices() except +
vector[string] getAvailableDevices() nogil except +
object getMetric(const string & deviceName, const string & name) except +
object getConfig(const string & deviceName, const string & name) except +

2
inference-engine/ie_bridges/python/src/openvino/offline_transformations/CMakeLists.txt
View File

@ -20,7 +20,9 @@ set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/offline_transformations_
# create target
cython_add_module(${TARGET_NAME} ${SOURCES})
add_dependencies(${TARGET_NAME} ie_api)
ov_python_disable_intel_warnings(${TARGET_NAME})
if(COMMAND ie_add_vs_version_file)
ie_add_vs_version_file(NAME ${TARGET_NAME}

2
inference-engine/ie_bridges/python/src/openvino/test_utils/CMakeLists.txt
View File

@ -20,7 +20,9 @@ set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/test_utils_api.pyx
# create target
cython_add_module(${TARGET_NAME} ${SOURCES})
add_dependencies(${TARGET_NAME} ie_api)
ov_python_disable_intel_warnings(${TARGET_NAME})
if(COMMAND ie_add_vs_version_file)
ie_add_vs_version_file(NAME ${TARGET_NAME}

5
inference-engine/ie_bridges/python/tests/conftest.py
View File

@ -21,11 +21,6 @@ def model_onnx_path():
test_onnx = os.path.join(path_to_repo, "models", "test_model", 'test_model.onnx')
return test_onnx
def model_prototxt_path():
path_to_repo = os.environ["MODELS_PATH"]
test_prototxt = os.path.join(path_to_repo, "models", "test_model", 'test_model.prototxt')
return test_prototxt
def image_path():
path_to_repo = os.environ["DATA_PATH"]
path_to_img = os.path.join(path_to_repo, 'validation_set', '224x224', 'dog.bmp')

18
inference-engine/ie_bridges/python/tests/test_ExecutableNetwork.py
View File

@ -82,24 +82,6 @@ def test_input_info(device):
del ie_core
def test_inputs_deprecated(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
exec_net = ie_core.load_network(net, device, num_requests=5)
with warnings.catch_warnings(record=True) as w:
assert len(exec_net.inputs) == 1
assert "data" in exec_net.inputs
assert isinstance(exec_net.inputs['data'], ie.DataPtr)
assert len(w) == 3
for i in range (len(w)):
assert "'inputs' property of ExecutableNetwork class is deprecated. " \
"To access DataPtrs user need to use 'input_data' property " \
"of InputInfoCPtr objects which " \
"can be accessed by 'input_info' property." in str(w[i].message)
del exec_net
del ie_core
def test_outputs(device):
ie_core = ie.IECore()
net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)

52
inference-engine/ie_bridges/python/tests/test_IECore.py
View File

@ -5,14 +5,16 @@ import os
import pytest
from sys import platform
from pathlib import Path
from threading import Thread
from time import sleep, time
from queue import Queue
from openvino.inference_engine import IENetwork, IECore, ExecutableNetwork
from conftest import model_path, plugins_path, model_onnx_path, model_prototxt_path
from conftest import model_path, plugins_path, model_onnx_path
test_net_xml, test_net_bin = model_path()
test_net_onnx = model_onnx_path()
test_net_prototxt = model_prototxt_path()
plugins_xml, plugins_win_xml, plugins_osx_xml = plugins_path()
@ -201,18 +203,6 @@ def test_read_network_from_onnx_as_path():
assert isinstance(net, IENetwork)
def test_read_network_from_prototxt():
ie = IECore()
net = ie.read_network(model=test_net_prototxt)
assert isinstance(net, IENetwork)
def test_read_network_from_prototxt_as_path():
ie = IECore()
net = ie.read_network(model=Path(test_net_prototxt))
assert isinstance(net, IENetwork)
def test_incorrect_xml():
ie = IECore()
with pytest.raises(Exception) as e:
@ -253,3 +243,37 @@ def test_net_from_buffer_valid():
o_net2 = ref_net.outputs
assert ii_net.keys() == ii_net2.keys()
assert o_net.keys() == o_net2.keys()
@pytest.mark.skipif(os.environ.get("TEST_DEVICE","CPU") != "GPU", reason=f"Device dependent test")
def test_load_network_release_gil(device):
running = True
message_queue = Queue()
def detect_long_gil_holds():
sleep_time = 0.01
latency_alert_threshold = 0.1
# Send a message to indicate the thread is running and ready to detect GIL locks
message_queue.put("ready to detect")
while running:
start_sleep = time()
sleep(sleep_time)
elapsed = time() - start_sleep
if elapsed > latency_alert_threshold:
# Send a message to the testing thread that a long GIL lock occurred
message_queue.put(latency_alert_threshold)
ie = IECore()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
# Wait for the GIL lock detector to be up and running
gil_hold_detection_thread = Thread(daemon=True, target=detect_long_gil_holds)
gil_hold_detection_thread.start()
# Wait to make sure the thread is started and checking for GIL holds
sleep(0.1)
assert message_queue.get(timeout=5) == "ready to detect"
# Run the function that should unlock the GIL
exec_net = ie.load_network(net, device)
# Ensure resources are closed
running = False
gil_hold_detection_thread.join(timeout=5)
# Assert there were never any long gil locks
assert message_queue.qsize() == 0, \
f"More than 0 GIL locks occured! Latency: {message_queue.get()})"

64
inference-engine/ie_bridges/python/tests/test_IENetwork.py
View File

@ -12,60 +12,12 @@ from conftest import model_path
test_net_xml, test_net_bin = model_path()
def test_create_ie_network_deprecated():
with warnings.catch_warnings(record=True) as w:
net = IENetwork(model=test_net_xml, weights=test_net_bin)
assert isinstance(net, IENetwork)
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "Reading network using constructor is deprecated. " \
"Please, use IECore.read_network() method instead" in str(w[0].message)
def test_incorrect_xml_deprecated():
with warnings.catch_warnings(record=True) as w:
with pytest.raises(Exception) as e:
IENetwork(model="./model.xml", weights=test_net_bin)
assert "Path to the model ./model.xml doesn't exist or it's a directory" in str(e.value)
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "Reading network using constructor is deprecated. " \
"Please, use IECore.read_network() method instead" in str(w[0].message)
def test_incorrect_bin_deprecated():
with warnings.catch_warnings(record=True) as w:
with pytest.raises(Exception) as e:
IENetwork(model=test_net_xml, weights="./model.bin")
assert "Path to the weights ./model.bin doesn't exist or it's a directory" in str(e.value)
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "Reading network using constructor is deprecated. " \
"Please, use IECore.read_network() method instead" in str(w[0].message)
def test_name():
ie = IECore()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
assert net.name == "test_model"
def test_inputs_deprecated():
ie = IECore()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
with warnings.catch_warnings(record=True) as w:
inp = net.inputs
assert isinstance(inp['data'], DataPtr)
assert inp['data'].layout == "NCHW"
assert inp['data'].precision == "FP32"
assert inp['data'].shape == [1, 3, 32, 32]
assert len(w) == 1
assert "'inputs' property of IENetwork class is deprecated. " \
"To access DataPtrs user need to use 'input_data' property " \
"of InputInfoPtr objects which " \
"can be accessed by 'input_info' property." in str(w[-1].message)
def test_input_info():
ie = IECore()
net = ie.read_network(model=test_net_xml, weights=test_net_bin)
@ -208,21 +160,7 @@ def test_reshape():
net.reshape({"data": (2, 3, 32, 32)})
def test_read_net_from_buffer_deprecated():
with warnings.catch_warnings(record=True) as w:
with open(test_net_bin, 'rb') as f:
bin = f.read()
with open(test_net_xml, 'rb') as f:
xml = f.read()
net = IENetwork(model=xml, weights=bin, init_from_buffer=True)
assert isinstance(net, IENetwork)
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "Reading network using constructor is deprecated. " \
"Please, use IECore.read_network() method instead" in str(w[0].message)
def test_net_from_buffer_valid_deprecated():
def test_net_from_buffer_valid():
ie = IECore()
with open(test_net_bin, 'rb') as f:
bin = f.read()

45
inference-engine/ie_bridges/python/tests/test_InferRequest.py
View File

@ -66,32 +66,6 @@ def test_output_blobs(device):
assert executable_network.requests[0].output_blobs['fc_out'].tensor_desc == td
def test_inputs_deprecated(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=2)
with warnings.catch_warnings(record=True) as w:
inputs = executable_network.requests[0].inputs
assert "'inputs' property of InferRequest is deprecated. " \
"Please instead use 'input_blobs' property." in str(w[-1].message)
del executable_network
del ie_core
del net
def test_outputs_deprecated(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=2)
with warnings.catch_warnings(record=True) as w:
outputs = executable_network.requests[0].outputs
assert "'outputs' property of InferRequest is deprecated. Please instead use 'output_blobs' property." in str(
w[-1].message)
del executable_network
del ie_core
del net
def test_inputs_list(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
@ -552,11 +526,10 @@ def test_query_state_write_buffer(device, input_shape, data_type, mode):
pytest.skip("Can't run on ARM plugin")
layout = ["C", "HW", "CHW", "NCHW"]
np_data_type = {"FP32": np.float32, "FP16": np.float16, "I32": np.int32}
from openvino.inference_engine import TensorDesc, Blob
from openvino.inference_engine import TensorDesc, Blob, format_map
net = ie.IENetwork(create_function_with_memory(input_shape, np_data_type[data_type]))
net = ie.IENetwork(create_function_with_memory(input_shape, format_map[data_type]))
ie_core = ie.IECore()
exec_net = ie_core.load_network(network=net, device_name=device, num_requests=1)
request = exec_net.requests[0]
@ -572,23 +545,23 @@ def test_query_state_write_buffer(device, input_shape, data_type, mode):
if mode == "set_init_memory_state":
# create initial value
const_init = 5
init_array = np.full(input_shape, const_init, dtype=np_data_type[mem_state.state.tensor_desc.precision])
init_array = np.full(input_shape, const_init, dtype=format_map[mem_state.state.tensor_desc.precision])
tensor_desc = TensorDesc(mem_state.state.tensor_desc.precision, input_shape, layout[len(input_shape) - 1])
blob = Blob(tensor_desc, init_array)
mem_state.state = blob
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
expected_res = np.full(input_shape, 1 + const_init, dtype=np_data_type[data_type])
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=format_map[data_type])})
expected_res = np.full(input_shape, 1 + const_init, dtype=format_map[data_type])
elif mode == "reset_memory_state":
# reset initial state of ReadValue to zero
mem_state.reset()
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=format_map[data_type])})
# always ones
expected_res = np.full(input_shape, 1, dtype=np_data_type[data_type])
expected_res = np.full(input_shape, 1, dtype=format_map[data_type])
else:
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=np_data_type[data_type])})
expected_res = np.full(input_shape, i, dtype=np_data_type[data_type])
res = exec_net.infer({"input_data": np.full(input_shape, 1, dtype=format_map[data_type])})
expected_res = np.full(input_shape, i, dtype=format_map[data_type])
assert np.allclose(res['MemoryAdd'], expected_res, atol=1e-6), \
"Expected values: {} \n Actual values: {} \n".format(expected_res, res)

4
inference-engine/ie_bridges/python/wheel/CMakeLists.txt
View File

@ -56,13 +56,13 @@ endif()
add_custom_command(TARGET ie_wheel
PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E rm -rf "${CMAKE_CURRENT_BINARY_DIR}/site-packages"
COMMAND ${CMAKE_COMMAND} -E remove_directory "${CMAKE_CURRENT_BINARY_DIR}/site-packages"
COMMAND ${PYTHON_EXECUTABLE} ${SETUP_PY} clean bdist_wheel
--dist-dir ${CMAKE_BINARY_DIR}/wheels
--build=${WHEEL_BUILD}
--plat-name=${WHEEL_PLATFORM}
POST_BUILD
COMMAND ${CMAKE_COMMAND} -E rm "${CMAKE_CURRENT_SOURCE_DIR}/.env"
COMMAND ${CMAKE_COMMAND} -E remove_directory "${CMAKE_CURRENT_SOURCE_DIR}/.env"
WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}"
COMMENT "Building Python wheel ${WHEEL_PACKAGE_NAME}"
VERBATIM

35
inference-engine/samples/CMakeLists.txt
View File

@ -56,7 +56,7 @@ set (CMAKE_RUNTIME_OUTPUT_DIRECTORY ${IE_MAIN_SAMPLES_DIR}/${BIN_FOLDER})
if (WIN32)
set_property (DIRECTORY APPEND PROPERTY COMPILE_DEFINITIONS _CRT_SECURE_NO_WARNINGS)
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_SCL_SECURE_NO_WARNINGS -DNOMINMAX")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_SCL_SECURE_NO_WARNINGS")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /EHsc") # no asynchronous structured exception handling
set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /LARGEADDRESSAWARE")
@ -68,23 +68,18 @@ if (WIN32)
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /Qdiag-disable:177")
endif()
# disable some noisy warnings
if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4251 /wd4275 /wd4267 /wd4819") #disable some warnings
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4251 /wd4275 /wd4267 /wd4819")
endif()
else()
# treating warnings as errors
if(TREAT_WARNING_AS_ERROR)
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror") #treating warnings as errors
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror")
endif()
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall")
set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall")
if (APPLE)
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=unused-command-line-argument")
elseif(UNIX)
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wuninitialized -Winit-self")
if(NOT CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wmaybe-uninitialized")
endif()
if (CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -diag-disable:177")
endif()
endif()
@ -92,6 +87,15 @@ if(APPLE)
set(CMAKE_MACOSX_RPATH ON)
endif()
if(CMAKE_SYSTEM_PROCESSOR MATCHES "^(arm64.*|aarch64.*|AARCH64.*)")
set(AARCH64 ON)
elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(arm.*|ARM.*)")
set(ARM ON)
endif()
if(ARM AND NOT CMAKE_CROSSCOMPILING)
add_compile_options(-march=armv7-a)
endif()
set(CMAKE_POLICY_DEFAULT_CMP0063 NEW)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_CXX_VISIBILITY_PRESET hidden)
@ -104,9 +108,6 @@ if(NOT DEFINED CMAKE_CXX_STANDARD)
set (CMAKE_CXX_STANDARD 11)
set (CMAKE_CXX_EXTENSIONS OFF)
set (CMAKE_CXX_STANDARD_REQUIRED ON)
if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set (CMAKE_CXX_FLAGS "-std=c++11 ${CMAKE_CXX_FLAGS}")
endif()
endif()
####################################
@ -135,10 +136,6 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/cnpy")
add_subdirectory(thirdparty/cnpy EXCLUDE_FROM_ALL)
endif()
if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall")
endif()
if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/common/utils")
add_subdirectory(common/utils)
endif()

16
inference-engine/samples/benchmark_app/benchmark_app.hpp
View File

@ -147,6 +147,14 @@ static constexpr char iop_message[] = "Optional. Specifies precision for input a
" Overwrites precision from ip and op options for "
"specified layers.";
static constexpr char input_image_scale_message[] = "Optional. Scale values to be used for the input image per channel.\n"
"Values to be provided in the [R, G, B] format. Can be defined for desired input of the model.\n"
"Example: -iscale data[255,255,255],info[255,255,255]\n";
static constexpr char input_image_mean_message[] = "Optional. Mean values to be used for the input image per channel.\n"
"Values to be provided in the [R, G, B] format. Can be defined for desired input of the model,\n"
"Example: -imean data[255,255,255],info[255,255,255]\n";
/// @brief Define flag for showing help message <br>
DEFINE_bool(h, false, help_message);
@ -259,6 +267,12 @@ DEFINE_string(cache_dir, "", cache_dir_message);
/// @brief Define flag for load network from model file by name without ReadNetwork <br>
DEFINE_bool(load_from_file, false, load_from_file_message);
/// @brief Define flag for using input image scale <br>
DEFINE_string(iscale, "", input_image_scale_message);
/// @brief Define flag for using input image mean <br>
DEFINE_string(imean, "", input_image_mean_message);
/**
* @brief This function show a help message
*/
@ -304,4 +318,6 @@ static void showUsage() {
std::cout << " -ip <value> " << inputs_precision_message << std::endl;
std::cout << " -op <value> " << outputs_precision_message << std::endl;
std::cout << " -iop \"<value>\" " << iop_message << std::endl;
std::cout << " -iscale " << input_image_scale_message << std::endl;
std::cout << " -imean " << input_image_mean_message << std::endl;
}

4
inference-engine/samples/benchmark_app/inputs_filling.cpp
View File

@ -91,7 +91,9 @@ void fillBlobImage(Blob::Ptr& inputBlob, const std::vector<std::string>& filePat
size_t offset = imageId * numChannels * width * height + (((app_info.layout == "NCHW") || (app_info.layout == "CHW"))
? (ch * width * height + h * width + w)
: (h * width * numChannels + w * numChannels + ch));
inputBlobData[offset] = static_cast<T>(vreader.at(imageId).get()[h * width * numChannels + w * numChannels + ch]);
inputBlobData[offset] =
(static_cast<T>(vreader.at(imageId).get()[h * width * numChannels + w * numChannels + ch]) - static_cast<T>(app_info.mean[ch])) /
static_cast<T>(app_info.scale[ch]);
}
}
}

4
inference-engine/samples/benchmark_app/main.cpp
View File

@ -380,7 +380,7 @@ int main(int argc, char* argv[]) {
batchSize = cnnNetwork.getBatchSize();
// Parse input shapes if specified
bool reshape = false;
app_inputs_info = getInputsInfo<InputInfo::Ptr>(FLAGS_shape, FLAGS_layout, FLAGS_b, inputInfo, reshape);
app_inputs_info = getInputsInfo<InputInfo::Ptr>(FLAGS_shape, FLAGS_layout, FLAGS_b, FLAGS_iscale, FLAGS_imean, inputInfo, reshape);
if (reshape) {
InferenceEngine::ICNNNetwork::InputShapes shapes = {};
for (auto& item : app_inputs_info)
@ -441,7 +441,7 @@ int main(int argc, char* argv[]) {
slog::info << "Import network took " << duration_ms << " ms" << slog::endl;
if (statistics)
statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS, {{"import network time (ms)", duration_ms}});
app_inputs_info = getInputsInfo<InputInfo::CPtr>(FLAGS_shape, FLAGS_layout, FLAGS_b, exeNetwork.GetInputsInfo());
app_inputs_info = getInputsInfo<InputInfo::CPtr>(FLAGS_shape, FLAGS_layout, FLAGS_b, FLAGS_iscale, FLAGS_imean, exeNetwork.GetInputsInfo());
if (batchSize == 0) {
batchSize = 1;
}

49
inference-engine/samples/benchmark_app/utils.cpp
View File

@ -88,6 +88,17 @@ std::vector<std::string> split(const std::string& s, char delim) {
return result;
}
std::vector<float> splitFloat(const std::string& s, char delim) {
std::vector<float> result;
std::stringstream ss(s);
std::string item;
while (getline(ss, item, delim)) {
result.push_back(std::stof(item));
}
return result;
}
std::vector<std::string> parseDevices(const std::string& device_string) {
std::string comma_separated_devices = device_string;
if (comma_separated_devices.find(":") != std::string::npos) {
@ -161,6 +172,44 @@ std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& sha
return ss.str();
}
std::map<std::string, std::vector<float>> parseScaleOrMean(const std::string& scale_mean, const benchmark_app::InputsInfo& inputs_info) {
// Format: data:[255,255,255],info[255,255,255]
std::map<std::string, std::vector<float>> return_value;
std::string search_string = scale_mean;
auto start_pos = search_string.find_first_of('[');
while (start_pos != std::string::npos) {
auto end_pos = search_string.find_first_of(']');
if (end_pos == std::string::npos)
break;
auto input_name = search_string.substr(0, start_pos);
auto input_value_string = search_string.substr(start_pos + 1, end_pos - start_pos - 1);
auto input_value = splitFloat(input_value_string, ',');
if (!input_name.empty()) {
if (inputs_info.count(input_name)) {
return_value[input_name] = input_value;
}
// ignore wrong input name
} else {
for (auto& item : inputs_info) {
if (item.second.isImage())
return_value[item.first] = input_value;
}
search_string.clear();
break;
}
search_string = search_string.substr(end_pos + 1);
if (search_string.empty() || search_string.front() != ',')
break;
search_string = search_string.substr(1);
start_pos = search_string.find_first_of('[');
}
if (!search_string.empty())
throw std::logic_error("Can't parse input parameter string: " + scale_mean);
return return_value;
}
#ifdef USE_OPENCV
void dump_config(const std::string& filename, const std::map<std::string, std::map<std::string, std::string>>& config) {
cv::FileStorage fs(filename, cv::FileStorage::WRITE);

30
inference-engine/samples/benchmark_app/utils.hpp
View File

@ -13,6 +13,8 @@ struct InputInfo {
InferenceEngine::Precision precision;
InferenceEngine::SizeVector shape;
std::string layout;
std::vector<float> scale;
std::vector<float> mean;
bool isImage() const;
bool isImageInfo() const;
size_t getDimentionByLayout(char character) const;
@ -31,6 +33,7 @@ std::map<std::string, std::string> parseNStreamsValuePerDevice(const std::vector
std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& shapes);
size_t getBatchSize(const benchmark_app::InputsInfo& inputs_info);
std::vector<std::string> split(const std::string& s, char delim);
std::map<std::string, std::vector<float>> parseScaleOrMean(const std::string& scale_mean, const benchmark_app::InputsInfo& inputs_info);
template <typename T>
std::map<std::string, std::string> parseInputParameters(const std::string parameter_string, const std::map<std::string, T>& input_info) {
@ -65,9 +68,11 @@ std::map<std::string, std::string> parseInputParameters(const std::string parame
template <typename T>
benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string, const std::string& layout_string, const size_t batch_size,
const std::map<std::string, T>& input_info, bool& reshape_required) {
const std::string& scale_string, const std::string& mean_string, const std::map<std::string, T>& input_info,
bool& reshape_required) {
std::map<std::string, std::string> shape_map = parseInputParameters(shape_string, input_info);
std::map<std::string, std::string> layout_map = parseInputParameters(layout_string, input_info);
reshape_required = false;
benchmark_app::InputsInfo info_map;
for (auto& item : input_info) {
@ -106,14 +111,33 @@ benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string, const s
}
info_map[name] = info;
}
// Update scale and mean
std::map<std::string, std::vector<float>> scale_map = parseScaleOrMean(scale_string, info_map);
std::map<std::string, std::vector<float>> mean_map = parseScaleOrMean(mean_string, info_map);
for (auto& item : info_map) {
if (item.second.isImage()) {
item.second.scale.assign({1, 1, 1});
item.second.mean.assign({0, 0, 0});
if (scale_map.count(item.first)) {
item.second.scale = scale_map.at(item.first);
}
if (mean_map.count(item.first)) {
item.second.mean = mean_map.at(item.first);
}
}
}
return info_map;
}
template <typename T>
benchmark_app::InputsInfo getInputsInfo(const std::string& shape_string, const std::string& layout_string, const size_t batch_size,
const std::map<std::string, T>& input_info) {
const std::string& scale_string, const std::string& mean_string, const std::map<std::string, T>& input_info) {
bool reshape_required = false;
return getInputsInfo<T>(shape_string, layout_string, batch_size, input_info, reshape_required);
return getInputsInfo<T>(shape_string, layout_string, batch_size, scale_string, mean_string, input_info, reshape_required);
}
#ifdef USE_OPENCV

34
inference-engine/samples/common/utils/include/samples/classification_results.h
View File

@ -54,21 +54,27 @@ private:
* @param output Vector of indexes for the top n places
*/
template <class T>
void topResults(unsigned int n, InferenceEngine::TBlob<T>& input, std::vector<unsigned>& output) {
InferenceEngine::SizeVector dims = input.getTensorDesc().getDims();
void topResults(unsigned int n, InferenceEngine::Blob::Ptr& input, std::vector<unsigned>& output) {
InferenceEngine::SizeVector dims = input->getTensorDesc().getDims();
size_t input_rank = dims.size();
if (!input_rank || !dims[0])
IE_THROW() << "Input blob has incorrect dimensions!";
size_t batchSize = dims[0];
std::vector<unsigned> indexes(input.size() / batchSize);
std::vector<unsigned> indexes(input->size() / batchSize);
n = static_cast<unsigned>(std::min<size_t>((size_t)n, input.size()));
n = static_cast<unsigned>(std::min<size_t>((size_t)n, input->size()));
output.resize(n * batchSize);
InferenceEngine::MemoryBlob::CPtr moutput = InferenceEngine::as<InferenceEngine::MemoryBlob>(input);
if (!moutput) {
IE_THROW() << "Output blob should be inherited from MemoryBlob";
}
// locked memory holder should be alive all time while access to its buffer happens
auto moutputHolder = moutput->rmap();
for (size_t i = 0; i < batchSize; i++) {
size_t offset = i * (input.size() / batchSize);
T* batchData = input.data();
size_t offset = i * (input->size() / batchSize);
T* batchData = moutputHolder.as<T*>();
batchData += offset;
std::iota(std::begin(indexes), std::end(indexes), 0);
@ -88,16 +94,15 @@ private:
* @param input 1D blob that contains probabilities
* @param output Vector of indexes for the top n places
*/
void topResults(unsigned int n, InferenceEngine::Blob& input, std::vector<unsigned>& output) {
void topResults(unsigned int n, InferenceEngine::Blob::Ptr& input, std::vector<unsigned>& output) {
#define TBLOB_TOP_RESULT(precision) \
case InferenceEngine::Precision::precision: { \
using myBlobType = InferenceEngine::PrecisionTrait<InferenceEngine::Precision::precision>::value_type; \
InferenceEngine::TBlob<myBlobType>& tblob = dynamic_cast<InferenceEngine::TBlob<myBlobType>&>(input); \
topResults(n, tblob, output); \
topResults<myBlobType>(n, input, output); \
break; \
}
switch (input.getTensorDesc().getPrecision()) {
switch (input->getTensorDesc().getPrecision()) {
TBLOB_TOP_RESULT(FP32);
TBLOB_TOP_RESULT(FP64);
TBLOB_TOP_RESULT(FP16);
@ -111,7 +116,7 @@ private:
TBLOB_TOP_RESULT(U64);
TBLOB_TOP_RESULT(I64);
default:
IE_THROW() << "cannot locate blob for precision: " << input.getTensorDesc().getPrecision();
IE_THROW() << "cannot locate blob for precision: " << input->getTensorDesc().getPrecision();
}
#undef TBLOB_TOP_RESULT
@ -129,7 +134,7 @@ public:
if (_imageNames.size() != _batchSize) {
throw std::logic_error("Batch size should be equal to the number of images.");
}
topResults(_nTop, *_outBlob, _results);
topResults(_nTop, _outBlob, _results);
}
/**
@ -146,18 +151,17 @@ public:
std::wcout << std::endl << std::endl;
printHeader();
InferenceEngine::MemoryBlob::CPtr moutput = InferenceEngine::as<InferenceEngine::MemoryBlob>(_outBlob);
auto moutputHolder = moutput->rmap();
for (size_t id = image_id * _nTop, cnt = 0; id < (image_id + 1) * _nTop; ++cnt, ++id) {
std::cout.precision(7);
/** Getting probability for resulting class **/
InferenceEngine::MemoryBlob::CPtr moutput = InferenceEngine::as<InferenceEngine::MemoryBlob>(_outBlob);
if (!moutput) {
throw std::logic_error("We expect _outBlob to be inherited from MemoryBlob in "
"ClassificationResult::print, "
"but by fact we were not able to cast _outBlob to MemoryBlob");
}
// locked memory holder should be alive all time while access to its buffer happens
auto moutputHolder = moutput->rmap();
const auto result =
moutputHolder
.as<const InferenceEngine::PrecisionTrait<InferenceEngine::Precision::FP32>::value_type*>()[_results.at(id) +

2
inference-engine/samples/hello_reshape_ssd/reshape_ssd_extension.hpp
View File

@ -109,7 +109,7 @@ public:
return std::make_shared<CustomReluOp>(new_args.at(0));
}
bool visit_attributes(ngraph::AttributeVisitor& visitor) override {
bool visit_attributes(ngraph::AttributeVisitor&) override {
return true;
}
};

2
inference-engine/samples/ngraph_function_creation_sample/main.cpp
View File

@ -108,7 +108,7 @@ TBlob<uint8_t>::CPtr ReadWeights(std::string filepath) {
std::shared_ptr<Function> createNgraphFunction() {
TBlob<uint8_t>::CPtr weightsPtr = ReadWeights(FLAGS_m);
if (weightsPtr->byteSize() != 1724336)
if (weightsPtr->byteSize() != 6897344)
IE_THROW() << "Incorrect weights file. This sample works only with LeNet "
"classification network.";

95
inference-engine/src/auto_plugin/auto_plugin.cpp
View File

@ -274,33 +274,110 @@ DeviceName AutoInferencePlugin::SelectDevice(const std::vector<DeviceName>& meta
}
std::vector<DeviceName> CPU;
std::vector<DeviceName> GPU;
std::vector<DeviceName> dGPU;
std::vector<DeviceName> iGPU;
std::vector<DeviceName> MYRIAD;
std::vector<DeviceName> VPUX;
for (auto& item : metaDevices) {
if (item.find("CPU") == 0) {
CPU.push_back(item);
continue;
}
if (item.find("MYRIAD") == 0) {
MYRIAD.push_back(item);
continue;
}
if (item.find("VPUX") == 0) {
VPUX.push_back(item);
continue;
}
if (item.find("GPU") == 0) {
GPU.push_back(item);
auto gpuFullDeviceName = GetCore()->GetMetric(item, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
if (gpuFullDeviceName.find("iGPU") != std::string::npos) {
iGPU.push_back(item);
} else if (gpuFullDeviceName.find("dGPU") != std::string::npos) {
dGPU.push_back(item);
}
continue;
}
}
if (CPU.empty() && GPU.empty()) {
if (CPU.empty() && dGPU.empty() && iGPU.empty() && MYRIAD.empty() && VPUX.empty()) {
IE_THROW(NotFound) << "No available device found";
}
// Sort GPU by name: GPU.2 > GPU.1 > GPU.0 > GPU, so we always choose the GPU[0] as best device
std::sort(GPU.begin(), GPU.end(), [](const DeviceName& a, const DeviceName& b)->bool{return b < a;});
for (auto&& item : GPU) {
// Priority of selecting device: dGPU > VPUX > iGPU > MYRIAD > CPU
if (!dGPU.empty()) {
for (auto&& item : dGPU) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto res = std::find(capability.begin(), capability.end(), networkPrecision);
if (res != capability.end()) {
auto supportNetwork = std::find(capability.begin(), capability.end(), networkPrecision);
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!VPUX.empty()) {
for (auto&& item : VPUX) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), networkPrecision);
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!iGPU.empty()) {
for (auto&& item : iGPU) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), networkPrecision);
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!MYRIAD.empty()) {
for (auto&& item : MYRIAD) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), networkPrecision);
if (supportNetwork != capability.end()) {
return item;
}
}
}
// If network is FP32 but there is no device support FP32, offload FP32 network to device support FP16.
if (networkPrecision == "FP32") {
if (!dGPU.empty()) {
for (auto&& item : dGPU) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), "FP16");
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!VPUX.empty()) {
for (auto&& item : VPUX) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), "FP16");
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!iGPU.empty()) {
for (auto&& item : iGPU) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), "FP16");
if (supportNetwork != capability.end()) {
return item;
}
}
} else if (!MYRIAD.empty()) {
for (auto&& item : MYRIAD) {
std::vector<std::string> capability = GetCore()->GetMetric(item, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
auto supportNetwork = std::find(capability.begin(), capability.end(), "FP16");
if (supportNetwork != capability.end()) {
return item;
}
}
}
}
if (CPU.empty()) {
IE_THROW() << "Cannot select any device";

4
inference-engine/src/cldnn_engine/cldnn_graph.cpp
View File

@ -88,11 +88,11 @@ void CLDNNGraph::Build() {
std::shared_ptr<cldnn::network> CLDNNGraph::BuildNetwork(std::shared_ptr<cldnn::program> program) {
OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNGraph::BuildNetwork");
auto network = std::make_shared<cldnn::network>(*program, m_stream_id);
auto network = std::make_shared<cldnn::network>(program, m_stream_id);
if (!m_config.graph_dumps_dir.empty() && m_stream_id == 0) {
static int net_id = 0;
auto steps_info = network->get_optimization_steps_info();
auto steps_info = network->get_optimizer_passes_info();
size_t step_idx = 0;
for (auto& step : steps_info) {
CNNNetwork net(GetExecGraphInfoByPrimitivesInfo(step.second, true));

2
inference-engine/src/cldnn_engine/cldnn_graph.h
View File

@ -51,8 +51,10 @@ public:
InferenceEngine::SizeVector GetOutputSize(std::string outName) const;
std::string MapOutputName(std::string outName) const;
std::string getName() const { return m_networkName; }
std::mutex& get_mutex() { return m_infer_mutex; }
protected:
std::mutex m_infer_mutex;
std::string m_networkName;
Config m_config;

953
inference-engine/src/cldnn_engine/cldnn_infer_request.cpp
View File

File diff suppressed because it is too large Load Diff

37
inference-engine/src/cldnn_engine/cldnn_infer_request.h
View File

@ -23,6 +23,7 @@ class CLDNNExecNetwork;
class CLDNNInferRequest : public InferenceEngine::IInferRequestInternal {
public:
using Ptr = std::shared_ptr<CLDNNInferRequest>;
// make sure all blobs and cldnn::memory objects
// are in place and valid
void checkBlobs() override;
@ -45,8 +46,9 @@ public:
void EnableProfiling() { m_useProfiling = true; }
void EnableStreams() { m_useStreams = true; }
protected:
std::map<std::string, cldnn::memory::ptr> inputsMemory;
private:
InferenceEngine::BlobMap _deviceOutputs;
std::map<std::string, cldnn::primitive_id> inputsMap;
std::map<std::string, cldnn::primitive_id> outputsMap;
bool m_useProfiling;
@ -58,24 +60,25 @@ protected:
std::map<std::string, std::vector<buf_info>> batchOutputs;
InferenceEngine::IStreamsExecutor* streamExecutor = nullptr;
InferenceEngine::Blob::Ptr createInputBlob(const InferenceEngine::TensorDesc& desc, uint8_t* mem_ptr = nullptr);
InferenceEngine::Blob::Ptr createOutputBlob(const InferenceEngine::TensorDesc& desc, uint8_t* mem_ptr = nullptr);
void copyOutputData(cldnn::memory::ptr outputMemory, InferenceEngine::Blob::Ptr bptr, buf_info* bi = nullptr);
void copyInputData(std::shared_ptr<cldnn::network> network, const cldnn::primitive_id &inputName,
void prepare_input(const cldnn::primitive_id &inputName, InferenceEngine::Blob::Ptr &inputBlob,
std::vector<cldnn::event::ptr>& dependencies);
void prepare_output(const cldnn::primitive_id& outputName, InferenceEngine::Blob::Ptr& outputBlob);
InferenceEngine::Blob::Ptr create_input_host_blob(const InferenceEngine::TensorDesc& desc, uint8_t* mem_ptr = nullptr);
InferenceEngine::Blob::Ptr create_output_host_blob(const InferenceEngine::TensorDesc& desc, uint8_t* mem_ptr = nullptr);
InferenceEngine::Blob::Ptr create_device_blob(const InferenceEngine::TensorDesc& desc, const cldnn::layout& layout);
void copy_output_data(cldnn::memory::ptr outputMemory, InferenceEngine::Blob::Ptr bptr, buf_info* bi = nullptr);
void copy_input_data(std::shared_ptr<cldnn::network> network, const cldnn::primitive_id &inputName,
const cldnn::layout& inputLayout, const InferenceEngine::Blob &inputBlob,
buf_info* bi = nullptr);
void input_attach(cldnn::primitive_id name, cldnn::memory::ptr inputMem);
void input_alloc(cldnn::primitive_id name, const cldnn::layout& layout);
void AllocateInputs();
void AllocateOutputs();
void AllocateInputsDyn();
void AllocateOutputsDyn();
void execAndParse();
void execAndParseDyn();
void PrepareInput(const cldnn::primitive_id &inputName, const InferenceEngine::Blob &inputBlob);
void PrepareInputDyn(const cldnn::primitive_id &inputName, const InferenceEngine::Blob &inputBlob);
void allocate_inputs();
void allocate_outputs();
void allocate_inputs_dynamic();
void allocate_outputs_dynamic();
void exec_and_parse(const std::vector<cldnn::event::ptr>& dependencies);
void exec_and_parse_dynamic();
};
}; // namespace CLDNNPlugin

1
inference-engine/src/cldnn_engine/cldnn_primitives_list.hpp
View File

@ -204,6 +204,7 @@ REGISTER_FACTORY(v5, Loop);
// ------------------------------ Supported v6 ops ------------------------------ //
REGISTER_FACTORY(v6, CTCGreedyDecoderSeqLen);
REGISTER_FACTORY(v6, MVN);
REGISTER_FACTORY(v6, GatherElements);
// ------------------------------ Supported v7 ops ------------------------------ //
REGISTER_FACTORY(v7, Gather);

8
inference-engine/src/cldnn_engine/cldnn_program.cpp
View File

@ -6,7 +6,6 @@
#include "ngraph/ops.hpp"
#include "ngraph_ops/nms_ie_internal.hpp"
#include "cldnn_itt.h"
#include "cldnn/runtime/debug_configuration.hpp"
using namespace InferenceEngine;
using namespace InferenceEngine::details;
@ -178,16 +177,11 @@ std::shared_ptr<cldnn::program> Program::BuildProgram(const std::vector<std::sha
bool createTopologyOnly) {
OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "Program::BuildProgram");
cldnn::build_options options;
GPU_DEBUG_GET_INSTANCE(debug_config);
if (!m_config.graph_dumps_dir.empty()) {
options.set_option(cldnn::build_option::graph_dumps_dir(m_config.graph_dumps_dir));
}
GPU_DEBUG_IF(!debug_config->dump_graphs.empty()) {
options.set_option(cldnn::build_option::graph_dumps_dir(debug_config->dump_graphs));
}
options.set_option(cldnn::build_option::optimize_data(true));
options.set_option(cldnn::build_option::tuning_config(m_config.tuningConfig));
@ -199,7 +193,7 @@ std::shared_ptr<cldnn::program> Program::BuildProgram(const std::vector<std::sha
return {};
} else {
OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "Program::CreateProgram");
auto program = std::make_shared<cldnn::program>(*m_engine, *m_topology, options);
auto program = cldnn::program::build_program(*m_engine, *m_topology, options);
CleanupBuild();
return program;

20
inference-engine/src/cldnn_engine/cldnn_remote_context.cpp
View File

@ -81,22 +81,13 @@ bool CLDNNRemoteBlobImpl::is_locked() const noexcept {
return lockedHolder != nullptr;
}
void CLDNNRemoteBlobImpl::allocate_if_needed() {
OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNRemoteBlobImpl::AllocateIfNeeded");
auto _impl = getContextImpl(m_context.lock());
_impl->acquire_lock();
if (m_memObject == nullptr) {
allocate();
}
_impl->release_lock();
}
void CLDNNRemoteBlobImpl::allocate() noexcept {
OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNRemoteBlobImpl::Allocate");
assert(m_memObject == nullptr);
std::shared_ptr<cldnn::engine> eng = getContextImpl(m_context.lock())->GetEngine();
auto _impl = getContextImpl(m_context.lock());
_impl->acquire_lock();
std::shared_ptr<cldnn::engine> eng = _impl->GetEngine();
switch (m_mem_type) {
case BlobType::BT_BUF_INTERNAL: {
@ -129,6 +120,7 @@ void CLDNNRemoteBlobImpl::allocate() noexcept {
default:
m_memObject.reset();
}
_impl->release_lock();
}
const std::shared_ptr<IAllocator>& CLDNNRemoteBlobImpl::getAllocator() const noexcept {
@ -154,7 +146,7 @@ void CLDNNRemoteBlobImpl::lock() const {
}
void CLDNNRemoteBlobImpl::unlock() const {
lockedHolder.release();
lockedHolder.reset();
}
LockedMemory<void> CLDNNRemoteBlobImpl::buffer() noexcept {

15
inference-engine/src/cldnn_engine/cldnn_remote_context.h
View File

@ -44,8 +44,8 @@ public:
explicit CLDNNRemoteBlobImpl(InferenceEngine::gpu::ClContext::Ptr context,
cldnn::stream& stream,
const cldnn::layout& layout,
cldnn::shared_handle mem,
cldnn::shared_surface surf,
cldnn::shared_handle mem = nullptr,
cldnn::shared_surface surf = 0,
uint32_t plane = 0,
BlobType mem_type = BT_BUF_INTERNAL);
@ -64,7 +64,6 @@ public:
bool is_allocated() const noexcept;
bool is_locked() const noexcept;
void allocate_if_needed();
cldnn::memory::ptr getMemory() { return m_memObject; }
protected:
@ -99,10 +98,10 @@ public:
cldnn::stream& stream,
const InferenceEngine::TensorDesc& desc,
const cldnn::layout& layout,
cldnn::shared_handle mem,
cldnn::shared_surface surf,
uint32_t plane,
CLDNNRemoteBlobImpl::BlobType mem_type)
cldnn::shared_handle mem = nullptr,
cldnn::shared_surface surf = 0,
uint32_t plane = 0,
CLDNNRemoteBlobImpl::BlobType mem_type = CLDNNRemoteBlobImpl::BlobType::BT_BUF_INTERNAL)
: _impl(context, stream, layout, mem, surf, plane, mem_type)
, TpublicAPI(desc) {}
@ -184,7 +183,7 @@ public:
* @brief Maps handle to heap memory accessible by any memory manipulation routines.
* @return Generic pointer to memory
*/
void* lock(void* handle, InferenceEngine::LockOp = InferenceEngine::LOCK_FOR_WRITE) noexcept override { return nullptr; };
void* lock(void* handle, InferenceEngine::LockOp = InferenceEngine::LOCK_FOR_WRITE) noexcept override { return handle; };
/**
* @brief Unmaps memory by handle with multiple sequential mappings of the same handle.
* The multiple sequential mappings of the same handle are suppose to get the same

8
inference-engine/src/cldnn_engine/ops/concat.cpp
View File

@ -12,14 +12,14 @@
namespace CLDNNPlugin {
static cldnn::concatenation::concatenation_axis GetConcatAxis(int32_t axis, size_t rank) {
if (axis >= rank)
unsigned cldnn_axis = axis >= 0 ? axis : axis + static_cast<int32_t>(rank);
if (cldnn_axis >= rank)
IE_THROW() << "Concatenation axis exceeds number of dimensions";
// Difference in dimension ordering between IE and clDNN,
// reverse spatial dimensions after batch and feature.
unsigned cldnn_axis = axis;
if (axis >= 2) {
auto spatial_axis = axis - 2;
if (cldnn_axis >= 2) {
auto spatial_axis = cldnn_axis - 2;
// Default and minimum number of dimensions is 4
auto spatial_size = std::max<size_t>(rank, 4) - 2;
cldnn_axis = spatial_size - spatial_axis - 1 + 2;

66
inference-engine/src/cldnn_engine/ops/gather_elements.cpp Normal file
View File

@ -0,0 +1,66 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "cldnn_program.h"
#include "cldnn_common_utils.h"
#include "ngraph/op/gather_elements.hpp"
#include "ngraph/op/constant.hpp"
#include "cldnn/primitives/gather_elements.hpp"
namespace CLDNNPlugin {
static cldnn::gather_elements::gather_elements_axis GetGatherAxis(int axis, unsigned rank) {
if (axis < 0)
axis += rank;
if (axis < 0 || axis >= rank)
IE_THROW() << "GatherElements axis is not correspond to number of dimensions";
// Difference in dimension ordering between IE and clDNN,
// reverse spatial dimensions after batch and feature.
unsigned cldnn_axis = axis;
if (axis >= 2) {
auto spatial_axis = axis - 2;
// Default and minimum number of dimensions is 4
auto spatial_size = std::max(rank, 4u) - 2;
cldnn_axis = spatial_size - spatial_axis - 1 + 2;
}
switch (cldnn_axis) {
case 0: return cldnn::gather_elements::gather_elements_axis::along_b;
case 1: return cldnn::gather_elements::gather_elements_axis::along_f;
case 2: return cldnn::gather_elements::gather_elements_axis::along_x;
case 3: return cldnn::gather_elements::gather_elements_axis::along_y;
case 4: return cldnn::gather_elements::gather_elements_axis::along_z;
case 5: return cldnn::gather_elements::gather_elements_axis::along_w;
default: IE_THROW() << "Unsupported GatherElements axis: " << axis;
}
return cldnn::gather_elements::gather_elements_axis::along_f; // shouldn't get here
}
void CreateGatherElementsOp(Program& p, const std::shared_ptr<ngraph::op::v6::GatherElements>& op) {
p.ValidateInputs(op, {2});
auto inputPrimitives = p.GetInputPrimitiveIDs(op);
std::string layerName = layer_type_name_ID(op);
size_t rank = op->get_input_shape(0).size();
int32_t axis = static_cast<int32_t>(op->get_axis());
auto outLayout = DefaultFormatForDims(op->get_output_shape(0).size());
auto primitive = cldnn::gather_elements(layerName,
inputPrimitives[0],
inputPrimitives[1],
outLayout,
CldnnTensorFromIEDims(op->get_output_shape(0)),
GetGatherAxis(axis, rank));
p.AddPrimitive(primitive);
p.AddPrimitiveToProfiler(op);
}
REGISTER_FACTORY_IMPL(v6, GatherElements);
} // namespace CLDNNPlugin

3
inference-engine/src/gna_plugin/backend/dnn.hpp
View File

@ -58,7 +58,8 @@ void AdvanceCnnOperationIfAllApplied(const std::vector<intel_dnn_component_t>& c
template <class T>
void AdvancePwlOperationIfAllApplied(const std::vector<intel_dnn_component_t>& component, int i, T*& operation) {
if (i == component.size() - 1 || (component[i + 1].operation != kDnnMaxPoolOp)) {
if (i == component.size() - 1 || ((component[i + 1].operation != kDnnMaxPoolOp)
&& (component[i + 1].operation != kDnnPiecewiselinearOp))) {
operation++;
}
}

4
inference-engine/src/gna_plugin/backend/dnn_types.h
View File

@ -227,7 +227,7 @@ OvGnaType OvGnaTypeIntFromBytes(T bytesPerElement) {
return r->second;
}
static std::string OvGnaTypeToString(OvGnaType type) {
inline std::string OvGnaTypeToString(OvGnaType type) {
static const std::map<OvGnaType, std::string> typeToString = {
{OvGnaTypeInt8, "OvGnaTypeInt8"},
{OvGnaTypeInt16, "OvGnaTypeInt16"},
@ -241,7 +241,7 @@ static std::string OvGnaTypeToString(OvGnaType type) {
return r->second;
}
static std::string OvGnaModeToString(OvGnaMode mode) {
inline std::string OvGnaModeToString(OvGnaMode mode) {
static const std::map<OvGnaMode, std::string> modeToString = {
{OvGnaModeDefault, "OvGnaModeDefault"},
{OvGnaModeDisabled, "OvGnaModeDisabled"},

6
inference-engine/src/gna_plugin/backend/gna_limitations.hpp
View File

@ -24,6 +24,10 @@ constexpr uint32_t noOfInputsLowPrecDivisor = 16;
constexpr uint32_t affineMaxBatchSize = 8;
constexpr uint32_t maxPoolMaxWindowSize = 6;
constexpr uint32_t copyMaxGrouping = 8;
namespace Cnn2D {
struct RangeLimit {
uint32_t min;
@ -87,6 +91,8 @@ class Validator {
static void ThrowIfNotEmpty(const std::string prefix, const std::string error);
public:
Validator() = default;
void ValidateCnn2D(std::string name, const uint32_t inHeight, const uint32_t inWidth,
const uint32_t inChannels, const uint32_t kH, const uint32_t kW, const uint32_t kN,
const uint32_t strideH, const uint32_t strideW, OvGnaType inPrecision) const;

18
inference-engine/src/gna_plugin/gna_plugin.cpp
View File

@ -63,6 +63,7 @@
#include "transformations/swap_input_matmul_gna.hpp"
#include "transformations/convert_matmul_to_pointwise_convolution.hpp"
#include "transformations/split_convolution_with_large_buffer_size.hpp"
#include "transformations/decompose_2d_conv.hpp"
#include "transformations/convert_padded2valid_conv.hpp"
#include <ngraph/opsets/opset7.hpp>
@ -673,6 +674,11 @@ void GNAPlugin::AddDebugProperties(const InferenceEngine::CNNLayerPtr layer,
void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
OV_ITT_SCOPED_TASK(itt::domains::GNAPlugin, "LoadNetwork");
std::shared_ptr<InferenceEngine::details::CNNNetworkImpl> convertedNetwork;
if (!gnaFlags->sw_fp32) {
InitGNADevice();
}
if (_network.getFunction()) {
CNNNetwork clonedNetwork = InferenceEngine::cloneNetwork(_network);
const auto& graph = clonedNetwork.getFunction();
@ -682,6 +688,11 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
manager.register_pass<ngraph::pass::ConvertPriorBox>();
manager.register_pass<ngraph::pass::CommonOptimizations>();
manager.register_pass<ConvertPadded2ValidConv>();
if (config.gnaCompileTarget == InferenceEngine::GNAConfigParams::GNA_TARGET_2_0) {
manager.register_pass<Decompose2DConvTransposedWithBiasAF>();
manager.register_pass<Decompose2DConvTransposedWithBias>();
manager.register_pass<Decompose2DConv>();
}
// TODO enable this transformation for networks with convolutions
if (!ngraph::op::util::has_op_with_type<ngraph::opset7::Convolution>(graph)) {
manager.register_pass<ConvertMatmulWithFqToPointWiseConvolution>();
@ -870,15 +881,16 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
// fill in extra storage with memory layers
graphCompiler.fillMemoryConnections(memoryPairs);
if (!graphCompiler.memory_connection.empty()) {
if (!graphCompiler.memory_connection.empty() && gnaFlags->gna_lib_async_threads_num != 1) {
// TODO: check if updating the number of threads is needed for sw_fp32
gnaFlags->gna_lib_async_threads_num = 1;
if (!gnaFlags->sw_fp32)
InitGNADevice();
}
if (gnaFlags->sw_fp32) {
gnamem.reset(new gna_memory_type(memory::make_polymorph<std::allocator<uint8_t>>()));
graphCompiler.setGNAMemoryPtr(gnamem);
} else {
InitGNADevice();
}
// keep inputs information and create input primitives

4
inference-engine/src/gna_plugin/gna_plugin_config.cpp
View File

@ -90,8 +90,8 @@ void Config::UpdateFromMap(const std::map<std::string, std::string>& config) {
}
}
auto scale_factor = InferenceEngine::CNNLayer::ie_parse_float(value);
if (fp32eq(scale_factor, 0.0f)) {
THROW_GNA_EXCEPTION << "input scale factor of 0.0f not supported";
if (fp32eq(scale_factor, 0.0f) || std::isinf(scale_factor)) {
THROW_GNA_EXCEPTION << "input scale factor of 0.0f or +-inf not supported";
}
// missing scale factors are set to be 1.0f
if (inputScaleFactors.size() <= input_index) {

2
inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
View File

@ -2173,7 +2173,7 @@ void MoveFakeQuantizeLayerIntoQuantParamsPass :: run() {
// Find all output layers connected to FQ
auto nextLayers = CNNNetGetAllNextLayersSkipCertain(*fqLayer, -1, donotSkip);
if (nextLayers.empty()) {
return;
continue;
}
if (isFQFuseAllowed) {

87
inference-engine/src/gna_plugin/transformations/convert_padded2valid_conv.cpp
View File

@ -14,72 +14,26 @@
#include <ngraph/rt_info.hpp>
#include <ngraph/pass/manager.hpp>
#include <ie_common.h>
#include "utils/transformation_helper.hpp"
using namespace GNAPluginNS;
NGRAPH_RTTI_DEFINITION(ConvertPadded2ValidConv, "ConvertPadded2ValidConv", 0);
struct ConvData {
size_t input_height;
size_t input_width;
size_t input_channel_count;
size_t filter_count;
size_t pads_begin_width;
size_t pads_begin_height;
size_t pads_end_width;
size_t pads_end_height;
ngraph::op::PadType padding_type;
ngraph::element::Type element_type;
};
static bool VerifyAndGetConvParams(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data) {
static bool VerifyAndGetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data) {
const auto& input = conv->input_value(0);
// We support only 2D conv batch 1
if (conv->get_dilations().size() != 2 ||
conv->get_strides().size() != 2 ||
input.get_shape()[0] != 1) {
// We support only batch 1
if (input.get_shape()[0] != 1) {
return false;
}
conv_data.padding_type = conv->get_auto_pad();
conv_data.input_channel_count = conv->input_value(0).get_shape()[1];
conv_data.input_height = conv->input_value(0).get_shape()[2];
conv_data.input_width = conv->input_value(0).get_shape()[3];
conv_data.filter_count = conv->input_value(1).get_shape()[0];
conv_data.pads_begin_height = conv->get_pads_begin()[0];
conv_data.pads_begin_width = conv->get_pads_begin()[1];
conv_data.pads_end_height = conv->get_pads_end()[0];
conv_data.pads_end_width = conv->get_pads_end()[1];
conv_data.element_type = conv->get_element_type();
GetConvData(conv, conv_data);
return conv_data.pads_begin_height || conv_data.pads_end_height || conv_data.pads_begin_width || conv_data.pads_end_width;
}
static bool TransposeOrderMatches(std::shared_ptr<ngraph::opset7::Transpose> transpose, std::vector<size_t> order) {
if (!transpose)
return false;
const ngraph::Output<ngraph::Node>& transpose_order = transpose->input_value(1);
auto transpose_order_dim = transpose_order.get_shape().size();
if (transpose_order_dim != 1 || transpose_order.get_shape()[0] != order.size())
return false;
auto const_with_order_values = std::dynamic_pointer_cast<ngraph::opset7::Constant>(transpose_order.get_node_shared_ptr());
if (!const_with_order_values)
return false;
const auto data = const_with_order_values->cast_vector<size_t>();
if (data.empty())
return false;
if (!std::equal(order.begin(), order.end(), data.begin()))
return false;
return true;
}
static bool VerifyBias(std::shared_ptr<ngraph::opset7::Add> bias, const size_t& filter_count) {
auto add_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(bias->input_value(0).get_node_shared_ptr());
@ -91,16 +45,6 @@ static bool VerifyBias(std::shared_ptr<ngraph::opset7::Add> bias, const size_t&
return (add_const && shape_size(add_const->get_shape()) == filter_count);
}
static std::shared_ptr<ngraph::opset7::StridedSlice> FlatCrop(ngraph::Output<ngraph::Node> input, size_t offset, size_t size) {
return std::make_shared<ngraph::opset7::StridedSlice>(
input, // data
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)0, offset}), // begin sice index
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)0, offset + size}), // end slice index
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)1, (size_t)1}), // strides
std::vector<int64_t>{1, 0}, // begin mask
std::vector<int64_t>{1, 0}); // end mask
}
static void InsertPadding(ngraph::OutputVector& input_rows_to_concat, size_t size, const std::shared_ptr<ngraph::opset7::Convolution>& conv,
const std::shared_ptr<ngraph::opset7::Constant> padding_const, size_t biggest_padding) {
@ -226,7 +170,7 @@ static bool Convert(std::shared_ptr<ngraph::Node> leading_transpose,
ConvData conv_data;
if (!VerifyAndGetConvParams(std::dynamic_pointer_cast<ngraph::opset7::Convolution>(conv), conv_data))
if (!VerifyAndGetConvData(std::dynamic_pointer_cast<ngraph::opset7::Convolution>(conv), conv_data))
return false;
// We are looking for Transpose(NHWC->NCHW) => Conv => Transpose(NCHW->NHWC)
@ -246,7 +190,7 @@ static bool Convert(std::shared_ptr<ngraph::Node> leading_transpose,
return true;
}
std::function<bool(ngraph::Output<ngraph::Node>)> consumers_and_rank(const size_t expected_count, const ngraph::Dimension& expected_rank) {
static std::function<bool(ngraph::Output<ngraph::Node>)> consumers_and_rank(const size_t expected_count, const ngraph::Dimension& expected_rank) {
return [=](ngraph::Output<ngraph::Node> output) -> bool {
return ngraph::pattern::consumers_count(expected_count) && ngraph::pattern::rank_equals(expected_rank);
};
@ -263,34 +207,35 @@ ConvertPadded2ValidConv::ConvertPadded2ValidConv() {
ngraph::pattern::consumers_count(1));
auto bias = ngraph::pattern::wrap_type<ngraph::opset7::Add>({conv, const_input},
ngraph::pattern::consumers_count(1));
auto fq = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({bias, const_input, const_input, const_input, const_input},
auto fq_bias = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({bias, const_input, const_input, const_input, const_input},
ngraph::pattern::consumers_count(1));
auto max_pool1 = ngraph::pattern::wrap_type<ngraph::opset7::MaxPool>({bias},
ngraph::pattern::consumers_count(1));
auto max_pool2 = ngraph::pattern::wrap_type<ngraph::opset7::MaxPool>({fq},
auto max_pool2 = ngraph::pattern::wrap_type<ngraph::opset7::MaxPool>({fq_bias},
ngraph::pattern::consumers_count(1));
auto af1 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({bias}, ngraph::pattern::consumers_count(1));
auto af2 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({fq}, ngraph::pattern::consumers_count(1));
ngraph::opset7::Sign, ngraph::opset7::Clamp>({fq_bias}, ngraph::pattern::consumers_count(1));
auto af3 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({max_pool1}, ngraph::pattern::consumers_count(1));
auto af4 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({max_pool2}, ngraph::pattern::consumers_count(1));
auto transpose_input = std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{conv, bias, max_pool1, max_pool2, fq, af1, af2, af3, af4});
auto fq_af = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({af4, const_input, const_input, const_input, const_input},
ngraph::pattern::consumers_count(1));
auto transpose_input =
std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{conv, bias, max_pool1, max_pool2, fq_bias, af1, af2, af3, af4, fq_af});
auto trailing_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({transpose_input, const_input},
consumers_and_rank(1, 4));
ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
const auto& pattern_map = m.get_pattern_value_map();
auto conv_output = conv->output(0).get_node_shared_ptr();
IE_ASSERT(conv_output != nullptr);
auto bias_node = std::dynamic_pointer_cast<ngraph::opset7::Add>(conv_output);
auto bias_it = pattern_map.find(bias);
auto bias_node = (bias_it == std::end(pattern_map) ? nullptr : bias_it->second.get_node_shared_ptr());
return Convert(pattern_map.at(leading_transpose).get_node_shared_ptr(), pattern_map.at(conv).get_node_shared_ptr(),
pattern_map.at(trailing_transpose).get_node_shared_ptr(), bias_node);

2
inference-engine/src/gna_plugin/transformations/convert_padded2valid_conv.hpp
View File

@ -17,7 +17,7 @@ namespace GNAPluginNS {
* |
* Transpose (NHWC -> NCHW) Transpose (NHWC -> NCHW)
* | |
* Convolution with padding Convolution with padding
* Convolution with padding Valid convolution
* | |
* Broadcast Bias (optional) Broadcast Bias (optional)
* | |

667
inference-engine/src/gna_plugin/transformations/decompose_2d_conv.cpp Normal file
View File

@ -0,0 +1,667 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <openvino/cc/ngraph/itt.hpp>
#include "transformations/decompose_2d_conv.hpp"
#include <memory>
#include <ngraph/opsets/opset7.hpp>
#include <ngraph/pattern/op/wrap_type.hpp>
#include <transformations/utils/utils.hpp>
#include <ngraph/pattern/op/or.hpp>
#include <ngraph/rt_info.hpp>
#include <ngraph/pass/manager.hpp>
#include <ie_common.h>
#include "utils/transformation_helper.hpp"
#include "backend/gna_limitations.hpp"
#include "layers/gna_convolution_layer.hpp"
using namespace GNAPluginNS;
NGRAPH_RTTI_DEFINITION(Decompose2DConv, "Decompose2DConv", 0);
NGRAPH_RTTI_DEFINITION(Decompose2DConvTransposedWithBias, "Decompose2DConvTransposedWithBias", 0);
NGRAPH_RTTI_DEFINITION(Decompose2DConvTransposedWithBiasAF, "Decompose2DConvTransposedWithBiasAF", 0);
struct GraphData {
std::shared_ptr<ngraph::opset7::Transpose>leading_transpose;
std::shared_ptr<ngraph::opset7::FakeQuantize>fq_conv;
std::shared_ptr<ngraph::opset7::Convolution>conv;
std::shared_ptr<ngraph::opset7::Transpose>trailing_transpose;
std::shared_ptr<ngraph::opset7::FakeQuantize>fq_bias;
std::shared_ptr<ngraph::opset7::MaxPool>max_pool;
std::shared_ptr<ngraph::op::util::UnaryElementwiseArithmetic>af;
std::shared_ptr<ngraph::opset7::FakeQuantize>fq_af;
std::shared_ptr<ngraph::Node>last_op_in_sequence_for_replacement;
std::shared_ptr<ngraph::Node>bias_const;
size_t conv_count;
size_t pool_size_width;
size_t pool_stride_width;
// TODO: currently 2D max pool is not supported
//size_t pool_size_height;
//size_t pool_stride_height;
};
static bool VerifyAndGetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data) {
const auto& input = conv->input_value(0);
const auto& filters = conv->input_value(1);
// We support only batch == 1
if (input.get_shape()[0] != 1) {
return false;
}
size_t filter_height = filters.get_shape()[2];
size_t filter_width = filters.get_shape()[3];
if (filter_width > GNALimitations::copyMaxGrouping || filter_height > GNALimitations::copyMaxGrouping) {
return false;
}
GetConvData(conv, conv_data);
IE_ASSERT(conv_data.output_channel_count == conv->get_output_shape(0)[1]);
return true;
}
static std::shared_ptr<ngraph::Node> VerifyBiasAndReshapeConst(std::shared_ptr<ngraph::opset7::Add> conv_bias, const ConvData& conv_data) {
auto add_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(conv_bias->input_value(1).get_node_shared_ptr());
if (add_const) {
auto bias_size = shape_size(add_const->get_shape());
// The add may be a normal add not conv bias, then we just go further
if (bias_size == conv_data.filter_count) {
return ngraph::op::util::make_try_fold<ngraph::opset7::Reshape>(add_const,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, ngraph::Shape{1, bias_size, 1, 1}), false);
}
}
// Bias size does not match (or dynamic bias), can't decompose such convolution
return nullptr;
}
static bool VerifyMaxPool(GraphData& graph_data, std::shared_ptr<ngraph::opset7::MaxPool> max_pool) {
auto pool_filter = max_pool->get_kernel();
auto pool_strides = max_pool->get_strides();
// Check Max Pool padding and limitations
if ((max_pool->get_auto_pad() != ngraph::op::PadType::VALID &&
(max_pool->get_auto_pad() != ngraph::op::PadType::EXPLICIT ||
max_pool->get_pads_begin() != ngraph::Shape({0, 0}) || max_pool->get_pads_end() != ngraph::Shape({0, 0}))) ||
pool_filter.size() != 2 || pool_strides.size() != 2 ||
pool_filter[0] > GNALimitations::maxPoolMaxWindowSize)
return false;
graph_data.pool_size_width = pool_filter[1];
graph_data.pool_stride_width = pool_strides[1];
return true;
}
static size_t CalculateConvCount(const ConvData& conv_data) {
// Check if split of plane due to GNA HW limitations of 768 filter elements is possible
size_t conv_count = 1;
size_t total_factorized_conv_channel_count = (conv_data.input_channel_count * conv_data.filter_height * conv_data.filter_width);
while (total_factorized_conv_channel_count / conv_count > GNALimitations::convFilterMaxSize ||
total_factorized_conv_channel_count % conv_count != 0 || conv_data.filter_channel_count % conv_count != 0)
conv_count++;
return conv_count;
}
static bool ShouldDecompose(GraphData& graph_data, const ConvData& conv_data) {
// Calculate the number of splits required
graph_data.conv_count = CalculateConvCount(conv_data);
// Concat (copy) layer limitation allows to split up to a certain limit
// Currently we are able to split only convolutions without pooling in horizontal dimension
if (graph_data.conv_count > GNALimitations::copyMaxGrouping ||
((graph_data.pool_size_width > 1 || graph_data.pool_stride_width > 1) && graph_data.conv_count > 1))
return false;
// GNA supported features or handled otherwise - there is no need to decompose such convolution
if (graph_data.conv_count == 1 && (((conv_data.input_height == 1 || conv_data.input_width == 1) &&
conv_data.filter_dilation_width == 1 && conv_data.filter_dilation_height == 1) ||
GNAConvolutionLayer::isMappableFrom2DTo1D(conv_data.input_height, conv_data.input_width, conv_data.filter_width, conv_data.filter_stride_width)))
return false;
return true;
}
static std::vector<std::shared_ptr<ngraph::Node>> Split2DConvFilters(std::shared_ptr<ngraph::opset7::Constant>& filters,
const bool& vertical_permute, const bool& horizontal_permute, const size_t& split_channels) {
if (!horizontal_permute && !vertical_permute && split_channels == 1)
return {filters};
std::vector <std::shared_ptr<ngraph::Node>> result;
ngraph::Shape reshape_shape;
auto flat_filters = filters->outputs();
const auto filter_shape = filters->get_output_shape(0);
IE_ASSERT(filter_shape.size() == 4);
if (split_channels > 1) {
const auto axis_node = ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
const auto split = std::make_shared<ngraph::opset7::Split>(filters, axis_node, split_channels);
flat_filters = split->outputs();
}
for (size_t split_index = 0; split_index < split_channels; split_index++) {
ngraph::Output<ngraph::Node>& flat_filter = flat_filters[split_index];
if (horizontal_permute && !vertical_permute) {
result.push_back(std::make_shared<ngraph::opset7::Transpose>(flat_filter,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, ngraph::Shape{0, 1, 3, 2})));
} else {
result.push_back(flat_filter.get_node_shared_ptr());
}
}
if (vertical_permute && horizontal_permute) {
reshape_shape = ngraph::Shape{filter_shape[0], filter_shape[1] * filter_shape[2] * filter_shape[3] / split_channels, 1, 1};
} else if (vertical_permute && !horizontal_permute) {
reshape_shape = ngraph::Shape{filter_shape[0], filter_shape[1] * filter_shape[2] / split_channels, 1, filter_shape[3]};
} else if (!vertical_permute && horizontal_permute) {
reshape_shape = ngraph::Shape{filter_shape[0], filter_shape[1] * filter_shape[3] / split_channels, filter_shape[2], 1};
} else {
reshape_shape = ngraph::Shape{filter_shape[0], filter_shape[1] / split_channels, filter_shape[2], filter_shape[3]};
}
for (auto &new_filter : result)
new_filter = ngraph::op::util::make_try_fold<ngraph::opset7::Reshape>(new_filter,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, reshape_shape), false);
return result;
}
static ngraph::OutputVector SplitInput(const GraphData& graph_data, ConvData& conv_data) {
// We need to have proper input shape first
ngraph::OutputVector split_planes;
auto padded_input_plane = std::make_shared<ngraph::opset7::Reshape>(graph_data.leading_transpose->input_value(0),
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2},
ngraph::Shape{1, shape_size(graph_data.leading_transpose->input_value(0).get_shape())}), false);
copy_runtime_info(graph_data.conv, padded_input_plane);
if (graph_data.conv_count > 1) {
// If we have split input plane and convolutions due to GNA limitation - we must sum their results at the end
conv_data.input_channel_count /= graph_data.conv_count;
auto reshape_before_transpose = std::make_shared<ngraph::opset7::Reshape>(padded_input_plane,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2},
{shape_size(padded_input_plane->get_shape()) / graph_data.conv_count, graph_data.conv_count}), false);
auto transpose_before_channel_wise_split = std::make_shared<ngraph::opset7::Transpose>(reshape_before_transpose,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 0})->output(0));
const auto axis_node = ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
const auto split = std::make_shared<ngraph::opset7::Split>(transpose_before_channel_wise_split, axis_node, graph_data.conv_count);
split_planes = split->outputs();
} else {
split_planes.push_back(padded_input_plane);
}
return split_planes;
}
static std::vector<std::shared_ptr<ngraph::Node>> SplitFilters(const GraphData& graph_data, ConvData& conv_data) {
// If the input plane exceeds GNA limits and we have split into several convolutions, then we need to split filter data as well;
// we also need to take filter height and potential dilation into account when modifying the filters
// Take account of fake quantize when getting filter values
auto filter_values = std::dynamic_pointer_cast<ngraph::opset7::Constant>(graph_data.fq_conv == nullptr ?
graph_data.conv->input_value(1).get_node_shared_ptr() : graph_data.fq_conv->input_value(0).get_node_shared_ptr());
bool vertical_permute = (conv_data.filter_height > 1);
bool horizontal_permute = (conv_data.filter_dilation_width > 1);
std::vector<std::shared_ptr<ngraph::Node>> h_1_filters{};
h_1_filters = Split2DConvFilters(filter_values, vertical_permute, horizontal_permute, graph_data.conv_count);
for (auto filter : h_1_filters)
copy_runtime_info(graph_data.conv, filter);
return h_1_filters;
}
static void TransformInput(const GraphData& graph_data, const ConvData& conv_data, ngraph::Output<ngraph::Node>& split_input_plane) {
/*
* Padded row - NHWC order
* |
* Split in vertical dim (filter height)
* / | \
* Concat
* |
* Transpose
*/
// First we need to prepare flat (height = 1) slices of input data proper for flattened (height = 1) filters created later on;
// the input datat is overlapping (duplicated)
ngraph::OutputVector dilated_input_planes;
for (size_t filter_height = 0; filter_height < conv_data.filter_height; filter_height++) {
size_t offset;
if (conv_data.filter_stride_height > 1) {
// Prepare strided slices of input data
for (size_t output_height = 0; output_height < conv_data.output_height; output_height++) {
offset = (filter_height * conv_data.filter_dilation_height + output_height * conv_data.filter_stride_height) *
conv_data.input_width * conv_data.input_channel_count;
auto slice = FlatCrop(split_input_plane, offset, conv_data.input_width * conv_data.input_channel_count);
copy_runtime_info(graph_data.conv, slice);
dilated_input_planes.push_back(slice);
}
} else {
offset = filter_height * conv_data.filter_dilation_height * conv_data.input_width * conv_data.input_channel_count;
auto slice = FlatCrop(split_input_plane, offset, conv_data.input_width * conv_data.input_channel_count * conv_data.output_height);
copy_runtime_info(graph_data.conv, slice);
dilated_input_planes.push_back(slice);
}
}
// Interleaving dilated input planes
std::shared_ptr<ngraph::Node> dilated_chunks_concat = std::make_shared<ngraph::opset7::Concat>(dilated_input_planes, 0);
// Additional reshape is required for strided slices of input intended for each filter row
if (conv_data.filter_stride_height > 1) {
dilated_chunks_concat = std::make_shared<ngraph::opset7::Reshape>(dilated_chunks_concat,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2},
{conv_data.filter_height, conv_data.input_width * conv_data.input_channel_count * conv_data.output_height}), false);
}
auto transposed_dilated_chunks = std::make_shared<ngraph::opset7::Transpose>(dilated_chunks_concat,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 0})->output(0));
// Flattening of interleaved input planes
auto flattened_dilated_transposed_input = std::make_shared<ngraph::opset7::Reshape>(transposed_dilated_chunks,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2},
{(size_t)1, conv_data.input_width * conv_data.input_channel_count * conv_data.output_height * conv_data.filter_height}), false);
copy_runtime_info(graph_data.conv, {dilated_chunks_concat, flattened_dilated_transposed_input, transposed_dilated_chunks });
split_input_plane = flattened_dilated_transposed_input;
}
static void InsertFQLayer(const std::shared_ptr<ngraph::opset7::FakeQuantize> fqLayer,
std::shared_ptr<ngraph::Node> lastNode) {
if (fqLayer != nullptr) {
lastNode = fqLayer->clone_with_new_inputs({lastNode,
fqLayer->input_value(1), fqLayer->input_value(2),
fqLayer->input_value(3), fqLayer->input_value(4)});
ngraph::copy_runtime_info(fqLayer, lastNode);
}
}
// Valid 1D (decomposed 2D) convolution wrapped with transposes NHWC => NCHW => conv => NCHW => NHWC
static std::shared_ptr<ngraph::Node> Create1DConv(const GraphData& graph_data, const ConvData& conv_data, const ngraph::Output<ngraph::Node>& input,
std::shared_ptr<ngraph::Node> filters, const size_t conv_index, const size_t h_index) {
// Transpose NHWC => NCHW
std::shared_ptr<ngraph::Node> nchw_input = std::make_shared<ngraph::opset7::Transpose>(input,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 3, 1, 2})->output(0));
// Fake quantize
InsertFQLayer(graph_data.fq_conv, filters);
// 1D Convolution
auto conv = std::make_shared<ngraph::opset7::Convolution>(nchw_input, filters,
ngraph::Strides{1, conv_data.filter_stride_width}, ngraph::CoordinateDiff{0, 0}, ngraph::CoordinateDiff{0, 0},
ngraph::Strides{1, 1}, ngraph::op::PadType::VALID);
std::string conv_name = graph_data.conv->get_friendly_name() + "_H_" + std::to_string(h_index) + "_CH_" + std::to_string(0);
conv->set_friendly_name(conv_name);
// Bias & fake quantize
std::shared_ptr<ngraph::Node> last_conv_block_op = conv;
if (graph_data.bias_const && conv_index == 0) {
last_conv_block_op = std::make_shared<ngraph::opset7::Add>(conv, graph_data.bias_const);
copy_runtime_info(graph_data.conv, last_conv_block_op);
InsertFQLayer(graph_data.fq_bias, last_conv_block_op);
}
// Max pooling
if ((graph_data.max_pool && graph_data.pool_size_width > 1) || graph_data.pool_stride_width > 1) {
last_conv_block_op = std::make_shared<ngraph::opset7::MaxPool>(last_conv_block_op,
ngraph::Strides{1, graph_data.pool_stride_width}, ngraph::Shape{0, 0}, ngraph::Shape{0, 0},
ngraph::Shape{1, graph_data.pool_size_width}, graph_data.max_pool->get_rounding_type(), ngraph::op::PadType::VALID);
}
// Activation function & fake quantize
if (graph_data.af && graph_data.conv_count == 1) {
last_conv_block_op = graph_data.af->copy_with_new_inputs({last_conv_block_op});
copy_runtime_info(conv, last_conv_block_op);
InsertFQLayer(graph_data.fq_af, last_conv_block_op);
}
// Transpose NCHW => NHWC
auto nhwc_output = std::make_shared<ngraph::opset7::Transpose>(last_conv_block_op,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 2, 3, 1})->output(0));
copy_runtime_info(graph_data.conv, {nchw_input, conv, nhwc_output});
return nhwc_output;
}
static std::shared_ptr<ngraph::Node> CreateDecomposedConv(const GraphData& graph_data, ConvData& conv_data,
ngraph::Output<ngraph::Node>& reduced_input_plane, const std::vector<std::shared_ptr<ngraph::Node>>& h_1_filters, const size_t conv_index) {
ngraph::OutputVector result_chunks;
std::shared_ptr<ngraph::Node> last_op;
bool horizontal_permute = (conv_data.filter_dilation_width > 1);
size_t h_1_filter_channel_count = (conv_data.input_channel_count * conv_data.filter_height);
for (size_t output_height = 0; output_height < conv_data.output_height; output_height++) {
size_t offset = output_height * conv_data.input_width * h_1_filter_channel_count;
auto row = (conv_data.output_height == 1) ? reduced_input_plane :
FlatCrop(reduced_input_plane, offset, conv_data.input_width * h_1_filter_channel_count);
/*
* Padded row
* |
* ??? <Dilation !=1> ???
* |
* Split in vertical dim
* / | \
* Concat
* |
* Permute
* |
* Transpose (NHWC => NCHW)
* |
* 1D Conv (Bias | MaxPooling)
* |
* Transpose (NCHW => NHWC)
*/
auto nhwc_conv_y_input = row;
if (horizontal_permute) {
// Horizontal split - transform input accordingly
ngraph::OutputVector dilated_chunks;
std::shared_ptr<ngraph::Node> dilated_chunks_concat = nhwc_conv_y_input.get_node_shared_ptr();
// We need to calculate some parameters in case horizontal stride > 1 is used, because if we use the ones available from the original convolution
// we won't take into account the fact horizontal strides will be supported by the newly created 1D convolution, and not by decomposition
size_t filter_dilation_width = conv_data.filter_width > 1 ? conv_data.filter_dilation_width : 1;
size_t output_width = (conv_data.input_width - (conv_data.filter_width + filter_dilation_width - 2));
if (conv_data.filter_width > 1) {
for (size_t filter_width = 0; filter_width < conv_data.filter_width; filter_width++) {
size_t offset = filter_width * conv_data.filter_dilation_width * h_1_filter_channel_count;
auto slice = FlatCrop(row, offset, h_1_filter_channel_count * output_width);
copy_runtime_info(graph_data.conv, slice);
dilated_chunks.push_back(slice);
}
dilated_chunks_concat = std::make_shared<ngraph::opset7::Concat>(dilated_chunks, 0);
}
auto transposed_dilated_chunks = std::make_shared<ngraph::opset7::Transpose>(dilated_chunks_concat,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 0})->output(0));
auto flattened_dilated_conv_input = std::make_shared<ngraph::opset7::Reshape>(transposed_dilated_chunks,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4},
ngraph::Shape{1, 1, output_width, h_1_filter_channel_count * conv_data.filter_width}), false);
copy_runtime_info(graph_data.conv, ngraph::NodeVector{flattened_dilated_conv_input, transposed_dilated_chunks, dilated_chunks_concat});
nhwc_conv_y_input = flattened_dilated_conv_input;
} else {
// If no horizontal split is done, only reshape is required before decomposed convolution
nhwc_conv_y_input = std::make_shared<ngraph::opset7::Reshape>(nhwc_conv_y_input,
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{4},
ngraph::Shape{1, 1, conv_data.input_width, h_1_filter_channel_count}), false);
}
// Pointwise convolutions
// Valid 1D convolution wrapped with transposes NHWC => NCHW => Conv => NCHW => NHWC
// Activation function can be fused with convolution only if it isn't split
auto nhwc_y_output = Create1DConv(graph_data, conv_data, nhwc_conv_y_input, h_1_filters[conv_index], conv_index, output_height);
result_chunks.push_back(nhwc_y_output);
last_op = nhwc_y_output;
}
// Horizontal dimemsion greater than 1
if (result_chunks.size() > 1) {
// Concat in horizontal dimension
// In NHWC index of H is 1
auto concatenated_sub_results = std::make_shared<ngraph::opset7::Concat>(result_chunks, 1);
copy_runtime_info(graph_data.conv, concatenated_sub_results);
last_op = concatenated_sub_results;
}
return last_op;
}
static void Decompose(const GraphData& graph_data, ConvData& conv_data) {
std::vector<std::shared_ptr<ngraph::Node>> partial_conv_results;
// Split input due to GNA filter element count limit
auto split_planes = SplitInput(graph_data, conv_data);
// Split filters due to GNA filter element count limit, 2D convolution shape, or dilations
auto h_1_filters = SplitFilters(graph_data, conv_data);
// Do transformations in each of the splits created above
for (size_t conv_index = 0; conv_index < graph_data.conv_count; conv_index++) {
ngraph::Output<ngraph::Node>& split_input_plane = split_planes[conv_index];
// Input data needs to be prepared before 2D convolution decomposition
if (conv_data.filter_height > 1 || conv_data.filter_stride_height > 1) {
TransformInput(graph_data, conv_data, split_input_plane);
}
auto flat_conv = CreateDecomposedConv(graph_data, conv_data, split_input_plane, h_1_filters, conv_index);
partial_conv_results.push_back(flat_conv);
}
std::shared_ptr<ngraph::Node> conv_result = partial_conv_results.front();
for (size_t i = 1; i < partial_conv_results.size(); i++) {
auto add_result = std::make_shared<ngraph::opset7::Add>(partial_conv_results[i], conv_result);
copy_runtime_info(graph_data.conv, add_result);
conv_result = add_result;
}
// TODO: Max Pool 2D case
//if (graph_data.max_pool && (graph_data.pool_size_height > 1 || graph_data.pool_stride_height > 1)) {
//}
// Activation function after trailing Transpose NCHW->NHWC
if (graph_data.af && graph_data.conv_count > 1) {
auto af_result = graph_data.af->copy_with_new_inputs({conv_result});
copy_runtime_info(graph_data.conv, af_result);
conv_result = af_result;
}
// We need to put the same name as before for the Convolution layer, so its output can be used as network result
std::string conv_result_name = graph_data.last_op_in_sequence_for_replacement->get_friendly_name();
replace_node(graph_data.last_op_in_sequence_for_replacement, conv_result);
conv_result->set_friendly_name(conv_result_name);
}
static bool Convert(std::shared_ptr<ngraph::Node> leading_transpose,
std::shared_ptr<ngraph::Node> fq_conv,
std::shared_ptr<ngraph::Node> conv,
std::shared_ptr<ngraph::Node> trailing_transpose,
std::shared_ptr<ngraph::Node> bias,
std::shared_ptr<ngraph::Node> fq_bias,
std::shared_ptr<ngraph::Node> max_pool,
std::shared_ptr<ngraph::Node> af,
std::shared_ptr<ngraph::Node> fq_af,
std::shared_ptr<ngraph::Node> last_op_for_replacement) {
GraphData graph_data{std::dynamic_pointer_cast<ngraph::opset7::Transpose>(leading_transpose),
std::dynamic_pointer_cast<ngraph::opset7::FakeQuantize>(fq_conv),
std::dynamic_pointer_cast<ngraph::opset7::Convolution>(conv),
std::dynamic_pointer_cast<ngraph::opset7::Transpose>(trailing_transpose),
std::dynamic_pointer_cast<ngraph::opset7::FakeQuantize>(fq_bias),
std::dynamic_pointer_cast<ngraph::opset7::MaxPool>(max_pool),
std::dynamic_pointer_cast<ngraph::op::util::UnaryElementwiseArithmetic>(af),
std::dynamic_pointer_cast<ngraph::opset7::FakeQuantize>(fq_af),
last_op_for_replacement, nullptr, 1, 1, 1};
ConvData conv_data;
if (!VerifyAndGetConvData(std::dynamic_pointer_cast<ngraph::opset7::Convolution>(conv), conv_data))
return false;
// We are looking for Transpose(NHWC->NCHW) => Conv => Transpose(NCHW->NHWC)
// or similar cases, so required network must be in NHWC order like in TF
if (!TransposeOrderMatches(std::dynamic_pointer_cast<ngraph::opset7::Transpose>(leading_transpose), {0, 3, 1, 2}))
return false;
if (!TransposeOrderMatches(std::dynamic_pointer_cast<ngraph::opset7::Transpose>(trailing_transpose), {0, 2, 3, 1}))
return false;
if (bias && !(graph_data.bias_const = VerifyBiasAndReshapeConst(std::dynamic_pointer_cast<ngraph::opset7::Add>(bias), conv_data)))
return false;
if (max_pool && !VerifyMaxPool(graph_data, std::dynamic_pointer_cast<ngraph::opset7::MaxPool>(max_pool)))
return false;
if (!ShouldDecompose(graph_data, conv_data))
return false;
// All checks applied - now we may start decomposition
Decompose(graph_data, conv_data);
return true;
}
static bool VerifyBias(std::shared_ptr<ngraph::Node> conv, std::shared_ptr<ngraph::Node> bias) {
auto add_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(bias->input_value(1).get_node_shared_ptr());
if (!add_const) {
add_const = std::dynamic_pointer_cast<ngraph::opset7::Constant>(bias->input_value(0).get_node_shared_ptr());
}
if (!add_const) {
auto bias_size = shape_size(add_const->get_shape());
auto conv_filter_count = conv->input_value(1).get_shape()[0];
if (bias_size == conv_filter_count)
return true;
}
return false;
}
Decompose2DConv::Decompose2DConv() {
MATCHER_SCOPE(Decompose2DConv);
auto const_input = ngraph::pattern::wrap_type<ngraph::opset7::Constant>();
auto leading_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({ngraph::pattern::any_input(), const_input},
consumers_and_rank(1, 4));
auto filters_const = ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::rank_equals(4));
auto fq_conv = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({const_input, const_input, const_input, const_input, const_input},
consumers_and_rank(1, 4));
auto filters = std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{filters_const, fq_conv});
auto conv = ngraph::pattern::wrap_type<ngraph::opset7::Convolution>({leading_transpose, filters},
consumers_and_rank(1, 4));
auto bias = ngraph::pattern::wrap_type<ngraph::opset7::Add>({conv, const_input},
ngraph::pattern::consumers_count(1));
auto fq_bias = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({bias, const_input, const_input, const_input, const_input},
ngraph::pattern::consumers_count(1));
auto max_pool1 = ngraph::pattern::wrap_type<ngraph::opset7::MaxPool>({bias},
ngraph::pattern::consumers_count(1));
auto max_pool2 = ngraph::pattern::wrap_type<ngraph::opset7::MaxPool>({fq_bias},
ngraph::pattern::consumers_count(1));
auto af1 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({bias}, ngraph::pattern::consumers_count(1));
auto af2 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({fq_bias}, ngraph::pattern::consumers_count(1));
auto af3 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({max_pool1}, ngraph::pattern::consumers_count(1));
auto af4 = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({max_pool2}, ngraph::pattern::consumers_count(1));
auto fq_af = ngraph::pattern::wrap_type<ngraph::opset7::FakeQuantize>({af4, const_input, const_input, const_input, const_input},
ngraph::pattern::consumers_count(1));
auto transpose_input =
std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{conv, bias, max_pool1, max_pool2, fq_bias, af1, af2, af3, af4, fq_af});
auto trailing_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({transpose_input, const_input},
consumers_and_rank(1, 4));
ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
const auto& pattern_map = m.get_pattern_value_map();
auto fq_conv_it = pattern_map.find(fq_conv);
auto fq_conv_node = (fq_conv_it == std::end(pattern_map) ? nullptr : fq_conv_it->second.get_node_shared_ptr());
auto bias_it = pattern_map.find(bias);
auto bias_node = (bias_it == std::end(pattern_map) ? nullptr : bias_it->second.get_node_shared_ptr());
auto fq_bias_it = pattern_map.find(fq_bias);
auto fq_bias_node = (fq_bias_it == std::end(pattern_map) ? nullptr : fq_bias_it->second.get_node_shared_ptr());
auto fq_af_it = pattern_map.find(fq_af);
auto fq_af_node = (fq_af_it == std::end(pattern_map) ? nullptr : fq_af_it->second.get_node_shared_ptr());
auto max_pool1_it = pattern_map.find(max_pool1);
auto max_pool2_it = pattern_map.find(max_pool2);
auto max_pool_node = (max_pool1_it == std::end(pattern_map) ?
((max_pool2_it == std::end(pattern_map) ? nullptr : max_pool2_it->second.get_node_shared_ptr())) : max_pool1_it->second.get_node_shared_ptr());
std::shared_ptr<ngraph::Node> af_node = nullptr;
std::vector<ngraph::pattern::PatternValueMap::const_iterator> af_it
{pattern_map.find(af1), pattern_map.find(af2), pattern_map.find(af3), pattern_map.find(af4)};
for (auto const& af : af_it) {
if (af != std::end(pattern_map)) {
af_node = af->second.get_node_shared_ptr();
break;
}
}
return Convert(pattern_map.at(leading_transpose).get_node_shared_ptr(), fq_conv_node, pattern_map.at(conv).get_node_shared_ptr(),
pattern_map.at(trailing_transpose).get_node_shared_ptr(), bias_node, fq_bias_node, max_pool_node, af_node, fq_af_node,
pattern_map.at(trailing_transpose).get_node_shared_ptr());
};
auto m = std::make_shared<ngraph::pattern::Matcher>(trailing_transpose, matcher_name);
this->register_matcher(m, callback);
}
Decompose2DConvTransposedWithBias::Decompose2DConvTransposedWithBias() {
MATCHER_SCOPE(Decompose2DConvTransposedWithBias);
auto const_input_i64 = ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::type_matches(ngraph::element::i64));
auto const_input = ngraph::pattern::wrap_type<ngraph::opset7::Constant>();
auto leading_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({ngraph::pattern::any_input(), const_input_i64},
consumers_and_rank(1, 4));
auto conv = ngraph::pattern::wrap_type<ngraph::opset7::Convolution>(
{leading_transpose, ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::rank_equals(4))},
consumers_and_rank(1, 4));
auto trailing_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({conv, const_input_i64},
consumers_and_rank(1, 4));
auto bias = ngraph::pattern::wrap_type<ngraph::opset7::Add>({trailing_transpose, const_input},
ngraph::pattern::consumers_count(1));
ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
const auto& pattern_map = m.get_pattern_value_map();
if (!VerifyBias(pattern_map.at(conv).get_node_shared_ptr(), pattern_map.at(bias).get_node_shared_ptr()))
return false;
return Convert(pattern_map.at(leading_transpose).get_node_shared_ptr(), nullptr, pattern_map.at(conv).get_node_shared_ptr(),
pattern_map.at(trailing_transpose).get_node_shared_ptr(), pattern_map.at(bias).get_node_shared_ptr(), nullptr, nullptr,
nullptr, nullptr, pattern_map.at(bias).get_node_shared_ptr());
};
auto m = std::make_shared<ngraph::pattern::Matcher>(bias, matcher_name);
this->register_matcher(m, callback);
}
Decompose2DConvTransposedWithBiasAF::Decompose2DConvTransposedWithBiasAF() {
MATCHER_SCOPE(Decompose2DConvTransposedWithBiasAF);
auto const_input_i64 = ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::type_matches(ngraph::element::i64));
auto const_input = ngraph::pattern::wrap_type<ngraph::opset7::Constant>();
auto leading_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({ngraph::pattern::any_input(), const_input_i64},
consumers_and_rank(1, 4));
auto conv = ngraph::pattern::wrap_type<ngraph::opset7::Convolution>(
{leading_transpose, ngraph::pattern::wrap_type<ngraph::opset7::Constant>(ngraph::pattern::rank_equals(4))},
consumers_and_rank(1, 4));
auto trailing_transpose = ngraph::pattern::wrap_type<ngraph::opset7::Transpose>({conv, const_input_i64},
consumers_and_rank(1, 4));
auto bias = ngraph::pattern::wrap_type<ngraph::opset7::Add>({trailing_transpose, const_input},
ngraph::pattern::consumers_count(1));
auto af = ngraph::pattern::wrap_type<ngraph::opset7::Relu, ngraph::opset7::Sigmoid,
ngraph::opset7::Tanh, ngraph::opset7::Abs, ngraph::opset7::Log, ngraph::opset7::Exp,
ngraph::opset7::Sign, ngraph::opset7::Clamp>({bias},
ngraph::pattern::consumers_count(1));
ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
const auto& pattern_map = m.get_pattern_value_map();
if (!VerifyBias(pattern_map.at(conv).get_node_shared_ptr(), pattern_map.at(bias).get_node_shared_ptr()))
return false;
return Convert(pattern_map.at(leading_transpose).get_node_shared_ptr(), nullptr, pattern_map.at(conv).get_node_shared_ptr(),
pattern_map.at(trailing_transpose).get_node_shared_ptr(), pattern_map.at(bias).get_node_shared_ptr(), nullptr,
nullptr, pattern_map.at(af).get_node_shared_ptr(), nullptr, pattern_map.at(af).get_node_shared_ptr());
};
auto m = std::make_shared<ngraph::pattern::Matcher>(af, matcher_name);
this->register_matcher(m, callback);
}

80
inference-engine/src/gna_plugin/transformations/decompose_2d_conv.hpp Normal file
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@ -0,0 +1,80 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include <ngraph/pass/graph_rewrite.hpp>
namespace GNAPluginNS {
/**
* @brief Decompose a 2D convolution, wrapped with transposes,
* to a set of valid 1D convolutions with padding added in front of the set:
*
* Padding
* |
* Transpose (NHWC -> NCHW) Transpose (NHWC -> NCHW)
* | |
* Convolution with padding Valid convolution
* | |
* Broadcast Bias (optional) Broadcast Bias (optional)
* | |
* Max Pooling (optional) Max Pooling (optional)
* | |
* Activation Function (optional) Activation Function (optional)
* | |
* Transpose (NCHW -> NHWC) Transpose (NCHW -> NHWC)
*
*/
class Decompose2DConv : public ngraph::pass::MatcherPass {
public:
NGRAPH_RTTI_DECLARATION;
Decompose2DConv();
};
/**
* @brief Decomopose a 2D convolution wrapped with transposes, with bias after trailing transpose,
* to a set of valid 1D convolutions with padding added in front of the set:
*
* Padding
* |
* Transpose (NHWC -> NCHW) Transpose (NHWC -> NCHW)
* | |
* Convolution with padding Valid convolution
* | |
* Transpose (NCHW -> NHWC) Transpose (NCHW -> NHWC)
* | |
* Broadcast Bias Broadcast Bias
*
*/
class Decompose2DConvTransposedWithBias : public ngraph::pass::MatcherPass {
public:
NGRAPH_RTTI_DECLARATION;
Decompose2DConvTransposedWithBias();
};
/**
* @brief Decomopose a 2D convolution wrapped with transposes, with bias
* to a set of valid 1D convolutions with padding added in front of the set:
*
* Padding
* |
* Transpose (NHWC -> NCHW) Transpose (NHWC -> NCHW)
* | |
* Convolution with padding Valid convolution
* | |
* Transpose (NCHW -> NHWC) Transpose (NCHW -> NHWC)
* | |
* Broadcast Bias Broadcast Bias
* | |
* Activation Function Activation Function
*
*/
class Decompose2DConvTransposedWithBiasAF : public ngraph::pass::MatcherPass {
public:
NGRAPH_RTTI_DECLARATION;
Decompose2DConvTransposedWithBiasAF();
};
} // namespace GNAPluginNS

75
inference-engine/src/gna_plugin/transformations/utils/transformation_helper.cpp Normal file
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@ -0,0 +1,75 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <ngraph/opsets/opset7.hpp>
#include <ngraph/pattern/op/wrap_type.hpp>
#include "transformation_helper.hpp"
namespace GNAPluginNS {
void GetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data) {
conv_data.output_height = conv->get_output_shape(0)[2];
conv_data.output_width = conv->get_output_shape(0)[3];
conv_data.input_channel_count = conv->input_value(0).get_shape()[1];
conv_data.input_height = conv->input_value(0).get_shape()[2];
conv_data.input_width = conv->input_value(0).get_shape()[3];
conv_data.filter_count = conv->input_value(1).get_shape()[0];
conv_data.filter_channel_count = conv->input_value(1).get_shape()[1];
conv_data.filter_height = conv->input_value(1).get_shape()[2];
conv_data.filter_width = conv->input_value(1).get_shape()[3];
conv_data.filter_dilation_height = conv->get_dilations()[0];
conv_data.filter_dilation_width = conv->get_dilations()[1];
conv_data.filter_stride_height = conv->get_strides()[0];
conv_data.filter_stride_width = conv->get_strides()[1];
conv_data.output_channel_count = conv_data.filter_count;
conv_data.pads_begin_height = conv->get_pads_begin()[0];
conv_data.pads_begin_width = conv->get_pads_begin()[1];
conv_data.pads_end_height = conv->get_pads_end()[0];
conv_data.pads_end_width = conv->get_pads_end()[1];
conv_data.padding_type = conv->get_auto_pad();
conv_data.element_type = conv->get_element_type();
}
std::function<bool(ngraph::Output<ngraph::Node>)> consumers_and_rank(const size_t expected_count, const ngraph::Dimension& expected_rank) {
return [=](ngraph::Output<ngraph::Node> output) -> bool {
return ngraph::pattern::consumers_count(expected_count)(output) && ngraph::pattern::rank_equals(expected_rank)(output);
};
}
bool TransposeOrderMatches(std::shared_ptr<ngraph::opset7::Transpose> transpose, std::vector<size_t> order) {
if (!transpose)
return false;
const ngraph::Output<ngraph::Node>& transpose_order = transpose->input_value(1);
auto transpose_order_dim = transpose_order.get_shape().size();
if (transpose_order_dim != 1 || transpose_order.get_shape()[0] != order.size())
return false;
auto const_with_order_values = std::dynamic_pointer_cast<ngraph::opset7::Constant>(transpose_order.get_node_shared_ptr());
if (!const_with_order_values)
return false;
const auto data = const_with_order_values->cast_vector<size_t>();
if (data.empty())
return false;
if (!std::equal(order.begin(), order.end(), data.begin()))
return false;
return true;
}
std::shared_ptr<ngraph::opset7::StridedSlice> FlatCrop(ngraph::Output<ngraph::Node> input, size_t offset, size_t size) {
return std::make_shared<ngraph::opset7::StridedSlice>(
input, // data
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)0, offset}), // begin sice index
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)0, offset + size}), // end slice index
ngraph::opset7::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {(size_t)1, (size_t)1}), // strides
std::vector<int64_t>{1, 0}, // begin mask
std::vector<int64_t>{1, 0}); // end mask
}
} // namespace GNAPluginNS

64
inference-engine/src/gna_plugin/transformations/utils/transformation_helper.hpp Normal file
View File

@ -0,0 +1,64 @@
// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
namespace GNAPluginNS {
struct ConvData {
size_t input_height;
size_t input_width;
size_t input_channel_count;
size_t filter_height;
size_t filter_width;
size_t filter_count;
size_t filter_channel_count;
size_t filter_dilation_height;
size_t filter_dilation_width;
size_t filter_stride_height;
size_t filter_stride_width;
size_t output_height;
size_t output_width;
size_t output_channel_count;
size_t pads_begin_width;
size_t pads_begin_height;
size_t pads_end_width;
size_t pads_end_height;
ngraph::op::PadType padding_type;
ngraph::element::Type element_type;
};
/**
* @brief gets all convolution related data into a struct for further processing
* @param conv convolution node to get data of
* @param conv_data convolution data structure to put data into
* @return void
*/
void GetConvData(std::shared_ptr<ngraph::opset7::Convolution> conv, ConvData& conv_data);
/**
* @brief ngraph matcher predicate fusing existing predicates for consumers count and rank of a layer
* @param expected_count expected consumers count for of node
* @param expected_rank expected node rank
* @return predicate function wrapper
*/
std::function<bool(ngraph::Output<ngraph::Node>)> consumers_and_rank(const size_t expected_count, const ngraph::Dimension& expected_rank);
/**
* @brief checks whether transpose matches a given order
* @param transpose transpose layer
* @param order order of transposition to be compared with
* @return true if the order matches, false otherwise
*/
bool TransposeOrderMatches(std::shared_ptr<ngraph::opset7::Transpose> transpose, std::vector<size_t> order);
/**
* @brief performs a crop of a flattened input tensor
* @param input input layer
* @param offset offset to start the crop at*
* @param size size of the crop
* @return pointer to the newly created slice
*/
std::shared_ptr<ngraph::opset7::StridedSlice> FlatCrop(ngraph::Output<ngraph::Node> input, size_t offset, size_t size);
} // namespace GNAPluginNS

2
inference-engine/src/hetero_plugin/hetero_infer_request.cpp
View File

@ -77,7 +77,7 @@ void HeteroInferRequest::SetBlob(const std::string& name, const InferenceEngine:
if (findInputAndOutputBlobByName(name, foundInput, foundOutput)) {
r->SetBlob(name, data, foundInput->getPreProcess());
}
} catch (const InferenceEngine::NotFound& ex) {}
} catch (const InferenceEngine::NotFound&) {}
}
}

62
inference-engine/src/inference_engine/CMakeLists.txt
View File

@ -12,11 +12,11 @@ if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO")
endif()
file (GLOB LIBRARY_SRC
${CMAKE_CURRENT_SOURCE_DIR}/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/cpp/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/threading/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/cpp/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/cpp_interfaces/interface/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/threading/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/cpp/*.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/cpp_interfaces/interface/*.cpp
)
# TODO: WA for OneHot pass usage in reshape
@ -30,38 +30,38 @@ set(LEGACY_LIBRARY_SHARED_SRCS
set_source_files_properties(${LEGACY_LIBRARY_SHARED_SRCS} PROPERTIES
COMPILE_DEFINITIONS "USE_STATIC_IE")
set(IE_STATIC_DEPENDENT_FILES ${CMAKE_CURRENT_SOURCE_DIR}/file_utils.cpp)
set(IE_STATIC_DEPENDENT_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/file_utils.cpp)
list(REMOVE_ITEM LIBRARY_SRC ${IE_STATIC_DEPENDENT_FILES})
file (GLOB LIBRARY_HEADERS
${CMAKE_CURRENT_SOURCE_DIR}/*.h
${CMAKE_CURRENT_SOURCE_DIR}/*.hpp
${CMAKE_CURRENT_SOURCE_DIR}/src/*.h
${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp
)
if (LINUX)
file (GLOB LIBRARY_SRC
${LIBRARY_SRC}
${CMAKE_CURRENT_SOURCE_DIR}/os/lin/*.cpp)
${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/*.cpp)
file (GLOB LIBRARY_HEADERS
${LIBRARY_HEADERS}
${CMAKE_CURRENT_SOURCE_DIR}/os/lin/*.hpp)
${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/*.hpp)
elseif (UNIX)
list (APPEND LIBRARY_SRC
${CMAKE_CURRENT_SOURCE_DIR}/os/lin/lin_shared_object_loader.cpp)
${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/lin_shared_object_loader.cpp)
endif()
if (WIN32)
file (GLOB LIBRARY_SRC
${LIBRARY_SRC}
${CMAKE_CURRENT_SOURCE_DIR}/os/win/*.cpp)
${CMAKE_CURRENT_SOURCE_DIR}/src/os/win/*.cpp)
file (GLOB LIBRARY_HEADERS
${LIBRARY_HEADERS}
${CMAKE_CURRENT_SOURCE_DIR}/os/win/*.hpp)
${CMAKE_CURRENT_SOURCE_DIR}/src/os/win/*.hpp)
endif()
if(ENABLE_SSE42)
file(GLOB SSE_SRC ${CMAKE_CURRENT_SOURCE_DIR}/cpu_x86_sse42/*.cpp)
file(GLOB SSE_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/cpu_x86_sse42/*.hpp)
file(GLOB SSE_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/cpu_x86_sse42/*.cpp)
file(GLOB SSE_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/cpu_x86_sse42/*.hpp)
list(APPEND LIBRARY_HEADERS ${SSE_HEADERS})
list(APPEND LIBRARY_SRC ${SSE_SRC})
@ -75,9 +75,9 @@ if(ENABLE_SSE42)
endif()
endif()
addVersionDefines(ie_version.cpp CI_BUILD_NUMBER)
addVersionDefines(src/ie_version.cpp CI_BUILD_NUMBER)
set (PUBLIC_HEADERS_DIR "${IE_MAIN_SOURCE_DIR}/include")
set (PUBLIC_HEADERS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include/ie")
file (GLOB_RECURSE PUBLIC_HEADERS
${PUBLIC_HEADERS_DIR}/*.hpp
@ -116,7 +116,7 @@ add_library(${TARGET_NAME}_obj OBJECT
${PUBLIC_HEADERS})
ie_faster_build(${TARGET_NAME}_obj
UNITY PCH PRIVATE "precomp.hpp"
UNITY PCH PRIVATE "src/precomp.hpp"
)
target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_INFERENCE_ENGINE_API
@ -128,7 +128,7 @@ target_include_directories(${TARGET_NAME}_obj SYSTEM PRIVATE $<TARGET_PROPERTY:n
$<TARGET_PROPERTY:ngraph::frontend_manager::static,INTERFACE_INCLUDE_DIRECTORIES>
$<TARGET_PROPERTY:xbyak,INTERFACE_INCLUDE_DIRECTORIES>)
target_include_directories(${TARGET_NAME}_obj PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}"
target_include_directories(${TARGET_NAME}_obj PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src"
"${IE_MAIN_SOURCE_DIR}/src/readers/ir_reader" # for ie_ir_version.hpp
$<TARGET_PROPERTY:${TARGET_NAME}_legacy,INTERFACE_INCLUDE_DIRECTORIES>
$<TARGET_PROPERTY:${TARGET_NAME}_transformations,INTERFACE_INCLUDE_DIRECTORIES>
@ -162,11 +162,12 @@ if (TBBBIND_2_4_FOUND)
endif()
target_link_libraries(${TARGET_NAME} PRIVATE pugixml::static openvino::itt ${CMAKE_DL_LIBS} Threads::Threads
ngraph ngraph::frontend_manager::static inference_engine_transformations)
ngraph::frontend_manager::static inference_engine_transformations
PUBLIC ngraph)
target_include_directories(${TARGET_NAME} INTERFACE
$<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
$<INSTALL_INTERFACE:${IE_CPACK_IE_DIR}/include>
$<INSTALL_INTERFACE:${IE_CPACK_IE_DIR}/include/ie>
PRIVATE $<TARGET_PROPERTY:${TARGET_NAME}_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
$<TARGET_PROPERTY:${TARGET_NAME}_legacy,INTERFACE_INCLUDE_DIRECTORIES>)
@ -194,7 +195,7 @@ if (TBBBIND_2_4_FOUND)
target_link_libraries(${TARGET_NAME}_s PRIVATE ${TBBBIND_2_4_IMPORTED_TARGETS})
endif()
target_include_directories(${TARGET_NAME}_s PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}"
target_include_directories(${TARGET_NAME}_s PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/src"
$<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
"${IE_MAIN_SOURCE_DIR}/src/legacy_api/src")
@ -216,7 +217,7 @@ set_target_properties(${TARGET_NAME} ${TARGET_NAME}_obj ${TARGET_NAME}_s
# Export for build tree
export(TARGETS ${TARGET_NAME} NAMESPACE IE::
export(TARGETS ngraph ${TARGET_NAME} NAMESPACE IE::
APPEND FILE "${CMAKE_BINARY_DIR}/InferenceEngineTargets.cmake")
# Export for developer package
@ -229,7 +230,14 @@ list(APPEND core_components ngraph)
list(APPEND PATH_VARS "IE_INCLUDE_DIR" "IE_NGRAPH_DIR"
"IE_PARALLEL_CMAKE")
if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND TBBROOT MATCHES ${TEMP})
# define variables for InferenceEngineConfig.cmake
if(THREADING MATCHES "^(TBB|TBB_AUTO)$")
set(IE_TBB_DIR "${TBB_DIR}")
list(APPEND PATH_VARS "IE_TBB_DIR")
endif()
# install only downloaded TBB, system one is not installed
if(THREADING MATCHES "^(TBB|TBB_AUTO)$" AND TBBROOT MATCHES ${TEMP})
ie_cpack_add_component(tbb REQUIRED)
list(APPEND core_components tbb)
@ -249,8 +257,6 @@ if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND TBBROOT MATCH
COMPONENT tbb)
set(IE_TBB_DIR_INSTALL "external/tbb/cmake")
set(IE_TBB_DIR "${TBB_DIR}")
list(APPEND PATH_VARS "IE_TBB_DIR")
install(FILES "${TBB}/cmake/TBBConfig.cmake"
"${TBB}/cmake/TBBConfigVersion.cmake"
@ -263,7 +269,7 @@ endif()
ie_cpack_add_component(core REQUIRED DEPENDS ${core_components})
ie_cpack_add_component(core_dev REQUIRED core ngraph_dev)
install(DIRECTORY "${PUBLIC_HEADERS_DIR}" DESTINATION ${IE_CPACK_IE_DIR}
install(DIRECTORY "${PUBLIC_HEADERS_DIR}" DESTINATION ${IE_CPACK_IE_DIR}/include
COMPONENT core_dev)
install(TARGETS ${TARGET_NAME} EXPORT InferenceEngineTargets
@ -301,7 +307,7 @@ configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceE
INSTALL_DESTINATION "${CMAKE_INSTALL_PREFIX}"
PATH_VARS ${PATH_VARS})
set(IE_INCLUDE_DIR "include")
set(IE_INCLUDE_DIR "include/ie")
set(IE_NGRAPH_DIR "../ngraph/cmake")
set(IE_TBB_DIR "${IE_TBB_DIR_INSTALL}")
set(IE_PARALLEL_CMAKE "share/ie_parallel.cmake")

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