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Move the following header files from backendsCommon to armnn/backends.
* MemCopyWorkload.hpp
* TensorHandle.hpp
* Workload.hpp
* WorkloadData.hpp
* WorkloadFactory.hpp
Replace them with forwarding headers and a pragma deprecation message.
Resolve the deprecation messages in Arm NN code.
Signed-off-by: Colm Donelan <colm.donelan@arm.com>
Change-Id: I47f116b30f86e478c9057795bc518c391a8ae514
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* Created include/armnnTestUtils directory
* Moved Arm NN test utils files into armnnTestUtils directory
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I03ac54c645c41c52650c4c03b6a58fb1481fef5d
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!android-nn-driver:6532
!armnn-internal-tests:372451
* Made fix to 2 out of 3 ConstTensor() constructors in Tensor.hpp to
throw InvalidArgumentException when TensorInfo isConstant parameter
is false.
* Added new ConstTensor() constructor in Tensor.cpp to accept vector<>.data()
using template<typename MemoryType>.
* Fixed runtime->GetOutputTensorInfo()/GetInputTensorInfo() methods and
called submethods to return TensorInfo& rather than TensorInfo.
* Fixed all failing unit tests for CpuRef/CpuAcc/GpuAcc to ensure any
ConstTensor created has it's TensorInfo isConstant set to true.
* Added unit tests in TensorTest.cpp to ensure ConstTensor constructors
throw InvalidArgumentException when TensorInfo isConstat parameter is
false.
* Added unit test to ensure an empty ConstTensor constructor will set
TensorInfo isConatant to true.
* Indentation fixes.
* Fix to arm_tensor.i to add isConstant parameter to TensorInfo
constructor. Added methods IsConstant() and SetConstant().
* Fix to const_tensor.py to throw ValueError when TensorInfo
isConstant is set to false when constructing a ConstTensor.
* Fixed PyArmnn unit tests to set TensorInfo isConstant to
True when ConstTensor is used.
* Added unit tests in test_const_tensor.py to ensure ConstTensor
constructors throw ValueError when TensorInfo isConstat parameter
is false.
Signed-off-by: Cathal Corbett <cathal.corbett@arm.com>
Change-Id: I44e440dd0422c366d31bbdbc77ad2b4db0bde148
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* This change is necessary because tflite uses a [1,H,W,I*M] format
and uses the I*M dimension for per axis quantization. Our previous
layout [M,I,H,W] can't handle the correlating quantization scales.
* Updates Onnx-, TfLiteParser and TfliteDelegate
* Updates the CpuRef, CpuAcc and GpuAcc backends
* Adjusts unit tests
* Adds test to ensure models with old layout can still be read and
executed
* Adds conversion function to previous layout [1,H,W,I*M] --> [M,I,H,W]
which can be used by backend developers
!android-nn-driver:5553
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ifef23368b8c3702cf315a5838d214f7dc13c0152
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* Used doctest in ArmNN unit tests
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Ia9cf5fc72775878885c5f864abf2c56b3a935f1a
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*All fold pad test are now in a separate file
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ic0b0436f6b0194404f9a3f1553e2f69524b63580
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