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path: root/src/armnn/optimizations/FoldPadIntoLayer2d.hpp
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2024-02-14Minor adjustment to the commit for MLCE-1165Tracy Narine
* Rewrote constexpr check to avoid a compile error Signed-off-by: Tracy Narine <tracy.narine@arm.com> Change-Id: I09a61314b1b4a5aa1e2baa52711f470802f04131
2024-02-13MLCE-1165 Model failing to load when pad is folded into Conv2dTracy Narine
* Skipping the optimization which folds pad and conv2d together for a specific case: 1x1 filter and padding size >= filter size Signed-off-by: Tracy Narine <tracy.narine@arm.com> Change-Id: I46944e9f736df1ff60469b2d2852e1bba01ab8cd
2023-01-20IVGCVSW-7013 Removing the check for constant layer in FoldPadTeresa Charlin
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com> Change-Id: Iaa3cff46217117aefdb92f281e9da2b0315f3af9
2022-12-12IVGCVSW-7209 Remove deprecated code due to be removed in 23.02Mike Kelly
* Removed weights and bias from Convolution, DepthwiseConv & FullyConnected layers * Removed the weight and bias ConstTensorHandles from the QueueDescriptors * Updated Workloads to take tensors from WorkloadInfo rather than the QueueDescriptors * Removed unused RedirectMembersToConstantInputs optimization and tests. Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com> Signed-off-by: Mike Kelly <mike.kelly@arm.com> Change-Id: I9ffcdc4a1c0dff725539dd69fc435b700bd98a56
2022-09-28IVGCVSW-7209 Delay one release the removal of weights and biasTeresa Charlin
* This affects only to the layers (not workloads) Conv, DWConv and FC Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com> Change-Id: I66a91ed1a78bc0464e00423c7fc7c28c91d199ce
2022-08-05GitHub #667: Neon fold padding into average pool 2D quantization bug fix.Cathal Corbett
* Originated from a GitHub issue: https://github.com/ARM-software/armnn/issues/667 * Initially, Arm NN supports the pool 2D operation because there is no padding on the pool2d. Neon failure occurs when padding is followed by average pool 2D due to folding optimization. * Here we prevent the folding optimization from happening for the above special case and add it in as a backend specific optimization. Signed-off-by: Cathal Corbett <cathal.corbett@arm.com> Change-Id: Ia0fd90c3a6b4b9d29c81106f154617d2e893e26b
2022-05-16IVGCVSW-6124 ConstTensorsAsInput: Conv2d - FrontEndKeith Davis
* Update Front-end and Tools. * Updated Serializer, Deserializer and unit tests to reflect this. * Updated TfLiteDelegate, TfLiteParser and OnnxParser. * Updated Ref. * Fixed resulting Neon / CL tests * Unified optimizers for conv2d ops * Optimizer Fix - Fp32ToBf16 * Partial implementation for ACL backends to fix VTS failures !android-nn-driver:7477 Signed-off-by: Keith Davis <keith.davis@arm.com> Change-Id: I5fb18877f7ee32643e15a9818945356274bb401b
2022-05-05IVGCVSW-6127 ConstTensorsAsInput: DepthwiseConvolution2dCathal Corbett
!android-nn-driver:7418 * Update Front-end and Tools. * Updated Serializer, Deserializer and unit tests to reflect this. * Updated TfLiteDelegate, TfLiteParser and OnnxParser. * Change NNDriver to new API. * Updated Ref. * Neon and Cl backend partially completed (Backend.cpp files). * Added dynamic or constant input EndToEnd tests. * Added ConstantTensorAsInputMemeberVariableRedirect Optimization. Signed-off-by: Cathal Corbett <cathal.corbett@arm.com> Change-Id: Ib18b6c10a093042e165e25237dc04a4c67ba82da
2022-02-07IVGCVSW-6635 Move MemCopyTestImpl from acl to armnnTestUtils.Colm Donelan
* Move MemCopyTestImpl.hpp from src/backends/aclCommon/test/ to include/armnnTestutils. * Refactor MemCopyTests in aclCommon, cl and Neon. * Introduce RefMemCopyTests to exercise this utility in x86 builds. Signed-off-by: Colm Donelan <colm.donelan@arm.com> Change-Id: I8824f013d3656658ed0a2904bb79384e3af68641
2021-05-24IVGCVSW-6069 Fold PAD into Depthwise ConvolutionTeresa Charlin
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com> Change-Id: Ib01629256309cfe17f341909d5b9bbbb09361422
2021-04-21Fold PAD into Pooling2d if possibleDiego Lopez Recas
Some models would add a PAD layer before a pooling when they can't express their padding configuration as SAME or VALID. ArmNN can potentially merge the two merge the two because pooling layers are described with explicit padding. The merge is possible if the extra padding is neutral in the combined pooling operation. A merged operation can only fuse paddings in the dimensions that accept explicit padding in a pooling operation, i.e. the spatial dimensions. Signed-off-by: Diego Lopez Recas <diego.lopez.recas@gmail.com> Signed-off-by: Colm Donelan <Colm.Donelan@arm.com> Change-Id: Icd54718dcd9e797c923456b7fa6e0213e288e668
2021-03-23Revert "Fold PAD into Pooling2d"Jim Flynn
This reverts commit 51ce7d487c761358de105f82ff90553570aedac0. Reason for revert: https://jira.arm.com/browse/IVGCVSW-5798 LargeGraph_TENSOR_FLOAT32 CTS tests failures Change-Id: Ib031a47f605340b2202ecf074ce96a8b54c51075
2021-03-22Fold PAD into Pooling2dDiego Lopez Recas
Some models would add a PAD layer before a pooling when they can't express their padding configuration as SAME or VALID. Arm NN can merge the two because pooling layers are described with explicit padding. Signed-off-by: Diego Lopez Recas <diego.lopez.recas@gmail.com> Change-Id: Id048186db6a005e0257bfbc1406c3b0dab2cdd58
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