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Change-Id: I8b59b9b94cbd132e1ff5157a4c59882719e12e3b
Reviewed-on: https://review.mlplatform.org/327
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
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Change-Id: I827b26239043a9e90d26c2583122648d2a45303a
Reviewed-on: https://review.mlplatform.org/317
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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Change-Id: Id0d4a07af24e2331161996083b0c1bab072bd405
Reviewed-on: https://review.mlplatform.org/322
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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Change-Id: I9e6e43a5839d04c2e4b4552c05446efb0a5074cf
Reviewed-on: https://review.mlplatform.org/232
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
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Change-Id: Ibdb7d875f8ff89bc210c63d389abef1ea1fd51d5
Reviewed-on: https://review.mlplatform.org/330
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
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Change-Id: Ic6a1f55f14d53896725afe426bc2e2acb1546589
Reviewed-on: https://review.mlplatform.org/343
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
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Change-Id: I13f6e4c600f39355f69e015409bf30dafdc5e3aa
Reviewed-on: https://review.mlplatform.org/332
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
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Change-Id: I3de6bb33746d52f8d8c337ab7776eccee8c205fb
Reviewed-on: https://review.mlplatform.org/328
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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Change-Id: Ice653e48211053bd3cd20a693bd76de6b4efc370
Reviewed-on: https://review.mlplatform.org/270
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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Change-Id: I7eae2e55cc0b0b7bbebb7617299daaca6f75f40c
Reviewed-on: https://review.mlplatform.org/292
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
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INESimpleFunctionNoBorder
Change-Id: Ia9fdc75b23e9a6208058f8406fb7b5fcd917de2c
Reviewed-on: https://review.mlplatform.org/311
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
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Adds support for Equal,NotEqual,Less,LessEqual,Greater,GreaterEqual
Change-Id: If0cdf4aae7f95c94709b195eee485f6663f45909
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Change-Id: I2a18f0acea382960a8bc71a8f56928a5998f0dd6
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Change-Id: Icadce1298f9e52dc6f4b0adc1556ede9dd9a2a2e
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Change-Id: I49b2e8b4200c9ed654736d9451e4ab9c073b4b10
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-Fixes NEWinogradConvolution memory manager usage
-Moves allocations in prepare staged for GEMMDispatchWrapper.
Change-Id: Ic1c709ee473eb4968f5a081f2bc26960f882f8db
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Change-Id: I29e35024e29781a6b943b568abec9c73649215e6
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Change-Id: I6ee2c0b670727fc808fa636c53ddfaec3a0036c9
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Change-Id: I62d937533967b29505d3ac8a51b513f0c6de8cd0
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-Simplifies import memory interface
-Changes the used of void** handles with appropriate interfaces.
Change-Id: I5918c855c11f46352058864623336b352162a4b7
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Change-Id: I6e7dee8bd615a5eff01c523f208a218574ee5eab
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kernels
Change-Id: I98183f95814442b6f3dbb67a1bdae99df05b9b01
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BoxWithNMSLimitKernel
COMPMID-1792: Accuracy issue in CLGenerateProposals
This patch does the following:
- Some fixes for GenerateProposals function and tests
- Adapting BoxWithNMSLimitKernel to only accept U32 tensors as keeps_size
- Update 3rdparty
- Adds a small tolerance for a GenerateProposals test
Change-Id: Ia8ec1cdfe941fe05003645e86deb9ea6a6044d74
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Introduced F32 accumulation for F16 winograd gemm and output transform
WinogradConvolution will be available for F16 only if fast math flag is enabled
Change-Id: I215593c205236a0f9669218437bb40b184ec6a4f
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Change-Id: I99e1c3939cfea4b9cb0ddfa313706f31b213ca89
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Change-Id: Ibc8d903c8d3c97b51dc8a3344197b56ad9d6c00e
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Change-Id: Ia8d4e46ce5d9bb366af15726bc208dc14583c6ae
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Change-Id: I69e995973597ba3927d29e4f6ed5438560e53d77
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In case of CIFG optimisation scratch buffer should have a size of
[batch_size, num_units * 3] else [batch_size, num_units * 4].
Change-Id: I43e46f7b52e791472f1196f36e9142240ba76c5c
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Change-Id: Ib0798cc17496b7817f5b5769b25d98913a33a69d
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Change-Id: I5bf5d751ec7c02d96c26a769f49d03ea23a248b7
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and 8 tensors (Part 1)
Creating special cases for concatening 2 and 4 tensors.
Change-Id: I6a739a494ae45011acb65369e353f9ef96970b90
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NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
Change-Id: I1d5bc4d24059917f9ddef0873dd3043b1f2320a8
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-Use raw string literals in regexp in CPUUtils.cpp
-Avoid implicit cast bool->int
Change-Id: I45a403ab8d0be02bb8dec267fe59545ad1074292
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Change-Id: I93b14106cda8a1f640cf5acf120d31e2ebdaf495
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Some systems don't have enough memory to run the VGG networks, for example
on systems with only 2GB memory the VGG example fails throwing a bad_alloc exception.
This patch introduces the concept of global memory policy in ACL, the policy
is a mechanism which could be used by the library's functions to try to reduce
memory consumption on systems with limited memory.
In this specific case the VGG examples set the policy to MINIMIZE. The GEMM
function checks if the policy is MINIMIZE and in this case does not use the
pretransposed weights path as this requires considerable more memory.
Change-Id: I53abc3c9c64d045d8306793ffc9d24b28e228b7b
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Change-Id: I57bbdbef85d1f6e8cf1d13324f9cc38a3e3f0cc3
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-Uses output quantization information for the activation layer.
-Updates checks for BoundedRelu at CL side.
Change-Id: I0447860e90f1c89b67b9ace3c8daad713f6c64e0
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Change-Id: I953f3b63aa4910650a1a3f6faea31beb4f6f376a
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CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat
Since we perform an element-wise operation, it is not necessary to pass the output_depth3d.
Change-Id: Ibfa07a0706e902acf59b444aa61e18a348162ea9
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The issue was related to CLIm2Col when the number of input channels was less than
the number of elements processed by each thread.
The bug has been fixed in the validate_and_configure_window() function setting the correct number of elements accessed
in the output tensor.
Also fixed an issue GEMM3D when we have a single output channel
Change-Id: I094292d0c7662599c4a4c3916ec5f5821df5faef
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Change-Id: I86679adff556b6ffc9929b35cbf1b59b3958bdb1
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Change-Id: I6d5f91579850906e1eb973ff6c5612195255e631
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Change-Id: I68c648a5246fcdc67a496602089f93d65eb1d601
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ArmNN reported an issue with padding in CLLSTMLayer. This was due to the fact
that some tensors were allocated before they were passed to some configure
functions which attempted to change the padding requirement on already allocated
memory.
Also, increase tolerance on number of mismatches for CLBBoxTransform FP16.
Change-Id: Iad75b012be895693d0e553f3ab85f1ca7144e882
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Change-Id: I5aae537372bf797fbb2a2bae81038f8963b041a9
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Change-Id: Iac6a95ba7f388e65b7f1c8865c3e9bf289b233ea
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155490
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
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Change-Id: I0cf221c706c3d957423941d3aa9a9262dcb00c00
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155593
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Anthony Barbier <anthony.barbier@arm.com>
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OpenCL
COMPMID-1424 - Add dot product support for CLDepthwise QASYMM8 3x3 NHWC non-unit stride
With this patch we are able to improve the performance of MobileNet v1-qasymm8 by 37 %
Tried to use the dot product instruction in CLDepthwise QASYMM8 3x3 NHWC non-unit stride
but I have not seen any benefit (maybe because we have few arithemtic operation and we
do not have more load instructions). However Depthwise convolution has been improved by
30%
Change-Id: Id768a99c2e53a04276707e427af5d0ec93419ada
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155082
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
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Change-Id: Ie1bcdd9dca2dc3b26003790a19cc80bb953385b2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155373
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
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