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matrix of GEMM/GEMMLowp
Change-Id: I77f2bfcc5d170bcc2428a2f27104942c1ec877d7
Reviewed-on: https://review.mlplatform.org/375
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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matrix of GEMM/GEMMLowp
Change-Id: I8c5fd4c8bcdffda1522c83158981ed92baa045f4
Reviewed-on: https://review.mlplatform.org/364
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
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Change-Id: Ia7fb128e1f3944d0d831e1d125a6db3e1d257106
Reviewed-on: https://review.mlplatform.org/355
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
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Change-Id: I14cea30ffa9ca735941b559bb272b8c476814a34
Reviewed-on: https://review.mlplatform.org/338
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
Reviewed-by: Isabella Gottardi <isabella.gottardi@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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-Removes shift from depth conversion tests.
-Changes Cast tolerance between float conversions to zero
Change-Id: I6c456f7d910eb3c02069f1e4d5df7b257d6d784e
Reviewed-on: https://review.mlplatform.org/341
Reviewed-by: Anthony Barbier <Anthony.barbier@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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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: Id74cc7ba8e5cabee6acd3798d4779f88b1f00a9b
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Change-Id: I49b2e8b4200c9ed654736d9451e4ab9c073b4b10
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Change-Id: I29e35024e29781a6b943b568abec9c73649215e6
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Change-Id: I6ee2c0b670727fc808fa636c53ddfaec3a0036c9
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Change-Id: I49f1d865f5e7562f1d80db849353a89ef77e6a9e
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Output of Priorbox should be independent of the input
data layout and should always be in NCHW format
Change-Id: Ie80cd4e51c78945b158c0db1af1923bdf8d7ea7b
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Fixes for:
- ReduceMean, reduction on the X axis for FP16 with 8 elements was
performed only up to a certain point. The fix now takes into account the
number of elements of the vector and does as many reductions as
necessary.
- YOLOLayer, activation for FP16 has to be performed on 32 bits until
the FP16 approximations is fixed.
Change-Id: I75373f4edd37de476e6fe1a56de3ef386b65c619
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NHWC reduction on 0 axis requires a lot of memory. Testing only
axis 1 and 2 for now.
Change-Id: I82e16a27b6dfc6b426e6294cde63c3d88cb41a09
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-Simplifies import memory interface
-Changes the used of void** handles with appropriate interfaces.
Change-Id: I5918c855c11f46352058864623336b352162a4b7
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-Adds NHWC support for FP16
Change-Id: I61addf8efecf511ac8cd5f8aa9afc3e09c476aaf
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Changed random distribution to [1, 2] as values close to
zero generate mismatches.
Change-Id: I4a00fc4f445b123dea624dd8459efce945f06126
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Change-Id: I770b044b67d93510ef65e556905135b34be7ea0a
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Change-Id: I6e7dee8bd615a5eff01c523f208a218574ee5eab
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kernels
Change-Id: I98183f95814442b6f3dbb67a1bdae99df05b9b01
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- Fixing a bug for which we did not scale the boxes before transforming them
- Adding the correct_transform_coords option to BoundingBoxTransformInfo
Change-Id: I40281254bcf87e7c8583c119e99562414fe59822
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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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Increase tolerance for FP16
Change-Id: I88f95da5471bbceb7449f453e2e33cf0bc4da23e
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Change-Id: I99e1c3939cfea4b9cb0ddfa313706f31b213ca89
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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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Added test cases to exercise the code path where the reshaping of B is performed on the fly.
Change-Id: Ifa4348e1054dc0019be3927f482adf64b18fd554
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Change-Id: Ib0798cc17496b7817f5b5769b25d98913a33a69d
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Change-Id: I5bf5d751ec7c02d96c26a769f49d03ea23a248b7
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Change-Id: Ibab049f09413258c99335b7da6b151530a1bd136
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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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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: I6d5f91579850906e1eb973ff6c5612195255e631
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Change-Id: I807ef84dbf893bd401dcac5c0fa3a4ee49aabc66
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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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The tab characters were corrupting the output JSON file of arm_compute_validation
Change-Id: I8792fd0e02393aef60341552b428111e969a3927
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Reduce the amount of precommit tests run in DirectConvolution,
Deconvolution and Pooling. Proper investigation scheduled for later.
Change-Id: Idc2510cf6877e7a605cead84f384852b609e3216
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/156466
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com>
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Also added the test case reported by ArmNN.
Change-Id: I9fe9a1b4f74267a3346529f3a597b37486593c4a
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155914
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
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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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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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