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| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-09-04 20:20:56 +0100 |
|---|---|---|
| committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-09-07 12:37:27 +0000 |
| commit | 8a14b2ca62c43a2691066ce374949c2501ae8315 (patch) | |
| tree | dc262d9b720ca1caadcc39ee065e66502889e354 /src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp | |
| parent | 903f8cca78502a9e3835e6ec42caa1f816274600 (diff) | |
| download | ComputeLibrary-8a14b2ca62c43a2691066ce374949c2501ae8315.tar.gz | |
COMPMID-3748: Compiler issue with Bfloat16 on gcc8
Treat bf16 memory on memset as raw memory by casting to void*. This
hides the class-memaccess warning and is safe for the current class
layout of arm_compute::bfloat16
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I5e242827d3737b4491d29abe7570eefee5b6edc1
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3928
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp b/src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp index 618a1baf07..d012cbfded 100644 --- a/src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp +++ b/src/core/NEON/kernels/NEDepthToSpaceLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 Arm Limited. + * Copyright (c) 2019-2020 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -71,7 +71,7 @@ NEDepthToSpaceLayerKernel::NEDepthToSpaceLayerKernel() void NEDepthToSpaceLayerKernel::configure(const ITensor *input, ITensor *output, int32_t block_shape) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - TensorShape output_shape = compute_depth_to_space_shape(input->info(), block_shape); + TensorShape output_shape = compute_depth_to_space_shape(input->info()->tensor_shape(), input->info()->data_layout(), block_shape); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); |