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| author | Manuel Bottini <manuel.bottini@arm.com> | 2019-04-08 13:18:00 +0100 |
|---|---|---|
| committer | Manuel Bottini <manuel.bottini@arm.com> | 2019-05-03 10:30:44 +0000 |
| commit | a788c2f7b143731704cdbc6a7f0016e4f38896d9 (patch) | |
| tree | bf8a3f9d3c61544466a4d64ca6ef1a120337b0f3 /tests/SimpleTensor.h | |
| parent | 01bbacb465da79d3b4d1a3f313b172fe295642f5 (diff) | |
| download | ComputeLibrary-a788c2f7b143731704cdbc6a7f0016e4f38896d9.tar.gz | |
COMPMID-2108: Fuse Activation Layer in CLDepthwiseConvolutionLayer3x3Kernels for F32
Change-Id: I39dd23696b6d8573e172a59b9e327b6a69886f08
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/973
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Usama Arif <usama.arif@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Diffstat (limited to 'tests/SimpleTensor.h')
| -rw-r--r-- | tests/SimpleTensor.h | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/tests/SimpleTensor.h b/tests/SimpleTensor.h index dd4a8bee2c..f0e9b15021 100644 --- a/tests/SimpleTensor.h +++ b/tests/SimpleTensor.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -280,7 +280,7 @@ SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_cha _quantization_info(quantization_info), _data_layout(data_layout) { - _buffer = support::cpp14::make_unique<T[]>(num_elements() * this->num_channels()); + _buffer = support::cpp14::make_unique<T[]>(this->_shape.total_size() * _num_channels); } template <typename T> @@ -293,8 +293,8 @@ SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor) _quantization_info(tensor.quantization_info()), _data_layout(tensor.data_layout()) { - _buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * num_channels()); - std::copy_n(tensor.data(), num_elements() * num_channels(), _buffer.get()); + _buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * _num_channels); + std::copy_n(tensor.data(), this->_shape.total_size() * _num_channels, _buffer.get()); } template <typename T> |