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author | Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com> | 2018-07-02 09:13:49 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:54:10 +0000 |
commit | 014333d73883c3872e458cedda5ccef586a7ccd4 (patch) | |
tree | 0f28bbc1ab769993af91b40e4584061f6ed6d3fa /tests/GLES_COMPUTE | |
parent | de01468bbfff3a7d8bcbba3bfdf5698fb2e3b267 (diff) | |
download | ComputeLibrary-014333d73883c3872e458cedda5ccef586a7ccd4.tar.gz |
COMPMID-970 : Remove QS8 / QS16 support
Removed Fixed point position arguments from test sources
Change-Id: I8343724723b71611fd501ed34de0866d3fb60e7e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/136382
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'tests/GLES_COMPUTE')
-rw-r--r-- | tests/GLES_COMPUTE/Helper.h | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/tests/GLES_COMPUTE/Helper.h b/tests/GLES_COMPUTE/Helper.h index 65f992623a..c04c6b608a 100644 --- a/tests/GLES_COMPUTE/Helper.h +++ b/tests/GLES_COMPUTE/Helper.h @@ -37,17 +37,16 @@ namespace test { /** Helper to create an empty tensor. * - * @param[in] shape Desired shape. - * @param[in] data_type Desired data type. - * @param[in] num_channels (Optional) It indicates the number of channels for each tensor element - * @param[in] fixed_point_position (Optional) Fixed point position that expresses the number of bits for the fractional part of the number when the tensor's data type is QS8 or QS16. + * @param[in] shape Desired shape. + * @param[in] data_type Desired data type. + * @param[in] num_channels (Optional) It indicates the number of channels for each tensor element * * @return Empty @ref GCTensor with the specified shape and data type. */ -inline GCTensor create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1, int fixed_point_position = 0) +inline GCTensor create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1) { GCTensor tensor; - tensor.allocator()->init(TensorInfo(shape, num_channels, data_type, fixed_point_position)); + tensor.allocator()->init(TensorInfo(shape, num_channels, data_type)); return tensor; } |