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authorVidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com>2018-07-02 09:13:49 +0100
committerAnthony Barbier <anthony.barbier@arm.com>2018-11-02 16:54:10 +0000
commit014333d73883c3872e458cedda5ccef586a7ccd4 (patch)
tree0f28bbc1ab769993af91b40e4584061f6ed6d3fa /tests/GLES_COMPUTE
parentde01468bbfff3a7d8bcbba3bfdf5698fb2e3b267 (diff)
downloadComputeLibrary-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.h11
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;
}