aboutsummaryrefslogtreecommitdiff
path: root/tests/NEON/Helper.h
diff options
context:
space:
mode:
Diffstat (limited to 'tests/NEON/Helper.h')
-rw-r--r--tests/NEON/Helper.h43
1 files changed, 2 insertions, 41 deletions
diff --git a/tests/NEON/Helper.h b/tests/NEON/Helper.h
index e64ac27ef3..e77615406e 100644
--- a/tests/NEON/Helper.h
+++ b/tests/NEON/Helper.h
@@ -24,11 +24,9 @@
#ifndef __ARM_COMPUTE_TEST_NEON_HELPER_H__
#define __ARM_COMPUTE_TEST_NEON_HELPER_H__
-#include "Globals.h"
-#include "TensorLibrary.h"
-
#include "arm_compute/runtime/Array.h"
-#include "arm_compute/runtime/Tensor.h"
+
+#include <algorithm>
namespace arm_compute
{
@@ -36,43 +34,6 @@ namespace test
{
namespace neon
{
-/** 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.
- *
- * @return Empty @ref Tensor with the specified shape and data type.
- */
-inline Tensor create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1, int fixed_point_position = 0)
-{
- Tensor tensor;
- tensor.allocator()->init(TensorInfo(shape, num_channels, data_type, fixed_point_position));
-
- return tensor;
-}
-
-/** Helper to create an empty tensor.
- *
- * @param[in] name File name from which to get the dimensions.
- * @param[in] data_type Desired data type.
- * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers
- *
- * @return Empty @ref Tensor with the specified shape and data type.
- */
-inline Tensor create_tensor(const std::string &name, DataType data_type, int fixed_point_position = 0)
-{
- constexpr unsigned int num_channels = 1;
-
- const RawTensor &raw = library->get(name);
-
- Tensor tensor;
- tensor.allocator()->init(TensorInfo(raw.shape(), num_channels, data_type, fixed_point_position));
-
- return tensor;
-}
-
template <typename T>
Array<T> create_array(const std::vector<T> &v)
{