xref: /aosp_15_r20/external/ComputeLibrary/tests/validation/reference/Unstack.cpp (revision c217d954acce2dbc11938adb493fc0abd69584f3)
1 /*
2  * Copyright (c) 2018 Arm Limited.
3  *
4  * SPDX-License-Identifier: MIT
5  *
6  * Permission is hereby granted, free of charge, to any person obtaining a copy
7  * of this software and associated documentation files (the "Software"), to
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10  * sell copies of the Software, and to permit persons to whom the Software is
11  * furnished to do so, subject to the following conditions:
12  *
13  * The above copyright notice and this permission notice shall be included in all
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15  *
16  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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22  * SOFTWARE.
23  */
24 #include "Unstack.h"
25 
26 #include "tests/validation/Helpers.h"
27 
28 namespace arm_compute
29 {
30 namespace test
31 {
32 namespace validation
33 {
34 namespace reference
35 {
36 namespace
37 {
expand_coordinates(Coordinates in_coord,size_t axis,size_t slice,size_t num_dimensions)38 inline Coordinates expand_coordinates(Coordinates in_coord, size_t axis, size_t slice, size_t num_dimensions)
39 {
40     /*
41         Reconstruct input_coord to read the corresponding value from the correct slice. This is done by adding an extra dimension
42         to the coordinates and shuffling around the values based on the info below.
43 
44         For example, if input tensor shape is (X, Y, Z, W);
45 
46         If axis == 0, each slice will have the shape (Y, Z, W) and there will be X slices
47 
48         If axis == 1, each slice will have the shape (X, Z, W) and there will be Y slices.
49     */
50     Coordinates expanded_coord;
51     expanded_coord.set_num_dimensions(num_dimensions);
52     expanded_coord.set(axis, slice);
53     for(size_t k = 0; k < axis; ++k)
54     {
55         expanded_coord.set(k, in_coord[k]);
56     }
57     for(size_t k = axis + 1; k < num_dimensions; ++k)
58     {
59         expanded_coord.set(k, in_coord[k - 1]);
60     }
61     return expanded_coord;
62 }
63 
64 template <typename T>
get_slice(const SimpleTensor<T> & input_tensor,size_t axis,size_t slice)65 SimpleTensor<T> get_slice(const SimpleTensor<T> &input_tensor, size_t axis, size_t slice)
66 {
67     TensorShape out_shape = input_tensor.shape();
68     out_shape.remove_dimension(axis);
69 
70     const size_t unpacked_num_dimensions(input_tensor.shape().num_dimensions());
71 
72     SimpleTensor<T> output{ out_shape, input_tensor.data_type() };
73 
74     Window win;
75     win.use_tensor_dimensions(out_shape);
76     execute_window_loop(win, [&](const Coordinates & id)
77     {
78         const Coordinates input_coords     = expand_coordinates(id, axis, slice, unpacked_num_dimensions);
79         *reinterpret_cast<T *>(output(id)) = *reinterpret_cast<const T *>(input_tensor(input_coords));
80     });
81 
82     return output;
83 }
84 } // namespace
85 
86 template <typename T>
unstack(const SimpleTensor<T> & input_tensor,std::vector<SimpleTensor<T>> & output_tensors,int axis)87 std::vector<SimpleTensor<T>> unstack(const SimpleTensor<T> &input_tensor, std::vector<SimpleTensor<T>> &output_tensors, int axis)
88 {
89     // Wrap around negative values
90     const unsigned int axis_u = wrap_around(axis, static_cast<int>(input_tensor.shape().num_dimensions()));
91     ARM_COMPUTE_ERROR_ON(axis_u >= input_tensor.shape().num_dimensions());
92     for(size_t k = 0; k < output_tensors.size(); ++k)
93     {
94         SimpleTensor<T>      &output    = output_tensors[k];
95         const SimpleTensor<T> kth_slice = get_slice(input_tensor, axis_u, k);
96         output                          = copy_tensor<T>(kth_slice);
97     }
98     return output_tensors;
99 }
100 
101 template std::vector<SimpleTensor<float>> unstack(const SimpleTensor<float> &input_tensor, std::vector<SimpleTensor<float>> &output_tensors, int axis);
102 template std::vector<SimpleTensor<half>> unstack(const SimpleTensor<half> &input_tensor, std::vector<SimpleTensor<half>> &output_tensors, int axis);
103 template std::vector<SimpleTensor<uint8_t>> unstack(const SimpleTensor<uint8_t> &input_tensor, std::vector<SimpleTensor<uint8_t>> &output_tensors, int axis);
104 
105 } // namespace reference
106 } // namespace validation
107 } // namespace test
108 } // namespace arm_compute
109