1 /*
2 * Copyright (c) 2019-2020 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
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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
14 * copies or substantial portions of the Software.
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,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/core/CL/OpenCL.h"
25 #include "arm_compute/core/Types.h"
26 #include "arm_compute/runtime/CL/CLHelpers.h"
27 #include "arm_compute/runtime/CL/CLScheduler.h"
28 #include "arm_compute/runtime/CL/Utils.h"
29 #include "arm_compute/runtime/CL/functions/CLPermute.h"
30 #include "utils/Utils.h"
31
32 using namespace arm_compute;
33 using namespace utils;
34
35 namespace
36 {
37 } // namespace
38
39 class CLCacheExample : public Example
40 {
41 public:
42 CLCacheExample() = default;
43
do_setup(int argc,char ** argv)44 bool do_setup(int argc, char **argv) override
45 {
46 std::cout << "Once the program has run and created the file cache.bin, rerun with --restore_cache." << std::endl;
47 CLScheduler::get().default_init();
48
49 if(argc > 1)
50 {
51 std::string argv1 = argv[1];
52 std::transform(argv1.begin(), argv1.end(), argv1.begin(), ::tolower);
53 if(argv1 == "--restore_cache")
54 {
55 // Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed
56 // compilation won't be required.
57 restore_program_cache_from_file();
58 }
59 else
60 {
61 std::cout << "Unkown option " << argv1 << std::endl;
62 }
63 }
64
65 // Initialise shapes
66 init_tensor(TensorShape(8U, 4U, 2U), tensor_nchw, DataType::U8, DataLayout::NCHW);
67 init_tensor(TensorShape(2U, 8U, 4U), tensor_nhwc, DataType::U8, DataLayout::NHWC);
68 init_tensor(TensorShape(8U, 4U, 2U), tensor_nchw_result, DataType::U8, DataLayout::NCHW);
69
70 // Create the permutation vector to turn a NCHW tensor to NHWC.
71 // The input tensor is NCHW, which means that the fastest changing coordinate is W=8U.
72 // For permutation vectors the fastest changing coordinate is the one on the left too.
73 // Each element in the permutation vector specifies a mapping from the source tensor to the destination one, thus if we
74 // use 2U in the permutation vector's first element we are telling the function to move the channels to the fastest
75 // changing coordinate in the destination tensor.
76
77 const PermutationVector vector_nchw_to_nhwc(2U, 0U, 1U);
78 permute_nhwc.configure(&tensor_nchw, &tensor_nhwc, vector_nchw_to_nhwc);
79
80 // Allocate and fill tensors
81 tensor_nhwc.allocator()->allocate();
82 tensor_nchw.allocator()->allocate();
83 fill_tensor(tensor_nchw);
84
85 // Demostrate autoconfigure for the output tensor
86 const PermutationVector vector_nhwc_to_nchw(1U, 2U, 0U);
87 permute_nchw.configure(&tensor_nhwc, &tensor_nchw_result, vector_nhwc_to_nchw);
88 tensor_nchw_result.allocator()->allocate();
89
90 // Save the opencl kernels to a file
91 save_program_cache_to_file();
92
93 return true;
94 }
do_run()95 void do_run() override
96 {
97 permute_nhwc.run();
98 permute_nchw.run();
99 }
do_teardown()100 void do_teardown() override
101 {
102 }
103
104 private:
validate_result(CLTensor & reference,CLTensor & result)105 void validate_result(CLTensor &reference, CLTensor &result)
106 {
107 reference.map();
108 result.map();
109 Window window;
110 window.use_tensor_dimensions(reference.info()->tensor_shape());
111 Iterator it_ref(&reference, window);
112 Iterator it_res(&result, window);
113 execute_window_loop(window, [&](const Coordinates &)
114 {
115 assert(*reinterpret_cast<unsigned char *>(it_ref.ptr()) == *reinterpret_cast<unsigned char *>(it_res.ptr()));
116 },
117 it_ref, it_res);
118 reference.unmap();
119 result.unmap();
120 }
121
fill_tensor(CLTensor & tensor)122 void fill_tensor(CLTensor &tensor)
123 {
124 tensor.map();
125 Window window;
126 window.use_tensor_dimensions(tensor.info()->tensor_shape());
127 Iterator it_tensor(&tensor, window);
128 unsigned char val(0);
129 execute_window_loop(window, [&](const Coordinates &)
130 {
131 *reinterpret_cast<unsigned char *>(it_tensor.ptr()) = val++;
132 },
133 it_tensor);
134 tensor.unmap();
135 }
init_tensor(const TensorShape shape,CLTensor & tensor,DataType type,DataLayout layout)136 void init_tensor(const TensorShape shape, CLTensor &tensor, DataType type, DataLayout layout)
137 {
138 tensor.allocator()->init(TensorInfo(shape, 1, type).set_data_layout(layout));
139 }
140
141 CLTensor tensor_nchw{};
142 CLTensor tensor_nhwc{};
143 CLTensor tensor_nchw_result{};
144 CLPermute permute_nhwc{};
145 CLPermute permute_nchw{};
146 };
147
148 /** Main program creating an example that demostrates how to load precompiled kernels from a file.
149 *
150 * @param[in] argc Number of arguments
151 * @param[in] argv Arguments
152 */
main(int argc,char ** argv)153 int main(int argc, char **argv)
154 {
155 return utils::run_example<CLCacheExample>(argc, argv);
156 }
157