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 "src/core/NEON/kernels/NESpaceToBatchLayerKernel.h"
25
26 #include "arm_compute/core/Helpers.h"
27 #include "arm_compute/core/ITensor.h"
28 #include "arm_compute/core/Types.h"
29 #include "arm_compute/core/Validate.h"
30 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
31 #include "src/core/NEON/wrapper/wrapper.h"
32 #include "src/core/helpers/AutoConfiguration.h"
33 #include "src/core/helpers/WindowHelpers.h"
34
35 #include <arm_neon.h>
36 #include <cstdint>
37
38 using namespace arm_compute::misc::shape_calculator;
39
40 namespace arm_compute
41 {
42 namespace
43 {
validate_arguments(const ITensorInfo * input,const ITensorInfo * block_info,const ITensorInfo * paddings,const ITensorInfo * output)44 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *block_info, const ITensorInfo *paddings, const ITensorInfo *output)
45 {
46 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, block_info, paddings, output);
47 ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
48 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(block_info, 1, DataType::S32);
49 ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
50 ARM_COMPUTE_RETURN_ERROR_ON(block_info->num_dimensions() > 1);
51 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(block_info->tensor_shape(), TensorShape{ 2 });
52 ARM_COMPUTE_RETURN_ERROR_ON(paddings->num_dimensions() > 2);
53 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(paddings->tensor_shape(), TensorShape{ 2, 2 });
54
55 // Validate output if initialized
56 if(output->total_size() != 0)
57 {
58 const DataLayout data_layout = input->data_layout();
59 const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
60 ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_channel] != output->tensor_shape()[idx_channel]);
61 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
62 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
63 }
64
65 return Status{};
66 }
validate_arguments_static(const ITensorInfo * input,const int block_shape_x,const int block_shape_y,const Size2D & padding_left,const Size2D & padding_right,const ITensorInfo * output)67 Status validate_arguments_static(const ITensorInfo *input, const int block_shape_x, const int block_shape_y, const Size2D &padding_left, const Size2D &padding_right,
68 const ITensorInfo *output)
69 {
70 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
71 ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
72 ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
73 ARM_COMPUTE_RETURN_ERROR_ON(block_shape_x < 1 || block_shape_y < 1);
74
75 // Validate output if initialized
76 if(output->total_size() != 0)
77 {
78 TensorShape expected_output_shape = misc::shape_calculator::compute_space_to_batch_shape(input, block_shape_x, block_shape_y, padding_left, padding_right);
79 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), expected_output_shape);
80 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
81 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
82 }
83
84 return Status{};
85 }
86 } // namespace
87
NESpaceToBatchLayerKernel()88 NESpaceToBatchLayerKernel::NESpaceToBatchLayerKernel()
89 : _input(nullptr), _block_shape(nullptr), _paddings(nullptr), _output(nullptr), _data_layout(DataLayout::UNKNOWN), _padding_left(), _block_shape_x(), _block_shape_y()
90 {
91 }
92
configure(const ITensor * input,const ITensor * block_shape,const ITensor * paddings,ITensor * output)93 void NESpaceToBatchLayerKernel::configure(const ITensor *input, const ITensor *block_shape, const ITensor *paddings, ITensor *output)
94 {
95 ARM_COMPUTE_ERROR_ON_NULLPTR(input, block_shape, paddings, output);
96 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), block_shape->info(), paddings->info(), output->info()));
97
98 _input = input;
99 _block_shape = block_shape;
100 _paddings = paddings;
101 _output = output;
102 _data_layout = input->info()->data_layout();
103
104 // Configure kernel window
105 Window win = calculate_max_window(*output->info(), Steps());
106 ICPPKernel::configure(win);
107 }
108
configure(const ITensor * input,const int block_shape_x,const int block_shape_y,const Size2D & padding_left,const Size2D & padding_right,ITensor * output)109 void NESpaceToBatchLayerKernel::configure(const ITensor *input, const int block_shape_x, const int block_shape_y, const Size2D &padding_left, const Size2D &padding_right,
110 ITensor *output)
111 {
112 ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
113
114 TensorShape output_shape = misc::shape_calculator::compute_space_to_batch_shape(input->info(), block_shape_x, block_shape_y, padding_left, padding_right);
115 auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info());
116
117 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_static(input->info(), block_shape_x, block_shape_y, padding_left, padding_right, output->info()));
118
119 _input = input;
120 _output = output;
121 _block_shape_x = block_shape_x;
122 _block_shape_y = block_shape_y;
123 _padding_left = padding_left;
124 _data_layout = input->info()->data_layout();
125
126 // Configure kernel window
127 Window win = calculate_max_window(*output->info(), Steps());
128 INEKernel::configure(win);
129 }
130
validate(const ITensorInfo * input,const ITensorInfo * block_shape,const ITensorInfo * paddings,const ITensorInfo * output)131 Status NESpaceToBatchLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *block_shape, const ITensorInfo *paddings, const ITensorInfo *output)
132 {
133 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, block_shape, paddings, output));
134 return Status{};
135 }
validate(const ITensorInfo * input,const int block_shape_x,const int block_shape_y,const Size2D & padding_left,const Size2D & padding_right,const ITensorInfo * output)136 Status NESpaceToBatchLayerKernel::validate(const ITensorInfo *input, const int block_shape_x, const int block_shape_y, const Size2D &padding_left, const Size2D &padding_right,
137 const ITensorInfo *output)
138 {
139 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_static(input, block_shape_x, block_shape_y, padding_left, padding_right, output));
140 return Status{};
141 }
142
run(const Window & window,const ThreadInfo & info)143 void NESpaceToBatchLayerKernel::run(const Window &window, const ThreadInfo &info)
144 {
145 ARM_COMPUTE_UNUSED(info);
146 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
147 ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window);
148
149 if(_block_shape != nullptr)
150 {
151 // Retrieve the block shapes dynamically
152 _block_shape_x = *(reinterpret_cast<const int *>(_block_shape->ptr_to_element(0)));
153 _block_shape_y = *(reinterpret_cast<const int *>(_block_shape->ptr_to_element(1)));
154 }
155
156 if(_paddings != nullptr)
157 {
158 const size_t pad_left_x = *reinterpret_cast<const size_t *>(_paddings->ptr_to_element({ 0, 0 }));
159 const size_t pad_left_y = *reinterpret_cast<const size_t *>(_paddings->ptr_to_element({ 1, 0 }));
160 _padding_left = Size2D(pad_left_x, pad_left_y);
161 }
162 const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
163 const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
164 const int batch_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
165 const int element_size = _input->info()->element_size();
166
167 const size_t height = _input->info()->dimension(height_idx);
168 const size_t width = _input->info()->dimension(width_idx);
169 const size_t batch_size = _input->info()->dimension(batch_idx);
170
171 Window slice_out = window.first_slice_window_3D();
172
173 int batch_id = 0;
174
175 // Main loop for NCHW and NHWC
176 if(_data_layout == DataLayout::NCHW)
177 {
178 do
179 {
180 Iterator out(_output, slice_out);
181 execute_window_loop(slice_out, [&](const Coordinates & id)
182 {
183 const size_t out_x = id.x();
184 const size_t out_y = id.y();
185 const size_t z = id.z();
186 const size_t pos_x = out_x * _block_shape_x + (batch_id / batch_size) % _block_shape_x;
187 const size_t pos_y = out_y * _block_shape_y + (batch_id / batch_size) / _block_shape_x;
188 if(pos_y >= _padding_left.y() && pos_y < _padding_left.y() + height && pos_x >= _padding_left.x() && pos_x < _padding_left.x() + width)
189 {
190 const int w = batch_id % batch_size;
191 const int in_x = pos_x - _padding_left.x();
192 const int in_y = pos_y - _padding_left.y();
193 Coordinates input_coords{ in_x, in_y, z, w };
194 memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
195 }
196 },
197 out);
198 ++batch_id;
199 }
200 while(window.slide_window_slice_3D(slice_out));
201 }
202 else
203 {
204 do
205 {
206 Iterator out(_output, slice_out);
207 execute_window_loop(slice_out, [&](const Coordinates & id)
208 {
209 const size_t out_x = id.y();
210 const size_t out_y = id.z();
211 const size_t z = id.x();
212 const size_t pos_x = out_x * _block_shape_x + (batch_id / batch_size) % _block_shape_x;
213 const size_t pos_y = out_y * _block_shape_y + (batch_id / batch_size) / _block_shape_x;
214 if(pos_y >= _padding_left.y() && pos_y < _padding_left.y() + height && pos_x >= _padding_left.x() && pos_x < _padding_left.x() + width)
215 {
216 const int w = batch_id % batch_size;
217 const int in_x = pos_x - _padding_left.x();
218 const int in_y = pos_y - _padding_left.y();
219 Coordinates input_coords{ z, in_x, in_y, w };
220 memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
221 }
222 },
223 out);
224 ++batch_id;
225 }
226 while(window.slide_window_slice_3D(slice_out));
227 }
228 }
229 } // namespace arm_compute
230