1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
|
/*
* Copyright (c) 2019-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/NEON/kernels/NESpaceToBatchLayerKernel.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <arm_neon.h>
#include <cstdint>
using namespace arm_compute::misc::shape_calculator;
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *block_info, const ITensorInfo *padddings, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, block_info, padddings, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(block_info, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
ARM_COMPUTE_RETURN_ERROR_ON(block_info->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(padddings->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON(padddings->tensor_shape()[1] != block_info->tensor_shape()[0]);
// Validate output if initialized
if(output->total_size() != 0)
{
const DataLayout data_layout = input->data_layout();
const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_channel] != output->tensor_shape()[idx_channel]);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
return Status{};
}
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,
const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN);
ARM_COMPUTE_RETURN_ERROR_ON(block_shape_x < 1 || block_shape_y < 1);
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
// Validate output if initialized
if(output->total_size() != 0)
{
const DataLayout data_layout = input->data_layout();
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const int idx_batch = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape()[idx_width] < padding_left.x() + padding_right.y());
ARM_COMPUTE_RETURN_ERROR_ON((input->tensor_shape()[idx_width] + padding_left.x() + padding_right.x()) % block_shape_x != 0);
ARM_COMPUTE_RETURN_ERROR_ON((input->tensor_shape()[idx_height] + padding_left.y() + padding_right.y()) % block_shape_y != 0);
ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape()[idx_channel] != output->tensor_shape()[idx_channel]);
ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape()[idx_batch] % (block_shape_x * block_shape_y) != 0);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
}
return Status{};
}
} // namespace
NESpaceToBatchLayerKernel::NESpaceToBatchLayerKernel()
: _input(nullptr), _block_shape(nullptr), _paddings(nullptr), _output(nullptr), _data_layout(DataLayout::UNKNOWN), _padding_left(), _block_shape_x(), _block_shape_y()
{
}
void NESpaceToBatchLayerKernel::configure(const ITensor *input, const ITensor *block_shape, const ITensor *paddings, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), block_shape->info(), paddings->info(), output->info()));
_input = input;
_block_shape = block_shape;
_paddings = paddings;
_output = output;
_data_layout = input->info()->data_layout();
// Configure kernel window
Window win = calculate_max_window(*output->info(), Steps());
ICPPKernel::configure(win);
}
void NESpaceToBatchLayerKernel::configure(const ITensor *input, const int block_shape_x, const int block_shape_y, const Size2D &padding_left, const Size2D &padding_right,
ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
TensorShape output_shape = misc::shape_calculator::compute_space_to_batch_shape(input->info(), block_shape_x, block_shape_y, padding_left, padding_right);
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info());
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_static(input->info(), block_shape_x, block_shape_y, padding_left, padding_right, output->info()));
_input = input;
_output = output;
_block_shape_x = block_shape_x;
_block_shape_y = block_shape_y;
_padding_left = padding_left;
_data_layout = input->info()->data_layout();
// Configure kernel window
Window win = calculate_max_window(*output->info(), Steps());
INEKernel::configure(win);
}
Status NESpaceToBatchLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *block_shape, const ITensorInfo *paddings, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, block_shape, paddings, output));
return Status{};
}
Status NESpaceToBatchLayerKernel::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)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_static(input, block_shape_x, block_shape_y, padding_left, padding_right, output));
return Status{};
}
void NESpaceToBatchLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window);
if(_block_shape != nullptr)
{
// Retrieve the block shapes dynamically
_block_shape_x = *(reinterpret_cast<const int *>(_block_shape->ptr_to_element(0)));
_block_shape_y = *(reinterpret_cast<const int *>(_block_shape->ptr_to_element(1)));
}
if(_paddings != nullptr)
{
const size_t pad_left_x = *reinterpret_cast<const size_t *>(_paddings->ptr_to_element({ 0, 0 }));
const size_t pad_left_y = *reinterpret_cast<const size_t *>(_paddings->ptr_to_element({ 1, 0 }));
_padding_left = Size2D(pad_left_x, pad_left_y);
}
const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
const int batch_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES);
const int element_size = _input->info()->element_size();
const size_t height = _input->info()->dimension(height_idx);
const size_t width = _input->info()->dimension(width_idx);
const size_t batch_size = _input->info()->dimension(batch_idx);
Window slice_out = window.first_slice_window_3D();
int batch_id = 0;
// Main loop for NCHW and NHWC
if(_data_layout == DataLayout::NCHW)
{
do
{
Iterator out(_output, slice_out);
execute_window_loop(slice_out, [&](const Coordinates & id)
{
const size_t out_x = id.x();
const size_t out_y = id.y();
const size_t z = id.z();
const size_t pos_x = out_x * _block_shape_x + (batch_id / batch_size) % _block_shape_x;
const size_t pos_y = out_y * _block_shape_y + (batch_id / batch_size) / _block_shape_x;
if(pos_y >= _padding_left.y() && pos_y < _padding_left.y() + height && pos_x >= _padding_left.x() && pos_x < _padding_left.x() + width)
{
const int w = batch_id % batch_size;
const int in_x = pos_x - _padding_left.x();
const int in_y = pos_y - _padding_left.y();
Coordinates input_coords{ in_x, in_y, z, w };
memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
}
},
out);
++batch_id;
}
while(window.slide_window_slice_3D(slice_out));
}
else
{
do
{
Iterator out(_output, slice_out);
execute_window_loop(slice_out, [&](const Coordinates & id)
{
const size_t out_x = id.y();
const size_t out_y = id.z();
const size_t z = id.x();
const size_t pos_x = out_x * _block_shape_x + (batch_id / batch_size) % _block_shape_x;
const size_t pos_y = out_y * _block_shape_y + (batch_id / batch_size) / _block_shape_x;
if(pos_y >= _padding_left.y() && pos_y < _padding_left.y() + height && pos_x >= _padding_left.x() && pos_x < _padding_left.x() + width)
{
const int w = batch_id % batch_size;
const int in_x = pos_x - _padding_left.x();
const int in_y = pos_y - _padding_left.y();
Coordinates input_coords{ z, in_x, in_y, w };
memcpy(out.ptr(), _input->ptr_to_element(input_coords), element_size);
}
},
out);
++batch_id;
}
while(window.slide_window_slice_3D(slice_out));
}
}
} // namespace arm_compute
|
