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
|
/*
* Copyright (c) 2017-2019 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/NEMinMaxLayerKernel.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include <algorithm>
#include <arm_neon.h>
#include <climits>
#include <cstddef>
using namespace arm_compute::misc::shape_calculator;
namespace arm_compute
{
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() < 3);
if(output->tensor_shape().total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
TensorShape output_shape = compute_min_max_shape(input);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
}
return Status{};
}
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
TensorShape output_shape = compute_min_max_shape(input);
// Output auto initialization if not yet initialized
auto_init_if_empty(*output, output_shape, 1, input->data_type());
constexpr unsigned int num_elems_processed_per_iteration = 1;
// Configure kernel window
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output, 0, 2);
bool window_changed = update_window_and_padding(win, input_access, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
return std::make_tuple(err, win);
}
} // namespace
NEMinMaxLayerKernel::NEMinMaxLayerKernel()
: _input(nullptr), _output(nullptr), _mtx()
{
}
void NEMinMaxLayerKernel::configure(const ITensor *input, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
_input = input;
_output = output;
auto win_config = validate_and_configure_window(input->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
INEKernel::configure(std::get<1>(win_config));
}
Status NEMinMaxLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get())));
return Status{};
}
void NEMinMaxLayerKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
const int x_start = window.x().start();
const int x_end = window.x().end();
Window window_output;
window_output.use_tensor_dimensions(_output->info()->tensor_shape());
window_output.set(Window::DimX, Window::Dimension(0, 1, 1));
// Handle X dimension manually to split into two loops
// First one will use vector operations, second one processes the left over pixels
Window window_input(window);
window_input.set(Window::DimX, Window::Dimension(0, 1, 1));
window_input.set(3, Window::Dimension(0, 1, 1));
Iterator input(_input, window_input);
Iterator output(_output, window_output);
execute_window_loop(window_output, [&](const Coordinates & id_batch)
{
float32x2_t carry_min = vdup_n_f32(std::numeric_limits<float>::max());
float32x2_t carry_max = vdup_n_f32(std::numeric_limits<float>::lowest());
float carry_min_scalar = std::numeric_limits<float>::max();
float carry_max_scalar = std::numeric_limits<float>::lowest();
execute_window_loop(window_input, [&](const Coordinates &)
{
int x = x_start;
const auto in_ptr = reinterpret_cast<const float *>(input.ptr() + id_batch[1] * _input->info()->strides_in_bytes()[3]);
// Vector loop
for(; x <= x_end - 8; x += 8)
{
const float32x4x2_t pixels = vld2q_f32(in_ptr + x);
const float32x4_t tmp_min1 = vminq_f32(pixels.val[0], pixels.val[1]);
const float32x4_t tmp_max1 = vmaxq_f32(pixels.val[0], pixels.val[1]);
const float32x2_t tmp_min2 = vmin_f32(vget_high_f32(tmp_min1), vget_low_f32(tmp_min1));
const float32x2_t tmp_max2 = vmax_f32(vget_high_f32(tmp_max1), vget_low_f32(tmp_max1));
carry_min = vmin_f32(tmp_min2, carry_min);
carry_max = vmax_f32(tmp_max2, carry_max);
}
// Process leftover pixels
for(; x < x_end; ++x)
{
const float pixel = in_ptr[x];
carry_min_scalar = std::min(pixel, carry_min_scalar);
carry_max_scalar = std::max(pixel, carry_max_scalar);
}
},
input);
// Reduce result
carry_min = vpmin_f32(carry_min, carry_min);
carry_max = vpmax_f32(carry_max, carry_max);
carry_min = vpmin_f32(carry_min, carry_min);
carry_max = vpmax_f32(carry_max, carry_max);
// Extract max/min values
const float min_i = std::min(vget_lane_f32(carry_min, 0), carry_min_scalar);
const float max_i = std::max(vget_lane_f32(carry_max, 0), carry_max_scalar);
auto out_ptr = reinterpret_cast<float *>(output.ptr());
// Perform reduction of local min/max values
update_min_max(out_ptr, min_i, max_i);
},
output);
}
void NEMinMaxLayerKernel::reset()
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
float32x2_t reset_values = vdup_n_f32(0.0f);
reset_values = vset_lane_f32(std::numeric_limits<float>::max(), reset_values, 0);
reset_values = vset_lane_f32(std::numeric_limits<float>::lowest(), reset_values, 1);
Window window_output;
window_output.use_tensor_dimensions(_output->info()->tensor_shape());
window_output.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator output(_output, window_output);
execute_window_loop(window_output, [&](const Coordinates &)
{
vst1_f32(reinterpret_cast<float *>(output.ptr()), reset_values);
},
output);
}
void NEMinMaxLayerKernel::update_min_max(float *out_ptr, float min, float max)
{
std::lock_guard<Mutex> lock(_mtx);
const float32x2_t old_min = vld1_dup_f32(out_ptr);
const float32x2_t old_max = vld1_dup_f32(out_ptr + 1);
const float32x2_t new_min = vmin_f32(vdup_n_f32(min), old_min);
const float32x2_t new_max = vmax_f32(vdup_n_f32(max), old_max);
vst1_f32(out_ptr, vzip_f32(new_min, new_max).val[0]);
}
} // namespace arm_compute
|
