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
|
/*
* Copyright (c) 2021 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 "ROIPoolingLayer.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/validation/Helpers.h"
#include <algorithm>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <>
SimpleTensor<float> roi_pool_layer(const SimpleTensor<float> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
{
ARM_COMPUTE_UNUSED(output_qinfo);
const size_t num_rois = rois.shape()[1];
const size_t values_per_roi = rois.shape()[0];
DataType output_data_type = src.data_type();
TensorShape input_shape = src.shape();
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
SimpleTensor<float> output(output_shape, output_data_type);
const int pooled_w = pool_info.pooled_width();
const int pooled_h = pool_info.pooled_height();
const float spatial_scale = pool_info.spatial_scale();
// get sizes of x and y dimensions in src tensor
const int width = src.shape()[0];
const int height = src.shape()[1];
// Move pointer across the fourth dimension
const size_t input_stride_w = input_shape[0] * input_shape[1] * input_shape[2];
const size_t output_stride_w = output_shape[0] * output_shape[1] * output_shape[2];
const auto *rois_ptr = reinterpret_cast<const uint16_t *>(rois.data());
// Iterate through pixel width (X-Axis)
for(size_t pw = 0; pw < num_rois; ++pw)
{
const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
const auto x1 = rois_ptr[values_per_roi * pw + 1];
const auto y1 = rois_ptr[values_per_roi * pw + 2];
const auto x2 = rois_ptr[values_per_roi * pw + 3];
const auto y2 = rois_ptr[values_per_roi * pw + 4];
//Iterate through pixel height (Y-Axis)
for(size_t fm = 0; fm < input_shape[2]; ++fm)
{
// Iterate through regions of interest index
for(size_t py = 0; py < pool_info.pooled_height(); ++py)
{
// Scale ROI
const int roi_anchor_x = support::cpp11::round(x1 * spatial_scale);
const int roi_anchor_y = support::cpp11::round(y1 * spatial_scale);
const int roi_width = std::max(support::cpp11::round((x2 - x1) * spatial_scale), 1.f);
const int roi_height = std::max(support::cpp11::round((y2 - y1) * spatial_scale), 1.f);
// Iterate over feature map (Z axis)
for(size_t px = 0; px < pool_info.pooled_width(); ++px)
{
auto region_start_x = static_cast<int>(std::floor((static_cast<float>(px) / pooled_w) * roi_width));
auto region_end_x = static_cast<int>(std::floor((static_cast<float>(px + 1) / pooled_w) * roi_width));
auto region_start_y = static_cast<int>(std::floor((static_cast<float>(py) / pooled_h) * roi_height));
auto region_end_y = static_cast<int>(std::floor((static_cast<float>(py + 1) / pooled_h) * roi_height));
region_start_x = std::min(std::max(region_start_x + roi_anchor_x, 0), width);
region_end_x = std::min(std::max(region_end_x + roi_anchor_x, 0), width);
region_start_y = std::min(std::max(region_start_y + roi_anchor_y, 0), height);
region_end_y = std::min(std::max(region_end_y + roi_anchor_y, 0), height);
// Iterate through the pooling region
if((region_end_x <= region_start_x) || (region_end_y <= region_start_y))
{
/* Assign element in tensor 'output' at coordinates px, py, fm, roi_indx, to 0 */
auto out_ptr = output.data() + px + py * output_shape[0] + fm * output_shape[0] * output_shape[1] + pw * output_stride_w;
*out_ptr = 0;
}
else
{
float curr_max = -std::numeric_limits<float>::max();
for(int j = region_start_y; j < region_end_y; ++j)
{
for(int i = region_start_x; i < region_end_x; ++i)
{
/* Retrieve element from input tensor at coordinates(i, j, fm, roi_batch) */
float in_element = *(src.data() + i + j * input_shape[0] + fm * input_shape[0] * input_shape[1] + roi_batch * input_stride_w);
curr_max = std::max(in_element, curr_max);
}
}
/* Assign element in tensor 'output' at coordinates px, py, fm, roi_indx, to curr_max */
auto out_ptr = output.data() + px + py * output_shape[0] + fm * output_shape[0] * output_shape[1] + pw * output_stride_w;
*out_ptr = curr_max;
}
}
}
}
}
return output;
}
/*
Template genericised method to allow calling of roi_pooling_layer with quantized 8 bit datatype
*/
template <>
SimpleTensor<uint8_t> roi_pool_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint16_t> &rois, const ROIPoolingLayerInfo &pool_info, const QuantizationInfo &output_qinfo)
{
const SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = roi_pool_layer<float>(src_tmp, rois, pool_info, output_qinfo);
SimpleTensor<uint8_t> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_qinfo);
return dst;
}
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
|
