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
|
/*
* Copyright (c) 2017-2020 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 "DepthConcatenateLayer.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> depthconcatenate_layer(const std::vector<SimpleTensor<T>> &srcs, SimpleTensor<T> &dst)
{
// Create reference
std::vector<TensorShape> shapes;
shapes.reserve(srcs.size());
for(const auto &src : srcs)
{
shapes.emplace_back(src.shape());
}
// Compute reference
int depth_offset = 0;
const int width_out = dst.shape().x();
const int height_out = dst.shape().y();
const int depth_out = dst.shape().z();
const int out_stride_z = width_out * height_out;
const int batches = dst.shape().total_size_upper(3);
auto have_different_quantization_info = [&](const SimpleTensor<T> &tensor)
{
return tensor.quantization_info() != dst.quantization_info();
};
if(srcs[0].data_type() == DataType::QASYMM8 && std::any_of(srcs.cbegin(), srcs.cend(), have_different_quantization_info))
{
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(int b = 0; b < batches; ++b)
{
// input tensors can have smaller width and height than the output, so for each output's slice we need to requantize 0 (as this is the value
// used in NEFillBorderKernel by NEDepthConcatenateLayer) using the corresponding quantization info for that particular slice/input tensor.
int slice = 0;
for(const auto &src : srcs)
{
auto ptr_slice = static_cast<T *>(dst(Coordinates(0, 0, slice, b)));
const auto num_elems_in_slice((dst.num_elements() / depth_out) * src.shape().z());
const UniformQuantizationInfo iq_info = src.quantization_info().uniform();
const UniformQuantizationInfo oq_info = dst.quantization_info().uniform();
std::transform(ptr_slice, ptr_slice + num_elems_in_slice, ptr_slice, [&](T)
{
return quantize_qasymm8(dequantize_qasymm8(0, iq_info), oq_info);
});
slice += src.shape().z();
}
}
}
else
{
std::fill_n(dst.data(), dst.num_elements(), 0);
}
for(const auto &src : srcs)
{
ARM_COMPUTE_ERROR_ON(depth_offset >= depth_out);
ARM_COMPUTE_ERROR_ON(batches != static_cast<int>(src.shape().total_size_upper(3)));
const int width = src.shape().x();
const int height = src.shape().y();
const int depth = src.shape().z();
const int x_diff = (width_out - width) / 2;
const int y_diff = (height_out - height) / 2;
const T *src_ptr = src.data();
for(int b = 0; b < batches; ++b)
{
const size_t offset_to_first_element = b * out_stride_z * depth_out + depth_offset * out_stride_z + y_diff * width_out + x_diff;
for(int d = 0; d < depth; ++d)
{
for(int r = 0; r < height; ++r)
{
if(src.data_type() == DataType::QASYMM8 && src.quantization_info() != dst.quantization_info())
{
const UniformQuantizationInfo iq_info = src.quantization_info().uniform();
const UniformQuantizationInfo oq_info = dst.quantization_info().uniform();
std::transform(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out, [&](T t)
{
const float dequantized_input = dequantize_qasymm8(t, iq_info);
return quantize_qasymm8(dequantized_input, oq_info);
});
src_ptr += width;
}
else
{
std::copy(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out);
src_ptr += width;
}
}
}
}
depth_offset += depth;
}
return dst;
}
template SimpleTensor<uint8_t> depthconcatenate_layer(const std::vector<SimpleTensor<uint8_t>> &srcs, SimpleTensor<uint8_t> &dst);
template SimpleTensor<float> depthconcatenate_layer(const std::vector<SimpleTensor<float>> &srcs, SimpleTensor<float> &dst);
template SimpleTensor<half> depthconcatenate_layer(const std::vector<SimpleTensor<half>> &srcs, SimpleTensor<half> &dst);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
|
