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
|
// Copyright 2016, Tobias Hermann.
// https://github.com/Dobiasd/frugally-deep
// Distributed under the MIT License.
// (See accompanying LICENSE file or at
// https://opensource.org/licenses/MIT)
#pragma once
#include "fdeep/common.hpp"
#include <cstddef>
#include <cstdlib>
#include <string>
namespace fdeep {
namespace internal {
class tensor_pos {
public:
// The dimensions are right-aligned (left-padded) compared to Keras.
// I.e., if you have a position (or shape) of (a, b) in Keras
// it corresponds to (0, 0, 0, a, b) in frugally-deep.
explicit tensor_pos(
std::size_t pos_dim_5,
std::size_t pos_dim_4,
std::size_t y,
std::size_t x,
std::size_t z)
: pos_dim_5_(pos_dim_5)
, pos_dim_4_(pos_dim_4)
, y_(y)
, x_(x)
, z_(z)
, rank_(5)
{
}
explicit tensor_pos(
std::size_t pos_dim_4,
std::size_t y,
std::size_t x,
std::size_t z)
: pos_dim_5_(0)
, pos_dim_4_(pos_dim_4)
, y_(y)
, x_(x)
, z_(z)
, rank_(4)
{
}
explicit tensor_pos(
std::size_t y,
std::size_t x,
std::size_t z)
: pos_dim_5_(0)
, pos_dim_4_(0)
, y_(y)
, x_(x)
, z_(z)
, rank_(3)
{
}
explicit tensor_pos(
std::size_t x,
std::size_t z)
: pos_dim_5_(0)
, pos_dim_4_(0)
, y_(0)
, x_(x)
, z_(z)
, rank_(2)
{
}
explicit tensor_pos(
std::size_t z)
: pos_dim_5_(0)
, pos_dim_4_(0)
, y_(0)
, x_(0)
, z_(z)
, rank_(1)
{
}
std::size_t rank() const
{
return rank_;
}
std::vector<std::size_t> dimensions() const
{
if (rank() == 5)
return { pos_dim_5_, pos_dim_4_, y_, x_, z_ };
if (rank() == 4)
return { pos_dim_4_, y_, x_, z_ };
if (rank() == 3)
return { y_, x_, z_ };
if (rank() == 2)
return { x_, z_ };
return { z_ };
}
std::size_t pos_dim_5_;
std::size_t pos_dim_4_;
std::size_t y_;
std::size_t x_;
std::size_t z_;
private:
std::size_t rank_;
};
inline tensor_pos create_tensor_pos_from_dims(
const std::vector<std::size_t>& dimensions)
{
assertion(dimensions.size() >= 1 && dimensions.size() <= 5,
"Invalid tensor-pos dimensions");
if (dimensions.size() == 5)
return tensor_pos(
dimensions[0],
dimensions[1],
dimensions[2],
dimensions[3],
dimensions[4]);
if (dimensions.size() == 4)
return tensor_pos(
dimensions[0],
dimensions[1],
dimensions[2],
dimensions[3]);
if (dimensions.size() == 3)
return tensor_pos(
dimensions[0],
dimensions[1],
dimensions[2]);
if (dimensions.size() == 2)
return tensor_pos(
dimensions[0],
dimensions[1]);
return tensor_pos(dimensions[0]);
}
inline tensor_pos tensor_pos_with_changed_rank(const tensor_pos& s, std::size_t rank)
{
assertion(rank >= 1 && rank <= 5, "Invalid target rank");
if (rank == 4) {
assertion(s.pos_dim_5_ == 0, "Invalid target rank");
return tensor_pos(s.pos_dim_4_, s.y_, s.x_, s.z_);
}
if (rank == 3) {
assertion(s.pos_dim_5_ == 0, "Invalid target rank");
assertion(s.pos_dim_4_ == 0, "Invalid target rank");
return tensor_pos(s.y_, s.x_, s.z_);
}
if (rank == 2) {
assertion(s.pos_dim_5_ == 0, "Invalid target rank");
assertion(s.pos_dim_4_ == 0, "Invalid target rank");
assertion(s.y_ == 0, "Invalid target rank");
return tensor_pos(s.x_, s.z_);
}
if (rank == 1) {
assertion(s.pos_dim_5_ == 0, "Invalid target rank");
assertion(s.pos_dim_4_ == 0, "Invalid target rank");
assertion(s.y_ == 0, "Invalid target rank");
assertion(s.x_ == 0, "Invalid target rank");
return tensor_pos(s.z_);
}
return tensor_pos(s.pos_dim_5_, s.pos_dim_4_, s.y_, s.x_, s.z_);
}
}
using tensor_pos = internal::tensor_pos;
}
|
