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
|
/*
COPYRIGHT
All contributions by Taiga Nomi
Copyright (c) 2013, Taiga Nomi
All rights reserved.
All other contributions:
Copyright (c) 2013-2016, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
LICENSE
The BSD 3-Clause License
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of tiny-dnn nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include "tiny_dnn/core/framework/device.fwd.h"
#include "tiny_dnn/core/params/conv_params.h"
namespace tiny_dnn {
namespace core {
class OpKernel; // delared below
class OpKernelConstruction {
public:
explicit OpKernelConstruction() {}
explicit OpKernelConstruction(Device* device, Params* params)
: device_(device), params_(params) {}
// Returns the device raw pointer
Device* device() const { return device_; }
// Returns the device raw pointer
Params* params() const { return params_; }
private:
Device* device_ = nullptr;
Params* params_ = nullptr;
};
class OpKernelContext {
public:
struct OpParams {
// the op kernel being computed.
OpKernel* op_kernel_ptr = nullptr;
// the device on which the kernel is running.
Device* device_ptr = nullptr;
// the layer on which kernel is runnning
layer* layer_ptr_ = nullptr;
// the operation params
Params* params_ptr_ = nullptr;
// parallelize operation
bool parallelize = false;
backend_t engine = default_engine();
};
explicit OpKernelContext(const std::vector<tensor_t*>& in_data,
std::vector<tensor_t*>& out_data)
: in_data_(in_data), out_data_(out_data) {
op_params_ = std::unique_ptr<OpParams>(new OpParams());
}
explicit OpKernelContext(const std::vector<tensor_t*>& in_data,
const std::vector<tensor_t*>& out_data,
std::vector<tensor_t*>& out_grad,
std::vector<tensor_t*>& in_grad)
: in_data_(in_data)
, out_data_(out_data)
, out_grad_(out_grad)
, in_grad_(in_grad) {
op_params_ = std::unique_ptr<OpParams>(new OpParams());
}
tensor_t& input(const int idx) const {
return *in_data_[idx];
}
tensor_t& output(const int idx) const {
return *out_data_[idx];
}
tensor_t& input_grad(const int idx) const {
return *in_grad_[idx];
}
tensor_t& output_grad(const int idx) const {
return *out_grad_[idx];
}
void setParams(Params* params) {
op_params_->params_ptr_ = params;
}
Params* params() const {
return op_params_->params_ptr_;
}
void setParallelize(const bool parallelize) {
op_params_->parallelize = parallelize;
}
bool parallelize() const {
return op_params_->parallelize;
}
void setDevice(Device* device) {
op_params_->device_ptr = device;
}
Device* device() const {
return op_params_->device_ptr;
}
void setLayer(layer* layer) {
op_params_->layer_ptr_ = layer;
}
layer* Layer() const {
return op_params_->layer_ptr_;
}
backend_t engine() const {
return op_params_->engine;
}
void setEngine(const backend_t engine) {
op_params_->engine = engine;
}
private:
std::vector<tensor_t*> in_data_;
std::vector<tensor_t*> out_data_;
std::vector<tensor_t*> out_grad_;
std::vector<tensor_t*> in_grad_;
std::unique_ptr<OpParams> op_params_;
};
class OpKernel {
public:
explicit OpKernel() {}
explicit OpKernel(const OpKernelConstruction& context)
: device_(context.device())
, params_(context.params()) {}
virtual ~OpKernel() {}
virtual void compute(const OpKernelContext& context) = 0;
protected:
Device* device_ = nullptr;
Params* params_ = nullptr;
};
} // namespace core
} // namespace tiny_dnn
|
