blob: 31d2b33fcb98f7f3d02e78bda42cc3584608f101 (
plain)
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
|
/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "ConvBackend.h"
#include <caffe/proto/caffe.pb.h>
#include <nnkit/support/caffe/Backend.h>
#include <nncc/core/ADT/kernel/Overlay.h>
#include <nncc/core/ADT/kernel/NCHWLayout.h>
#include <stdex/Memory.h>
using stdex::make_unique;
std::unique_ptr<nnkit::Backend> ConvBackend::create(const nnsuite::conv::Model &model)
{
::caffe::NetParameter param;
param.set_name("conv");
// Create 'Input' layer
{
auto input = param.add_layer();
input->set_name("input");
input->set_type("Input");
input->add_top(model.ifm_name());
auto input_param = new ::caffe::InputParameter{};
auto input_shape = input_param->add_shape();
input_shape->add_dim(1);
input_shape->add_dim(model.ifm_shape().depth());
input_shape->add_dim(model.ifm_shape().height());
input_shape->add_dim(model.ifm_shape().width());
input->set_allocated_input_param(input_param);
}
// Create 'Convolution' layer
{
auto conv = param.add_layer();
conv->set_name("conv");
conv->set_type("Convolution");
conv->add_bottom(model.ifm_name());
conv->add_top(model.ofm_name());
const auto &ker_shape = model.ker_shape();
auto ker_blob_shape = new ::caffe::BlobShape{};
ker_blob_shape->add_dim(ker_shape.count());
ker_blob_shape->add_dim(ker_shape.depth());
ker_blob_shape->add_dim(ker_shape.height());
ker_blob_shape->add_dim(ker_shape.width());
auto ker_blob = conv->add_blobs();
for (uint32_t n = 0; n < ker_shape.count(); ++n)
{
for (uint32_t ch = 0; ch < ker_shape.depth(); ++ch)
{
for (uint32_t row = 0; row < ker_shape.height(); ++row)
{
for (uint32_t col = 0; col < ker_shape.width(); ++col)
{
ker_blob->add_data(model.ker_data().at(n, ch, row, col));
}
}
}
}
ker_blob->set_allocated_shape(ker_blob_shape);
auto conv_param = new ::caffe::ConvolutionParameter{};
conv_param->set_num_output(model.ker_shape().count());
conv_param->set_bias_term(false);
conv_param->add_kernel_size(model.ker_shape().height());
conv_param->add_kernel_size(model.ker_shape().width());
conv->set_allocated_convolution_param(conv_param);
}
auto net = make_unique<::caffe::Net<float>>(param);
return make_unique<nnkit::support::caffe::Backend<float>>(std::move(net));
}
|
