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
|
/*
* 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 "Scale.h"
#include "IRBuilder.h"
#include <coco/IR/FeatureLayouts.h>
#include <morph/caffe.h>
#include <cassert>
using namespace nncc::core::ADT;
using namespace morph::caffe;
namespace caffeimport
{
void ScaleBuilder::build(const ::caffe::LayerParameter &layer, GraphBuilderContext *context) const
{
coco::Module *module = context->module();
coco::Data *data = context->data();
coco::Block *blk = context->block();
std::map<std::string, tensor::Shape> &shape_ctx = context->shape_ctx();
std::map<std::string, coco::Bag *> &bag_ctx = context->bag_ctx();
WeightContext &weight_ctx = context->weight_ctx();
// TODO Support Scale layer with 2 bottoms
assert(layer.bottom().size() == 1);
assert(layer.top().size() == 1);
assert(layer.has_scale_param());
const auto ¶m = layer.scale_param();
assert(param.axis() == 1);
assert(!param.has_num_axes());
assert(weight_ctx.blob_count(layer.name()) >= 1);
// NOTE The shape of "Scale" output is same as that of its input
// NOTE The current implementation assumes that input/output is of feature type
// TODO Support generic tensor arguments
auto shape = shape_ctx.at(layer.bottom(0));
coco::Bag *last_bag = bag_ctx.at(layer.bottom(0));
// Create channel-wise multiplication
{
auto in_bag = last_bag;
auto in_obj = module->entity()->object()->create<coco::FeatureObject>();
in_obj->bag(in_bag);
in_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
auto factor_bag = module->entity()->bag()->create(num_elements(shape));
auto factor_obj = module->entity()->object()->create<coco::FeatureObject>();
factor_obj->bag(factor_bag);
factor_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(shape)));
auto out_bag = module->entity()->bag()->create(num_elements(shape));
auto out_obj = module->entity()->object()->create<coco::FeatureObject>();
out_obj->bag(out_bag);
out_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
auto mul_op = op_builder(module).load(factor_obj).load(in_obj).mul().pop();
auto mul_ins = instr_builder(module).eval(out_obj, mul_op);
blk->instr()->append(mul_ins);
// Fill "factor" data
{
data->f32()->allocate(factor_bag);
auto span = data->f32()->weight(factor_bag);
auto blob = weight_ctx.blob_get(layer.name(), 0);
for (uint32_t ch = 0; ch < factor_obj->shape().depth(); ++ch)
{
span[ch] = blob->data(ch);
}
}
// Update "last_bag"
last_bag = out_bag;
}
assert(last_bag != nullptr);
// Create bias addition (as channel-wise addition)
if (param.bias_term())
{
assert(weight_ctx.blob_count(layer.name()) >= 2);
auto in_bag = last_bag; /* Use the output of the last computation as an input */
auto in_obj = module->entity()->object()->create<coco::FeatureObject>();
in_obj->bag(in_bag);
in_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
auto bias_bag = module->entity()->bag()->create(num_elements(shape));
auto bias_obj = module->entity()->object()->create<coco::FeatureObject>();
bias_obj->bag(bias_bag);
bias_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(shape)));
auto out_bag = module->entity()->bag()->create(num_elements(shape));
auto out_obj = module->entity()->object()->create<coco::FeatureObject>();
out_obj->bag(out_bag);
out_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
auto add_op = op_builder(module).load(bias_obj).load(in_obj).add().pop();
auto add_ins = instr_builder(module).eval(out_obj, add_op);
blk->instr()->append(add_ins);
// Fill bias data
{
data->f32()->allocate(bias_bag);
auto bias_span = data->f32()->weight(bias_bag);
auto bias_blob = weight_ctx.blob_get(layer.name(), 1);
for (uint32_t ch = 0; ch < bias_obj->shape().depth(); ++ch)
{
bias_span[ch] = bias_blob->data(ch);
}
}
// Update "last_bag"
last_bag = out_bag;
}
// Update bag and shape context
{
const auto &out_name = layer.top(0);
const auto &out_bag = last_bag;
const auto &out_shape = shape;
bag_ctx[out_name] = out_bag;
shape_ctx[out_name] = out_shape;
}
}
} // namespace caffeimport
|
