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
|
/*
* Copyright (c) 2018-2019 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 "arm_compute/graph/nodes/DepthwiseConvolutionLayerNode.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
#include "arm_compute/graph/Utils.h"
namespace arm_compute
{
namespace graph
{
DepthwiseConvolutionLayerNode::DepthwiseConvolutionLayerNode(PadStrideInfo info, int depth_multiplier, DepthwiseConvolutionMethod method,
QuantizationInfo out_quant_info)
: _info(std::move(info)), _depth_multiplier(depth_multiplier), _method(method), _out_quant_info(std::move(out_quant_info)), _fused_activation()
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
}
int DepthwiseConvolutionLayerNode::depth_multiplier() const
{
return _depth_multiplier;
}
void DepthwiseConvolutionLayerNode::set_depthwise_convolution_method(DepthwiseConvolutionMethod method)
{
_method = method;
}
DepthwiseConvolutionMethod DepthwiseConvolutionLayerNode::depthwise_convolution_method() const
{
return _method;
}
PadStrideInfo DepthwiseConvolutionLayerNode::convolution_info() const
{
return _info;
}
ActivationLayerInfo DepthwiseConvolutionLayerNode::fused_activation() const
{
return _fused_activation;
}
void DepthwiseConvolutionLayerNode::set_fused_activation(ActivationLayerInfo fused_activation)
{
_fused_activation = fused_activation;
}
TensorDescriptor DepthwiseConvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
const TensorDescriptor &weights_descriptor,
const PadStrideInfo &info,
int depth_multiplier)
{
unsigned int output_width = 0;
unsigned int output_height = 0;
const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT);
const unsigned int input_channels = get_dimension_size(input_descriptor, DataLayoutDimension::CHANNEL);
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier);
return output_descriptor;
}
bool DepthwiseConvolutionLayerNode::forward_descriptors()
{
if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
dst->desc() = configure_output(0);
return true;
}
return false;
}
TensorDescriptor DepthwiseConvolutionLayerNode::configure_output(size_t idx) const
{
ARM_COMPUTE_UNUSED(idx);
const Tensor *src = input(0);
const Tensor *weights = input(1);
ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info, _depth_multiplier);
if(!_out_quant_info.empty())
{
output_info.quant_info = _out_quant_info;
}
return output_info;
}
NodeType DepthwiseConvolutionLayerNode::type() const
{
return DepthwiseConvolutionLayerNode::node_type;
}
void DepthwiseConvolutionLayerNode::accept(INodeVisitor &v)
{
v.visit(*this);
}
} // namespace graph
} // namespace arm_compute
|
