1 /*
2 * Copyright (c) 2018-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/graph/nodes/FullyConnectedLayerNode.h"
25
26 #include "arm_compute/core/Utils.h"
27 #include "arm_compute/graph/Graph.h"
28 #include "arm_compute/graph/INodeVisitor.h"
29
30 namespace arm_compute
31 {
32 namespace graph
33 {
FullyConnectedLayerNode(unsigned int num_outputs,QuantizationInfo out_quant_info,FullyConnectedLayerInfo fc_info,FastMathHint fast_math_hint)34 FullyConnectedLayerNode::FullyConnectedLayerNode(unsigned int num_outputs, QuantizationInfo out_quant_info, FullyConnectedLayerInfo fc_info, FastMathHint fast_math_hint)
35 : _num_outputs(num_outputs), _out_quant_info(std::move(out_quant_info)), _info(fc_info), _fast_math_hint(fast_math_hint)
36 {
37 _input_edges.resize(3, EmptyEdgeID);
38 _outputs.resize(1, NullTensorID);
39 }
set_fast_math_hint(FastMathHint hint)40 void FullyConnectedLayerNode::set_fast_math_hint(FastMathHint hint)
41 {
42 _fast_math_hint = hint;
43 }
44
fast_math_hint() const45 FastMathHint FullyConnectedLayerNode::fast_math_hint() const
46 {
47 return _fast_math_hint;
48 }
49
set_fused_activation(ActivationLayerInfo fused_activation)50 void FullyConnectedLayerNode::set_fused_activation(ActivationLayerInfo fused_activation)
51 {
52 _info.activation_info = fused_activation;
53 }
54
compute_weights_descriptor(const TensorDescriptor & input_descriptor,unsigned int num_outputs,FullyConnectedLayerInfo fc_info,const QuantizationInfo & weights_quant_info)55 TensorDescriptor FullyConnectedLayerNode::compute_weights_descriptor(const TensorDescriptor &input_descriptor,
56 unsigned int num_outputs,
57 FullyConnectedLayerInfo fc_info,
58 const QuantizationInfo &weights_quant_info)
59 {
60 unsigned int num_weights = 1;
61 unsigned int num_dimensions = input_descriptor.shape.num_dimensions();
62 // Ignore the batch dimension if there is one:
63 if(num_dimensions == 2 || num_dimensions == 4)
64 {
65 num_dimensions--;
66 }
67 for(unsigned int i = 0; i < num_dimensions; i++)
68 {
69 num_weights *= input_descriptor.shape[i];
70 }
71
72 TensorDescriptor weights_descriptor = input_descriptor;
73 weights_descriptor.shape = TensorShape(num_weights, num_outputs);
74
75 // If weights are tranposed, use tranposed shape
76 if(!fc_info.transpose_weights)
77 {
78 weights_descriptor.shape = TensorShape(num_outputs, num_weights);
79 }
80
81 // Set quantization info if present
82 if(!weights_quant_info.empty())
83 {
84 weights_descriptor.quant_info = weights_quant_info;
85 }
86
87 return weights_descriptor;
88 }
89
compute_output_descriptor(const TensorDescriptor & input_descriptor,unsigned int num_outputs,const QuantizationInfo & out_quant_info)90 TensorDescriptor FullyConnectedLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
91 unsigned int num_outputs,
92 const QuantizationInfo &out_quant_info)
93 {
94 // Note: Only 1D batch space is supported at the moment
95 unsigned int batches = input_descriptor.shape[1];
96 if(input_descriptor.shape.num_dimensions() > 2)
97 {
98 batches = input_descriptor.shape[3];
99 }
100
101 // Set descriptor shape
102 TensorDescriptor output_descriptor = input_descriptor;
103 output_descriptor.shape = TensorShape(num_outputs, batches);
104
105 // Set quantization info if present
106 if(!out_quant_info.empty())
107 {
108 output_descriptor.quant_info = out_quant_info;
109 }
110
111 return output_descriptor;
112 }
113
info() const114 FullyConnectedLayerInfo FullyConnectedLayerNode::info() const
115 {
116 return _info;
117 }
118
forward_descriptors()119 bool FullyConnectedLayerNode::forward_descriptors()
120 {
121 if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
122 {
123 Tensor *dst = output(0);
124 ARM_COMPUTE_ERROR_ON(dst == nullptr);
125 dst->desc() = configure_output(0);
126 return true;
127 }
128 return false;
129 }
130
configure_output(size_t idx) const131 TensorDescriptor FullyConnectedLayerNode::configure_output(size_t idx) const
132 {
133 ARM_COMPUTE_UNUSED(idx);
134 const Tensor *src = input(0);
135 ARM_COMPUTE_ERROR_ON(src == nullptr);
136
137 return compute_output_descriptor(src->desc(), _num_outputs, _out_quant_info);
138 }
139
type() const140 NodeType FullyConnectedLayerNode::type() const
141 {
142 return NodeType::FullyConnectedLayer;
143 }
144
accept(INodeVisitor & v)145 void FullyConnectedLayerNode::accept(INodeVisitor &v)
146 {
147 v.visit(*this);
148 }
149 } // namespace graph
150 } // namespace arm_compute