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
|
//
// Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn/INetwork.hpp>
#include <GraphUtils.hpp>
#include <TestUtils.hpp>
#include <doctest/doctest.h>
using namespace armnn;
TEST_SUITE("GpuFsaOptimizedNetwork")
{
TEST_CASE("SingleConv2dSupportedOptimizedNetwork")
{
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
INetworkPtr network(INetwork::Create());
TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
TensorInfo outputInfo({ 1, 3, 3, 1 }, DataType::Float32);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
Convolution2dDescriptor desc;
desc.m_BiasEnabled = true;
desc.m_DataLayout = DataLayout::NHWC;
auto inputLayer = network->AddInputLayer(0, "input");
auto weightLayer = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer = network->AddConvolution2dLayer(desc, "conv2d");
auto outputLayer = network->AddOutputLayer(1, "output");
inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
weightLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1));
weightLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2));
biasLayer->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
std::vector<BackendId> backends = { "GpuFsa" };
OptimizerOptionsOpaque optimizedOptions;
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
CHECK(optNet);
Graph& graph = GetGraphForTesting(optNet.get());
// Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<OutputLayer>));
}
TEST_CASE("TwoConv2dSupportedOptimizedNetwork")
{
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
INetworkPtr network(INetwork::Create());
TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
TensorInfo intermediateInfo({ 1, 3, 3, 1 }, DataType::Float32);
TensorInfo outputInfo({ 1, 1, 1, 1 }, DataType::Float32);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
Convolution2dDescriptor desc;
desc.m_BiasEnabled = true;
desc.m_DataLayout = DataLayout::NHWC;
auto inputLayer = network->AddInputLayer(0, "input");
auto weightLayer1 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer1 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer1 = network->AddConvolution2dLayer(desc, "conv2d");
auto weightLayer2 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer2 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer2 = network->AddConvolution2dLayer(desc, "conv2d");
auto outputLayer = network->AddOutputLayer(0, "output");
inputLayer->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(0));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
weightLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(1));
weightLayer1->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(2));
biasLayer1->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer1->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(0));
convLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
weightLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(1));
weightLayer2->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(2));
biasLayer2->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer2->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
convLayer2->GetOutputSlot(0).SetTensorInfo(outputInfo);
std::vector<BackendId> backends = { "GpuFsa" };
OptimizerOptionsOpaque optimizedOptions;
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
CHECK(optNet);
Graph& graph = GetGraphForTesting(optNet.get());
// Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<OutputLayer>));
}
TEST_CASE("ElementwiseBinaryAddSupportedOptimizedNetwork")
{
using namespace armnn;
const float qScale = 1.0f;
const int32_t qOffset = 0;
const TensorShape& input1Shape = { 2, 2, 2 };
const TensorShape& input2Shape = { 2, 2, 2 };
const TensorShape& outputShape = { 2, 2, 2 };
TensorInfo input1TensorInfo(input1Shape, DataType::Float32, qScale, qOffset, true);
TensorInfo input2TensorInfo(input2Shape, DataType::Float32, qScale, qOffset, true);
TensorInfo outputTensorInfo(outputShape, DataType::Float32, qScale, qOffset);
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
INetworkPtr network(INetwork::Create());
IConnectableLayer* input1 = network->AddInputLayer(0, "input0");
IConnectableLayer* input2 = network->AddInputLayer(1, "input1");
ElementwiseBinaryDescriptor desc;
desc.m_Operation = BinaryOperation::Add;
IConnectableLayer* elementwiseBinaryLayer = network->AddElementwiseBinaryLayer(desc, "elementwiseBinary");
IConnectableLayer* output = network->AddOutputLayer(2, "output");
Connect(input1, elementwiseBinaryLayer, input1TensorInfo, 0, 0);
Connect(input2, elementwiseBinaryLayer, input2TensorInfo, 0, 1);
Connect(elementwiseBinaryLayer, output, outputTensorInfo, 0, 0);
std::vector<BackendId> backends = { "GpuFsa" };
OptimizerOptionsOpaque optimizedOptions;
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
CHECK(optNet);
Graph& graph = GetGraphForTesting(optNet.get());
// Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<InputLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<OutputLayer>));
}
}
|
