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Aron Virginas-Tar70104002018-10-24 15:33:28 +01001//
2// Copyright © 2017 Arm Ltd. All rights reserved.
3// SPDX-License-Identifier: MIT
4//
5
6#include <armnn/ArmNN.hpp>
Aron Virginas-Tarc9cc8042018-11-01 16:15:57 +00007#include <Graph.hpp>
8#include <Network.hpp>
Aron Virginas-Tar70104002018-10-24 15:33:28 +01009
Aron Virginas-Tarc9cc8042018-11-01 16:15:57 +000010#include <reference/RefWorkloadFactory.hpp>
Aron Virginas-Tar70104002018-10-24 15:33:28 +010011
12#include <boost/test/unit_test.hpp>
keidav01738c2e62018-12-11 16:14:20 +000013#include <test/GraphUtils.hpp>
Aron Virginas-Tar70104002018-10-24 15:33:28 +010014
15BOOST_AUTO_TEST_SUITE(RefOptimizedNetwork)
16
17BOOST_AUTO_TEST_CASE(OptimizeValidateCpuRefWorkloads)
18{
19 const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
20
21 armnn::Network net;
22
23 armnn::NormalizationDescriptor nmDesc;
24 armnn::ActivationDescriptor acDesc;
25
26 // in
27 // |
28 // nm
29 // / |
30 // ac |
31 // \ |
32 // ml
33 // |
34 // sm
35 // |
36 // ot
37 armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
38 layer->GetOutputSlot(0).SetTensorInfo(desc);
39
40 armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
41
42 layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
43 normLayer->GetOutputSlot(0).SetTensorInfo(desc);
44
45 layer = net.AddActivationLayer(acDesc, "ac");
46
47 normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
48 layer->GetOutputSlot(0).SetTensorInfo(desc);
49
50 armnn::IConnectableLayer* prevLayer = layer;
51 layer = net.AddMultiplicationLayer("ml");
52
53 prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
54 normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
55 layer->GetOutputSlot(0).SetTensorInfo(desc);
56
57 prevLayer = layer;
58 armnn::SoftmaxDescriptor softmaxDescriptor;
59 layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
60
61 prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
62 layer->GetOutputSlot(0).SetTensorInfo(desc);
63
64 prevLayer = layer;
65 layer = net.AddOutputLayer(0, "ot");
66
67 prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
68
69 armnn::IRuntime::CreationOptions options;
70 armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
71
72 std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
73 armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
74 static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph().AllocateDynamicBuffers();
75 BOOST_CHECK(optNet);
76
77 // Validates workloads.
78 armnn::RefWorkloadFactory fact;
79 for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
80 {
81 BOOST_CHECK_NO_THROW(
82 layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
83 }
84}
85
86BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefPermuteLayer)
87{
88 // Create runtime in which test will run
89 armnn::IRuntime::CreationOptions options;
90 armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
91
92 std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
93
94 // build up the structure of the network
95 armnn::INetworkPtr net(armnn::INetwork::Create());
96
97 armnn::IConnectableLayer* input = net->AddInputLayer(0);
98
99 armnn::PermuteDescriptor descriptor({0, 2, 3, 1});
100 armnn::IConnectableLayer* permute = net->AddPermuteLayer(descriptor);
101
102 armnn::IConnectableLayer* output = net->AddOutputLayer(0);
103
104 input->GetOutputSlot(0).Connect(permute->GetInputSlot(0));
105 permute->GetOutputSlot(0).Connect(output->GetInputSlot(0));
106
107 input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
108 permute->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 4, 1, 4 }, armnn::DataType::Float32));
109
110 // optimize the network
111 armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
112
113 for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
114 {
115 BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
116 }
117}
118
119BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsCpuRefMeanLayer)
120{
121 // Create runtime in which test will run
122 armnn::IRuntime::CreationOptions options;
123 armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
124
125 std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
126
127 // build up the structure of the network
128 armnn::INetworkPtr net(armnn::INetwork::Create());
129
130 armnn::IConnectableLayer* input = net->AddInputLayer(0);
131
132 armnn::MeanDescriptor descriptor({ 0, 1 }, false);
133 armnn::IConnectableLayer* meanLayer = net->AddMeanLayer(descriptor);
134
135 armnn::IConnectableLayer* output = net->AddOutputLayer(0);
136
137 input->GetOutputSlot(0).Connect(meanLayer->GetInputSlot(0));
138 meanLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
139
140 input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 4, 3, 2 }, armnn::DataType::Float32));
141 meanLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 2 }, armnn::DataType::Float32));
142
143 // optimize the network
144 armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
145
146 for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
147 {
148 BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
149 }
150}
151
keidav01738c2e62018-12-11 16:14:20 +0000152BOOST_AUTO_TEST_CASE(DebugTestOnCpuRef)
153{
154 armnn::Network net;
155
156 armnn::ActivationDescriptor activation1Descriptor;
157 activation1Descriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
158 activation1Descriptor.m_A = 1.f;
159 activation1Descriptor.m_B = -1.f;
160
161 // Defines layers.
162 auto input = net.AddInputLayer(0, "InputLayer");
163 auto activation = net.AddActivationLayer(activation1Descriptor, "ActivationLayer");
164 auto output = net.AddOutputLayer(0, "OutputLayer");
165
166 // Connects layers.
167 input->GetOutputSlot(0).Connect(activation->GetInputSlot(0));
168 activation->GetOutputSlot(0).Connect(output->GetInputSlot(0));
169
170 armnn::TensorShape shape({4});
171 armnn::TensorInfo info(shape, armnn::DataType::Float32);
172 input->GetOutputSlot(0).SetTensorInfo(info);
173 activation->GetOutputSlot(0).SetTensorInfo(info);
174
175 armnn::IRuntime::CreationOptions options;
176 armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
177
178 std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
179
180 armnn::OptimizerOptions optimizerOptions;
181 optimizerOptions.m_Debug = true;
182
183 armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec(),
184 optimizerOptions);
185
186 const armnn::Graph& graph = static_cast<armnn::OptimizedNetwork*>(optimizedNet.get())->GetGraph();
187 // Tests that all layers are present in the graph.
188 BOOST_TEST(graph.GetNumLayers() == 5);
189
190 // Tests that the vertices exist and have correct names.
191 BOOST_TEST(GraphHasNamedLayer(graph, "InputLayer"));
192 BOOST_TEST(GraphHasNamedLayer(graph, "DebugLayerAfterInputLayer"));
193 BOOST_TEST(GraphHasNamedLayer(graph, "ActivationLayer"));
194 BOOST_TEST(GraphHasNamedLayer(graph, "DebugLayerAfterActivationLayer"));
195 BOOST_TEST(GraphHasNamedLayer(graph, "OutputLayer"));
196}
197
Aron Virginas-Tar70104002018-10-24 15:33:28 +0100198BOOST_AUTO_TEST_SUITE_END()