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
|
//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../TestUtils.hpp"
#include <Optimizer.hpp>
#include <doctest/doctest.h>
TEST_SUITE("Optimizer")
{
using namespace armnn::optimizations;
TEST_CASE("RedirectMembersToConstantInputsFullyConnectedTest")
{
armnn::Graph graph;
const armnn::TensorInfo inputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32);
const armnn::TensorInfo weightsInfo({ 4 }, armnn::DataType::Float32, 0.0f, 0, true);
const armnn::TensorInfo biasesInfo ({ 2 }, armnn::DataType::Float32, 0.0f, 0, true);
// Check if isConstant is enabled for weights and biases tensor info.
CHECK(weightsInfo.IsConstant());
CHECK(biasesInfo.IsConstant());
armnn::FullyConnectedDescriptor desc;
desc.m_BiasEnabled = true;
desc.m_ConstantWeights = false;
// Create the simple test network with Weights and Biases as inputs to a FullyConnected layer.
auto input = graph.AddLayer<armnn::InputLayer>(0, "Input");
auto weights = graph.AddLayer<armnn::ConstantLayer>("Weights");
auto biases = graph.AddLayer<armnn::ConstantLayer>("Biases");
auto fcLayer = graph.AddLayer<armnn::FullyConnectedLayer>(desc, "FullyConnected");
auto output = graph.AddLayer<armnn::OutputLayer>(1, "Output");
float expectedWeightsData[] = { 1.0f, 1.0f, 1.0f, 1.0f };
float expectedBiasesData[] = { 2.0f, 2.0f };
// Set the m_LayerOutput for the optimizer to point to.
armnn::ConstTensor weightsTensor(weightsInfo, &expectedWeightsData);
armnn::ConstTensor biasesTensor(biasesInfo, &expectedBiasesData);
weights->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weightsTensor);
biases->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(biasesTensor);
input->GetOutputSlot().SetTensorInfo(inputInfo);
weights->GetOutputSlot().SetTensorInfo(weightsInfo);
biases->GetOutputSlot().SetTensorInfo(biasesInfo);
fcLayer->GetOutputSlot().SetTensorInfo(outputInfo);
// Connect up the layers
input->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(0));
weights->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(1));
biases->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(2));
fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// Member variables should be null before optimization.
CHECK(fcLayer->m_Weight == nullptr);
CHECK(fcLayer->m_Bias == nullptr);
// Run the optimizer
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs()));
// Check if member variables are not null and shape is set correctly.
CHECK(fcLayer->m_Weight != nullptr);
CHECK(fcLayer->m_Bias != nullptr);
CHECK(fcLayer->m_Weight->GetTensorInfo().GetShape() == weightsInfo.GetShape());
CHECK(fcLayer->m_Bias->GetTensorInfo().GetShape() == biasesInfo.GetShape());
// Check whether data matches expected float data
const float* weightsData = fcLayer->m_Weight->GetConstTensor<float>();
CHECK(weightsData[0] == expectedWeightsData[0]);
CHECK(weightsData[1] == expectedWeightsData[1]);
CHECK(weightsData[2] == expectedWeightsData[2]);
CHECK(weightsData[3] == expectedWeightsData[3]);
const float* biasesData = fcLayer->m_Bias->GetConstTensor<float>();
CHECK(biasesData[0] == expectedBiasesData[0]);
CHECK(biasesData[1] == expectedBiasesData[1]);
}
}
|
