Add ConstTensorsAsInput support for Conv3d
* Constant weights and biases are now stored as Constant layers.
* Updated Serializer, Deserializer and unit tests to reflect this.
* Updated TfLiteParser.
* Updated Ref backend to handle constant weights and
bias as inputs rather than reading from member variables.
* Added Conv3d EndToEnd test.
* Added NCDHW DataLayout and unit tests.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I10cdd354ca5f1c748730f92ffdb36bf810f83c8e
diff --git a/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp
new file mode 100644
index 0000000..33bf9a1
--- /dev/null
+++ b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp
@@ -0,0 +1,167 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "EndToEndTestImpl.hpp"
+#include "QuantizeHelper.hpp"
+
+#include <ResolveType.hpp>
+
+#include <backendsCommon/test/CommonTestUtils.hpp>
+#include <backendsCommon/test/DataLayoutUtils.hpp>
+
+#include <map>
+#include <vector>
+
+namespace
+{
+
+armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor,
+ const armnn::TensorInfo& inputInfo,
+ const armnn::TensorInfo& weightsInfo,
+ const armnn::TensorInfo& biasInfo,
+ const armnn::TensorInfo& outputInfo,
+ const armnn::ConstTensor& weights,
+ const armnn::ConstTensor& biases)
+{
+ using namespace armnn;
+
+ INetworkPtr network(INetwork::Create());
+ IConnectableLayer* input = network->AddInputLayer(0, "input");
+ armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights");
+ armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias");
+ IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d");
+ IConnectableLayer* output = network->AddOutputLayer(0, "output");
+
+ Connect(input, convolution3d, inputInfo, 0, 0);
+ Connect(weightsLayer, convolution3d, weightsInfo, 0, 1);
+ Connect(biasLayer, convolution3d, biasInfo, 0, 2);
+ Connect(convolution3d, output, outputInfo, 0, 0);
+
+ return network;
+}
+
+} // anonymous namespace
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType>
+void Convolution3dEndToEnd(const std::vector<armnn::BackendId>& backends,
+ armnn::DataLayout dataLayout)
+{
+ using namespace armnn;
+ using T = ResolveType<ArmnnType>;
+ using BT = ResolveType<ArmnnBType>;
+
+ const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
+ const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
+
+ TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset);
+ TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
+ TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true);
+ TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true);
+
+ std::vector<float> inputData =
+ {
+ 0.0f, 1.0f, 2.0f, 3.0f, 4.0f,
+ 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
+ 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
+ 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
+
+ 20.0f, 21.0f, 22.0f, 23.0f, 24.0f,
+ 25.0f, 26.0f, 27.0f, 28.0f, 29.0f,
+ 30.0f, 31.0f, 32.0f, 33.0f, 34.0f,
+ 35.0f, 36.0f, 37.0f, 38.0f, 39.0f,
+ 40.0f, 41.0f, 42.0f, 43.0f, 44.0f,
+
+ 45.0f, 46.0f, 47.0f, 48.0f, 49.0f,
+ 50.0f, 51.0f, 52.0f, 53.0f, 54.0f,
+ 55.0f, 56.0f, 57.0f, 58.0f, 59.0f,
+ 60.0f, 61.0f, 62.0f, 63.0f, 64.0f,
+ 65.0f, 66.0f, 67.0f, 68.0f, 69.0f,
+
+ 70.0f, 71.0f, 72.0f, 73.0f, 74.0f,
+ 75.0f, 76.0f, 77.0f, 78.0f, 79.0f,
+ 80.0f, 81.0f, 82.0f, 83.0f, 84.0f,
+ 85.0f, 86.0f, 87.0f, 88.0f, 89.0f,
+ 90.0f, 91.0f, 92.0f, 93.0f, 94.0f,
+ 95.0f, 96.0f, 97.0f, 98.0f, 99.0f,
+
+ 100.0f, 101.0f, 102.0f, 103.0f, 104.0f,
+ 105.0f, 106.0f, 107.0f, 108.0f, 109.0f,
+ 110.0f, 111.0f, 112.0f, 113.0f, 114.0f,
+ 115.0f, 116.0f, 117.0f, 118.0f, 119.0f,
+ 120.0f, 121.0f, 122.0f, 123.0f, 124.0f
+ };
+
+ std::vector<float> weightsData =
+ {
+ 1.0f, 1.0f, 1.0f,
+ 1.0f, 1.0f, 1.0f,
+ 1.0f, 1.0f, 1.0f,
+
+ 0.0f, 0.0f, 0.0f,
+ 0.0f, 0.0f, 0.0f,
+ 0.0f, 0.0f, 0.0f,
+
+ 1.0f, 1.0f, 1.0f,
+ 1.0f, 1.0f, 1.0f,
+ 1.0f, 1.0f, 1.0f,
+ };
+
+ std::vector<float> biasesData = { 1.f };
+
+ std::vector<float> expectedOutputData =
+ {
+ 559.0f, 595.0f,
+
+ 739.0f, 775.0f,
+
+ 1459.0f, 1495.0f,
+
+ 1639.0f, 1675.0f,
+ };
+
+ Convolution3dDescriptor descriptor;
+ descriptor.m_PadLeft = 0;
+ descriptor.m_PadRight = 0;
+ descriptor.m_PadTop = 0;
+ descriptor.m_PadBottom = 0;
+ descriptor.m_PadFront = 0;
+ descriptor.m_PadBack = 0;
+ descriptor.m_StrideX = 2;
+ descriptor.m_StrideY = 2;
+ descriptor.m_StrideZ = 2;
+ descriptor.m_BiasEnabled = true;
+ descriptor.m_DataLayout = dataLayout;
+
+ // Permute input and output if NCDHW.
+ if (dataLayout == DataLayout::NCDHW)
+ {
+ PermuteTensorNdhwcToNcdhw(inputInfo, inputData);
+ PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData);
+ }
+
+ // Quantize data
+ std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
+ std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
+ std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
+
+ std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
+
+ ConstTensor weights(weightsInfo, qWeightsData);
+ ConstTensor biases(biasesInfo, qBiasesData);
+
+ INetworkPtr network = CreateConvolution3dNetwork(descriptor,
+ inputInfo,
+ weightsInfo,
+ biasesInfo,
+ outputInfo,
+ weights,
+ biases);
+
+ EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
+ { { 0, qInputData } },
+ { { 0, qExpectedOutputData } },
+ backends);
+}