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

1 files changed, 167 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Convolution3dEndToEndTestImpl.hpp
new file mode 100644
index 0000000000..33bf9a180b
--- /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);
+}