IVGCVSW-7209 Remove deprecated code due to be removed in 23.02
* Removed weights and bias from Convolution, DepthwiseConv & FullyConnected
layers
* Removed the weight and bias ConstTensorHandles from the QueueDescriptors
* Updated Workloads to take tensors from WorkloadInfo rather than the
QueueDescriptors
* Removed unused RedirectMembersToConstantInputs optimization and tests.
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I9ffcdc4a1c0dff725539dd69fc435b700bd98a56
diff --git a/src/armnnTestUtils/CreateWorkload.hpp b/src/armnnTestUtils/CreateWorkload.hpp
index 905b8fa..0846d21 100644
--- a/src/armnnTestUtils/CreateWorkload.hpp
+++ b/src/armnnTestUtils/CreateWorkload.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
@@ -495,10 +495,6 @@
TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Weight->Allocate();
-
armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
weightsTensorInfo.SetConstant();
@@ -562,12 +558,6 @@
TensorShape weightShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 5, 3} : TensorShape{2, 5, 3, 3};
TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
-
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
weightsTensorInfo.SetConstant();
@@ -662,12 +652,6 @@
TensorShape biasShape = TensorShape{ 32 };
TensorShape inputShape = TensorShape{ 1, 32, 149, 149 };
TensorShape outputShape = TensorShape{ 1, 32, 147, 147 };
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo(biasShape, GetBiasDataType(DataType)));
-
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
armnn::TensorInfo weightsTensorInfo(weightShape, DataType, inputsQScale);
weightsTensorInfo.SetConstant();
@@ -1132,12 +1116,6 @@
armnn::TensorInfo biasTensorInfo(biasShape, GetBiasDataType(DataType), inputsQScale);
biasTensorInfo.SetConstant();
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(biasTensorInfo);
-
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
-
// Creates extra layers.
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
auto const weights = graph.AddLayer<ConstantLayer>("weights");
@@ -1170,8 +1148,6 @@
CHECK(queueDescriptor.m_Inputs.size() == 3);
CHECK(queueDescriptor.m_Outputs.size() == 1);
- CHECK((queueDescriptor.m_Weight->GetTensorInfo() == weightsTensorInfo));
- CHECK((queueDescriptor.m_Bias->GetTensorInfo() == biasTensorInfo));
// Returns so we can do extra, backend-specific tests.
return workload;
@@ -1203,9 +1179,6 @@
DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType)); // [ 1, H, W, I*M ]
- layer->m_Weight->Allocate();
// Creates extra layers.
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
@@ -1252,10 +1225,6 @@
float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
- layer->m_Weight->Allocate();
-
armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
weightsTensorInfo.SetConstant();
@@ -1301,12 +1270,6 @@
float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
- // As optimization isn't run member variables need to be updated.
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale));
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
-
armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
armnn::TensorInfo biasesTensorInfo({7}, GetBiasDataType(DataType), inputsQScale);
weightsTensorInfo.SetConstant();