diff options
Diffstat (limited to 'src/armnn/layers/QuantizedLstmLayer.cpp')
| -rw-r--r-- | src/armnn/layers/QuantizedLstmLayer.cpp | 124 |
1 files changed, 95 insertions, 29 deletions
diff --git a/src/armnn/layers/QuantizedLstmLayer.cpp b/src/armnn/layers/QuantizedLstmLayer.cpp index c82e34f983..ebe320718c 100644 --- a/src/armnn/layers/QuantizedLstmLayer.cpp +++ b/src/armnn/layers/QuantizedLstmLayer.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2017,2019-2023 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2017,2019-2024 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "QuantizedLstmLayer.hpp" @@ -80,7 +80,11 @@ QuantizedLstmLayer* QuantizedLstmLayer::Clone(Graph& graph) const std::vector<TensorShape> QuantizedLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const { - ARMNN_ASSERT(inputShapes.size() == 3); + if (inputShapes.size() != 3) + { + throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + + "\" - should be \"3\"."); + } // Get input values for validation unsigned int numBatches = inputShapes[0][0]; @@ -108,35 +112,97 @@ void QuantizedLstmLayer::ValidateTensorShapesFromInputs() GetInputSlot(2).GetTensorInfo().GetShape() // previousOutputIn }); - ARMNN_ASSERT(inferredShapes.size() == 2); + if (inferredShapes.size() != 2) + { + throw armnn::LayerValidationException("inferredShapes has " + + std::to_string(inferredShapes.size()) + + " element(s) - should only have 2."); + } // Check weights and bias for nullptr - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToInputWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToInputWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToForgetWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToForgetWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToCellWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToCellWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputToOutputWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputToOutputWeights should not be null."); - - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToInputWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToInputWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToForgetWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToForgetWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToCellWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToCellWeights should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_RecurrentToOutputWeights != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_RecurrentToOutputWeights should not be null."); - - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_InputGateBias != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_InputGateBias should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_ForgetGateBias != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_ForgetGateBias should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_CellBias != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_CellBias should not be null."); - ARMNN_ASSERT_MSG(m_QuantizedLstmParameters.m_OutputGateBias != nullptr, - "QuantizedLstmLayer: m_QuantizedLstmParameters.m_OutputGateBias should not be null."); + if (!m_QuantizedLstmParameters.m_InputToInputWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_InputToInputWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_InputToForgetWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_InputToForgetWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_InputToCellWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_InputToCellWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_InputToOutputWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_InputToOutputWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_RecurrentToInputWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_RecurrentToInputWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_RecurrentToForgetWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_RecurrentToForgetWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_RecurrentToCellWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_RecurrentToCellWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_RecurrentToOutputWeights) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_RecurrentToOutputWeights " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_InputGateBias) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_InputGateBias " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_ForgetGateBias) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_ForgetGateBias " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_CellBias) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_CellBias " + "should not be null."); + } + + if (!m_QuantizedLstmParameters.m_OutputGateBias) + { + throw armnn::LayerValidationException("QuantizedLstmLayer: " + "m_QuantizedLstmParameters.m_OutputGateBias " + "should not be null."); + } // Check output TensorShape(s) match inferred shape ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QuantizedLstmLayer"); |