Lines Matching +full:- +full:- +full:model
2 // Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
18 bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& da… in ConvertOperation() argument
23 return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Add); in ConvertOperation()
25 return ConvertAveragePool2d(operation, model, data); in ConvertOperation()
27 return ConvertConcatenation(operation, model, data); in ConvertOperation()
29 return ConvertConv2d(operation, model, data); in ConvertOperation()
31 return ConvertDepthToSpace(operation, model, data); in ConvertOperation()
33 return ConvertDepthwiseConv2d(operation, model, data); in ConvertOperation()
35 return ConvertDequantize(operation, model, data); in ConvertOperation()
37 return ConvertFloor(operation, model, data); in ConvertOperation()
39 return ConvertFullyConnected(operation, model, data); in ConvertOperation()
41 return ConvertLocalResponseNormalization(operation, model, data); in ConvertOperation()
43 return ConvertLogistic(operation, model, data); in ConvertOperation()
45 return ConvertLstm(operation, model, data); in ConvertOperation()
47 return ConvertL2Normalization(operation, model, data); in ConvertOperation()
49 return ConvertL2Pool2d(operation, model, data); in ConvertOperation()
51 return ConvertMaxPool2d(operation, model, data); in ConvertOperation()
53 return ConvertElementwiseBinary(operation, model, data, armnn::BinaryOperation::Mul); in ConvertOperation()
55 return ConvertReLu(operation, model, data); in ConvertOperation()
57 return ConvertReLu1(operation, model, data); in ConvertOperation()
59 return ConvertReLu6(operation, model, data); in ConvertOperation()
61 return ConvertSoftmax(operation, model, data); in ConvertOperation()
63 return ConvertSpaceToDepth(operation, model, data); in ConvertOperation()
65 return ConvertTanH(operation, model, data); in ConvertOperation()
67 return ConvertReshape(operation, model, data); in ConvertOperation()
69 return ConvertResizeBilinear(operation, model, data); in ConvertOperation()
76 bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData… in ConvertAveragePool2d() argument
79 …tPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::Average, model, data); in ConvertAveragePool2d()
82 bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData… in ConvertConcatenation() argument
85 return ::ConvertConcatenation<hal_1_0::HalPolicy>(operation, model, data); in ConvertConcatenation()
88 bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data) in ConvertConv2d() argument
91 return ::ConvertConv2d<hal_1_0::HalPolicy>(operation, model, data); in ConvertConv2d()
94 bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData&… in ConvertDepthToSpace() argument
97 return ::ConvertDepthToSpace<hal_1_0::HalPolicy>(operation, model, data); in ConvertDepthToSpace()
100 bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionDa… in ConvertDepthwiseConv2d() argument
103 return ::ConvertDepthwiseConv2d<hal_1_0::HalPolicy>(operation, model, data); in ConvertDepthwiseConv2d()
106 bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& d… in ConvertDequantize() argument
109 return ::ConvertDequantize<hal_1_0::HalPolicy>(operation, model, data); in ConvertDequantize()
113 const Model& model, in ConvertElementwiseBinary() argument
118 return ::ConvertElementwiseBinary<hal_1_0::HalPolicy>(operation, model, data, binaryOperation); in ConvertElementwiseBinary()
121 bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data) in ConvertFloor() argument
124 return ::ConvertFloor<hal_1_0::HalPolicy>(operation, model, data); in ConvertFloor()
127 bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionDat… in ConvertFullyConnected() argument
130 return ::ConvertFullyConnected<hal_1_0::HalPolicy>(operation, model, data); in ConvertFullyConnected()
134 const Model& model, in ConvertLocalResponseNormalization() argument
138 return ::ConvertLocalResponseNormalization<hal_1_0::HalPolicy>(operation, model, data); in ConvertLocalResponseNormalization()
141 bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& dat… in ConvertLogistic() argument
144 return ::ConvertLogistic<hal_1_0::HalPolicy>(operation, model, data); in ConvertLogistic()
147 bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) in ConvertLstm() argument
152 …// 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size… in ConvertLstm()
154 … LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data); in ConvertLstm()
159 …// 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, out… in ConvertLstm()
160 …utHandle outputStateIn = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 18, model, data); in ConvertLstm()
165 …// 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_u… in ConvertLstm()
166 …nputHandle cellStateIn = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 19, model, data); in ConvertLstm()
173 // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
176 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 2, model, data); in ConvertLstm()
177 // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
180 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 3, model, data); in ConvertLstm()
181 // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
184 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 4, model, data); in ConvertLstm()
185 // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
188 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 6, model, data); in ConvertLstm()
189 // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
192 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 7, model, data); in ConvertLstm()
193 // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
196 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 8, model, data); in ConvertLstm()
197 … // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. in ConvertLstm()
199 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 13, model, data); in ConvertLstm()
200 // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. in ConvertLstm()
202 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 14, model, data); in ConvertLstm()
203 … // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. in ConvertLstm()
205 ConvertOperationInputToConstTensorPin<hal_1_0::HalPolicy>(operation, 15, model, data); in ConvertLstm()
221 …// 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of sh… in ConvertLstm()
226 model, in ConvertLstm()
232 …// 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, o… in ConvertLstm()
238 model, in ConvertLstm()
244 …// 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of sha… in ConvertLstm()
248 model, in ConvertLstm()
254 …// 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of sh… in ConvertLstm()
258 model, in ConvertLstm()
264 …// 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of sh… in ConvertLstm()
268 model, in ConvertLstm()
274 …// 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [nu… in ConvertLstm()
278 model, in ConvertLstm()
284 … // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape in ConvertLstm()
289 model, in ConvertLstm()
295 …// 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [ou… in ConvertLstm()
299 model, in ConvertLstm()
317 // Get the mandatory input scalars (actually 1-D tensors of size 1): in ConvertLstm()
320 …// 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, … in ConvertLstm()
323 // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. in ConvertLstm()
327 …if (!GetInputActivationFunctionFromTensor<hal_1_0::HalPolicy>(operation, 20, activation, model, da… in ConvertLstm()
328 … !GetInputScalar<hal_1_0::HalPolicy>(operation, 21, OperandType::FLOAT32, cellClip, model, data) || in ConvertLstm()
329 … !GetInputScalar<hal_1_0::HalPolicy>(operation, 22, OperandType::FLOAT32, projClip, model, data)) in ConvertLstm()
335 …// 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, n… in ConvertLstm()
337 const Operand* scratchBuffer = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model); in ConvertLstm()
342 …// 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_siz… in ConvertLstm()
343 const Operand* outputStateOut = GetOutputOperand<hal_1_0::HalPolicy>(operation, 1, model); in ConvertLstm()
348 …// 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size,… in ConvertLstm()
349 const Operand* cellStateOut = GetOutputOperand<hal_1_0::HalPolicy>(operation, 2, model); in ConvertLstm()
354 …// 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_si… in ConvertLstm()
356 const Operand* output = GetOutputOperand<hal_1_0::HalPolicy>(operation, 3, model); in ConvertLstm()
400 return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights," in ConvertLstm()
414 …return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provid… in ConvertLstm()
415 … " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__); in ConvertLstm()
432 paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); in ConvertLstm()
433 paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); in ConvertLstm()
434 paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); in ConvertLstm()
435 paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); in ConvertLstm()
436 paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); in ConvertLstm()
437 paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); in ConvertLstm()
438 paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); in ConvertLstm()
439 paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); in ConvertLstm()
440 paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); in ConvertLstm()
445 paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); in ConvertLstm()
446 paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); in ConvertLstm()
449 paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); in ConvertLstm()
451 paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); in ConvertLstm()
456 paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); in ConvertLstm()
459 paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); in ConvertLstm()
465 paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); in ConvertLstm()
466 paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); in ConvertLstm()
491 armnn::IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm"); in ConvertLstm()
492 layer->SetBackendId(setBackend); in ConvertLstm()
494 input.Connect(layer->GetInputSlot(0)); in ConvertLstm()
495 outputStateIn.Connect(layer->GetInputSlot(1)); in ConvertLstm()
496 cellStateIn.Connect(layer->GetInputSlot(2)); in ConvertLstm()
498 … return (SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, 0, model, data) && in ConvertLstm()
499 … SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 1, *layer, 1, model, data) && in ConvertLstm()
500 … SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 2, *layer, 2, model, data) && in ConvertLstm()
501 SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 3, *layer, 3, model, data)); in ConvertLstm()
504 bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionDa… in ConvertL2Normalization() argument
507 return ::ConvertL2Normalization<hal_1_0::HalPolicy>(operation, model, data); in ConvertL2Normalization()
510 bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& dat… in ConvertL2Pool2d() argument
513 …onvertPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::L2, model, data); in ConvertL2Pool2d()
516 bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& da… in ConvertMaxPool2d() argument
519 …nvertPooling2d<hal_1_0::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::Max, model, data); in ConvertMaxPool2d()
522 bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data) in ConvertReLu() argument
525 return ::ConvertReLu<hal_1_0::HalPolicy>(operation, model, data); in ConvertReLu()
528 bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data) in ConvertReLu1() argument
531 return ::ConvertReLu1<hal_1_0::HalPolicy>(operation, model, data); in ConvertReLu1()
534 bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data) in ConvertReLu6() argument
537 return ::ConvertReLu6<hal_1_0::HalPolicy>(operation, model, data); in ConvertReLu6()
540 bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data) in ConvertSoftmax() argument
544 … LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data); in ConvertSoftmax()
550 const Operand* outputOperand = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model); in ConvertSoftmax()
563 if (!GetInputFloat32<hal_1_0::HalPolicy>(operation, 1, desc.m_Beta, model, data)) in ConvertSoftmax()
583 armnn::IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc); in ConvertSoftmax()
584 layer->SetBackendId(setBackend); in ConvertSoftmax()
589 input.Connect(layer->GetInputSlot(0)); in ConvertSoftmax()
591 return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data); in ConvertSoftmax()
594 bool HalPolicy::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData&… in ConvertSpaceToDepth() argument
598 … LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data); in ConvertSpaceToDepth()
614 …GetInputScalar<hal_1_0::HalPolicy>(operation, 1, OperandType::INT32, desc.m_BlockSize, model, data… in ConvertSpaceToDepth()
621 const Operand* output = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model); in ConvertSpaceToDepth()
648 armnn::IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc); in ConvertSpaceToDepth()
649 layer->SetBackendId(setBackend); in ConvertSpaceToDepth()
654 input.Connect(layer->GetInputSlot(0)); in ConvertSpaceToDepth()
656 return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data); in ConvertSpaceToDepth()
659 bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data) in ConvertTanH() argument
662 return ::ConvertTanH<hal_1_0::HalPolicy>(operation, model, data); in ConvertTanH()
665 bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data) in ConvertReshape() argument
668 return ::ConvertReshape<hal_1_0::HalPolicy>(operation, model, data); in ConvertReshape()
671 bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionDat… in ConvertResizeBilinear() argument
675 … LayerInputHandle input = ConvertToLayerInputHandle<hal_1_0::HalPolicy>(operation, 0, model, data); in ConvertResizeBilinear()
681 const Operand* output = GetOutputOperand<hal_1_0::HalPolicy>(operation, 0, model); in ConvertResizeBilinear()
714 …putScalar<hal_1_0::HalPolicy>(operation, 1, OperandType::INT32, desc.m_TargetWidth, model, data) || in ConvertResizeBilinear()
715 …nputScalar<hal_1_0::HalPolicy>(operation, 2, OperandType::INT32, desc.m_TargetHeight, model, data)) in ConvertResizeBilinear()
720 armnn::IConnectableLayer* layer = data.m_Network->AddResizeLayer(desc); in ConvertResizeBilinear()
721 layer->SetBackendId(setBackend); in ConvertResizeBilinear()
726 layer->GetOutputSlot(0).SetTensorInfo(outputInfo); in ConvertResizeBilinear()
727 input.Connect(layer->GetInputSlot(0)); in ConvertResizeBilinear()
729 return SetupAndTrackLayerOutputSlot<hal_1_0::HalPolicy>(operation, 0, *layer, model, data); in ConvertResizeBilinear()