diff options
Diffstat (limited to 'test/Lstm.cpp')
| -rw-r--r-- | test/Lstm.cpp | 119 |
1 files changed, 77 insertions, 42 deletions
diff --git a/test/Lstm.cpp b/test/Lstm.cpp index 56812326..579524ca 100644 --- a/test/Lstm.cpp +++ b/test/Lstm.cpp @@ -5,6 +5,8 @@ #include "DriverTestHelpers.hpp" #include "OperationsUtils.h" +#include "../1.0/HalPolicy.hpp" + #include <boost/array.hpp> #include <boost/test/unit_test.hpp> #include <boost/test/data/test_case.hpp> @@ -15,8 +17,10 @@ BOOST_AUTO_TEST_SUITE(LstmTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; +using ArmnnDriver = armnn_driver::ArmnnDriver; using DriverOptions = armnn_driver::DriverOptions; +using HalPolicy = armnn_driver::hal_1_0::HalPolicy; + using namespace driverTestHelpers; using namespace android::hardware; @@ -128,99 +132,130 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, armnn::Compute compute) { auto driver = std::make_unique<ArmnnDriver>(DriverOptions(compute)); - V1_0::Model model = {}; + HalPolicy::Model model = {}; // Inputs: // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - AddInputOperand(model, inputDimensions); + AddInputOperand<HalPolicy>(model, inputDimensions); // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size], where “num_units” corresponds to the number of cell units. - AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue, V1_0::OperandType::TENSOR_FLOAT32, - CreateNoValueLifeTime(inputToInputWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + inputToInputWeightsDimensions, + inputToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(inputToInputWeightsDimensions)); // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size]. - AddTensorOperand(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. - AddTensorOperand(model, inputToCellWeightsDimensions, inputToCellWeightsValue); + AddTensorOperand<HalPolicy>(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand<HalPolicy>(model, inputToCellWeightsDimensions, inputToCellWeightsValue); // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size]. - AddTensorOperand(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); + AddTensorOperand<HalPolicy>(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., // “num_units”), or the second dimension of the “projection_weights”, if defined. - AddTensorOperand(model, recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + recurrentToInputWeightsDimensions, + recurrentToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. - AddTensorOperand(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); + AddTensorOperand<HalPolicy>(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. - AddTensorOperand(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); + AddTensorOperand<HalPolicy>(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. - AddTensorOperand(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); + AddTensorOperand<HalPolicy>(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, cellToInputWeightsDimensions, cellToInputWeightsValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToInputWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + cellToInputWeightsDimensions, + cellToInputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToInputWeightsDimensions)); // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, cellToForgetWeightsDimensions, cellToForgetWeightsValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToForgetWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + cellToForgetWeightsDimensions, + cellToForgetWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToForgetWeightsDimensions)); // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, cellToOutputWeightsDimensions, cellToOutputWeightsValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToOutputWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + cellToOutputWeightsDimensions, + cellToOutputWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(cellToOutputWeightsDimensions)); // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, inputGateBiasDimensions, inputGateBiasValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(inputGateBiasDimensions)); + AddTensorOperand<HalPolicy>(model, + inputGateBiasDimensions, + inputGateBiasValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(inputGateBiasDimensions)); // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, forgetGateBiasDimensions, forgetGateBiasValue); + AddTensorOperand<HalPolicy>(model, forgetGateBiasDimensions, forgetGateBiasValue); // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, cellBiasDimensions, cellBiasValue); + AddTensorOperand<HalPolicy>(model, cellBiasDimensions, cellBiasValue); // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - AddTensorOperand(model, outputGateBiasDimensions, outputGateBiasValue); + AddTensorOperand<HalPolicy>(model, outputGateBiasDimensions, outputGateBiasValue); // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [output_size, num_units]. - AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionWeightsDimensions)); + AddTensorOperand<HalPolicy>(model, + projectionWeightsDimensions, + projectionWeightsValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(projectionWeightsDimensions)); // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - AddTensorOperand(model, projectionBiasDimensions, projectionBiasValue, - V1_0::OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionBiasDimensions)); + AddTensorOperand<HalPolicy>(model, + projectionBiasDimensions, + projectionBiasValue, + HalPolicy::OperandType::TENSOR_FLOAT32, + CreateNoValueLifeTime(projectionBiasDimensions)); // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - AddInputOperand(model, outputStateInDimensions); + AddInputOperand<HalPolicy>(model, outputStateInDimensions); // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - AddInputOperand(model, cellStateInDimensions); + AddInputOperand<HalPolicy>(model, cellStateInDimensions); // Constant scalar values (the VTS test adds these as tensors of dim {}) // 20: The activation function: A value indicating the activation function: // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - AddTensorOperand(model, activationFunctionDimensions, - activationFunctionValue, V1_0::OperandType::INT32); + AddTensorOperand<HalPolicy>(model, + activationFunctionDimensions, + activationFunctionValue, + HalPolicy::OperandType::INT32); // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. // If set to 0.0 then clipping is disabled. - AddTensorOperand(model, cellClippingThresholdDimensions, - cellClippingThresholdValue, V1_0::OperandType::FLOAT32); + AddTensorOperand<HalPolicy>(model, + cellClippingThresholdDimensions, + cellClippingThresholdValue, + HalPolicy::OperandType::FLOAT32); // 22: The clipping threshold: for the output from the projection layer, such that values are bound within // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - AddTensorOperand(model, projectionClippingThresholdDimensions, - projectionClippingThresholdValue, V1_0::OperandType::FLOAT32); + AddTensorOperand<HalPolicy>(model, + projectionClippingThresholdDimensions, + projectionClippingThresholdValue, + HalPolicy::OperandType::FLOAT32); // Outputs: // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with // CIFG, or [batch_size, num_units * 3] without CIFG. - AddOutputOperand(model, scratchBufferDimensions); + AddOutputOperand<HalPolicy>(model, scratchBufferDimensions); // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - AddOutputOperand(model, outputStateOutDimensions); + AddOutputOperand<HalPolicy>(model, outputStateOutDimensions); // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - AddOutputOperand(model, cellStateOutDimensions); + AddOutputOperand<HalPolicy>(model, cellStateOutDimensions); // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is // effectively the same as the current “output state (out)” value. - AddOutputOperand(model, outputDimensions); + AddOutputOperand<HalPolicy>(model, outputDimensions); // make the lstm operation model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::LSTM; + model.operations[0].type = HalPolicy::OperationType::LSTM; model.operations[0].inputs = hidl_vec<uint32_t> {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22}; model.operations[0].outputs = hidl_vec<uint32_t> {23, 24, 25, 26}; |