diff options
Diffstat (limited to 'test/Lstm.cpp')
| -rw-r--r-- | test/Lstm.cpp | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/test/Lstm.cpp b/test/Lstm.cpp index 66f2cf02..b1b7c9d5 100644 --- a/test/Lstm.cpp +++ b/test/Lstm.cpp @@ -137,7 +137,7 @@ void LstmTestImpl(const hidl_vec<uint32_t>& 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, OperandType::TENSOR_FLOAT32, + AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue, V1_0::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]. @@ -151,7 +151,7 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, // [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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(recurrentToInputWeightsDimensions)); + V1_0::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); @@ -163,16 +163,16 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, AddTensorOperand(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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToInputWeightsDimensions)); + V1_0::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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToForgetWeightsDimensions)); + V1_0::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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToOutputWeightsDimensions)); + V1_0::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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(inputGateBiasDimensions)); + V1_0::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); // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. @@ -182,10 +182,10 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [output_size, num_units]. AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionWeightsDimensions)); + V1_0::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, - OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionBiasDimensions)); + V1_0::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); @@ -196,15 +196,15 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, // 20: The activation function: A value indicating the activation function: // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. AddTensorOperand(model, activationFunctionDimensions, - activationFunctionValue, OperandType::INT32); + activationFunctionValue, V1_0::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, OperandType::FLOAT32); + cellClippingThresholdValue, V1_0::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, OperandType::FLOAT32); + projectionClippingThresholdValue, V1_0::OperandType::FLOAT32); // Outputs: // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with @@ -261,7 +261,7 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, float* outputData = static_cast<float*>(static_cast<void*>(outputMemory->getPointer())); // make the prepared model and run the execution - android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver); + android::sp<V1_0::IPreparedModel> preparedModel = PrepareModel(model, *driver); if (preparedModel.get() != nullptr) { Execute(preparedModel, request); |