diff options
Diffstat (limited to 'test/Lstm.cpp')
| -rw-r--r-- | test/Lstm.cpp | 35 |
1 files changed, 27 insertions, 8 deletions
diff --git a/test/Lstm.cpp b/test/Lstm.cpp index ea0405c6..66f2cf02 100644 --- a/test/Lstm.cpp +++ b/test/Lstm.cpp @@ -56,6 +56,17 @@ bool TolerantCompareEqual(float a, float b, float tolerance = 0.00001f) return rd < tolerance; } +// Helper function to create an OperandLifeTime::NO_VALUE for testing. +// To be used on optional input operands that have no values - these are valid and should be tested. +OperandLifeTime CreateNoValueLifeTime(const hidl_vec<uint32_t>& dimensions) +{ + // Only create a NO_VALUE for optional operands that have no elements + if (dimensions.size() == 0 || dimensions[0] == 0) + { + return OperandLifeTime::NO_VALUE; + } + return OperandLifeTime::CONSTANT_COPY; +} } // anonymous namespace // Add our own tests here since we fail the lstm tests which Google supplies (because of non-const weights) @@ -126,7 +137,8 @@ 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); + AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue, 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); @@ -138,7 +150,8 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, // 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); + AddTensorOperand(model, recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + 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); @@ -149,13 +162,17 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, // [num_units, output_size]. 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); + AddTensorOperand(model, cellToInputWeightsDimensions, cellToInputWeightsValue, + 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); + AddTensorOperand(model, cellToForgetWeightsDimensions, cellToForgetWeightsValue, + 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); + AddTensorOperand(model, cellToOutputWeightsDimensions, cellToOutputWeightsValue, + 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); + AddTensorOperand(model, inputGateBiasDimensions, inputGateBiasValue, + 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]. @@ -164,9 +181,11 @@ void LstmTestImpl(const hidl_vec<uint32_t>& inputDimensions, AddTensorOperand(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); + AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue, + 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); + AddTensorOperand(model, projectionBiasDimensions, projectionBiasValue, + 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); |