diff options
Diffstat (limited to '1.2/HalPolicy.cpp')
| -rw-r--r-- | 1.2/HalPolicy.cpp | 82 |
1 files changed, 19 insertions, 63 deletions
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp index 7e9a2233..5d6274fc 100644 --- a/1.2/HalPolicy.cpp +++ b/1.2/HalPolicy.cpp @@ -2010,6 +2010,8 @@ bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, Conv bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) { + ALOGV("hal_1_2::HalPolicy::ConvertLstm()"); + // 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. @@ -2035,27 +2037,27 @@ bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, Conv // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size]. const ConstTensorPin inputToForgetWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 2)); // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size]. const ConstTensorPin inputToCellWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 3, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 3)); // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, input_size]. const ConstTensorPin inputToOutputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 4, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 4)); // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. const ConstTensorPin recurrentToForgetWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 6, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 6)); // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. const ConstTensorPin recurrentToCellWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 7, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 7)); // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [num_units, output_size]. const ConstTensorPin recurrentToOutputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 8, model, data); + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 8)); // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 13, model, data); @@ -2083,56 +2085,21 @@ bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, Conv // 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. const ConstTensorPin inputToInputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 1, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 1, true)); // 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. const ConstTensorPin recurrentToInputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 5, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 5, true)); // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. const ConstTensorPin cellToInputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 9, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 9, true)); // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. const ConstTensorPin cellToForgetWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 10, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 10, true)); // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. const ConstTensorPin cellToOutputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 11, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 11, true)); // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, @@ -2146,14 +2113,7 @@ bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, Conv // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape // [output_size, num_units]. const ConstTensorPin projectionWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 16, - model, - data, - g_DontPermute, - nullptr, - true); - + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 16, true)); // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, @@ -2196,14 +2156,8 @@ bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, Conv // Get the normalization tensors // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. // Used to rescale normalized inputs to activation at input gate. - const ConstTensorPin inputLayerNormWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 23, - model, - data, - g_DontPermute, - nullptr, - true); + const ConstTensorPin inputLayerNormWeightsPin + (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 23, true)); // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. // Used to rescale normalized inputs to activation at forget gate. @@ -2357,7 +2311,9 @@ bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, Conv IsDynamicTensor(cellStateOutInfo) || IsDynamicTensor(outputInfo)) { - return Fail("%s: Dynamic output tensors are not supported", __func__); + return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__, + IsDynamicTensor(scratchBufferInfo), IsDynamicTensor(outputStateOutInfo), + IsDynamicTensor(cellStateOutInfo), IsDynamicTensor(outputInfo)); } // Basic parameters |