diff options
Diffstat (limited to 'runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp')
-rw-r--r-- | runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp | 60 |
1 files changed, 60 insertions, 0 deletions
diff --git a/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp b/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp new file mode 100644 index 000000000..0484ac4b5 --- /dev/null +++ b/runtimes/tests/neural_networks_test/generated/models/lstm3_state2.model.cpp @@ -0,0 +1,60 @@ +// Generated file (from: lstm3_state2.mod.py). Do not edit +void CreateModel(Model *model) { + OperandType type9(Type::FLOAT32, {}); + OperandType type8(Type::INT32, {}); + OperandType type5(Type::TENSOR_FLOAT32, {0}); + OperandType type4(Type::TENSOR_FLOAT32, {16,20}); + OperandType type6(Type::TENSOR_FLOAT32, {2, 16}); + OperandType type7(Type::TENSOR_FLOAT32, {2, 20}); + OperandType type0(Type::TENSOR_FLOAT32, {2, 5}); + OperandType type10(Type::TENSOR_FLOAT32, {2, 80}); + OperandType type2(Type::TENSOR_FLOAT32, {20, 16}); + OperandType type1(Type::TENSOR_FLOAT32, {20, 5}); + OperandType type3(Type::TENSOR_FLOAT32, {20}); + // Phase 1, operands + auto input = model->addOperand(&type0); + auto input_to_input_weights = model->addOperand(&type1); + auto input_to_forget_weights = model->addOperand(&type1); + auto input_to_cell_weights = model->addOperand(&type1); + auto input_to_output_weights = model->addOperand(&type1); + auto recurrent_to_intput_weights = model->addOperand(&type2); + auto recurrent_to_forget_weights = model->addOperand(&type2); + auto recurrent_to_cell_weights = model->addOperand(&type2); + auto recurrent_to_output_weights = model->addOperand(&type2); + auto cell_to_input_weights = model->addOperand(&type3); + auto cell_to_forget_weights = model->addOperand(&type3); + auto cell_to_output_weights = model->addOperand(&type3); + auto input_gate_bias = model->addOperand(&type3); + auto forget_gate_bias = model->addOperand(&type3); + auto cell_gate_bias = model->addOperand(&type3); + auto output_gate_bias = model->addOperand(&type3); + auto projection_weights = model->addOperand(&type4); + auto projection_bias = model->addOperand(&type5); + auto output_state_in = model->addOperand(&type6); + auto cell_state_in = model->addOperand(&type7); + auto activation_param = model->addOperand(&type8); + auto cell_clip_param = model->addOperand(&type9); + auto proj_clip_param = model->addOperand(&type9); + auto scratch_buffer = model->addOperand(&type10); + auto output_state_out = model->addOperand(&type6); + auto cell_state_out = model->addOperand(&type7); + auto output = model->addOperand(&type6); + // Phase 2, operations + static int32_t activation_param_init[] = {4}; + model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1); + static float cell_clip_param_init[] = {0.0f}; + model->setOperandValue(cell_clip_param, cell_clip_param_init, sizeof(float) * 1); + static float proj_clip_param_init[] = {0.0f}; + model->setOperandValue(proj_clip_param, proj_clip_param_init, sizeof(float) * 1); + model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output}); + // Phase 3, inputs and outputs + model->identifyInputsAndOutputs( + {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in}, + {scratch_buffer, output_state_out, cell_state_out, output}); + assert(model->isValid()); +} + +bool is_ignored(int i) { + static std::set<int> ignore = {0}; + return ignore.find(i) != ignore.end(); +} |