// Generated file (from: svdf_state.mod.py). Do not edit
void CreateModel(Model *model) {
OperandType type5(Type::INT32, {});
OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
OperandType type3(Type::TENSOR_FLOAT32, {4});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto weights_feature = model->addOperand(&type1);
auto weights_time = model->addOperand(&type2);
auto bias = model->addOperand(&type3);
auto state_in = model->addOperand(&type4);
auto rank_param = model->addOperand(&type5);
auto activation_param = model->addOperand(&type5);
auto state_out = model->addOperand(&type4);
auto output = model->addOperand(&type6);
// Phase 2, operations
static int32_t rank_param_init[] = {1};
model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
static int32_t activation_param_init[] = {0};
model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input, weights_feature, weights_time, bias, state_in},
{state_out, output});
assert(model->isValid());
}
// Generated file (from: rnn_state_relaxed.mod.py). Do not edit
void CreateModel(Model *model) {
OperandType type5(Type::INT32, {});
OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
OperandType type3(Type::TENSOR_FLOAT32, {16});
OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
// Phase 1, operands
auto input = model->addOperand(&type0);
auto weights = model->addOperand(&type1);
auto recurrent_weights = model->addOperand(&type2);
auto bias = model->addOperand(&type3);
auto hidden_state_in = model->addOperand(&type4);
auto activation_param = model->addOperand(&type5);
auto hidden_state_out = model->addOperand(&type4);
auto output = model->addOperand(&type4);
// Phase 2, operations
static int32_t activation_param_init[] = {1};
model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
// Phase 3, inputs and outputs
model->identifyInputsAndOutputs(
{input, weights, recurrent_weights, bias, hidden_state_in},
{hidden_state_out, output});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
// Generated file (from: lstm2_relaxed.mod.py). Do not edit
void CreateModel(Model *model) {
OperandType type8(Type::FLOAT32, {});
OperandType type7(Type::INT32, {});
OperandType type5(Type::TENSOR_FLOAT32, {0,0});
OperandType type3(Type::TENSOR_FLOAT32, {0});
OperandType type9(Type::TENSOR_FLOAT32, {1, 12});
OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
OperandType type4(Type::TENSOR_FLOAT32, {4});
// 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(&type4);
auto cell_to_output_weights = model->addOperand(&type4);
auto input_gate_bias = model->addOperand(&type4);
auto forget_gate_bias = model->addOperand(&type4);
auto cell_gate_bias = model->addOperand(&type4);
auto output_gate_bias = model->addOperand(&type4);
auto projection_weights = model->addOperand(&type5);
auto projection_bias = model->addOperand(&type3);
auto output_state_in = model->addOperand(&type6);
auto cell_state_in = model->addOperand(&type6);
auto activation_param = model->addOperand(&type7);
auto cell_clip_param = model->addOperand(&type8);
auto proj_clip_param = model->addOperand(&type8);
auto scratch_buffer = model->addOperand(&type9);
auto output_state_out = model->addOperand(&type6);
auto cell_state_out = model->addOperand(&type6);
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});
// Phase 4: set relaxed execution
model->relaxComputationFloat32toFloat16(true);
assert(model->isValid());
}
int main()
{
type1();
printf("\n");
type2();
printf("\n");
type3();
printf("\n");
type4();
printf("\n");
type5();
printf("\n");
rectange_interview();
return 0;
}
