const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(model::operation::ResizeBilinearNode::Input::INPUT)};
- const auto height_index{node.param().height_index};
- const auto width_index{node.param().width_index};
- (void)height_index;
- (void)width_index;
auto ofm_alloc = _tensor_builder->at(ofm_index).get();
auto ifm_alloc = _tensor_builder->at(ifm_index).get();
const auto ofm_index{node.getOutputs().at(0)};
const auto ifm_index{node.getInputs().at(model::operation::ResizeBilinearNode::Input::INPUT)};
- const auto height_index{node.param().height_index};
- const auto width_index{node.param().width_index};
- (void)height_index;
- (void)width_index;
auto ofm_alloc = _tensor_builder->at(ofm_index).get();
auto ifm_alloc = _tensor_builder->at(ifm_index).get();
};
_map[ANEURALNETWORKS_RESIZE_BILINEAR] = [](const OperationFactory::Param &init_param,
- neurun::model::Operands &) {
+ neurun::model::Operands &operands) {
assert(init_param.input_count == 3 && init_param.output_count == 1);
OperandIndexSequence outputs{init_param.outputs[0]};
OperandIndexSequence inputs{init_param.inputs[0]};
operation::ResizeBilinearNode::Param param;
- param.height_index = OperandIndex{init_param.inputs[1]};
- param.width_index = OperandIndex{init_param.inputs[2]};
+ param.height_out = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>();
+ param.width_out = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>();
return new operation::ResizeBilinearNode{inputs, outputs, param};
};