| /* Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ |
| |
| #include "tensorflow/lite/kernels/internal/types.h" |
| |
| namespace tflite { |
| |
| namespace reference_ops { |
| |
| template <typename T1, typename T2, typename T3, typename Cmp> |
| void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, |
| const T3* input2_data, const RuntimeShape& output_shape, |
| T2* output_data, const Cmp& cmp) { |
| TFLITE_DCHECK_GT(input1_shape.DimensionsCount(), 0); |
| TFLITE_DCHECK_EQ(input1_shape.DimensionsCount() - 1, |
| output_shape.DimensionsCount()); |
| int axis = input2_data[0]; |
| if (axis < 0) { |
| axis += input1_shape.DimensionsCount(); |
| } |
| const int axis_size = input1_shape.Dims(axis); |
| |
| int outer_size = 1; |
| for (int i = 0; i < axis; ++i) { |
| TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i)); |
| outer_size *= input1_shape.Dims(i); |
| } |
| |
| int inner_size = 1; |
| const int dims_count = input1_shape.DimensionsCount(); |
| for (int i = axis + 1; i < dims_count; ++i) { |
| TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1)); |
| inner_size *= input1_shape.Dims(i); |
| } |
| for (int outer = 0; outer < outer_size; ++outer) { |
| for (int inner = 0; inner < inner_size; ++inner) { |
| auto min_max_value = input1_data[outer * axis_size * inner_size + inner]; |
| T2 min_max_index = 0; |
| for (int i = 1; i < axis_size; ++i) { |
| const auto& curr_value = |
| input1_data[(outer * axis_size + i) * inner_size + inner]; |
| if (cmp(curr_value, min_max_value)) { |
| min_max_value = curr_value; |
| min_max_index = static_cast<T2>(i); |
| } |
| } |
| output_data[outer * inner_size + inner] = min_max_index; |
| } |
| } |
| } |
| } // namespace reference_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ |