| /* 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_PRELU_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ |
| |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/compatibility.h" |
| #include "tensorflow/lite/kernels/internal/types.h" |
| |
| namespace tflite { |
| |
| namespace reference_ops { |
| |
| // Broadcast prelu to output_shape for quantized uint8/int8 data. |
| template <typename T> |
| inline void BroadcastPrelu4DSlow( |
| const PreluParams& params, const RuntimeShape& input_shape, |
| const T* input_data, const RuntimeShape& alpha_shape, const T* alpha_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| TFLITE_DCHECK_LE(input_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(alpha_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 4); |
| const RuntimeShape extended_output_shape = |
| RuntimeShape::ExtendedShape(4, output_shape); |
| NdArrayDesc<4> desc1; |
| NdArrayDesc<4> desc2; |
| NdArrayDescsForElementwiseBroadcast(input_shape, alpha_shape, &desc1, &desc2); |
| |
| for (int b = 0; b < extended_output_shape.Dims(0); ++b) { |
| for (int y = 0; y < extended_output_shape.Dims(1); ++y) { |
| for (int x = 0; x < extended_output_shape.Dims(2); ++x) { |
| for (int c = 0; c < extended_output_shape.Dims(3); ++c) { |
| int output_index = Offset(extended_output_shape, b, y, x, c); |
| int input_index = SubscriptToIndex(desc1, b, y, x, c); |
| const int32 input_value = |
| params.input_offset + input_data[input_index]; |
| int32 output_value; |
| if (input_value >= 0) { |
| output_value = MultiplyByQuantizedMultiplier( |
| input_value, params.output_multiplier_1, params.output_shift_1); |
| } else { |
| auto alpha_index = SubscriptToIndex(desc2, b, y, x, c); |
| const int32 alpha_value = |
| params.alpha_offset + alpha_data[alpha_index]; |
| |
| output_value = MultiplyByQuantizedMultiplier( |
| input_value * alpha_value, params.output_multiplier_2, |
| params.output_shift_2); |
| } |
| output_value += params.output_offset; |
| |
| const int32 quantized_min = std::numeric_limits<T>::min(); |
| const int32 quantized_max = std::numeric_limits<T>::max(); |
| const int32 clamped_output = |
| std::min(quantized_max, std::max(quantized_min, output_value)); |
| output_data[output_index] = static_cast<T>(clamped_output); |
| } |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape, |
| const T* input_data, const RuntimeShape& alpha_shape, |
| const T* alpha_data, const RuntimeShape& output_shape, |
| T* output_data) { |
| const int32 quantized_min = std::numeric_limits<T>::min(); |
| const int32 quantized_max = std::numeric_limits<T>::max(); |
| |
| const int flat_size = |
| MatchingElementsSize(input_shape, alpha_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| const int32 input_value = params.input_offset + input_data[i]; |
| int32 output_value; |
| if (input_value >= 0) { |
| output_value = MultiplyByQuantizedMultiplier( |
| input_value, params.output_multiplier_1, params.output_shift_1); |
| } else { |
| const int32 alpha_value = params.alpha_offset + alpha_data[i]; |
| |
| output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value, |
| params.output_multiplier_2, |
| params.output_shift_2); |
| } |
| output_value += params.output_offset; |
| |
| const int32 clamped_output = |
| std::min(quantized_max, std::max(quantized_min, output_value)); |
| output_data[i] = static_cast<T>(clamped_output); |
| } |
| } |
| |
| } // namespace reference_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ |