wehub-resource-sync
96 lines
3.9 KiB
C++
96 lines
3.9 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
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#include <algorithm>
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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#include "tensorflow/lite/kernels/internal/reference/broadcast_loop.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace reference_ops {
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// Broadcast prelu to output_shape for quantized uint8_t/int8_t data.
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template <typename T, typename U>
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inline void BroadcastPrelu4DSlow(
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const PreluParams& params, const RuntimeShape& input_shape,
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const T* input_data, const RuntimeShape& alpha_shape, const U* alpha_data,
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const RuntimeShape& output_shape, T* output_data) {
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const int32_t quantized_min = std::numeric_limits<T>::min();
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const int32_t quantized_max = std::numeric_limits<T>::max();
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auto op = [¶ms, quantized_min, quantized_max](T input_val, U alpha_val) {
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const int32_t input_value = params.input_offset + input_val;
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int32_t output_value;
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if (input_value >= 0) {
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output_value = MultiplyByQuantizedMultiplier(
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input_value, params.output_multiplier_1, params.output_shift_1);
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} else {
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const int32_t alpha_value = params.alpha_offset + alpha_val;
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output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value,
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params.output_multiplier_2,
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params.output_shift_2);
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}
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output_value += params.output_offset;
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const int32_t clamped_output =
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std::min(quantized_max, std::max(quantized_min, output_value));
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return static_cast<T>(clamped_output);
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};
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BroadcastBinaryOpSimple(input_shape, input_data, alpha_shape, alpha_data,
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output_shape, output_data, op);
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}
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template <typename T, typename U>
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inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape,
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const T* input_data, const RuntimeShape& alpha_shape,
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const U* alpha_data, const RuntimeShape& output_shape,
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T* output_data) {
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const int32_t quantized_min = std::numeric_limits<T>::min();
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const int32_t quantized_max = std::numeric_limits<T>::max();
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const int flat_size =
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MatchingElementsSize(input_shape, alpha_shape, output_shape);
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for (int i = 0; i < flat_size; ++i) {
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const int32_t input_value = params.input_offset + input_data[i];
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int32_t output_value;
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if (input_value >= 0) {
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output_value = MultiplyByQuantizedMultiplier(
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input_value, params.output_multiplier_1, params.output_shift_1);
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} else {
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const int32_t alpha_value = params.alpha_offset + alpha_data[i];
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output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value,
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params.output_multiplier_2,
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params.output_shift_2);
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}
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output_value += params.output_offset;
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const int32_t clamped_output =
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std::min(quantized_max, std::max(quantized_min, output_value));
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output_data[i] = static_cast<T>(clamped_output);
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}
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}
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} // namespace reference_ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
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