81 lines
3.2 KiB
C++
81 lines
3.2 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_BINARY_FUNCTION_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_
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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/types.h"
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namespace tflite {
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namespace reference_ops {
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// Also appears to duplicate MinimumMaximum.
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//
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// R: Result type. T1: Input 1 type. T2: Input 2 type.
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template <typename R, typename T1, typename T2>
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inline void BroadcastBinaryFunction4DSlow(
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const RuntimeShape& unextended_input1_shape, const T1* input1_data,
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const RuntimeShape& unextended_input2_shape, const T2* input2_data,
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const RuntimeShape& unextended_output_shape, R* output_data,
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R (*func)(T1, T2)) {
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TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
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TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
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TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
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const RuntimeShape output_shape =
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RuntimeShape::ExtendedShape(4, unextended_output_shape);
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NdArrayDesc<4> desc1;
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NdArrayDesc<4> desc2;
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NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
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unextended_input2_shape, &desc1, &desc2);
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for (int b = 0; b < output_shape.Dims(0); ++b) {
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for (int y = 0; y < output_shape.Dims(1); ++y) {
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for (int x = 0; x < output_shape.Dims(2); ++x) {
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for (int c = 0; c < output_shape.Dims(3); ++c) {
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auto out_idx = Offset(output_shape, b, y, x, c);
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auto in1_idx = SubscriptToIndex(desc1, b, y, x, c);
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auto in2_idx = SubscriptToIndex(desc2, b, y, x, c);
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auto in1_val = input1_data[in1_idx];
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auto in2_val = input2_data[in2_idx];
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output_data[out_idx] = func(in1_val, in2_val);
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}
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}
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}
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}
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}
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// R: Result type. T1: Input 1 type. T2: Input 2 type.
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template <typename R, typename T1, typename T2>
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inline void BinaryFunction(const RuntimeShape& input1_shape,
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const T1* input1_data,
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const RuntimeShape& input2_shape,
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const T2* input2_data,
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const RuntimeShape& output_shape, R* output_data,
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R (*func)(T1, T2)) {
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const int flat_size =
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MatchingFlatSize(input1_shape, input2_shape, output_shape);
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for (int i = 0; i < flat_size; ++i) {
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output_data[i] = func(input1_data[i], input2_data[i]);
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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_BINARY_FUNCTION_H_
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