91 lines
3.3 KiB
C++
91 lines
3.3 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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#include <stdint.h>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace add_n {
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constexpr int kInputTensor1 = 0;
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constexpr int kOutputTensor = 0;
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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int num_inputs = NumInputs(node);
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TF_LITE_ENSURE(context, num_inputs >= 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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output->type = input1->type;
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// Check that all input tensors have the same shape and type.
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for (int i = kInputTensor1 + 1; i < num_inputs; ++i) {
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const TfLiteTensor* input = GetInput(context, node, i);
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TF_LITE_ENSURE(context, HaveSameShapes(input1, input));
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TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input->type);
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}
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// Use the first input node's dimension to be the dimension of the output
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// node.
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TfLiteIntArray* input1_dims = input1->dims;
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TfLiteIntArray* output_dims = TfLiteIntArrayCopy(input1_dims);
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return context->ResizeTensor(context, output, output_dims);
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}
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template <typename T>
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void EvalAddN(TfLiteContext* context, TfLiteNode* node) {
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// TODO(haoliang): Initialize all_inputs only once during init.
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VectorOfTensors<T> all_inputs(*context, *node->inputs);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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int num_inputs = NumInputs(node);
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const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
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reference_ops::AddN<T>(GetTensorShape(input1), num_inputs, all_inputs.data(),
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GetTensorData<T>(output));
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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if (output->type == kTfLiteFloat32) {
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EvalAddN<float>(context, node);
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} else if (output->type == kTfLiteInt32) {
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EvalAddN<int32_t>(context, node);
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} else {
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context->ReportError(context,
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"AddN only supports FLOAT32|INT32 now, got %s.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace add_n
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TfLiteRegistration* Register_ADD_N() {
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static TfLiteRegistration r = {/*init*/ nullptr, /*free*/ nullptr,
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add_n::Prepare, add_n::Eval};
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return &r;
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}
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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