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