118 lines
3.9 KiB
C++
118 lines
3.9 KiB
C++
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#include <stdint.h>
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/* Copyright 2017 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 <string.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 expand_dims {
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// Input indices
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enum { kInput = 0, kAxis };
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namespace {
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TfLiteStatus ExpandTensorDim(TfLiteContext* context, const TfLiteTensor& input,
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int axis, TfLiteTensor* output) {
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const TfLiteIntArray& input_dims = *input.dims;
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if (axis < 0) {
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axis = input_dims.size + 1 + axis;
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}
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TF_LITE_ENSURE(context, axis <= input_dims.size);
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TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims.size + 1);
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for (int i = 0; i < output_dims->size; ++i) {
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if (i < axis) {
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output_dims->data[i] = input_dims.data[i];
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} else if (i == axis) {
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output_dims->data[i] = 1;
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} else {
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output_dims->data[i] = input_dims.data[i - 1];
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}
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}
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return context->ResizeTensor(context, output, output_dims);
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}
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TfLiteStatus GetAxisValueFromTensor(TfLiteContext* context,
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const TfLiteTensor& axis, int* axis_value) {
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TF_LITE_ENSURE_EQ(context, NumElements(&axis), 1);
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switch (axis.type) {
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case kTfLiteInt32:
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*axis_value = *GetTensorData<int32_t>(&axis);
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return kTfLiteOk;
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case kTfLiteInt64:
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*axis_value = *GetTensorData<int64_t>(&axis);
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return kTfLiteOk;
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default:
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return kTfLiteError;
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}
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}
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} // namespace
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* input = GetInput(context, node, kInput);
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const TfLiteTensor* axis = GetInput(context, node, kAxis);
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TfLiteTensor* output = GetOutput(context, node, 0);
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output->type = input->type;
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if (IsConstantTensor(axis)) {
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int axis_value;
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TF_LITE_ENSURE_OK(context,
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GetAxisValueFromTensor(context, *axis, &axis_value));
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return ExpandTensorDim(context, *input, axis_value, output);
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}
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SetTensorToDynamic(output);
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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// Just copy input to output.
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const TfLiteTensor* input = GetInput(context, node, kInput);
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TfLiteTensor* output = GetOutput(context, node, 0);
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const TfLiteTensor* axis = GetInput(context, node, kAxis);
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if (IsDynamicTensor(output)) {
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int axis_value;
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TF_LITE_ENSURE_OK(context,
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GetAxisValueFromTensor(context, *axis, &axis_value));
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TF_LITE_ENSURE_OK(context,
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ExpandTensorDim(context, *input, axis_value, output));
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}
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if (output->type == kTfLiteString) {
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TfLiteTensorRealloc(input->bytes, output);
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}
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memcpy(output->data.raw, input->data.raw, input->bytes);
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return kTfLiteOk;
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}
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} // namespace expand_dims
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TfLiteRegistration* Register_EXPAND_DIMS() {
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static TfLiteRegistration r = {nullptr, nullptr, expand_dims::Prepare,
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expand_dims::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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