wehub-resource-sync
119 lines
4.1 KiB
C++
119 lines
4.1 KiB
C++
/* Copyright 2023 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 <cstring>
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#include <memory>
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#include "tensorflow/lite/core/c/c_api_types.h"
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#include "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/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 bitcast {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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TfLiteStatus CalculateShape(TfLiteContext* context, const TfLiteTensor* input,
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const TfLiteTensor* output,
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TfLiteIntArray** output_shape) {
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const int dims = NumDimensions(input);
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auto input_type = input->type;
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auto output_type = output->type;
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size_t input_type_size, output_type_size;
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TF_LITE_ENSURE_STATUS(GetSizeOfType(context, input_type, &input_type_size));
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TF_LITE_ENSURE_STATUS(GetSizeOfType(context, output_type, &output_type_size));
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TfLiteIntArray* shape = nullptr;
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if (input_type_size > output_type_size) {
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// If the input datatype T is larger than the output datatype type then the
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// shape changes from [...] to [..., sizeof(T)/sizeof(type)].
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shape = TfLiteIntArrayCreate(dims + 1);
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for (int i = 0; i < dims; ++i) {
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shape->data[i] = input->dims->data[i];
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}
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shape->data[dims] = input_type_size / output_type_size;
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} else if (input_type_size < output_type_size) {
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// If T is smaller than type, the operator requires that the rightmost
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// dimension be equal to sizeof(type)/sizeof(T). The shape then goes from
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// [..., sizeof(type)/sizeof(T)] to [...].
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TF_LITE_ENSURE_EQ(context, input->dims->data[dims - 1],
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output_type_size / input_type_size);
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shape = TfLiteIntArrayCreate(dims - 1);
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for (int i = 0; i < dims - 1; ++i) {
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shape->data[i] = input->dims->data[i];
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}
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} else {
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// Same element type size.
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shape = TfLiteIntArrayCopy(input->dims);
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}
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*output_shape = shape;
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return kTfLiteOk;
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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TfLiteIntArray* output_size = nullptr;
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TF_LITE_ENSURE_OK(context,
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CalculateShape(context, input, output, &output_size));
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return context->ResizeTensor(context, output, output_size);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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// Only copy data if input and output do not share a buffer.
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if (output->data.data != input->data.data) {
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memcpy(output->data.data, input->data.data, input->bytes);
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}
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return kTfLiteOk;
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}
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} // namespace bitcast
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TfLiteRegistration* Register_BITCAST() {
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static TfLiteRegistration r = {
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nullptr,
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nullptr,
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bitcast::Prepare,
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bitcast::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0,
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/*registration_external=*/nullptr,
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/*async_kernel=*/nullptr,
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kTfLiteInplaceOpInput0Shared | kTfLiteInplaceOpDataUnmodified};
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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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