wehub-resource-sync
115 lines
4.2 KiB
C++
115 lines
4.2 KiB
C++
/* Copyright 2021 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 "tensorflow/lite/kernels/internal/reference/broadcast_args.h"
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#include <algorithm>
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#include <cstdint>
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#include <memory>
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#include "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor.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 broadcast_args {
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constexpr int kShape1Tensor = 0;
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constexpr int kShape2Tensor = 1;
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constexpr int kOutputTensor = 0;
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struct BroadcastArgsContext {
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BroadcastArgsContext(TfLiteContext* context, TfLiteNode* node) {
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shape1 = GetInput(context, node, kShape1Tensor);
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shape2 = GetInput(context, node, kShape2Tensor);
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output = GetOutput(context, node, kOutputTensor);
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}
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const TfLiteTensor* shape1;
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const TfLiteTensor* shape2;
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TfLiteTensor* output;
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};
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TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node);
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE(context, NumInputs(node) == 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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BroadcastArgsContext op_context(context, node);
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TF_LITE_ENSURE(context, op_context.shape1->type == kTfLiteInt32 ||
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op_context.shape1->type == kTfLiteInt64);
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TF_LITE_ENSURE_EQ(context, op_context.shape1->type, op_context.shape2->type);
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TF_LITE_ENSURE_EQ(context, op_context.shape1->type, op_context.output->type);
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// Ensures the shapes are 1D tensor.
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.shape1), 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.shape2), 1);
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// Resizing the shape of the output tensor.
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TfLiteIntArray* output_shape = TfLiteIntArrayCreate(1);
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output_shape->data[0] = std::max(SizeOfDimension(op_context.shape1, 0),
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SizeOfDimension(op_context.shape2, 0));
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if (IsConstantOrPersistentTensor(op_context.shape1) &&
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IsConstantOrPersistentTensor(op_context.shape2)) {
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SetTensorToPersistentRo(op_context.output);
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TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, op_context.output,
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output_shape));
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return EvalImpl(context, node);
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}
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return context->ResizeTensor(context, op_context.output, output_shape);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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BroadcastArgsContext op_context(context, node);
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if (IsConstantOrPersistentTensor(op_context.output)) {
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return kTfLiteOk;
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} else {
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return EvalImpl(context, node);
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}
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}
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TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node) {
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BroadcastArgsContext op_context(context, node);
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#define TF_LITE_BROADCAST_ARG(data_type) \
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reference_ops::BroadcastArgs(GetTensorShape(op_context.shape1), \
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GetTensorData<data_type>(op_context.shape1), \
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GetTensorShape(op_context.shape2), \
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GetTensorData<data_type>(op_context.shape2), \
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GetTensorShape(op_context.output), \
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GetTensorData<data_type>(op_context.output))
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if (op_context.output->type == kTfLiteInt32) {
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TF_LITE_BROADCAST_ARG(int32_t);
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} else {
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TF_LITE_BROADCAST_ARG(int64_t);
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}
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#undef TF_LITE_BROADCAST_ARG
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return kTfLiteOk;
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}
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} // namespace broadcast_args
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TfLiteRegistration* Register_BROADCAST_ARGS() {
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static TfLiteRegistration r = {nullptr, nullptr, broadcast_args::Prepare,
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broadcast_args::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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