112 lines
3.8 KiB
C++
112 lines
3.8 KiB
C++
/* Copyright 2020 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/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/optimized/optimized_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 cumsum {
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static const int kInputTensor = 0;
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static const int kAxisTensor = 1;
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static const int kOutputTensor = 0;
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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, kInputTensor);
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const TfLiteTensor* axis = GetInput(context, node, kAxisTensor);
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TF_LITE_ENSURE(context, input->type == kTfLiteInt32 ||
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input->type == kTfLiteFloat32 ||
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input->type == kTfLiteInt64);
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TF_LITE_ENSURE_EQ(context, axis->type, kTfLiteInt32);
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TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
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TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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TfLiteIntArray* output_shape = TfLiteIntArrayCopy(input->dims);
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return context->ResizeTensor(context, output, output_shape);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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const TfLiteTensor* axis_tensor = GetInput(context, node, kAxisTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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auto* params = reinterpret_cast<TfLiteCumsumParams*>(node->builtin_data);
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int axis = *GetTensorData<int>(axis_tensor);
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if (axis < 0) axis += NumDimensions(input);
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if (axis < 0 || axis >= NumDimensions(input)) {
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TF_LITE_KERNEL_LOG(context, "Invalid axis: ", axis);
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return kTfLiteError;
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}
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switch (input->type) {
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case kTfLiteInt32: {
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optimized_ops::CumSum(GetTensorData<int>(input), GetTensorShape(input),
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axis, params->exclusive, params->reverse,
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GetTensorData<int>(output));
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break;
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}
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case kTfLiteInt64: {
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optimized_ops::CumSum(GetTensorData<int64_t>(input),
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GetTensorShape(input), axis, params->exclusive,
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params->reverse, GetTensorData<int64_t>(output));
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break;
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}
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case kTfLiteFloat32: {
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optimized_ops::CumSum(GetTensorData<float>(input), GetTensorShape(input),
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axis, params->exclusive, params->reverse,
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GetTensorData<float>(output));
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break;
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}
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default: {
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TF_LITE_KERNEL_LOG(
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context,
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"Unsupported input type, cumsum only supports int32 & float32.");
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return kTfLiteError;
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}
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}
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return kTfLiteOk;
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}
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} // namespace cumsum
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TfLiteRegistration* Register_CUMSUM() {
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static TfLiteRegistration r = {nullptr, nullptr, cumsum::Prepare,
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cumsum::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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