wehub-resource-sync
138 lines
5.1 KiB
C++
138 lines
5.1 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 <stdint.h>
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#include "tensorflow/lite/core/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 segment_sum {
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static const int kInputDataTensor = 0;
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static const int kInputSegmentIdsTensor = 1;
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static const int kOutputTensor = 0;
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TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
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const TfLiteTensor* data,
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const TfLiteTensor* segment_ids,
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TfLiteTensor* output) {
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// Segment ids should be of same cardinality as first input dimension and they
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// should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
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const int segment_id_size = segment_ids->dims->data[0];
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TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
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int previous_segment_id = -1;
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for (int i = 0; i < segment_id_size; i++) {
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const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
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if (i == 0) {
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TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
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} else {
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int delta = current_segment_id - previous_segment_id;
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TF_LITE_ENSURE(context, delta == 0 || delta == 1);
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}
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previous_segment_id = current_segment_id;
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}
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const int max_index = previous_segment_id;
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const int data_rank = NumDimensions(data);
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TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
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output_shape->data[0] = max_index + 1;
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for (int i = 1; i < data_rank; ++i) {
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output_shape->data[i] = data->dims->data[i];
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}
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return context->ResizeTensor(context, output, output_shape);
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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), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* data;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputDataTensor, &data));
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const TfLiteTensor* segment_ids;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputSegmentIdsTensor,
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&segment_ids));
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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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TF_LITE_ENSURE(context,
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data->type == kTfLiteInt32 || data->type == kTfLiteFloat32);
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TF_LITE_ENSURE_EQ(context, segment_ids->type, kTfLiteInt32);
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if (!IsConstantOrPersistentTensor(data) ||
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!IsConstantOrPersistentTensor(segment_ids)) {
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SetTensorToDynamic(output);
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return kTfLiteOk;
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}
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return ResizeOutputTensor(context, data, segment_ids, output);
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* data;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputDataTensor, &data));
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const TfLiteTensor* segment_ids;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputSegmentIdsTensor,
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&segment_ids));
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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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if (IsDynamicTensor(output)) {
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TF_LITE_ENSURE_OK(context,
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ResizeOutputTensor(context, data, segment_ids, output));
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}
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#define TF_LITE_SEGMENT_SUM(dtype) \
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reference_ops::SegmentSum<dtype>( \
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GetTensorShape(data), GetTensorData<dtype>(data), \
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GetTensorShape(segment_ids), GetTensorData<int32_t>(segment_ids), \
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GetTensorShape(output), GetTensorData<dtype>(output));
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switch (data->type) {
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case kTfLiteInt32:
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TF_LITE_SEGMENT_SUM(int32_t);
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break;
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case kTfLiteFloat32:
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TF_LITE_SEGMENT_SUM(float);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Currently SegmentSum doesn't support type: %s",
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TfLiteTypeGetName(data->type));
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return kTfLiteError;
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}
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#undef TF_LITE_SEGMENT_SUM
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return kTfLiteOk;
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}
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} // namespace segment_sum
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TfLiteRegistration* Register_SEGMENT_SUM() {
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static TfLiteRegistration r = {nullptr, nullptr, segment_sum::Prepare,
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segment_sum::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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