wehub-resource-sync
216 lines
8.1 KiB
C++
216 lines
8.1 KiB
C++
/* Copyright 2019 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/c_api_types.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/internal/types.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 where {
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constexpr int kInputConditionTensor = 0;
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constexpr int kOutputTensor = 0;
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template <typename T>
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TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
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const TfLiteTensor* cond_tensor,
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TfLiteTensor* output_tensor) {
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// Output tensor should have shape:
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// (num_true, cond_rank), where num_true denotes the number of true values
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// in condition.
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const RuntimeShape& cond_shape = GetTensorShape(cond_tensor);
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const int size = cond_shape.FlatSize();
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const int cond_rank = cond_shape.DimensionsCount();
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const T* cond_data = GetTensorData<T>(cond_tensor);
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int true_count = 0;
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for (int i = 0; i < size; ++i) {
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if (cond_data[i] != T(0)) {
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true_count++;
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}
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}
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TfLiteIntArray* output_dims = TfLiteIntArrayCreate(2);
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output_dims->data[0] = true_count;
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output_dims->data[1] = cond_rank;
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return context->ResizeTensor(context, output_tensor, output_dims);
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}
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template <typename T>
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TfLiteStatus PrepareOutput(TfLiteContext* context,
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const TfLiteTensor* cond_tensor,
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TfLiteTensor* output) {
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// As output will be a 2D tensor of indices, use int64 to be consistent with
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// tensorflow.
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output->type = kTfLiteInt64;
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// Exit early if cond is a non-const tensor. Set output tensor to dynamic so
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// output size can be determined in Eval.
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if (!IsConstantOrPersistentTensor(cond_tensor)) {
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SetTensorToDynamic(output);
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return kTfLiteOk;
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}
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return ResizeOutputTensor<T>(context, cond_tensor, output);
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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* cond_tensor;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputConditionTensor,
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&cond_tensor));
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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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switch (cond_tensor->type) {
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case kTfLiteBool:
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return PrepareOutput<bool>(context, cond_tensor, output);
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case kTfLiteFloat32:
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return PrepareOutput<float>(context, cond_tensor, output);
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case kTfLiteInt64:
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return PrepareOutput<int64_t>(context, cond_tensor, output);
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case kTfLiteInt32:
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return PrepareOutput<int32_t>(context, cond_tensor, output);
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case kTfLiteInt8:
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return PrepareOutput<int8_t>(context, cond_tensor, output);
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case kTfLiteUInt8:
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return PrepareOutput<uint8_t>(context, cond_tensor, output);
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case kTfLiteUInt32:
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return PrepareOutput<uint32_t>(context, cond_tensor, output);
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default:
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TF_LITE_KERNEL_LOG(context,
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"Condition tensor has unsupported type: '%s'.",
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TfLiteTypeGetName(cond_tensor->type));
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return kTfLiteError;
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}
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* cond_tensor;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputConditionTensor,
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&cond_tensor));
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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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switch (cond_tensor->type) {
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case kTfLiteBool:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<bool>(context, cond_tensor, output));
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break;
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case kTfLiteFloat32:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<float>(context, cond_tensor, output));
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break;
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case kTfLiteInt64:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<int64_t>(context, cond_tensor, output));
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break;
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case kTfLiteInt32:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<int32_t>(context, cond_tensor, output));
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break;
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case kTfLiteInt8:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<int8_t>(context, cond_tensor, output));
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break;
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case kTfLiteUInt8:
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TF_LITE_ENSURE_OK(
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context, ResizeOutputTensor<uint8_t>(context, cond_tensor, output));
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break;
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case kTfLiteUInt32:
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TF_LITE_ENSURE_OK(context, ResizeOutputTensor<uint32_t>(
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context, cond_tensor, output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Condition tensor has unsupported type: '%s'.",
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TfLiteTypeGetName(cond_tensor->type));
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return kTfLiteError;
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}
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}
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TfLiteIntArray* dims = cond_tensor->dims;
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if (dims->size == 0) {
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// Scalar tensors are not supported.
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TF_LITE_KERNEL_LOG(context, "Where op requires condition w/ rank > 0");
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return kTfLiteError;
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}
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switch (cond_tensor->type) {
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case kTfLiteBool:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<bool>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteFloat32:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<float>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteInt64:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<int64_t>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteInt32:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<int32_t>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteInt8:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<int8_t>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteUInt8:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<uint8_t>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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case kTfLiteUInt32:
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reference_ops::SelectTrueCoords(GetTensorShape(cond_tensor),
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GetTensorData<uint32_t>(cond_tensor),
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GetTensorData<int64_t>(output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Condition tensor has unsupported type: '%s'.",
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TfLiteTypeGetName(cond_tensor->type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace where
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TfLiteRegistration* Register_WHERE() {
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static TfLiteRegistration r = {/*init*/ nullptr, /*free*/ nullptr,
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where::Prepare, where::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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