wehub-resource-sync
291 lines
9.7 KiB
C++
291 lines
9.7 KiB
C++
/* Copyright 2018 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/arg_min_max.h"
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#include <stdint.h>
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#include <functional>
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#include "tensorflow/lite/core/c/builtin_op_data.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/optimized/optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.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 arg_min_max {
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constexpr int kInputTensor = 0;
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constexpr int kAxis = 1;
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constexpr int kOutputTensor = 0;
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TfLiteStatus ResizeOutput(TfLiteContext* context, const TfLiteTensor* input,
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const TfLiteTensor* axis, TfLiteTensor* output) {
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int axis_value;
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// Retrieve all 8 bytes when axis type is kTfLiteInt64 to avoid data loss.
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if (axis->type == kTfLiteInt64) {
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axis_value = static_cast<int>(*GetTensorData<int64_t>(axis));
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} else {
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axis_value = *GetTensorData<int>(axis);
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}
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if (axis_value < 0) {
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axis_value += NumDimensions(input);
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}
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TF_LITE_ENSURE(context, axis_value >= 0);
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TF_LITE_ENSURE(context, axis_value < NumDimensions(input));
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// Copy the input dimensions to output except the axis dimension.
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TfLiteIntArray* output_dims = TfLiteIntArrayCreate(NumDimensions(input) - 1);
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int j = 0;
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for (int i = 0; i < NumDimensions(input); ++i) {
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if (i != axis_value) {
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output_dims->data[j] = SizeOfDimension(input, i);
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++j;
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}
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}
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return context->ResizeTensor(context, output, output_dims);
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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* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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const TfLiteTensor* axis;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kAxis, &axis));
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// Make sure the axis is only 1 dimension.
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TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
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// Make sure the axis is only either int32 or int64.
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TF_LITE_ENSURE(context,
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axis->type == kTfLiteInt32 || axis->type == kTfLiteInt64);
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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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auto* params = reinterpret_cast<TfLiteArgMaxParams*>(node->builtin_data);
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switch (params->output_type) {
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case kTfLiteInt32:
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output->type = kTfLiteInt32;
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break;
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case kTfLiteInt64:
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output->type = kTfLiteInt64;
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Unknown index output data type: %d",
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params->output_type);
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return kTfLiteError;
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}
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// Check conditions for different types.
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switch (input->type) {
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case kTfLiteFloat32:
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case kTfLiteUInt8:
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case kTfLiteInt8:
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case kTfLiteInt32:
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case kTfLiteBool:
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Unknown input type: %d, only float32, int types "
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"and bool are supported",
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input->type);
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return kTfLiteError;
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}
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TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
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if (IsConstantOrPersistentTensor(axis)) {
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TF_LITE_ENSURE_STATUS(ResizeOutput(context, input, axis, output));
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} else {
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SetTensorToDynamic(output);
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) {
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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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const TfLiteTensor* axis;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kAxis, &axis));
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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_STATUS(ResizeOutput(context, input, axis, output));
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}
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#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type) \
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optimized_ops::ArgMinMax( \
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GetTensorShape(input), GetTensorData<data_type>(input), \
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GetTensorData<axis_type>(axis), GetTensorShape(output), \
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GetTensorData<output_type>(output), is_arg_max)
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if (axis->type == kTfLiteInt32) {
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switch (output->type) {
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case kTfLiteInt32: {
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switch (input->type) {
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case kTfLiteFloat32:
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TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t);
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break;
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case kTfLiteUInt8:
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TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int32_t);
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break;
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case kTfLiteInt8:
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TF_LITE_ARG_MIN_MAX(int8_t, int32_t, int32_t);
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break;
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case kTfLiteInt32:
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TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int32_t);
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break;
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case kTfLiteBool:
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TF_LITE_ARG_MIN_MAX(bool, int32_t, int32_t);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Only float32, uint8, int8, int32 and bool are "
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"supported currently, got %s.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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} break;
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case kTfLiteInt64: {
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switch (input->type) {
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case kTfLiteFloat32:
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TF_LITE_ARG_MIN_MAX(float, int32_t, int64_t);
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break;
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case kTfLiteUInt8:
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TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int64_t);
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break;
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case kTfLiteInt8:
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TF_LITE_ARG_MIN_MAX(int8_t, int32_t, int64_t);
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break;
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case kTfLiteInt32:
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TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int64_t);
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break;
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case kTfLiteBool:
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TF_LITE_ARG_MIN_MAX(bool, int32_t, int64_t);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Only float32, uint8, int8, int32 and bool are "
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"supported currently, got %s.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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} break;
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default:
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TF_LITE_KERNEL_LOG(
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context, "Only int32 and int64 are supported currently, got %s.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else {
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switch (output->type) {
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case kTfLiteInt32: {
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switch (input->type) {
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case kTfLiteFloat32:
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TF_LITE_ARG_MIN_MAX(float, int64_t, int32_t);
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break;
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case kTfLiteUInt8:
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TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int32_t);
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break;
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case kTfLiteInt8:
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TF_LITE_ARG_MIN_MAX(int8_t, int64_t, int32_t);
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break;
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case kTfLiteInt32:
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TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int32_t);
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break;
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case kTfLiteBool:
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TF_LITE_ARG_MIN_MAX(bool, int64_t, int32_t);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Only float32, uint8, int8, int32 and bool are "
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"supported currently, got %s.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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} break;
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case kTfLiteInt64: {
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switch (input->type) {
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case kTfLiteFloat32:
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TF_LITE_ARG_MIN_MAX(float, int64_t, int64_t);
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break;
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case kTfLiteUInt8:
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TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int64_t);
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break;
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case kTfLiteInt8:
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TF_LITE_ARG_MIN_MAX(int8_t, int64_t, int64_t);
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break;
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case kTfLiteInt32:
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TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int64_t);
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break;
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case kTfLiteBool:
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TF_LITE_ARG_MIN_MAX(bool, int64_t, int64_t);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Only float32, uint8, int8, int32 and bool are "
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"supported currently, got %s.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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} break;
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default:
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TF_LITE_KERNEL_LOG(
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context, "Only int32 and int64 are supported currently, got %s.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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}
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#undef TF_LITE_ARG_MIN_MAX
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return kTfLiteOk;
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}
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TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) {
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return Eval(context, node, false);
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}
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TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) {
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return Eval(context, node, true);
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}
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} // namespace arg_min_max
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TfLiteRegistration* Register_ARG_MAX() {
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static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare,
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arg_min_max::ArgMaxEval};
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return &r;
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}
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TfLiteRegistration* Register_ARG_MIN() {
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static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare,
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arg_min_max::ArgMinEval};
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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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