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/*
* Copyright (C) 2020 Texas Instruments Incorporated - http://www.ti.com/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "tensorflow/lite/kernels/internal/tensor.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tidl_op.h"
#include "subgraph_runtime.h"
#include <iostream>
using namespace std;
namespace tidl {
namespace tidl_subgraph_op {
using tflite::GetInput;
using tflite::GetOutput;
using tflite::GetTensorData;
using tflite::NumDimensions;
using tflite::NumInputs;
using tflite::NumOutputs;
struct OpData {
int dummy; // Use OpData to pass data from Init() to Prepare()/Eval()
};
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
auto* op_data = new OpData;
const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
// A single subgraph with subgraph index of 0
TidlInitSubgraph(1, 0);
return op_data;
}
void Free(TfLiteContext* context, void* buffer) {
TidlFreeSubgraph(1, 0);
delete reinterpret_cast<OpData*>(buffer);
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Populate the data initialized during Init() call
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// Get the input and output tensors
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 0);
// Allocate buffer for the output
int num_dims = NumDimensions(output);
TfLiteIntArray* output_size = TfLiteIntArrayCreate(num_dims);
for (int i=0; i<num_dims; ++i) {
output_size->data[i] = output->dims->data[i];
}
return context->ResizeTensor(context, output, output_size);
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
// Populate the data initialized during Init() call
OpData* data = reinterpret_cast<OpData*>(node->user_data);
// Get the input and output tensors
TfLiteTensor* output = GetOutput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 0);
float* input_data = input->data.f;
float* output_data = output->data.f;
#ifdef DEBUG
FILE *filePtr;
filePtr = fopen("floatArray_tidl_input","w");
std::cout << "input dimension: ";
int num_dims = NumDimensions(input);
int input_size = 1;
for (int i=0; i<num_dims; i++) {
input_size = input_size*input->dims->data[i];
std::cout << input->dims->data[i] << "x";
}
std::cout << std::endl;
for (int i = 0; i < input_size; i++) {
fprintf(filePtr, "%.10g\t", input_data[i]);
if (i%9 == 8)
fprintf(filePtr, "\n");
}
#endif
// A single subgraph with subgraph index of 0,
// batch size 1, sinlge input tensor, and single output tensor
TidlRunSubgraph(1, 0, 1, 1, 1, &input_data, &output_data);
#ifdef DEBUG
FILE *filePtrOut;
filePtrOut = fopen("floatArray_tidl_output","w");
std::cout << "output dimension: ";
num_dims = NumDimensions(output);
int output_size = 1;
for (int i=0; i<num_dims; i++) {
output_size = output_size*output->dims->data[i];
std::cout << output->dims->data[i] << "x";
}
std::cout << std::endl;
for (int i = 0; i < output_size; i++) {
fprintf(filePtrOut, "%.10g\t", output_data[i]);
if (i%9 == 8)
fprintf(filePtrOut, "\n");
}
#endif
return kTfLiteOk;
}
} //namespace tidl_subgraph_op
TfLiteRegistration* RegisterTidlSubgraphOp() {
static TfLiteRegistration r = {
tidl_subgraph_op::Init, tidl_subgraph_op::Free,
tidl_subgraph_op::Prepare, tidl_subgraph_op::Eval};
return &r;
}
}//namespace tidl
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