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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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 "support/tflite/kernels/SquaredDifference.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include <iostream>
namespace tflite
{
namespace ops
{
namespace custom
{
namespace nnfw
{
namespace SquaredDifference
{
void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length)
{
return nullptr;
}
void FreeSquaredDifference(TfLiteContext *context, void *buffer) {}
TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node)
{
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor *input1 = GetInput(context, node, 0);
const TfLiteTensor *input2 = GetInput(context, node, 1);
TfLiteTensor *output = GetOutput(context, node, 0);
TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
TF_LITE_ENSURE_EQ(context, input1->type, output->type);
return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input1->dims));
}
TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node)
{
const TfLiteTensor *input1 = GetInput(context, node, 0);
const TfLiteTensor *input2 = GetInput(context, node, 1);
TfLiteTensor *output = GetOutput(context, node, 0);
size_t elements = NumElements(input1);
switch (input1->type)
{
case kTfLiteFloat32:
{
const float *in1 = input1->data.f;
const float *in2 = input2->data.f;
const float *in_end1 = in1 + elements;
float *out = output->data.f;
for (; in1 < in_end1; in1++, in2++, out++)
*out = ((*in1 - *in2) * (*in1 - *in2));
return kTfLiteOk;
}
case kTfLiteInt32:
{
const int *in1 = input1->data.i32;
const int *in2 = input2->data.i32;
const int *in_end1 = in1 + elements;
int *out = output->data.i32;
for (; in1 < in_end1; in1++, in2++, out++)
*out = ((*in1 - *in2) * (*in1 - *in2));
return kTfLiteOk;
}
case kTfLiteInt64:
{
const int64_t *in1 = input1->data.i64;
const int64_t *in2 = input1->data.i64;
const int64_t *in_end1 = in1 + elements;
int64_t *out = output->data.i64;
for (; in1 < in_end1; in1++, in2++, out++)
*out = ((*in1 - *in2) * (*in1 - *in2));
return kTfLiteOk;
}
default:
{
context->ReportError(context, "InputType is %d Unsupported", input1->type);
return kTfLiteError;
}
}
}
} // namespace SquaredDifference
} // nnfw
} // namespace custom
} // namespace ops
} // namespace tflite
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