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/* Copyright 2018 The TensorFlow Authors. 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 "tensorflow/contrib/lite/context.h"
#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"
#include "tensorflow/contrib/lite/string_util.h"
namespace tflite {
namespace ops {
namespace builtin {
namespace comparisons {
constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;
TfLiteStatus ComparisonPrepare(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, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
// Don't support string and bool.
TF_LITE_ENSURE(context,
input1->type != kTfLiteString || input1->type != kTfLiteBool);
// Currently only support tensors have the same type.
TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
output->type = kTfLiteBool;
bool requires_broadcast = !HaveSameShapes(input1, input2);
TfLiteIntArray* output_size = nullptr;
if (requires_broadcast) {
TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
context, input1, input2, &output_size));
} else {
output_size = TfLiteIntArrayCopy(input1->dims);
}
return context->ResizeTensor(context, output, output_size);
}
#define TF_LITE_COMPARISON(type, opname, requires_broadcast) \
requires_broadcast \
? reference_ops::Broadcast##opname( \
GetTensorData<type>(input1), GetTensorDims(input1), \
GetTensorData<type>(input2), GetTensorDims(input2), \
GetTensorData<bool>(output), GetTensorDims(output)) \
: reference_ops::opname( \
GetTensorData<type>(input1), GetTensorDims(input1), \
GetTensorData<type>(input2), GetTensorDims(input2), \
GetTensorData<bool>(output), GetTensorDims(output));
TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
// TODO(renjieliu): Support quantized data.
switch (input1->type) {
case kTfLiteFloat32:
TF_LITE_COMPARISON(float, Greater, requires_broadcast);
break;
case kTfLiteInt32:
TF_LITE_COMPARISON(int32_t, Greater, requires_broadcast);
break;
case kTfLiteInt64:
TF_LITE_COMPARISON(int64_t, Greater, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
// TODO(renjieliu): Support quantized data.
switch (input1->type) {
case kTfLiteFloat32:
TF_LITE_COMPARISON(float, GreaterEqual, requires_broadcast);
break;
case kTfLiteInt32:
TF_LITE_COMPARISON(int32_t, GreaterEqual, requires_broadcast);
break;
case kTfLiteInt64:
TF_LITE_COMPARISON(int64_t, GreaterEqual, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
// TODO(renjieliu): Support quantized data.
switch (input1->type) {
case kTfLiteFloat32:
TF_LITE_COMPARISON(float, Less, requires_broadcast);
break;
case kTfLiteInt32:
TF_LITE_COMPARISON(int32_t, Less, requires_broadcast);
break;
case kTfLiteInt64:
TF_LITE_COMPARISON(int64_t, Less, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
bool requires_broadcast = !HaveSameShapes(input1, input2);
// TODO(renjieliu): Support quantized data.
switch (input1->type) {
case kTfLiteFloat32:
TF_LITE_COMPARISON(float, LessEqual, requires_broadcast);
break;
case kTfLiteInt32:
TF_LITE_COMPARISON(int32_t, LessEqual, requires_broadcast);
break;
case kTfLiteInt64:
TF_LITE_COMPARISON(int64_t, LessEqual, requires_broadcast);
break;
default:
context->ReportError(context,
"Does not support type %d, requires float|int",
input1->type);
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace comparisons
TfLiteRegistration* Register_GREATER() {
static TfLiteRegistration r = {nullptr, nullptr,
comparisons::ComparisonPrepare,
comparisons::GreaterEval};
return &r;
}
TfLiteRegistration* Register_GREATER_EQUAL() {
static TfLiteRegistration r = {nullptr, nullptr,
comparisons::ComparisonPrepare,
comparisons::GreaterEqualEval};
return &r;
}
TfLiteRegistration* Register_LESS() {
static TfLiteRegistration r = {
nullptr, nullptr, comparisons::ComparisonPrepare, comparisons::LessEval};
return &r;
}
TfLiteRegistration* Register_LESS_EQUAL() {
static TfLiteRegistration r = {nullptr, nullptr,
comparisons::ComparisonPrepare,
comparisons::LessEqualEval};
return &r;
}
} // namespace builtin
} // namespace ops
} // namespace tflite
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