| /* Copyright 2023 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/lite/kernels/internal/reference/comparisons.h" |
| |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| |
| namespace tflite { |
| |
| namespace { |
| |
| struct OpData { |
| ComparisonParams params; |
| }; |
| |
| constexpr int kInputTensor1 = 0; |
| constexpr int kInputTensor2 = 1; |
| constexpr int kOutputTensor = 0; |
| |
| TfLiteStatus EqualEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteBool: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<bool>(input1), input2_shape, |
| tflite::micro::GetTensorData<bool>(input2), output_shape, |
| output_data) |
| : reference_ops::EqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<bool>(input1), input2_shape, |
| tflite::micro::GetTensorData<bool>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::EqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::EqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::EqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::EqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| // TODO(renjieliu): Refactor the logic to avoid duplications. |
| TfLiteStatus NotEqualEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteBool: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowNotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<bool>(input1), input2_shape, |
| tflite::micro::GetTensorData<bool>(input2), output_shape, |
| output_data) |
| : reference_ops::NotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<bool>(input1), input2_shape, |
| tflite::micro::GetTensorData<bool>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowNotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::NotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowNotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::NotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowNotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::NotEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowNotEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::NotEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowGreaterEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::GreaterEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::LessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| const OpData* data = static_cast<const OpData*>(node->user_data); |
| |
| const TfLiteEvalTensor* input1 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor1); |
| const TfLiteEvalTensor* input2 = |
| tflite::micro::GetEvalInput(context, node, kInputTensor2); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); |
| RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); |
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); |
| bool* output_data = tflite::micro::GetTensorData<bool>(output); |
| |
| bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); |
| switch (input1->type) { |
| case kTfLiteFloat32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data) |
| : reference_ops::LessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<float>(input1), input2_shape, |
| tflite::micro::GetTensorData<float>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt32: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int32_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int32_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt64: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessEqualNoScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int64_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int64_t>(input2), output_shape, |
| output_data); |
| break; |
| case kTfLiteInt8: |
| requires_broadcast |
| ? reference_ops::Broadcast4DSlowLessEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data) |
| : reference_ops::LessEqualWithScaling( |
| data->params, input1_shape, |
| tflite::micro::GetTensorData<int8_t>(input1), input2_shape, |
| tflite::micro::GetTensorData<int8_t>(input2), output_shape, |
| output_data); |
| break; |
| default: |
| MicroPrintf("Type %s (%d) not supported.", |
| TfLiteTypeGetName(input1->type), input1->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); |
| return context->AllocatePersistentBuffer(context, sizeof(OpData)); |
| } |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| OpData* data = static_cast<OpData*>(node->user_data); |
| |
| MicroContext* micro_context = GetMicroContext(context); |
| |
| TfLiteTensor* input1 = |
| micro_context->AllocateTempInputTensor(node, kInputTensor1); |
| TF_LITE_ENSURE(context, input1 != nullptr); |
| TfLiteTensor* input2 = |
| micro_context->AllocateTempInputTensor(node, kInputTensor2); |
| TF_LITE_ENSURE(context, input2 != nullptr); |
| |
| if (input1->type == kTfLiteInt8) { |
| auto input1_offset = -input1->params.zero_point; |
| auto input2_offset = -input2->params.zero_point; |
| const int kLeftShift = 8; |
| |
| int32_t input1_multiplier; |
| int input1_shift; |
| QuantizeMultiplierSmallerThanOneExp( |
| static_cast<double>(input1->params.scale), &input1_multiplier, |
| &input1_shift); |
| int32_t input2_multiplier; |
| int input2_shift; |
| QuantizeMultiplierSmallerThanOneExp( |
| static_cast<double>(input2->params.scale), &input2_multiplier, |
| &input2_shift); |
| |
| data->params.left_shift = kLeftShift; |
| data->params.input1_offset = input1_offset; |
| data->params.input1_multiplier = input1_multiplier; |
| data->params.input1_shift = input1_shift; |
| data->params.input2_offset = input2_offset; |
| data->params.input2_multiplier = input2_multiplier; |
| data->params.input2_shift = input2_shift; |
| } |
| |
| micro_context->DeallocateTempTfLiteTensor(input1); |
| micro_context->DeallocateTempTfLiteTensor(input2); |
| |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_EQUAL() { |
| return tflite::micro::RegisterOp(Init, Prepare, EqualEval); |
| } |
| |
| TFLMRegistration Register_NOT_EQUAL() { |
| return tflite::micro::RegisterOp(Init, Prepare, NotEqualEval); |
| } |
| |
| TFLMRegistration Register_GREATER() { |
| return tflite::micro::RegisterOp(Init, Prepare, GreaterEval); |
| } |
| |
| TFLMRegistration Register_GREATER_EQUAL() { |
| return tflite::micro::RegisterOp(Init, Prepare, GreaterEqualEval); |
| } |
| |
| TFLMRegistration Register_LESS() { |
| return tflite::micro::RegisterOp(Init, Prepare, LessEval); |
| } |
| |
| TFLMRegistration Register_LESS_EQUAL() { |
| return tflite::micro::RegisterOp(Init, Prepare, LessEqualEval); |
| } |
| |
| } // namespace tflite |
