mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
39209b412a
Added 16bit operator support for below operators: 1. unpack 2. transpose 3. comparison BUG=fixes #3044
619 lines
26 KiB
C++
619 lines
26 KiB
C++
/* Copyright 2025 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/comparisons.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.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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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/micro_log.h"
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namespace tflite {
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namespace {
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struct OpData {
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ComparisonParams params;
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};
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constexpr int kInputTensor1 = 0;
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constexpr int kInputTensor2 = 1;
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constexpr int kOutputTensor = 0;
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TfLiteStatus EqualEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
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RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
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RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
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bool* output_data = tflite::micro::GetTensorData<bool>(output);
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bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteBool:
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requires_broadcast
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? reference_ops::Broadcast4DSlowEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<bool>(input1), input2_shape,
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tflite::micro::GetTensorData<bool>(input2), output_shape,
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output_data)
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: reference_ops::EqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<bool>(input1), input2_shape,
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tflite::micro::GetTensorData<bool>(input2), output_shape,
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output_data);
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break;
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case kTfLiteFloat32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data)
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: reference_ops::EqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data)
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: reference_ops::EqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt64:
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requires_broadcast
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? reference_ops::Broadcast4DSlowEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data)
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: reference_ops::EqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt8:
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requires_broadcast
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? reference_ops::Broadcast4DSlowEqualWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data)
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: reference_ops::EqualWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data);
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break;
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default:
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MicroPrintf("Type %s (%d) not supported.",
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TfLiteTypeGetName(input1->type), input1->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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// TODO(renjieliu): Refactor the logic to avoid duplications.
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TfLiteStatus NotEqualEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
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RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
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RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
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bool* output_data = tflite::micro::GetTensorData<bool>(output);
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bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteBool:
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requires_broadcast
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? reference_ops::Broadcast4DSlowNotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<bool>(input1), input2_shape,
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tflite::micro::GetTensorData<bool>(input2), output_shape,
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output_data)
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: reference_ops::NotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<bool>(input1), input2_shape,
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tflite::micro::GetTensorData<bool>(input2), output_shape,
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output_data);
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break;
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case kTfLiteFloat32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowNotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data)
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: reference_ops::NotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowNotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data)
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: reference_ops::NotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt64:
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requires_broadcast
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? reference_ops::Broadcast4DSlowNotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data)
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: reference_ops::NotEqualNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt8:
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requires_broadcast
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? reference_ops::Broadcast4DSlowNotEqualWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data)
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: reference_ops::NotEqualWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data);
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break;
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default:
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MicroPrintf("Type %s (%d) not supported.",
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TfLiteTypeGetName(input1->type), input1->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
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RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
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RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
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bool* output_data = tflite::micro::GetTensorData<bool>(output);
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bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteFloat32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowGreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data)
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: reference_ops::GreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt32:
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requires_broadcast
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? reference_ops::Broadcast4DSlowGreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data)
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: reference_ops::GreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt64:
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requires_broadcast
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? reference_ops::Broadcast4DSlowGreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data)
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: reference_ops::GreaterNoScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int64_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt8:
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requires_broadcast
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? reference_ops::Broadcast4DSlowGreaterWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data)
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: reference_ops::GreaterWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int8_t>(input2), output_shape,
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output_data);
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break;
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case kTfLiteInt16:
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requires_broadcast
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? reference_ops::Broadcast4DSlowGreaterWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int16_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int16_t>(input2), output_shape,
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output_data)
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: reference_ops::GreaterWithScaling(
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data->params, input1_shape,
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tflite::micro::GetTensorData<int16_t>(input1), input2_shape,
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tflite::micro::GetTensorData<int16_t>(input2), output_shape,
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output_data);
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break;
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default:
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MicroPrintf("Type %s (%d) not supported.",
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TfLiteTypeGetName(input1->type), input1->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData* data = static_cast<const OpData*>(node->user_data);
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|
|
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const TfLiteEvalTensor* input1 =
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tflite::micro::GetEvalInput(context, node, kInputTensor1);
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const TfLiteEvalTensor* input2 =
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tflite::micro::GetEvalInput(context, node, kInputTensor2);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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|
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RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
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RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
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RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
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bool* output_data = tflite::micro::GetTensorData<bool>(output);
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bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
|
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switch (input1->type) {
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case kTfLiteFloat32:
|
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requires_broadcast
|
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? reference_ops::Broadcast4DSlowGreaterEqualNoScaling(
|
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data->params, input1_shape,
|
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
|
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output_data)
|
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: reference_ops::GreaterEqualNoScaling(
|
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data->params, input1_shape,
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tflite::micro::GetTensorData<float>(input1), input2_shape,
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tflite::micro::GetTensorData<float>(input2), output_shape,
|
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output_data);
|
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break;
|
|
case kTfLiteInt32:
|
|
requires_broadcast
|
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? reference_ops::Broadcast4DSlowGreaterEqualNoScaling(
|
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data->params, input1_shape,
|
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
|
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
|
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output_data)
|
|
: reference_ops::GreaterEqualNoScaling(
|
|
data->params, input1_shape,
|
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tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
|
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tflite::micro::GetTensorData<int32_t>(input2), output_shape,
|
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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,
|
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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 ComparisonsPrepare(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, ComparisonsPrepare, EqualEval);
|
|
}
|
|
|
|
TFLMRegistration Register_NOT_EQUAL() {
|
|
return tflite::micro::RegisterOp(Init, ComparisonsPrepare, NotEqualEval);
|
|
}
|
|
|
|
TFLMRegistration Register_GREATER() {
|
|
return tflite::micro::RegisterOp(Init, ComparisonsPrepare, GreaterEval);
|
|
}
|
|
|
|
TFLMRegistration Register_GREATER_EQUAL() {
|
|
return tflite::micro::RegisterOp(Init, ComparisonsPrepare, GreaterEqualEval);
|
|
}
|
|
|
|
TFLMRegistration Register_LESS() {
|
|
return tflite::micro::RegisterOp(Init, ComparisonsPrepare, LessEval);
|
|
}
|
|
|
|
TFLMRegistration Register_LESS_EQUAL() {
|
|
return tflite::micro::RegisterOp(Init, ComparisonsPrepare, LessEqualEval);
|
|
}
|
|
|
|
} // namespace tflite
|