blob: bb00edb67125043f424c77e050e383c69e652a1a [file] [log] [blame]
/* Copyright 2022 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/pad.h"
#include <string.h>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h"
#include "tensorflow/lite/micro/micro_log.h"
namespace tflite {
namespace {
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
#if !defined(VISION_P6)
return context->AllocatePersistentBuffer(context, sizeof(OpDataPad));
#else
void* data =
context->AllocatePersistentBuffer(context, sizeof(XtensaPadData));
if (InitXtensaContext()) {
return nullptr;
}
return data;
#endif // defined(VISION_P6)
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
MicroContext* micro_context = GetMicroContext(context);
TFLITE_DCHECK(node->user_data != nullptr);
#if defined(VISION_P6)
XtensaPadData* op_data_xtensa = static_cast<XtensaPadData*>(node->user_data);
OpDataPad* data = &op_data_xtensa->reference_op_data;
#else
OpDataPad* data = static_cast<OpDataPad*>(node->user_data);
#endif
TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
TfLiteTensor* input =
micro_context->AllocateTempInputTensor(node, /*index=*/0);
TF_LITE_ENSURE(context, input != nullptr);
TfLiteTensor* paddings =
micro_context->AllocateTempInputTensor(node, /*index=*/1);
TF_LITE_ENSURE(context, paddings != nullptr);
TfLiteTensor* constant_values =
NumInputs(node) == 3
? micro_context->AllocateTempInputTensor(node, /*index=*/2)
: nullptr;
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, /*index=*/0);
TF_LITE_ENSURE(context, output != nullptr);
TF_LITE_ENSURE_EQ(context, input->type, output->type);
// Current implementations rely on the inputs being <= 4D.
TF_LITE_ENSURE(context, NumDimensions(input) <=
reference_ops::PadKernelMaxDimensionCount());
if (constant_values != nullptr) {
TF_LITE_ENSURE_EQ(context, input->type, constant_values->type);
// Ensure that constant_values is a scalar.
TF_LITE_ENSURE_EQ(context, NumElements(constant_values), 1);
}
// There must be a pair of paddings for each output dimension.
TF_LITE_ENSURE_EQ(context, GetTensorShape(paddings).FlatSize(),
output->dims->size * 2);
// On Micro, outputs must be properly sized by the converter.
// NOTE: This data is only available because the paddings buffer is stored in
// the flatbuffer:
TF_LITE_ENSURE(context, IsConstantTensor(paddings));
const int32_t* paddings_data = GetTensorData<int32_t>(paddings);
for (int i = 0; i < output->dims->size; i++) {
int output_dim = output->dims->data[i];
int expected_dim =
input->dims->data[i] + paddings_data[i * 2] + paddings_data[i * 2 + 1];
TF_LITE_ENSURE_EQ(context, output_dim, expected_dim);
}
// Calculate OpDataPad:
data->params.resizing_category = ResizingCategory::kGenericResize;
const int paddings_total = GetTensorShape(paddings).FlatSize();
if (paddings_total == 8 && (paddings_data[0] == 0 && paddings_data[1] == 0) &&
(paddings_data[6] == 0 && paddings_data[7] == 0)) {
data->params.resizing_category = ResizingCategory::kImageStyle;
}
const int num_input_dimensions = NumDimensions(input);
data->params.left_padding_count = num_input_dimensions;
data->params.right_padding_count = num_input_dimensions;
for (int idx = num_input_dimensions - 1; idx >= 0; --idx) {
data->params.left_padding[idx] = paddings_data[idx * 2];
data->params.right_padding[idx] = paddings_data[idx * 2 + 1];
}
if (input->type == kTfLiteInt8) {
if (constant_values == nullptr) {
// Quantized Pad requires that 0 is represented in the quantized
// range.
TF_LITE_ENSURE(context, output->params.zero_point >=
std::numeric_limits<int8_t>::min());
TF_LITE_ENSURE(context, output->params.zero_point <=
std::numeric_limits<int8_t>::max());
} else {
// Quantized Pad requires that 'constant_values' is represented in the
// same quantized range as the input and output tensors.
TF_LITE_ENSURE_EQ(context, output->params.zero_point,
constant_values->params.zero_point);
TF_LITE_ENSURE_EQ(context, static_cast<double>(output->params.scale),
static_cast<double>(constant_values->params.scale));
}
data->output_zero_point = output->params.zero_point;
}
micro_context->DeallocateTempTfLiteTensor(input);
micro_context->DeallocateTempTfLiteTensor(paddings);
if (constant_values != nullptr) {
micro_context->DeallocateTempTfLiteTensor(constant_values);
}
micro_context->DeallocateTempTfLiteTensor(output);
#if defined(VISION_P6)
TF_LITE_ENSURE_OK(context, PadPrepareVision(context, node));
#endif // VISION_P6
return kTfLiteOk;
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
#if defined(VISION_P6)
XtensaPadData* op_data_xtensa = static_cast<XtensaPadData*>(node->user_data);
OpDataPad* data = &op_data_xtensa->reference_op_data;
#else
OpDataPad* data = static_cast<OpDataPad*>(node->user_data);
#endif
const TfLiteEvalTensor* input =
tflite::micro::GetEvalInput(context, node, /*index=*/0);
const TfLiteEvalTensor* constant_values =
NumInputs(node) == 3
? tflite::micro::GetEvalInput(context, node, /*index=*/2)
: nullptr;
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, /*index=*/0);
switch (input->type) {
case kTfLiteFloat32: {
float pad_value =
constant_values == nullptr
? 0.f
: *tflite::micro::GetTensorData<float>(constant_values);
if (data->params.resizing_category == ResizingCategory::kImageStyle) {
reference_ops::PadImageStyle(
data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<float>(input), &pad_value,
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
} else {
reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<float>(input),
&pad_value, tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<float>(output));
}
} break;
case kTfLiteInt8: {
#if defined(VISION_P6)
PadEvalVision(*op_data_xtensa, input, output);
#else
int8_t pad_value;
if (constant_values == nullptr) {
pad_value = static_cast<uint8_t>(data->output_zero_point);
} else {
pad_value = *tflite::micro::GetTensorData<int8_t>(constant_values);
}
if (data->params.resizing_category == ResizingCategory::kImageStyle) {
reference_ops::PadImageStyle(
data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input), &pad_value,
tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
} else {
reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int8_t>(input),
&pad_value, tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int8_t>(output));
}
#endif
} break;
case kTfLiteInt16: {
int16_t pad_value =
constant_values == nullptr
? 0
: *tflite::micro::GetTensorData<int16_t>(constant_values);
#if defined(HIFI4)
/* NNLib currently only supports up to 4D input tensors */
if (tflite::micro::GetTensorShape(input).DimensionsCount() == 4) {
const TfLiteEvalTensor* paddings =
tflite::micro::GetEvalInput(context, node, /*index=*/1);
int32_t err = xa_nn_pad_16_16(
tflite::micro::GetTensorData<int16_t>(output),
tflite::micro::GetTensorShape(output).DimsData(),
tflite::micro::GetTensorData<int16_t>(input),
tflite::micro::GetTensorShape(input).DimsData(),
tflite::micro::GetTensorData<int32_t>(paddings),
tflite::micro::GetTensorShape(paddings).DimsData(),
tflite::micro::GetTensorShape(output).DimensionsCount(),
tflite::micro::GetTensorShape(input).DimensionsCount(),
tflite::micro::GetTensorShape(paddings).DimensionsCount(),
pad_value);
if (err != 0) return kTfLiteError;
} else {
#endif // defined(HIFI4)
reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int16_t>(input),
&pad_value, tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int16_t>(output));
#if defined(HIFI4)
}
#endif // defined(HIFI4)
} break;
case kTfLiteInt32: {
int32_t pad_value =
constant_values == nullptr
? 0
: *tflite::micro::GetTensorData<int32_t>(constant_values);
reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
tflite::micro::GetTensorData<int32_t>(input),
&pad_value, tflite::micro::GetTensorShape(output),
tflite::micro::GetTensorData<int32_t>(output));
} break;
default:
MicroPrintf("Type %s not currently supported by Pad.",
TfLiteTypeGetName(input->type));
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace
TFLMRegistration Register_PAD() {
return tflite::micro::RegisterOp(Init, Prepare, Eval);
}
// Also register Pad as PadV2.
TFLMRegistration Register_PADV2() {
return tflite::micro::RegisterOp(Init, Prepare, Eval);
}
} // namespace tflite