fff2c83262
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages. We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`). PiperOrigin-RevId: 332520146 Change-Id: I405d986cfc653aaafcfdf4162c0acbd46220b921
270 lines
10 KiB
C++
270 lines
10 KiB
C++
/* Copyright 2019 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/pooling.h"
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/kernels/internal/reference/integer_ops/pooling.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/kernels/padding.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace pooling {
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namespace {
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constexpr int kInputTensor = 0;
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constexpr int kOutputTensor = 0;
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struct OpData {
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TfLitePaddingValues padding;
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int32_t activation_min;
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int32_t activation_max;
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float activation_min_f32;
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float activation_max_f32;
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};
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TfLiteStatus CalculateOpData(const TfLiteContext* context,
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const TfLitePoolParams* params,
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const TfLiteTensor* input,
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const TfLiteTensor* output, OpData* data) {
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// input: batch, height, width, channel
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int height = SizeOfDimension(input, 1);
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int width = SizeOfDimension(input, 2);
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int out_height, out_width;
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data->padding = ComputePaddingHeightWidth(
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params->stride_height, params->stride_width,
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/*dilation_rate_height=*/1,
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/*dilation_rate_width=*/1, height, width, params->filter_height,
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params->filter_width, params->padding, &out_height, &out_width);
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return kTfLiteOk;
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}
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void AverageEvalFloat(const TfLiteContext* context, const TfLiteNode* node,
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const TfLitePoolParams* params, const OpData* data,
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const TfLiteEvalTensor* input, TfLiteEvalTensor* output) {
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PoolParams op_params;
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op_params.stride_height = params->stride_height;
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op_params.stride_width = params->stride_width;
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op_params.filter_height = params->filter_height;
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op_params.filter_width = params->filter_width;
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op_params.padding_values.height = data->padding.height;
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op_params.padding_values.width = data->padding.width;
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op_params.float_activation_min = data->activation_min_f32;
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op_params.float_activation_max = data->activation_max_f32;
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reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output));
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}
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void AverageEvalQuantized(TfLiteContext* context, const TfLiteNode* node,
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const TfLitePoolParams* params, const OpData* data,
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const TfLiteEvalTensor* input,
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TfLiteEvalTensor* output) {
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TFLITE_DCHECK(input->type == kTfLiteUInt8 || input->type == kTfLiteInt8);
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PoolParams op_params;
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op_params.stride_height = params->stride_height;
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op_params.stride_width = params->stride_width;
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op_params.filter_height = params->filter_height;
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op_params.filter_width = params->filter_width;
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op_params.padding_values.height = data->padding.height;
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op_params.padding_values.width = data->padding.width;
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op_params.quantized_activation_min = data->activation_min;
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op_params.quantized_activation_max = data->activation_max;
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if (input->type == kTfLiteUInt8) {
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reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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} else {
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reference_integer_ops::AveragePool(
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op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<int8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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}
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}
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void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node,
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TfLitePoolParams* params, const OpData* data,
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const TfLiteEvalTensor* input, TfLiteEvalTensor* output) {
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tflite::PoolParams op_params;
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op_params.stride_height = params->stride_height;
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op_params.stride_width = params->stride_width;
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op_params.filter_height = params->filter_height;
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op_params.filter_width = params->filter_width;
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op_params.padding_values.height = data->padding.height;
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op_params.padding_values.width = data->padding.width;
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op_params.float_activation_min = data->activation_min_f32;
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op_params.float_activation_max = data->activation_max_f32;
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reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output));
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}
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void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node,
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TfLitePoolParams* params, const OpData* data,
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const TfLiteEvalTensor* input, TfLiteEvalTensor* output) {
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tflite::PoolParams op_params;
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op_params.stride_height = params->stride_height;
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op_params.stride_width = params->stride_width;
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op_params.filter_height = params->filter_height;
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op_params.filter_width = params->filter_width;
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op_params.padding_values.height = data->padding.height;
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op_params.padding_values.width = data->padding.width;
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op_params.quantized_activation_min = data->activation_min;
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op_params.quantized_activation_max = data->activation_max;
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if (input->type == kTfLiteUInt8) {
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reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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} else {
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reference_integer_ops::MaxPool(
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op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<int8_t>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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}
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}
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} // namespace
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TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->builtin_data != nullptr);
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auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
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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* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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// Inputs and outputs share the same type, guaranteed by the converter.
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switch (input->type) {
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case kTfLiteFloat32:
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AverageEvalFloat(context, node, params, data, input, output);
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break;
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case kTfLiteUInt8:
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case kTfLiteInt8:
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AverageEvalQuantized(context, node, params, data, input, output);
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Input type %s is not currently supported",
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TfLiteTypeGetName(input->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 MaxEval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->builtin_data != nullptr);
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auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
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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* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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switch (input->type) {
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case kTfLiteFloat32:
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MaxEvalFloat(context, node, params, data, input, output);
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break;
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case kTfLiteUInt8:
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case kTfLiteInt8:
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MaxEvalQuantized(context, node, params, data, input, output);
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.",
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TfLiteTypeGetName(input->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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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpData));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->builtin_data != nullptr);
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auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
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TFLITE_DCHECK(node->user_data != nullptr);
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OpData* data = static_cast<OpData*>(node->user_data);
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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TF_LITE_ENSURE(context, input != nullptr);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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TF_LITE_ENSURE(context, output != nullptr);
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TF_LITE_ENSURE_STATUS(CalculateOpData(context, params, input, output, data));
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if (input->type == kTfLiteFloat32) {
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CalculateActivationRange(params->activation, &data->activation_min_f32,
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&data->activation_max_f32);
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} else if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) {
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CalculateActivationRangeQuantized(context, params->activation, output,
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&data->activation_min,
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&data->activation_max);
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}
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return kTfLiteOk;
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}
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} // namespace pooling
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TfLiteRegistration Register_AVERAGE_POOL_2D() {
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return {/*init=*/pooling::Init,
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/*free=*/nullptr,
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/*prepare=*/pooling::Prepare,
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/*invoke=*/pooling::AverageEval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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TfLiteRegistration Register_MAX_POOL_2D() {
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return {/*init=*/pooling::Init,
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/*free=*/nullptr,
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/*prepare=*/pooling::Prepare,
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/*invoke=*/pooling::MaxEval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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