81 lines
3.3 KiB
C++
81 lines
3.3 KiB
C++
/* Copyright 2017 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.
|
|
==============================================================================*/
|
|
#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_
|
|
#define TENSORFLOW_LITE_KERNELS_PADDING_H_
|
|
|
|
#include "tensorflow/lite/c/builtin_op_data.h"
|
|
|
|
namespace tflite {
|
|
|
|
// TODO(renjieliu): Migrate others to use ComputePaddingWithLeftover.
|
|
inline int ComputePadding(int stride, int dilation_rate, int in_size,
|
|
int filter_size, int out_size) {
|
|
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
|
|
int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2;
|
|
return padding > 0 ? padding : 0;
|
|
}
|
|
|
|
// It's not guaranteed that padding is symmetric. It's important to keep
|
|
// offset for algorithms need all paddings.
|
|
inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size,
|
|
int filter_size, int out_size,
|
|
int* offset) {
|
|
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
|
|
int total_padding =
|
|
((out_size - 1) * stride + effective_filter_size - in_size);
|
|
total_padding = total_padding > 0 ? total_padding : 0;
|
|
*offset = total_padding % 2;
|
|
return total_padding / 2;
|
|
}
|
|
|
|
// Matching GetWindowedOutputSize in TensorFlow.
|
|
inline int ComputeOutSize(TfLitePadding padding, int image_size,
|
|
int filter_size, int stride, int dilation_rate = 1) {
|
|
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
|
|
switch (padding) {
|
|
case kTfLitePaddingSame:
|
|
return (image_size + stride - 1) / stride;
|
|
case kTfLitePaddingValid:
|
|
return (image_size + stride - effective_filter_size) / stride;
|
|
default:
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
inline TfLitePaddingValues ComputePaddingHeightWidth(
|
|
int stride_height, int stride_width, int dilation_rate_height,
|
|
int dilation_rate_width, int in_height, int in_width, int filter_height,
|
|
int filter_width, TfLitePadding padding, int* out_height, int* out_width) {
|
|
*out_width = ComputeOutSize(padding, in_width, filter_width, stride_width,
|
|
dilation_rate_width);
|
|
*out_height = ComputeOutSize(padding, in_height, filter_height, stride_height,
|
|
dilation_rate_height);
|
|
|
|
TfLitePaddingValues padding_values;
|
|
int offset = 0;
|
|
padding_values.height =
|
|
ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height,
|
|
filter_height, *out_height, &offset);
|
|
padding_values.height_offset = offset;
|
|
padding_values.width =
|
|
ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width,
|
|
filter_width, *out_width, &offset);
|
|
padding_values.width_offset = offset;
|
|
return padding_values;
|
|
}
|
|
} // namespace tflite
|
|
|
|
#endif // TENSORFLOW_LITE_KERNELS_PADDING_H_
|