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Add NeonDepthwiseConvTransposeFunction.

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Adaptive_data_structure_for_SwitchOrderLayer
hedaoyuan 8 years ago
parent 40d47fae95
commit 840104c99a
3 changed files with 164 additions and 41 deletions
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19
paddle/function/neon/NeonDepthwiseConv.cpp
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@ -64,9 +64,10 @@ public:
// padding the input
float* inputPadding = inputData;
int padInputHeight = inputHeight + 2 * paddingH();
int padInputWidth = inputWidth + 2 * paddingW();
if (paddingH() > 0 || paddingW() > 0) {
int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) *
(inputWidth + 2 * paddingW());
int newSize = batchSize * inputChannels * padInputHeight * padInputWidth;
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
neon::Padding<float>::run(inputData,
@ -74,12 +75,8 @@ public:
batchSize * inputChannels,
inputHeight,
inputWidth,
paddingH(),
paddingW());
// height and width of padding data
inputHeight += 2 * paddingH();
inputWidth += 2 * paddingW();
padInputHeight,
padInputWidth);
}
std::function<void(
@ -101,14 +98,14 @@ public:
for (int i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
inputHeight,
inputWidth,
padInputHeight,
padInputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * inputHeight * inputWidth;
inputPadding += inputChannels * padInputHeight * padInputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}

62
paddle/function/neon/NeonDepthwiseConv.h
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@ -477,39 +477,40 @@ struct DepthwiseConvKernel<4, 2> {
template <class T>
struct Padding {
static void run(const T* src,
T* dest,
static void run(const T* input,
T* inputPadding,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
int padInputHeight,
int padInputWidth) {
const int paddingHeight = (padInputHeight - inputHeight) / 2;
const int paddingWidth = (padInputWidth - inputWidth) / 2;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T));
inputPadding += padInputWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
*inputPadding++ = T(0);
}
memcpy(dest, src, inputWidth * sizeof(T));
dest += inputWidth;
src += inputWidth;
memcpy(inputPadding, input, inputWidth * sizeof(T));
inputPadding += inputWidth;
input += inputWidth;
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
*inputPadding++ = T(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T));
inputPadding += padInputWidth * paddingHeight;
}
}
}
@ -518,47 +519,48 @@ struct Padding {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <>
struct Padding<float> {
static void run(const float* src,
float* dest,
static void run(const float* input,
float* inputPadding,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
int padInputHeight,
int padInputWidth) {
const int paddingHeight = (padInputHeight - inputHeight) / 2;
const int paddingWidth = (padInputWidth - inputWidth) / 2;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float));
inputPadding += padInputWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
*inputPadding++ = float(0);
}
int step = inputWidth >> 2;
int remain = inputWidth & 3;
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(src);
vst1q_f32(dest, s0);
src += 4;
dest += 4;
float32x4_t s0 = vld1q_f32(input);
vst1q_f32(inputPadding, s0);
input += 4;
inputPadding += 4;
}
for (int r = 0; r < remain; r++) {
*dest++ = *src++;
*inputPadding++ = *input++;
}
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
*inputPadding++ = float(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float));
inputPadding += padInputWidth * paddingHeight;
}
}
}

124
paddle/function/neon/NeonDepthwiseConvTranspose.cpp
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@ -0,0 +1,124 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "NeonDepthwiseConv.h"
#include "paddle/function/ConvOp.h"
namespace paddle {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <DeviceType Device>
class NeonDepthwiseConvTransposeFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
int batchSize = input[0];
int inputChannels = input[1];
int inputHeight = input[2];
int inputWidth = input[3];
int filterHeight = getFilterHeight(filter);
int filterWidth = getFilterWidth(filter);
int outputChannels = output[1];
int outputHeight = output[2];
int outputWidth = output[3];
int filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
CHECK_EQ(paddingH(), paddingW());
CHECK_EQ(filterHeight, filterWidth);
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
// padding the input, input -> inputPadding
float* inputPadding = inputData;
int padInputHeight =
(inputHeight - 1) * strideH() + 2 * filterHeight - 1 - 2 * paddingH();
int padInputWidth =
(inputWidth - 1) * strideW() + 2 * filterWidth - 1 - 2 * paddingW();
if (padInputHeight > inputHeight || padInputWidth > inputWidth) {
int newSize = batchSize * inputChannels * padInputHeight * padInputWidth;
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
neon::Padding<float>::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
padInputHeight,
padInputWidth);
}
std::function<void(
const float*, const float*, int, int, int, int, int, int, float*)>
DepthWiseConv;
if (filterWidth == 3) {
DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run;
} else if (filterWidth == 4) {
DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run;
} else {
LOG(FATAL) << "Not supported";
}
for (int i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
padInputHeight,
padInputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * padInputHeight * padInputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
#ifndef PADDLE_TYPE_DOUBLE
REGISTER_TYPED_FUNC(NeonDepthwiseConvTranspose,
CPU,
NeonDepthwiseConvTransposeFunction);
#endif
#endif
} // namespace paddle
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