hedaoyuan
8 years ago
parent
692259e071
commit
0dffe68ca9
@ -0,0 +1,227 @@
|
||||
/* 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 "neon_util.h"
|
||||
#include "paddle/function/ConvOp.h"
|
||||
#include "paddle/function/Im2Col.h"
|
||||
|
||||
namespace paddle {
|
||||
|
||||
namespace neon {
|
||||
|
||||
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
|
||||
|
||||
template <int filterSize, int stride>
|
||||
struct DepthwiseConvKernel {};
|
||||
|
||||
inline float32_t conv3x3(float32x4_t r0,
|
||||
float32x4_t r1,
|
||||
float32x4_t r2,
|
||||
float32x4_t k0,
|
||||
float32x4_t k1,
|
||||
float32x4_t k2) {
|
||||
float32x4_t tmp;
|
||||
tmp = vmulq_f32(r0, k0);
|
||||
tmp = vmlaq_f32(tmp, r1, k1);
|
||||
tmp = vmlaq_f32(tmp, r2, k2);
|
||||
return vaddvq_f32(tmp);
|
||||
}
|
||||
|
||||
/**
|
||||
* Each step calculates four elements of the output.
|
||||
* First step:
|
||||
* R0[0, 1, 2, 3...] * K[0][0]
|
||||
* R0[1, 2, 3, 4...] * K[0][1]
|
||||
* R0[2, 3, 4, 5...] * K[0][2]
|
||||
* R1[0, 1, 2, 3...] * K[1][0]
|
||||
* R1[1, 2, 3, 4...] * K[1][1]
|
||||
* R1[2, 3, 4, 5...] * K[1][2]
|
||||
* R2[0, 1, 2, 3...] * K[2][0]
|
||||
* R2[1, 2, 3, 4...] * K[2][1]
|
||||
* + R2[2, 3, 4, 5...] * K[2][2]
|
||||
* ------------------------------
|
||||
* Output[0, 1, 2, 3]
|
||||
*/
|
||||
template <>
|
||||
struct DepthwiseConvKernel<3, 1> {
|
||||
static void run(const float* inputData,
|
||||
const float* filterData,
|
||||
int inputHeight,
|
||||
int inputWidth,
|
||||
int outputChannels,
|
||||
int outputHeight,
|
||||
int outputWidth,
|
||||
int filterMultiplier,
|
||||
float* outputData) {
|
||||
const int steps = outputWidth >> 2;
|
||||
const int remain = outputWidth & 3;
|
||||
for (int c = 0; c < outputChannels; c++, filterData += 9) {
|
||||
// Load the filters
|
||||
float32x4_t k[3];
|
||||
k[0] = vld1q_f32(filterData);
|
||||
k[1] = vld1q_f32(filterData + 3);
|
||||
k[2] = vld1q_f32(filterData + 6);
|
||||
k[0] = vsetq_lane_f32(0.f, k[0], 3);
|
||||
k[1] = vsetq_lane_f32(0.f, k[1], 3);
|
||||
k[2] = vsetq_lane_f32(0.f, k[2], 3);
|
||||
|
||||
const float* r0 =
|
||||
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
|
||||
const float* r1 = r0 + inputWidth;
|
||||
const float* r2 = r0 + inputWidth * 2;
|
||||
float32x4_t input[3][3];
|
||||
for (int h = 0; h < outputHeight; h++) {
|
||||
for (int s = 0; s < steps; s++) {
|
||||
// Load the inputs
|
||||
float32x4_t tmp;
|
||||
input[0][0] = vld1q_f32(r0);
|
||||
tmp = vld1q_f32(r0 + 4);
|
||||
input[0][1] = vextq_f32(input[0][0], tmp, 1);
|
||||
input[0][2] = vextq_f32(input[0][0], tmp, 2);
|
||||
input[1][0] = vld1q_f32(r1);
|
||||
tmp = vld1q_f32(r1 + 4);
|
||||
input[1][1] = vextq_f32(input[1][0], tmp, 1);
|
||||
input[1][2] = vextq_f32(input[1][0], tmp, 2);
|
||||
input[2][0] = vld1q_f32(r2);
|
||||
tmp = vld1q_f32(r2 + 4);
|
||||
input[2][1] = vextq_f32(input[2][0], tmp, 1);
|
||||
input[2][2] = vextq_f32(input[2][0], tmp, 2);
|
||||
|
||||
float32x4_t tmp1 = vdupq_n_f32(0.f);
|
||||
float32x4_t tmp2 = vdupq_n_f32(0.f);
|
||||
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
|
||||
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
|
||||
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
|
||||
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
|
||||
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
|
||||
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
|
||||
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
|
||||
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
|
||||
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
|
||||
tmp1 = vaddq_f32(tmp1, tmp2);
|
||||
|
||||
vst1q_f32(outputData, tmp1);
|
||||
r0 += 4;
|
||||
r1 += 4;
|
||||
r2 += 4;
|
||||
outputData += 4;
|
||||
}
|
||||
|
||||
for (int r = 0; r < remain; r++) {
|
||||
float32x4_t i0 = vld1q_f32(r0);
|
||||
float32x4_t i1 = vld1q_f32(r1);
|
||||
float32x4_t i2 = vld1q_f32(r2);
|
||||
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
|
||||
r0++;
|
||||
r1++;
|
||||
r2++;
|
||||
outputData++;
|
||||
}
|
||||
|
||||
r0 += 2;
|
||||
r1 += 2;
|
||||
r2 += 2;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <DeviceType Device>
|
||||
class NeonDepthwiseConvFunction : 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();
|
||||
|
||||
size_t batchSize = input[0];
|
||||
size_t inputChannels = input[1];
|
||||
size_t inputHeight = input[2];
|
||||
size_t inputWidth = input[3];
|
||||
size_t filterHeight = getFilterHeight(filter);
|
||||
size_t filterWidth = getFilterWidth(filter);
|
||||
size_t outputChannels = output[1];
|
||||
size_t outputHeight = output[2];
|
||||
size_t outputWidth = output[3];
|
||||
size_t filterMultiplier = outputChannels / groups_;
|
||||
CHECK_EQ(inputChannels, groups_);
|
||||
|
||||
// only support
|
||||
CHECK_EQ(strideH(), strideW());
|
||||
CHECK_EQ(filterHeight, filterWidth);
|
||||
CHECK_EQ(filterHeight, size_t(3));
|
||||
CHECK_LT(strideH(), size_t(3));
|
||||
|
||||
float* inputData = inputs[0].data<float>();
|
||||
float* filterData = inputs[1].data<float>();
|
||||
float* outputData = outputs[0].data<float>();
|
||||
|
||||
// padding the input
|
||||
float* inputPadding = inputData;
|
||||
if (paddingH() > 0 || paddingW() > 0) {
|
||||
int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) *
|
||||
(inputWidth + 2 * paddingW());
|
||||
resizeBuffer<Device>(newSize);
|
||||
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
|
||||
Padding<float>::run(inputData,
|
||||
inputPadding,
|
||||
batchSize * inputChannels,
|
||||
inputHeight,
|
||||
inputWidth,
|
||||
paddingH(),
|
||||
paddingW());
|
||||
|
||||
// height and width of padding data
|
||||
inputHeight += 2 * paddingH();
|
||||
inputWidth += 2 * paddingW();
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < batchSize; i++) {
|
||||
DepthwiseConvKernel<3, 1>::run(inputPadding,
|
||||
filterData,
|
||||
inputHeight,
|
||||
inputWidth,
|
||||
outputChannels,
|
||||
outputHeight,
|
||||
outputWidth,
|
||||
filterMultiplier,
|
||||
outputData);
|
||||
|
||||
inputPadding += inputChannels * inputHeight * inputWidth;
|
||||
outputData += outputChannels * outputHeight * outputWidth;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction);
|
||||
|
||||
#endif
|
||||
|
||||
} // namespace neon
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,25 @@
|
||||
/* 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. */
|
||||
|
||||
#pragma once
|
||||
|
||||
namespace paddle {
|
||||
|
||||
namespace neon {
|
||||
|
||||
template <int filterSize, int stride>
|
||||
struct DepthwiseConvKernel {};
|
||||
|
||||
} // namespace neon
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,47 @@
|
||||
/* 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. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
|
||||
|
||||
#include <arm_neon.h>
|
||||
|
||||
namespace paddle {
|
||||
|
||||
namespace neon {
|
||||
|
||||
inline float32x4_t vld1q_f32_aligned(const float* p) {
|
||||
return vld1q_f32(
|
||||
(const float*)__builtin_assume_aligned(p, sizeof(float32x4_t)));
|
||||
}
|
||||
|
||||
#ifndef __aarch64__
|
||||
inline float32_t vaddvq_f32(float32x4_t a) {
|
||||
float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a));
|
||||
return vget_lane_f32(vpadd_f32(v, v), 0);
|
||||
}
|
||||
|
||||
inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
|
||||
float32x4_t b,
|
||||
float32x4_t v,
|
||||
const int lane) {
|
||||
return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
|
||||
}
|
||||
#endif
|
||||
|
||||
} // namespace neon
|
||||
} // namespace paddle
|
||||
|
||||
#endif
|
||||
Loading…
Reference in new issue