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提交 07329c60 编写于 作者: Z ZhenWang
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add int8_t type pooling support.

上级 475d7e91
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Showing with 933 addition and 76 deletion
src/operators/kernel/central-arm-func/pool_arm_func.h
浏览文件 @ 07329c60
......@@ -23,20 +23,22 @@ namespace paddle_mobile {
namespace operators {
using framework::Tensor;
inline void PoolBasic(std::string pooling_type, std::vector<int> ksize,
std::vector<int> strides, std::vector<int> paddings,
const Tensor *in_x, Tensor *out) {
template <typename T, typename S>
void PoolBasic(std::string pooling_type, std::vector<int> ksize,
std::vector<int> strides, std::vector<int> paddings,
const Tensor *in_x, Tensor *out) {
if (pooling_type == "max") {
math::PoolFunctor<CPU, math::MaxPool<float>, float> pool2d_forward;
math::MaxPool<float> pool_process;
math::PoolFunctor<CPU, math::MaxPool<T>, T> pool2d_forward;
math::MaxPool<T> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
} else if (pooling_type == "avg") {
math::PoolFunctor<CPU, math::AvgPool<float>, float> pool2d_forward;
math::AvgPool<float> pool_process;
math::PoolFunctor<CPU, math::AvgPool<T, S>, T> pool2d_forward;
math::AvgPool<T, S> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
}
}
template <typename P>
void PoolCompute(const PoolParam<CPU> &param) {
const Tensor *in_x = param.Input();
......@@ -52,50 +54,65 @@ void PoolCompute(const PoolParam<CPU> &param) {
LOG(paddle_mobile::LogLevel::kLOG_ERROR)
<< "Pool op only supports 2D and 3D input.";
}
if (param.isGlobalPooling()) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
if (ksize[0] == 3 && ksize[0] == ksize[1]) {
if (pooling_type == "max") {
if (strides[0] == strides[1] && strides[0] == 1 &&
paddings[0] == paddings[1] && paddings[1] == 1) {
math::Pool3x3Maxs1p1(in_x, out);
if (in_x->type() == typeid(int8_t)) {
if (pooling_type == "max" && ksize[0] == 3 && ksize[0] == ksize[1]) {
if (strides[0] == strides[1] && strides[0] == 1) {
math::Pool3x3Maxs1_int8(in_x, out, paddings[0], paddings[1]);
} else if (strides[0] == strides[1] && strides[0] == 2) {
math::Pool3x3Maxs2_int8(in_x, out, paddings[0], paddings[1]);
} else {
math::Pool3x3Max(strides, paddings, in_x, out);
}
} else if (pooling_type == "avg") {
if (strides[0] == strides[1] && strides[0] == 1 &&
paddings[0] == paddings[1] && paddings[1] == 1) {
math::Pool3x3Avgs1p1(in_x, out);
} else {
math::Pool3x3Avg(strides, paddings, in_x, out);
math::Pool3x3Max_int8(strides, paddings, in_x, out);
}
} else {
PoolBasic<int8_t, int32_t>(pooling_type, ksize, strides, paddings, in_x,
out);
}
} else {
if (ksize[0] == 3 && ksize[0] == ksize[1]) {
if (pooling_type == "max") {
if (strides[0] == strides[1] && strides[0] == 1 &&
paddings[0] == paddings[1] && paddings[1] == 1) {
math::Pool3x3Maxs1p1(in_x, out);
} else {
math::Pool3x3Max(strides, paddings, in_x, out);
}
} else if (pooling_type == "avg") {
if (strides[0] == strides[1] && strides[0] == 1 &&
paddings[0] == paddings[1] && paddings[1] == 1) {
math::Pool3x3Avgs1p1(in_x, out);
} else {
math::Pool3x3Avg(strides, paddings, in_x, out);
}
}
} else if (ksize[0] == 2 && ksize[0] == ksize[1] && strides[0] == 2 &&
strides[0] == strides[1] && paddings[0] == paddings[1] &&
paddings[1] == 0) {
} else if (ksize[0] == 2 && ksize[0] == ksize[1] && strides[0] == 2 &&
strides[0] == strides[1] && paddings[0] == paddings[1] &&
paddings[1] == 0) {
#if __ARM_NEON
#if __aarch64__
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
PoolBasic<float, float>(pooling_type, ksize, strides, paddings, in_x, out);
#else
/// todo: fix bug in Pool2x2
if (pooling_type == "max") {
math::Pool2x2Maxs2p0(strides, paddings, in_x, out);
} else if (pooling_type == "avg") {
math::Pool2x2Avgs2p0(strides, paddings, in_x, out);
}
/// todo: fix bug in Pool2x2
if (pooling_type == "max") {
math::Pool2x2Maxs2p0(strides, paddings, in_x, out);
} else if (pooling_type == "avg") {
math::Pool2x2Avgs2p0(strides, paddings, in_x, out);
}
#endif
#else
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
PoolBasic<float, float>(pooling_type, ksize, strides, paddings, in_x, out);
#endif // __ARM_NEON
} else {
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
} else {
PoolBasic<float, float>(pooling_type, ksize, strides, paddings, in_x,
out);
}
}
}
......
src/operators/math/pool_3x3.cpp
浏览文件 @ 07329c60
......@@ -38,6 +38,7 @@ void Pool3x3Avgs1p1(const Tensor *input, Tensor *output) {
const int input_width = static_cast<int>(input->dims()[3]);
const int output_height = static_cast<int>(output->dims()[2]);
const int output_width = static_cast<int>(output->dims()[3]);
output->mutable_data<float>();
const int hxw = input_height * input_width;
......@@ -472,7 +473,7 @@ void Pool3x3Maxs1p1(const Tensor *input, Tensor *output) {
const int inputdata_channel_stride = h_in * w_in;
const int input_batch_stride = output_channels * inputdata_channel_stride;
const int output_batch_stride = output_channels * outputdata_channel_stride;
float *out_data = output->data<float>();
float *out_data = output->mutable_data<float>();
const float *input_data = input->data<float>();
for (int k = 0; k < batch_size; ++k) {
#pragma omp parallel for
......
src/operators/math/pool_3x3.h
浏览文件 @ 07329c60
......@@ -28,15 +28,21 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
namespace math {
using framework::Tensor;
using std::vector;
void Pool3x3Avgs1p1(const Tensor *input, Tensor *output);
void Pool3x3Maxs1p1(const Tensor *input, Tensor *output);
void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output);
void Pool3x3Avg(vector<int> strides, vector<int> paddings, const Tensor *in_x,
Tensor *out);
void Pool3x3Avgs1p1(const framework::Tensor *input, framework::Tensor *output);
void Pool3x3Maxs1p1(const framework::Tensor *input, framework::Tensor *output);
void Pool3x3Max(std::vector<int> strides, std::vector<int> paddings,
const framework::Tensor *input, framework::Tensor *output);
void Pool3x3Avg(std::vector<int> strides, std::vector<int> paddings,
const framework::Tensor *in_x, framework::Tensor *out);
void Pool3x3Maxs1_int8(const framework::Tensor *input,
framework::Tensor *output, int32_t pad_h, int32_t pad_w);
void Pool3x3Maxs2_int8(const framework::Tensor *input,
framework::Tensor *output, int32_t pad_h, int32_t pad_w);
void Pool3x3Max_int8(const std::vector<int> &strides,
const std::vector<int> &paddings,
const framework::Tensor *input, framework::Tensor *output);
} // namespace math
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/pool_3x3_int8.cpp 0 → 100644
浏览文件 @ 07329c60
/* Copyright (c) 2018 PaddlePaddle 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. */
#ifdef POOL_OP
#ifdef _OPENMP
#include <omp.h>
#endif
#include "framework/tensor.h"
#include "operators/math/pool_3x3.h"
#if __ARM_NEON
#include <arm_neon.h>
#endif // __ARM_NEON
#include <climits>
#include <iostream>
namespace paddle_mobile {
namespace operators {
namespace math {
using framework::Tensor;
using std::max;
using std::min;
using std::vector;
template <typename T>
static void make_paddings(const Tensor *input, Tensor *padded_input,
int32_t top, int32_t bottom, int32_t left,
int32_t right, T value) {
const int32_t batch_size = input->dims()[0];
const int32_t c_in = input->dims()[1];
const int32_t h_in = input->dims()[2];
const int32_t w_in = input->dims()[3];
const int32_t h_padded = h_in + top + bottom;
const int32_t w_padded = w_in + left + right;
padded_input->Resize({batch_size, c_in, h_padded, w_padded});
T *padded_input_data = padded_input->mutable_data<T>();
const T *input_data = input->data<T>();
const int32_t input_channel_stride = h_in * w_in;
const int32_t input_batch_stride = c_in * input_channel_stride;
const int32_t padded_channel_stride = h_padded * w_padded;
const int32_t padded_batch_stride = c_in * padded_channel_stride;
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int j = 0; j < c_in; ++j) {
const T *img_in = input_data + j * input_channel_stride;
T *img_padded = padded_input_data + j * padded_channel_stride;
int k = 0;
for (; k < top; ++k) {
for (int l = 0; l < w_padded; ++l) {
img_padded[l] = value;
}
img_padded += w_padded;
}
for (; k < top + h_in; ++k) {
int l = 0;
for (; l < left; ++l) {
img_padded[l] = value;
}
memcpy(img_padded + left, img_in, w_in * sizeof(T));
l += w_in;
img_in += w_in;
for (; l < w_padded; ++l) {
img_padded[l] = value;
}
img_padded += w_padded;
}
for (; k < h_padded; ++k) {
for (int l = 0; l < w_padded; ++l) {
img_padded[l] = value;
}
img_padded += w_padded;
}
}
input_data += input_batch_stride;
padded_input_data += padded_batch_stride;
}
// input_data = input->data<T>();
// std::cout << "+++++++++++++++++++Origin begin++++++++++++++++++++"
// << std::endl;
// for (int i = 0; i < 1; ++i) {
// for (int j = 0; j < 1; ++j) {
// const T *img_in = input_data + j * input_channel_stride;
// for (int k = 0; k < h_in; ++k) {
// for (int l = 0; l < w_in; ++l) {
// std::cout << (int32_t)*img_in << "\t";
// img_in++;
// }
// std::cout << std::endl;
// }
// }
// input_data += input_batch_stride;
// }
// std::cout << "+++++++++++++++++++Origin end++++++++++++++++++++" <<
// std::endl;
//
// padded_input_data = padded_input->data<T>();
// std::cout << "******************Padding begin**********************"
// << std::endl;
// for (int i = 0; i < 1; ++i) {
// for (int j = 0; j < 1; ++j) {
// T *img_padded = padded_input_data + j * padded_channel_stride;
// for (int k = 0; k < h_padded; ++k) {
// for (int l = 0; l < w_padded; ++l) {
// std::cout << (int32_t)*img_padded << "\t";
// img_padded++;
// }
// std::cout << std::endl;
// }
// }
// padded_input_data += padded_batch_stride;
// }
// std::cout << "******************Padding end**********************"
// << std::endl;
}
void Pool3x3Maxs1_int8(const Tensor *input, Tensor *output, int32_t pad_h,
int32_t pad_w) {
Tensor padded_input;
if (pad_h != 0 && pad_w != 0) {
int8_t value = -SCHAR_MAX;
make_paddings(input, &padded_input, pad_h, pad_h, pad_w, pad_w, value);
input = &padded_input;
}
const int32_t batch_size = input->dims()[0];
const int32_t h_in = input->dims()[2];
const int32_t w_in = input->dims()[3];
const int8_t *input_data = input->data<int8_t>();
const int32_t output_channels = output->dims()[1];
const int32_t h_out = output->dims()[2];
int32_t w_out = output->dims()[3];
int8_t *output_data = output->mutable_data<int8_t>();
const int32_t outputdata_channel_stride = h_out * w_out;
const int32_t inputdata_channel_stride = h_in * w_in;
const int32_t input_batch_stride = output_channels * inputdata_channel_stride;
const int32_t output_batch_stride =
output_channels * outputdata_channel_stride;
// std::cout << "h_out = " << h_out << ", w_out=" << w_out << std::endl;
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int j = 0; j < output_channels; ++j) {
const int8_t *img_in = input_data + j * inputdata_channel_stride;
int8_t *img_out = output_data + j * outputdata_channel_stride;
for (int k = 0; k < h_out; ++k) {
const int8_t *row0 = img_in + k * w_in;
const int8_t *row1 = img_in + (k + 1) * w_in;
const int8_t *row2 = img_in + (k + 2) * w_in;
#if __ARM_NEON
int32_t nw = w_out >> 4;
int32_t left_w = w_out & 0xf;
int32_t nw1 = left_w >> 3;
int32_t left_w1 = left_w & 0x7;
#if __aarch64__
// TODO(wzzju)
#else
if (nw > 0) {
#define LOOP_LABEL "1"
// result: q15
asm volatile(
"vld1.8 {q0}, [%[row0]]! \n\t" // q0=0-15
"vld1.8 {q2}, [%[row1]]! \n\t"
"vld1.8 {q4}, [%[row2]]! \n\t"
LOOP_LABEL
": \n\t"
"vld1.8 {q1}, [%[row0]]! \n\t" // q1=16-31
"vext.8 q6, q0, q1, #1 \n\t"
"vext.8 q7, q0, q1, #2 \n\t"
"vld1.8 {q3}, [%[row1]]! \n\t"
"vmax.s8 q15, q0, q6 \n\t"
"vmax.s8 q15, q15, q7 \n\t"
"vext.8 q6, q2, q3, #1 \n\t"
"vext.8 q7, q2, q3, #2 \n\t"
"vld1.8 {q5}, [%[row2]]! \n\t"
"vmax.s8 q14, q2, q6 \n\t"
"vmax.s8 q14, q14, q7 \n\t"
"vext.8 q6, q4, q5, #1 \n\t"
"vext.8 q7, q4, q5, #2 \n\t"
"vmax.s8 q13, q4, q6 \n\t"
"vmax.s8 q13, q13, q7 \n\t"
"vmax.s8 q15, q15, q14 \n\t"
"vmax.s8 q15, q15, q13 \n\t"
"vmov.s8 q0, q1 \n\t"
"vmov.s8 q2, q3 \n\t"
"vmov.s8 q4, q5 \n\t"
"vst1.8 {q15}, [%[img_out]]! \n\t"
"subs %[nw], #1 \n\t"
"bne " LOOP_LABEL
"b \n\t"
"sub %[row0], #16 \n\t"
"sub %[row1], #16 \n\t"
"sub %[row2], #16 \n\t"
: [nw] "+r"(nw), [row0] "+r"(row0), [row1] "+r"(row1),
[row2] "+r"(row2), [img_out] "+r"(img_out)
:
: "cc", "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q13", "q14", "q15");
#undef LOOP_LABEL
}
if (nw1 > 0 || left_w1 > 0) {
#define PADDLE_LABEL_LESS8 "1"
#define PADDLE_LABEL_LESS8_SAVE "2"
#define PADDLE_LABEL_OVER "3"
// result: d15
asm volatile(
"vld1.8 {d0}, [%[row0]]! \n\t" // d0=0-8
"vld1.8 {d2}, [%[row1]]! \n\t"
"vld1.8 {d4}, [%[row2]]! \n\t"
"mov r0, #1 \n\t"
"cmp %[nw1], #0 \n\t"
"beq " PADDLE_LABEL_LESS8
"f\n\t"
"vld1.8 {d1}, [%[row0]]! \n\t" // d1=9-15
"vext.8 d6, d0, d1, #1 \n\t"
"vext.8 d7, d0, d1, #2 \n\t"
"vld1.8 {d3}, [%[row1]]! \n\t"
"vmax.s8 d15, d0, d6 \n\t"
"vmax.s8 d15, d15, d7 \n\t"
"vext.8 d6, d2, d3, #1 \n\t"
"vext.8 d7, d2, d3, #2 \n\t"
"vld1.8 {d5}, [%[row2]]! \n\t"
"vmax.s8 d14, d2, d6 \n\t"
"vmax.s8 d14, d14, d7 \n\t"
"vext.8 d6, d4, d5, #1 \n\t"
"vext.8 d7, d4, d5, #2 \n\t"
"vmax.s8 d13, d4, d6 \n\t"
"vmax.s8 d13, d13, d7 \n\t"
"vmax.s8 d15, d15, d14 \n\t"
"vmax.s8 d15, d15, d13 \n\t"
"vmov.s8 d0, d1 \n\t"
"vmov.s8 d2, d3 \n\t"
"vmov.s8 d4, d5 \n\t"
"vst1.8 {d15}, [%[img_out]]! \n\t"
PADDLE_LABEL_LESS8
": \n\t"
"cmp %[left_w1], #0 \n\t"
"beq " PADDLE_LABEL_OVER
"f\n\t"
"vld1.8 {d1}, [%[row0]] \n\t" // d1=9-15
"vext.8 d6, d0, d1, #1 \n\t"
"vext.8 d7, d0, d1, #2 \n\t"
"vld1.8 {d3}, [%[row1]] \n\t"
"vmax.s8 d15, d0, d6 \n\t"
"vmax.s8 d15, d15, d7 \n\t"
"vext.8 d6, d2, d3, #1 \n\t"
"vext.8 d7, d2, d3, #2 \n\t"
"vld1.8 {d5}, [%[row2]] \n\t"
"vmax.s8 d14, d2, d6 \n\t"
"vmax.s8 d14, d14, d7 \n\t"
"vext.8 d6, d4, d5, #1 \n\t"
"vext.8 d7, d4, d5, #2 \n\t"
"vmax.s8 d13, d4, d6 \n\t"
"vmax.s8 d13, d13, d7 \n\t"
"vmax.s8 d15, d15, d14 \n\t"
"vmax.s8 d15, d15, d13 \n\t"
PADDLE_LABEL_LESS8_SAVE
": \n\t"
"vst1.8 {d15}, [%[img_out]], r0\n\t"
"add %[row0], %[row0], #1 \n\t"
"add %[row1], %[row1], #1 \n\t"
"add %[row2], %[row2], #1 \n\t"
"vext.8 d15, d15, d15, #1 \n\t"
"subs %[left_w1], #1 \n\t"
"bgt " PADDLE_LABEL_LESS8_SAVE "b \n\t"
PADDLE_LABEL_OVER ": \n\t"
: [nw1] "+r"(nw1), [left_w1] "+r"(left_w1), [row0] "+r"(row0),
[row1] "+r"(row1), [row2] "+r"(row2), [img_out] "+r"(img_out)
:
: "cc", "memory", "r0", "d0", "d1", "d2", "d3", "d4", "d5", "d6",
"d7", "d13", "d14", "d15");
#undef PADDLE_LABEL_OVER
#undef PADDLE_LABEL_LESS8_SAVE
#undef PADDLE_LABEL_LESS8
}
#endif // __aarch64__
#else
int32_t left = w_out;
while (left > 0) {
const int8_t max0 = std::max(std::max(row0[0], row0[1]), row0[2]);
const int8_t max1 = std::max(std::max(row1[0], row1[1]), row1[2]);
const int8_t max2 = std::max(std::max(row2[0], row2[1]), row2[2]);
*img_out = std::max(std::max(max0, max1), max2);
row0 += 1;
row1 += 1;
row2 += 1;
img_out++;
left--;
}
#endif // __ARM_NEON
}
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
}
void Pool3x3Maxs2_int8(const Tensor *input, Tensor *output, int32_t pad_h,
int32_t pad_w) {
Tensor padded_input;
if (pad_h != 0 && pad_w != 0) {
int8_t value = -SCHAR_MAX;
make_paddings(input, &padded_input, pad_h, pad_h, pad_w, pad_w, value);
input = &padded_input;
}
const int32_t batch_size = input->dims()[0];
const int32_t h_in = input->dims()[2];
const int32_t w_in = input->dims()[3];
const int32_t output_channels = output->dims()[1];
const int32_t h_out = output->dims()[2];
int32_t w_out = output->dims()[3];
const int32_t outputdata_channel_stride = h_out * w_out;
const int32_t inputdata_channel_stride = h_in * w_in;
const int32_t output_batch_stride =
output_channels * outputdata_channel_stride;
const int32_t input_batch_stride = output_channels * inputdata_channel_stride;
const int8_t *input_data = input->data<int8_t>();
int8_t *output_data = output->mutable_data<int8_t>();
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int j = 0; j < output_channels; ++j) {
const int8_t *img_in = input_data + j * inputdata_channel_stride;
int8_t *img_out = output_data + j * outputdata_channel_stride;
for (int k = 0; k < h_out; ++k) {
const int8_t *row0 = img_in + 2 * k * w_in;
const int8_t *row1 = img_in + (2 * k + 1) * w_in;
const int8_t *row2 = img_in + (2 * k + 2) * w_in;
#if __ARM_NEON
int32_t nw = w_out >> 4;
int32_t left_w = w_out & 0xf;
int32_t nw1 = left_w >> 3;
int32_t left_w1 = left_w & 0x7;
#if __aarch64__
// TODO(wzzju)
#else
if (nw > 0) {
#define LOOP_LABEL "1"
// result: q15
asm volatile(
"vld2.8 {q0, q1}, [%[row0]]! \n\t" // q0=0-30, q1=1-31
"vld2.8 {q2, q3}, [%[row1]]! \n\t"
"vld2.8 {q4, q5}, [%[row2]]! \n\t" LOOP_LABEL
": \n\t"
"vmax.s8 q15, q0, q1 \n\t"
"vld2.8 {q6, q7}, [%[row0]]! \n\t" // q0=32-62, q1=33-63
"vmax.s8 q14, q2, q3 \n\t"
"vmax.s8 q13, q4, q5 \n\t"
"vld2.8 {q8, q9}, [%[row1]]! \n\t"
"vext.8 q0, q0, q6, #1 \n\t"
"vmax.s8 q15, q15, q0 \n\t"
"vld2.8 {q10, q11}, [%[row2]]! \n\t"
"vext.8 q2, q2, q8, #1 \n\t"
"vmax.s8 q14, q14, q2 \n\t"
"vext.8 q4, q4, q10, #1 \n\t"
"vmax.s8 q13, q13, q4 \n\t"
"vmax.s8 q15, q15, q14 \n\t"
"vmax.s8 q15, q15, q13 \n\t"
"vmov.s8 q0, q6 \n\t"
"vmov.s8 q1, q7 \n\t"
"vmov.s8 q2, q8 \n\t"
"vmov.s8 q3, q9 \n\t"
"vmov.s8 q4, q10 \n\t"
"vmov.s8 q5, q11 \n\t"
"vst1.8 {q15}, [%[img_out]]! \n\t"
"subs %[nw], #1 \n\t"
"bne " LOOP_LABEL
"b \n\t"
"sub %[row0], #32 \n\t"
"sub %[row1], #32 \n\t"
"sub %[row2], #32 \n\t"
: [nw] "+r"(nw), [row0] "+r"(row0), [row1] "+r"(row1),
[row2] "+r"(row2), [img_out] "+r"(img_out)
:
: "cc", "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q9", "q10", "q11", "q13", "q14", "q15");
#undef LOOP_LABEL
}
if (nw1 > 0 || left_w1 > 0) {
#define PADDLE_LABEL_LESS8 "1"
#define PADDLE_LABEL_LESS8_SAVE "2"
#define PADDLE_LABEL_OVER "3"
// result: d15
asm volatile(
"vld2.8 {d0, d1}, [%[row0]]! \n\t" // d0=0-14, d1=1-15
"vld2.8 {d2, d3}, [%[row1]]! \n\t"
"vld2.8 {d4, d5}, [%[row2]]! \n\t"
"mov r0, #1 \n\t"
"cmp %[nw1], #0 \n\t"
"beq " PADDLE_LABEL_LESS8
"f\n\t"
"vmax.s8 d15, d0, d1 \n\t"
"vld2.8 {d6, d7}, [%[row0]]! \n\t" // d0=32-62, d1=33-63
"vmax.s8 d14, d2, d3 \n\t"
"vmax.s8 d13, d4, d5 \n\t"
"vld2.8 {d8, d9}, [%[row1]]! \n\t"
"vext.8 d0, d0, d6, #1 \n\t"
"vmax.s8 d15, d15, d0 \n\t"
"vld2.8 {d10, d11}, [%[row2]]! \n\t"
"vext.8 d2, d2, d8, #1 \n\t"
"vmax.s8 d14, d14, d2 \n\t"
"vext.8 d4, d4, d10, #1 \n\t"
"vmax.s8 d13, d13, d4 \n\t"
"vmax.s8 d15, d15, d14 \n\t"
"vmax.s8 d15, d15, d13 \n\t"
"vmov.s8 d0, d6 \n\t"
"vmov.s8 d1, d7 \n\t"
"vmov.s8 d2, d8 \n\t"
"vmov.s8 d3, d9 \n\t"
"vmov.s8 d4, d10 \n\t"
"vmov.s8 d5, d11 \n\t"
"vst1.8 {d15}, [%[img_out]]! \n\t"
PADDLE_LABEL_LESS8
": \n\t"
"cmp %[left_w1], #0 \n\t"
"beq " PADDLE_LABEL_OVER
"f\n\t"
"vmax.s8 d15, d0, d1 \n\t"
"vld2.8 {d6, d7}, [%[row0]] \n\t" // d0=32-62, d1=33-63
"vmax.s8 d14, d2, d3 \n\t"
"vmax.s8 d13, d4, d5 \n\t"
"vld2.8 {d8, d9}, [%[row1]] \n\t"
"vext.8 d0, d0, d6, #1 \n\t"
"vmax.s8 d15, d15, d0 \n\t"
"vld2.8 {d10, d11}, [%[row2]] \n\t"
"vext.8 d2, d2, d8, #1 \n\t"
"vmax.s8 d14, d14, d2 \n\t"
"vext.8 d4, d4, d10, #1 \n\t"
"vmax.s8 d13, d13, d4 \n\t"
"vmax.s8 d15, d15, d14 \n\t"
"vmax.s8 d15, d15, d13 \n\t"
PADDLE_LABEL_LESS8_SAVE
": \n\t"
"vst1.8 {d15}, [%[img_out]], r0\n\t"
"add %[row0], %[row0], #2 \n\t"
"add %[row1], %[row1], #2 \n\t"
"add %[row2], %[row2], #2 \n\t"
"vext.8 d15, d15, d15, #1 \n\t"
"subs %[left_w1], #1 \n\t"
"bgt " PADDLE_LABEL_LESS8_SAVE "b \n\t"
PADDLE_LABEL_OVER ": \n\t"
: [nw1] "+r"(nw1), [left_w1] "+r"(left_w1), [row0] "+r"(row0),
[row1] "+r"(row1), [row2] "+r"(row2), [img_out] "+r"(img_out)
:
: "cc", "memory", "r0", "d0", "d1", "d2", "d3", "d4", "d5", "d6",
"d7", "d8", "d9", "d10", "d11", "d13", "d14", "d15");
#undef PADDLE_LABEL_OVER
#undef PADDLE_LABEL_LESS8_SAVE
#undef PADDLE_LABEL_LESS8
}
#endif // __aarch64__
#else
int32_t left = w_out;
while (left > 0) {
const int8_t max0 = std::max(std::max(row0[0], row0[1]), row0[2]);
const int8_t max1 = std::max(std::max(row1[0], row1[1]), row1[2]);
const int8_t max2 = std::max(std::max(row2[0], row2[1]), row2[2]);
*img_out = std::max(std::max(max0, max1), max2);
row0 += 2;
row1 += 2;
row2 += 2;
img_out++;
left--;
}
#endif // __ARM_NEON
}
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
}
void Pool3x3Max_int8(const vector<int> &strides, const vector<int> &paddings,
const Tensor *input, Tensor *output) {
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
const int input_width = input->dims()[3];
const int output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
// const int _kernel_size = 3;
const int stride = strides[0];
// const int stride_width = strides[1];
const int padding = paddings[0];
// const int padding_width = paddings[1];
const int8_t negative_max = -SCHAR_MAX;
const int input_channel_stride = input_height * input_width;
const int output_channel_stride = output_height * output_width;
const int8_t *input_data = input->data<int8_t>();
int8_t *output_data = output->mutable_data<int8_t>();
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
for (int i = 0; i < batch_size; ++i) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
const int8_t *input_seg = input_data + c * input_channel_stride;
int8_t *output_seg = output_data + c * output_channel_stride;
for (int ph = 0; ph < output_height; ph++) {
int hstart = ph * stride - padding;
int hend = min(hstart + 3, input_height);
hstart = max(hstart, 0);
for (int pw = 0; pw < output_width; pw++) {
int wstart = pw * stride - padding;
int wend = min(wstart + 3, input_width);
wstart = max(wstart, 0);
const int8_t *pos1 = input_seg + hstart * input_width + wstart;
const int8_t *pos2 = input_seg + (hstart + 1) * input_width + wstart;
const int8_t *pos3 = input_seg + (hstart + 2) * input_width + wstart;
int8_t *output_ptr = output_seg + ph * output_width + pw;
if (hend - hstart != 3 || wend - wstart != 3) {
int8_t max_value = -SCHAR_MAX;
for (int h = hstart; h < hend; h++) {
for (int w = wstart; w < wend; w++) {
int8_t value = input_seg[h * input_width + w];
if (value > max_value) {
max_value = value;
}
}
}
output_seg[ph * output_width + pw] = max_value;
} else {
#if __ARM_NEON
#if __aarch64__
// TODO(wzzju)
#else
asm volatile(
"vld1.8 {d0}, [%[pos1]] \n\t"
"vld1.8 {d1}, [%[pos2]] \n\t"
"vld1.8 {d2}, [%[pos3]] \n\t"
"vmax.s8 d3, d0, d1 \n\t"
"vmax.s8 d4, d2, d3 \n\t"
"vmov.s8 d4[3], %[negative_max] \n\t"
"vpmax.s8 d5, d4, d4 \n\t"
"vpmax.s8 d6, d5, d5 \n\t"
"vst1.8 {d6[0]},[%[output_ptr]] \n\t"
:
: [pos1] "r"(pos1), [pos2] "r"(pos2), [pos3] "r"(pos3),
[output_ptr] "r"(output_ptr), [negative_max] "r"(negative_max)
: "memory", "q0", "q1", "q2", "q3");
#endif
#else
const int8_t max0 = std::max(std::max(pos1[0], pos1[1]), pos1[2]);
const int8_t max1 = std::max(std::max(pos2[0], pos2[1]), pos2[2]);
const int8_t max2 = std::max(std::max(pos3[0], pos3[1]), pos3[2]);
*output_ptr = std::max(std::max(max0, max1), max2);
#endif // __ARM_NEON
}
}
}
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
}
} // namespace math
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/math/pooling.cpp
浏览文件 @ 07329c60
......@@ -70,15 +70,15 @@ class PoolFunctor<CPU, PoolProcess, T> {
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
T ele = pool_process.initial();
auto ele = pool_process.initial();
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[ph * output_width + pw] = ele;
pool_process.finalize(static_cast<float>(pool_size), &ele);
output_data[ph * output_width + pw] = static_cast<T>(ele);
}
}
input_data += input_stride;
......@@ -88,8 +88,10 @@ class PoolFunctor<CPU, PoolProcess, T> {
}
};
template class PoolFunctor<CPU, math::AvgPool<float>, float>;
template class PoolFunctor<CPU, math::AvgPool<float, float>, float>;
template class PoolFunctor<CPU, math::MaxPool<float>, float>;
template class PoolFunctor<CPU, math::AvgPool<int8_t, int32_t>, int8_t>;
template class PoolFunctor<CPU, math::MaxPool<int8_t>, int8_t>;
} // namespace math
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/pooling.h
浏览文件 @ 07329c60
......@@ -16,6 +16,8 @@ limitations under the License. */
#pragma once
#include <climits>
#include <cmath>
#include "common/log.h"
#include "framework/tensor.h"
#include "pool_2x2.h"
......@@ -37,24 +39,42 @@ namespace math {
* in pool pooling, and finally takes the average.
* MaxPoolGrad and AvgPoolGrad are gradient operations respectively.
*/
template <class T>
template <typename T>
class MaxPool {
public:
inline T initial() { return static_cast<T>(-FLT_MAX); }
inline T initial() {
if (typeid(T) == typeid(int8_t)) {
return static_cast<T>(-SCHAR_MAX);
}
return static_cast<T>(-FLT_MAX);
}
inline void compute(const T &x, T *y) { *y = *y > x ? *y : x; }
inline void finalize(const T &pool_field, T *y) {}
};
template <class T>
template <typename Itype, typename Otype>
class AvgPool {
public:
inline T initial() { return static_cast<T>(0); }
inline void compute(const T &x, T *y) { *y += x; }
inline void finalize(const T &pool_field, T *y) { *y /= pool_field; }
inline Otype initial() { return static_cast<Otype>(0); }
inline void compute(const Itype &x, Otype *y) { *y += x; }
inline void finalize(const float &pool_field, Otype *y) {
if (typeid(Itype) == typeid(int8_t)) {
float tmp = *y / pool_field;
if (tmp > SCHAR_MAX) {
*y = SCHAR_MAX;
} else if (tmp < -SCHAR_MAX) {
*y = -SCHAR_MAX;
} else {
*y = static_cast<Otype>(std::round(tmp));
}
} else {
*y /= pool_field;
}
}
};
template <typename DeviceType, typename PoolProcess, typename T>
......
test/CMakeLists.txt
浏览文件 @ 07329c60
......@@ -269,8 +269,8 @@ if (NOT FOUND_MATCH)
#gen test
ADD_EXECUTABLE(test-pool operators/test_pool_op.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-pool paddle-mobile)
ADD_EXECUTABLE(test-pool-op operators/test_pool_op.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-pool-op paddle-mobile)
#gen test
ADD_EXECUTABLE(test-softmax operators/test_softmax_op.cpp test_helper.h test_include.h executor_for_test.h)
......
test/operators/test_fusion_conv_add_relu_int8_op.cpp
浏览文件 @ 07329c60
......@@ -251,7 +251,7 @@ int TestConvOp(int in_channels, int in_height, int in_width, int out_channels) {
attrs["groups"].Set<int>(1);
attrs["axis"].Set<int>(0);
auto *op = new operators::FusionConvAddReluInt8Op<CPU, int8_t>(
auto *op = new operators::FusionConvAddReluInt8Op<CPU, T>(
"fusion_conv_add_relu_int8", inputs, outputs, attrs, scope);
op->InferShape();
op->Init();
......
test/operators/test_pool_op.cpp
浏览文件 @ 07329c60
......@@ -12,30 +12,279 @@ 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 <iostream>
#include "../test_include.h"
#include "operators/kernel/central-arm-func/pool_arm_func.h"
#include "operators/pool_op.h"
int main() {
paddle_mobile::framework::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string(g_googlenet));
if (program.originProgram == nullptr) {
DLOG << "program read file";
namespace paddle_mobile {
static int PoolOutputSize(int input_size, int filter_size, int padding,
int stride, bool ceil_mode) {
int output_size;
if (!ceil_mode) {
output_size = (input_size - filter_size + 2 * padding) / stride + 1;
} else {
output_size =
(input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
}
return output_size;
}
template <typename T>
static void PoolAvgPad0(std::vector<int> ksize, std::vector<int> strides,
const framework::Tensor *input,
framework::Tensor *out) {
const int32_t batch_size = input->dims()[0];
const int32_t input_c = input->dims()[1];
const int32_t input_h = input->dims()[2];
const int32_t input_w = input->dims()[3];
const int32_t out_c = out->dims()[1];
const int32_t out_h = out->dims()[2];
const int32_t out_w = out->dims()[3];
const int32_t kernel_h = ksize[0];
const int32_t kernel_w = ksize[1];
const int32_t stride_h = strides[0];
const int32_t stride_w = strides[1];
const int32_t inputdata_channel_stride = input_h * input_w;
const int32_t input_batch_stride = input_c * inputdata_channel_stride;
const int32_t outputdata_channel_stride = out_h * out_w;
const int32_t output_batch_stride = out_c * outputdata_channel_stride;
T *out_data = out->mutable_data<T>();
const T *input_data = input->data<T>();
const T **rows = new const T *[kernel_h];
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < out_c; ++j) {
const T *img_in = input_data + j * inputdata_channel_stride;
T *img_out = out_data + j * outputdata_channel_stride;
for (int k = 0; k < out_h; ++k) {
for (int m = 0; m < kernel_h; ++m) {
rows[m] = img_in + (stride_h * k + m) * input_w;
}
int32_t left = out_w;
while (left > 0) {
float tmp = 0;
for (int m = 0; m < kernel_h; ++m) {
for (int l = 0; l < kernel_w; ++l) {
tmp += rows[m][l];
}
}
if (typeid(T) == typeid(int8_t)) {
tmp = tmp / (kernel_h * kernel_w);
if (tmp < -127) {
*img_out = -127;
} else if (tmp > 127) {
*img_out = 127;
} else {
*img_out = static_cast<T>(std::round(tmp));
}
} else {
*img_out = static_cast<T>(tmp / (kernel_h * kernel_w));
}
for (int m = 0; m < kernel_h; ++m) {
rows[m] += stride_w;
}
img_out++;
left--;
}
}
}
input_data += input_batch_stride;
out_data += output_batch_stride;
}
delete[] rows;
}
template <typename T, int CeilMode, int PoolType, int Kernel, int Pad,
int Stride>
int TestPoolOp(int in_channels, int in_height, int in_width) {
int kernel_h = Kernel;
int kernel_w = Kernel;
int pad_h = Pad;
int pad_w = Pad;
int stride_h = Stride;
int stride_w = Stride;
bool ceil_mode = CeilMode != 0;
std::string pooling_type = (PoolType == 0 ? "max" : "avg");
int batch_size = 1;
int input_c = in_channels;
int input_h = in_height;
int input_w = in_width;
framework::DDim input_shape =
framework::make_ddim({batch_size, input_c, input_h, input_w});
std::vector<int64_t> output_shape_v({batch_size, input_c});
output_shape_v.push_back(
PoolOutputSize(input_h, kernel_h, pad_h, stride_h, ceil_mode));
output_shape_v.push_back(
PoolOutputSize(input_w, kernel_w, pad_w, stride_w, ceil_mode));
framework::DDim output_shape = framework::make_ddim(output_shape_v);
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::PoolOp<paddle_mobile::CPU, float>>
executor(program, "pool2d");
VariableNameMap inputs;
VariableNameMap outputs;
auto scope = std::make_shared<framework::Scope>();
inputs["X"] = std::vector<std::string>({"input"});
outputs["Out"] = std::vector<std::string>({"output"});
paddle_mobile::framework::Tensor input;
SetupTensor<float>(&input, {1, 64, 112, 112}, static_cast<float>(0),
static_cast<float>(1));
auto out_ddim = paddle_mobile::framework::make_ddim({1, 64, 56, 56});
auto output =
executor.Predict(input, "conv2d_0.tmp_1", "pool2d_0.tmp_0", out_ddim);
auto input_var = scope.get()->Var("input");
auto input = input_var->template GetMutable<framework::LoDTensor>();
SetupTensor<T>(input, input_shape, -127, 127);
float *output_ptr = output->data<float>();
for (int j = 0; j < output->numel(); ++j) {
DLOG << " value of output: " << output_ptr[j];
auto output_var = scope.get()->Var("output");
framework::AttributeMap attrs;
attrs["pooling_type"].SetString(pooling_type);
attrs["ksize"].Set<vector<int>>(std::vector<int>({kernel_h, kernel_w}));
attrs["strides"].Set<vector<int>>(std::vector<int>({stride_h, stride_w}));
attrs["paddings"].Set<vector<int>>(std::vector<int>({pad_h, pad_w}));
attrs["ceil_mode"].Set<bool>(false);
attrs["global_pooling"].Set<bool>(false);
auto *op = new operators::PoolOp<CPU, float>("pool2d", inputs, outputs, attrs,
scope);
op->InferShape();
op->Init();
op->Run();
framework::Tensor output_cmp;
output_cmp.mutable_data<T>(output_shape);
if (pooling_type == "avg" && pad_h == 0 && pad_h == pad_w) {
PoolAvgPad0<T>(std::vector<int>{kernel_h, kernel_w},
std::vector<int>{stride_h, stride_w}, input, &output_cmp);
} else {
if (typeid(T) == typeid(int8_t)) {
operators::PoolBasic<int8_t, int32_t>(
pooling_type, std::vector<int>{kernel_h, kernel_w},
std::vector<int>{stride_h, stride_w}, std::vector<int>{pad_h, pad_w},
input, &output_cmp);
} else {
operators::PoolBasic<float, float>(
pooling_type, std::vector<int>{kernel_h, kernel_w},
std::vector<int>{stride_h, stride_w}, std::vector<int>{pad_h, pad_w},
input, &output_cmp);
}
}
// compare results
int eq = 0;
int neq = 0;
auto output = output_var->template Get<framework::LoDTensor>();
const T *output_data = output->data<T>();
T *output_cmp_data = output_cmp.data<T>();
for (int i = 0; i < output->numel(); ++i) {
PADDLE_MOBILE_ENFORCE(output_data[i] == output_cmp_data[i],
"The execution of test_pool_op is failed!");
if (output_data[i] == output_cmp_data[i]) {
++eq;
} else {
++neq;
}
}
std::cout << "eq = " << eq << ", neq = " << neq << std::endl;
delete op;
return 0;
}
} // namespace paddle_mobile
int main(int argc, char *argv[]) {
if (argc < 4) {
LOG(paddle_mobile::kLOG_INFO)
<< "Usage:\n"
<< " ./test-pool-op in_channels in_height in_width \n"
<< " params:\n"
<< " -in_channels: int, input image's channels\n"
<< " -in_height: int, input image's height\n"
<< " -in_width: int, input image's width\n";
return 1;
}
int in_channels = atoi(argv[1]);
int in_height = atoi(argv[2]);
int in_width = atoi(argv[3]);
// kernel = 3, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "float, ceil_mode=false, pooling_type=max, kernel=3, pad=1, stride=1";
paddle_mobile::TestPoolOp<float, 0, 0, 3, 1, 1>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 2
LOG(paddle_mobile::kLOG_INFO)
<< "float, ceil_mode=false, pooling_type=max, kernel=3, pad=0, stride=2";
paddle_mobile::TestPoolOp<float, 0, 0, 3, 0, 2>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=0, stride=1";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 0, 1>(in_channels, in_height,
in_width);
// kernel = 3, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=1, stride=1";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 1, 1>(in_channels, in_height,
in_width);
// kernel = 3, pad = 2, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=2, stride=1";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 2, 1>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 2
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=0, stride=2";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 0, 2>(in_channels, in_height,
in_width);
// kernel = 3, pad = 1, stride = 2
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=1, stride=2";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 1, 2>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 2
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=2, stride=2";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 2, 2>(in_channels, in_height,
in_width);
// kernel = 3, pad = 3, stride = 3
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=max, kernel=3, pad=3, stride=3";
paddle_mobile::TestPoolOp<int8_t, 0, 0, 3, 3, 3>(in_channels, in_height,
in_width);
// kernel = 7, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=avg, kernel=7, pad=0, stride=1";
paddle_mobile::TestPoolOp<int8_t, 0, 1, 7, 0, 1>(in_channels, in_height,
in_width);
// kernel = 7, pad = 0, stride = 2
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=avg, kernel=7, pad=0, stride=2";
paddle_mobile::TestPoolOp<int8_t, 0, 1, 7, 0, 2>(in_channels, in_height,
in_width);
// kernel = 7, pad = 0, stride = 3
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=avg, kernel=7, pad=0, stride=3";
paddle_mobile::TestPoolOp<int8_t, 0, 1, 7, 0, 3>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=avg, kernel=3, pad=0, stride=1";
paddle_mobile::TestPoolOp<int8_t, 0, 1, 3, 0, 1>(in_channels, in_height,
in_width);
// kernel = 3, pad = 0, stride = 3
LOG(paddle_mobile::kLOG_INFO)
<< "int8_t, ceil_mode=false, pooling_type=avg, kernel=3, pad=0, stride=3";
paddle_mobile::TestPoolOp<int8_t, 0, 1, 3, 0, 3>(in_channels, in_height,
in_width);
// kernel = 7, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "float, ceil_mode=false, pooling_type=avg, kernel=7, pad=0, stride=1";
paddle_mobile::TestPoolOp<float, 0, 1, 7, 0, 1>(in_channels, in_height,
in_width);
// kernel = 7, pad = 0, stride = 4
LOG(paddle_mobile::kLOG_INFO)
<< "float, ceil_mode=false, pooling_type=avg, kernel=7, pad=0, stride=4";
paddle_mobile::TestPoolOp<float, 0, 1, 7, 0, 4>(in_channels, in_height,
in_width);
// kernel = 5, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO)
<< "float, ceil_mode=false, pooling_type=avg, kernel=5, pad=0, stride=1";
paddle_mobile::TestPoolOp<float, 0, 1, 5, 0, 1>(in_channels, in_height,
in_width);
}
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