未验证 提交 059b2784 编写于 作者: T tensor-tang 提交者: GitHub

Merge pull request #12408 from tensor-tang/refine/im2col

Refine CPU im2col padding with 1
...@@ -14,6 +14,7 @@ limitations under the License. */ ...@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h" #include "paddle/fluid/operators/math/im2col.h"
#include <vector> #include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -35,61 +36,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO, ...@@ -35,61 +36,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
PADDLE_ENFORCE(im.dims().size() == 3); PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col->dims().size() == 5); PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
// TODO(TJ): change me to template
// further optimaze:
// 1. padding != 1
// 2. could also support stride_h != 1
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 && if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 &&
dilation[1] == 1 && padding[0] == 0 && padding[1] == 0) { dilation[1] == 1) {
int col_matrix_width = output_width * output_height; if (padding[0] == 0 && padding[1] == 0) {
size_t copy_size = sizeof(T) * output_width; im2col_sh1sw1dh1dw1ph0pw0<T>(im, col);
for (int oh = 0; oh < output_height; ++oh) { return;
const T* im_data_start = im_data + oh * im_width; } else if (padding[0] == 1 && padding[1] == 1) {
T* dst_data = col_data + oh * output_width; im2col_sh1sw1dh1dw1ph1pw1<T>(im, col);
for (int ic = 0; ic < im_channels; ++ic) { return;
const T* src_data = im_data_start + ic * im_height * im_width;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
return;
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
} }
// TODO(TJ): complete padding >=2
} }
im2col_common<T>(im, dilation, stride, padding, col);
} }
}; };
......
/* Copyright (c) 2016 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. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
/**
* The most common im2col algorithm.
* Support dilation, stride and padding.
*/
template <typename T>
inline void im2col_common(const framework::Tensor& im,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 0
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int col_matrix_width = output_width * output_height;
int im_size = im_height * im_width;
size_t copy_size = sizeof(T) * output_width;
const T* im_data_oh = im_data;
T* dst_data_oh = col_data;
for (int oh = 0; oh < output_height; ++oh) {
const T* src_data_ic = im_data_oh;
T* dst_data = dst_data_oh;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = src_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
src_data_ic = src_data_ic + im_size;
}
im_data_oh = im_data_oh + im_width;
dst_data_oh = dst_data_oh + output_width;
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 1
* and filter_width == 1 have a special implementation
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
constexpr int plh = 1;
constexpr int prh = 1;
constexpr int plw = 1;
constexpr int prw = 1;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int im_size = im_height * im_width;
int col_matrix_width = output_width * output_height;
int col_block_fh = filter_width * col_matrix_width; // fw*oh*ow
int col_block_ic = filter_height * col_block_fh; // fh*fw*oh*ow
// fill height padding
{
size_t copy_size = sizeof(T) * output_width;
T* col_start_l = col_data;
T* col_start_r = col_data + (filter_height - 1) * col_block_fh +
col_matrix_width - output_width;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_l = col_start_l;
T* dst_data_r = col_start_r;
for (int kw = 0; kw < filter_width; ++kw) {
std::memset(dst_data_l, 0, copy_size);
std::memset(dst_data_r, 0, copy_size);
dst_data_l = dst_data_l + col_matrix_width;
dst_data_r = dst_data_r + col_matrix_width;
}
col_start_l = col_start_l + col_block_ic;
col_start_r = col_start_r + col_block_ic;
}
}
auto pad = static_cast<T>(0);
if (filter_width == 1) {
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
T* dst_data = dst_data_kh;
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width - 1;
*dst_data = pad;
++dst_data;
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// fill core
size_t copy_size = sizeof(T) * (output_width - plw - prw);
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start =
im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + col_matrix_width;
continue;
}
std::memcpy(dst_data + plw, src_data, copy_size);
dst_data = dst_data + col_matrix_width;
src_data = src_data + im_width;
}
}
}
return;
}
// filter_width != 1
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (T* dst_data :
{dst_data_kh, dst_data_kh + (filter_width - prw) * col_matrix_width +
output_width - 1}) {
// TODO(TJ): from plh, saving repeated assignment
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width;
}
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// TODO(TJ): use array like: size_t copy_size[kw]={sizeof(T) *
// (output_width-1)}
// length of copy_size is equal kw.
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + filter_width * col_matrix_width;
continue;
}
// TODO(TJ): reuse plw-kw outside this for
// try to unify
for (int kw = 0; kw < plw; ++kw) {
std::memcpy(dst_data + (plw - kw), src_data,
sizeof(T) * (output_width - (plw - kw)));
dst_data = dst_data + col_matrix_width;
}
for (int kw = plw; kw < filter_width - prw; ++kw) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * output_width);
dst_data = dst_data + col_matrix_width;
}
int i = 1;
for (int kw = filter_width - prw; kw < filter_width; ++kw, ++i) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * (output_width - i));
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
}
} // namespace math
} // namespace operators
} // namespace paddle
...@@ -14,7 +14,9 @@ limitations under the License. */ ...@@ -14,7 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h" #include "paddle/fluid/operators/math/im2col.h"
#include <gtest/gtest.h> #include <gtest/gtest.h>
#include <sys/time.h>
#include <vector> #include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
template <typename DeviceContext, typename Place> template <typename DeviceContext, typename Place>
void testIm2col() { void testIm2col() {
...@@ -160,82 +162,111 @@ void testIm2col() { ...@@ -160,82 +162,111 @@ void testIm2col() {
delete context; delete context;
} }
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
paddle::framework::Tensor input;
paddle::framework::Tensor output;
paddle::framework::Tensor ref_output;
std::vector<int> padding({ph, pw});
std::vector<int> stride({1, 1}); // stride_y, stride_x
std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1;
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1;
float* input_ptr =
input.mutable_data<float>({ic, ih, iw}, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] = static_cast<float>(i + 1);
}
paddle::platform::CPUPlace place;
paddle::platform::CPUDeviceContext context(place);
output.mutable_data<float>({ic, fh, fw, output_height, output_width}, place);
ref_output.mutable_data<float>({ic, fh, fw, output_height, output_width},
place);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO,
paddle::platform::CPUDeviceContext, float>
im2col;
im2col(context, input, dilation, stride, padding, &output);
auto ref_im2col = [&](
const paddle::framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& padding,
paddle::framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const float* im_data = im.data<float>();
float* col_data = col->data<float>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? 0.f
: im_data[im_idx];
}
}
}
};
ref_im2col(input, dilation, stride, padding, &ref_output);
float* out_cfo_ptr = output.data<float>();
float* out_ref_ptr = ref_output.data<float>();
for (int i = 0; i < output.numel(); ++i) {
EXPECT_EQ(out_cfo_ptr[i], out_ref_ptr[i]);
}
}
TEST(math, im2col) { TEST(math, im2col) {
testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>(); testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
testIm2colCPU(/*ic*/ 3, /*ih*/ 5, /*iw*/ 5, /*fh*/ 3, /*fw*/ 2, /*ph*/ 0,
/*pw*/ 0);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 1);
#ifdef PADDLE_WITH_CUDA #ifdef PADDLE_WITH_CUDA
testIm2col<paddle::platform::CUDADeviceContext, testIm2col<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace>(); paddle::platform::CUDAPlace>();
#endif #endif
} }
#define PREPARE_IM2COL_CPU \
paddle::platform::CPUPlace place; \
paddle::platform::CPUDeviceContext context(place); \
paddle::framework::Tensor input; \
paddle::framework::Tensor out; \
paddle::framework::Tensor ref; \
std::vector<int> padding({ph, pw}); \
std::vector<int> stride({1, 1}); \
std::vector<int> dilation({1, 1}); \
float* input_ptr = input.mutable_data<float>({ic, ih, iw}, place); \
for (int i = 0; i < input.numel(); ++i) { \
input_ptr[i] = static_cast<float>(i + 1); \
} \
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1; \
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1; \
out.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
ref.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
paddle::operators::math::Im2ColFunctor< \
paddle::operators::math::ColFormat::kCFO, \
paddle::platform::CPUDeviceContext, float> \
im2col
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
im2col(context, input, dilation, stride, padding, &out);
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
float* ref_data = ref.data<float>();
float* out_data = out.data<float>();
for (int i = 0; i < out.numel(); ++i) {
EXPECT_EQ(out_data[i], ref_data[i]);
}
}
void benchIm2col(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
constexpr int repeat = 100;
auto GetCurrentMs = []() -> double {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
};
auto t1 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
im2col(context, input, dilation, stride, padding, &out);
}
auto t2 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
}
auto t3 = GetCurrentMs();
LOG(INFO) << "before: " << (t3 - t2) / repeat
<< ",after: " << (t2 - t1) / repeat
<< ",boost: " << ((t3 - t2) / (t2 - t1) - 1) * 100 << "%";
}
TEST(math, im2col_cputest) {
// padding_h == padding_w
for (int p = 0; p < 4; ++p) {
// width == height
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 5, /*fh*/ 4, /*fw*/ 4, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 2, /*ph*/ p,
/*pw*/ p);
// height != width
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 1, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 5, /*fh*/ 3, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
// filter == 1
testIm2colCPU(/*ic*/ 3, /*ih*/ 4, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 3, /*ih*/ 3, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
}
// padding_h != padding_w
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 2);
// benchmark
for (int p : {0, 1}) {
for (int k : {1, 3, 5}) {
LOG(INFO) << "padding == " << p << ", filter == " << k;
benchIm2col(/*ic*/ 3, /*ih*/ 224, /*iw*/ 224, /*fh*/ k, /*fw*/ k,
/*ph*/ p, /*pw*/ p);
}
}
}
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