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未验证 提交 3898080e 编写于 作者: Z zyfncg 提交者： GitHub
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[Phi] Move roi_align grad kernel and infershape from fuild to phi (#40556) 

* move roi_align_grad kernel

* move roi_align grad kernel and infershape to phi

* remove roi_align infershape
上级 44d46d03
隐藏空白更改
内联 并排
Showing with 646 addition and 286 deletion
paddle/fluid/imperative/prepared_operator.h
浏览文件 @ 3898080e
......@@ -341,7 +341,6 @@ void BuildDygraphPhiKernelContext(
}
for (size_t i = 0; i < attr_names.size(); ++i) {
VLOG(1) << "############## attr_name: " << i << " : " << attr_names[i];
if (attr_defs[i].type_index == std::type_index(typeid(phi::ScalarArray))) {
if (attrs.find(attr_names[i]) !=
attrs.end()) { // shape is in the attribute
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 3898080e
......@@ -9,9 +9,12 @@ 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 "paddle/fluid/operators/roi_align_op.h"
#include <memory>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/ternary.h"
namespace paddle {
namespace operators {
......@@ -23,79 +26,6 @@ class ROIAlignOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
platform::errors::NotFound("Input(X) of ROIAlignOp "
"is not found."));
PADDLE_ENFORCE_EQ(ctx->HasInput("ROIs"), true,
platform::errors::NotFound("Input(ROIs) of ROIAlignOp "
"is not found."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::NotFound("Output(Out) of ROIAlignOp "
"is not found."));
auto input_dims = ctx->GetInputDim("X");
auto rois_dims = ctx->GetInputDim("ROIs");
if (ctx->HasInput("RoisNum")) {
auto rois_num_dims = ctx->GetInputDim("RoisNum");
PADDLE_ENFORCE_EQ(
rois_num_dims.size(), 1,
platform::errors::InvalidArgument("The size of RoisNum should be 1"
", but received size = %d",
rois_num_dims.size()));
}
PADDLE_ENFORCE_EQ(
input_dims.size(), 4,
platform::errors::InvalidArgument(
"The format of Input(X) in"
"RoIAlignOp is NCHW. And the rank of input must be 4. "
"But received rank = %d",
input_dims.size()));
PADDLE_ENFORCE_EQ(rois_dims.size(), 2, platform::errors::InvalidArgument(
"The rank of Input(ROIs) "
"in RoIAlignOp should be 2. "
"But the rank of RoIs is %d",
rois_dims.size()));
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(rois_dims[1], 4,
platform::errors::InvalidArgument(
"The second dimension "
"of Input(ROIs) should be 4. But received the "
"dimension = %d",
rois_dims[1]));
}
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
PADDLE_ENFORCE_GT(pooled_height, 0,
platform::errors::InvalidArgument(
"The 'pooled_height' attribute in RoIAlignOp is "
"invalid. The height must be greater than 0. But "
"received 'pooled_height' = %d",
pooled_height));
PADDLE_ENFORCE_GT(pooled_width, 0,
platform::errors::InvalidArgument(
"The 'pooled_width' attribute in RoIAlignOp is "
"invalid. The width must be greater than 0. But "
"received 'pooled_width' = %d",
pooled_width));
PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
platform::errors::InvalidArgument(
"The 'spatial_scale' attribute in RoIAlignOp is "
"invalid. The scale must be greater than 0. But "
"received 'spatial_scale' = %f",
spatial_scale));
auto out_dims = input_dims;
out_dims[0] = rois_dims[0];
out_dims[1] = input_dims[1];
out_dims[2] = pooled_height;
out_dims[3] = pooled_width;
ctx->SetOutputDim("Out", out_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
......@@ -221,17 +151,16 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(RoiAlignGradNoNeedBufVarsInferer, "X");
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(roi_align, RoiAlignInferShapeFunctor,
PD_INFER_META(phi::RoiAlignInferMeta));
REGISTER_OPERATOR(roi_align, ops::ROIAlignOp, ops::ROIAlignOpMaker,
ops::ROIAlignGradMaker<paddle::framework::OpDesc>,
ops::ROIAlignGradMaker<paddle::imperative::OpBase>);
ops::ROIAlignGradMaker<paddle::imperative::OpBase>,
RoiAlignInferShapeFunctor);
REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp,
ops::RoiAlignGradNoNeedBufVarsInferer);
REGISTER_OP_CPU_KERNEL(
roi_align_grad,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, int>);
REGISTER_OP_VERSION(roi_align)
.AddCheckpoint(
R"ROC(
......
paddle/fluid/operators/roi_align_op.h 已删除 100644 → 0
浏览文件 @ 44d46d03
/* 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. */
#pragma once
#include <algorithm>
#include <limits>
#include <numeric>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <class T>
void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
const T out_grad_this_bin, const T count,
T* batch_grad_data) {
int x_low, y_low, x_high, y_high;
T w1, w2, w3, w4;
if (y < -1.0 || y > height || x < -1.0 || x > width) {
w1 = w2 = w3 = w4 = 0;
x_low = x_high = y_low = y_high = -1;
return;
}
y = y <= 0 ? 0 : y;
x = x <= 0 ? 0 : x;
y_low = static_cast<int>(y);
x_low = static_cast<int>(x);
if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = static_cast<T>(y_low);
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = static_cast<T>(x_low);
} else {
x_high = x_low + 1;
}
T ly = y - y_low, lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
T diff1 = out_grad_this_bin * w1 / count;
T diff2 = out_grad_this_bin * w2 / count;
T diff3 = out_grad_this_bin * w3 / count;
T diff4 = out_grad_this_bin * w4 / count;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
*(batch_grad_data + y_low * width + x_low) += diff1;
*(batch_grad_data + y_low * width + x_high) += diff2;
*(batch_grad_data + y_high * width + x_low) += diff3;
*(batch_grad_data + y_high * width + x_high) += diff4;
}
}
template <typename DeviceContext, typename T>
class CPUROIAlignGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
auto in_dims = in->dims();
auto aligned = ctx.Attr<bool>("aligned");
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num = rois->dims()[0];
if (!in_grad) {
return;
}
Tensor roi_batch_id_list;
roi_batch_id_list.Resize({rois_num});
int* roi_batch_id_data =
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
int rois_batch_size;
if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
rois_batch_size = rois_num_t->numel();
auto* rois_num_data = rois_num_t->data<int>();
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (int i = start; i < start + rois_num_data[n]; ++i) {
roi_batch_id_data[i] = n;
}
start += rois_num_data[n];
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
for (int n = 0; n < rois_batch_size; ++n) {
for (std::size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
}
in_grad->mutable_data<T>(ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
phi::funcs::SetConstant<DeviceContext, T> set_zero;
set_zero(dev_ctx, in_grad, static_cast<T>(0));
int output_grad_size = out_grad->numel();
if ((!out_grad->IsInitialized()) || (output_grad_size <= 0)) {
return;
}
const T* rois_data = rois->data<T>();
const T* out_grad_data = out_grad->data<T>();
T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
auto in_stride = phi::stride(in->dims());
auto roi_stride = phi::stride(rois->dims());
auto out_stride = phi::stride(out_grad->dims());
T roi_offset = aligned ? T(0.5) : 0;
for (int n = 0; n < rois_num; ++n) {
int roi_batch_idx = roi_batch_id_data[n];
T roi_xmin = rois_data[0] * spatial_scale - roi_offset;
T roi_ymin = rois_data[1] * spatial_scale - roi_offset;
T roi_xmax = rois_data[2] * spatial_scale - roi_offset;
T roi_ymax = rois_data[3] * spatial_scale - roi_offset;
T roi_width = roi_xmax - roi_xmin;
T roi_height = roi_ymax - roi_ymin;
roi_width = std::max(roi_width, static_cast<T>(1.));
roi_height = std::max(roi_height, static_cast<T>(1.));
if (!aligned) {
roi_width = std::max(roi_width, static_cast<T>(1.));
roi_height = std::max(roi_height, static_cast<T>(1.));
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
for (int c = 0; c < channels; ++c) {
T* batch_grad_data =
in_grad_data + roi_batch_idx * in_stride[0] + c * in_stride[1];
const T* batch_out_grad_data =
out_grad_data + n * out_stride[0] + c * out_stride[1];
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
int pool_index = ph * pooled_width + pw;
T out_grad_this_bin = batch_out_grad_data[pool_index];
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_height / pooled_height);
int roi_bin_grid_w = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_width / pooled_width);
T count = roi_bin_grid_h * roi_bin_grid_w;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
const T y = roi_ymin + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h);
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T x = roi_xmin + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
bilinear_interpolate_gradient(height, width, y, x,
out_grad_this_bin, count,
batch_grad_data);
}
}
}
}
}
rois_data += roi_stride[0];
}
}
};
} // namespace operators
} // namespace paddle
paddle/phi/infermeta/backward.h
浏览文件 @ 3898080e
......@@ -21,6 +21,10 @@ limitations under the License. */
namespace phi {
// Common InferMeta Functions for backward operators.
//
// NOTE: The InferMeta Functions in this file are arranged in alphabetic order.
void BilinearTensorProductGradInferMeta(const MetaTensor& x,
const MetaTensor& y,
const MetaTensor& weight,
......
paddle/phi/infermeta/binary.h
浏览文件 @ 3898080e
......@@ -29,6 +29,8 @@ namespace phi {
// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
//
// The InferMeta Functions in this file are arranged in alphabetic order.
void AllValueCompareInferMeta(const MetaTensor& x,
const MetaTensor& y,
......
paddle/phi/infermeta/multiary.h
浏览文件 @ 3898080e
......@@ -18,6 +18,23 @@ limitations under the License. */
#include "paddle/phi/core/meta_tensor.h"
namespace phi {
// Common InferMeta Functions for multiary operators, The format like:
//
// 1. The number of input MetaTensor is more than 3:
// void [FunctionDesc|OpName]InferMeta(const MetaTensor& x,
// const MetaTensor& y,
// const MetaTensor& z,
// const MetaTensor& w,
// ...,
// MetaTensor* out) {}
//
// 2. There are `const vector<MetaTensor*>&` in params:
// void [FunctionDesc|OpName]InferMeta(const vector<MetaTensor*>& x,
// ...,
// MetaTensor* out) {}
//
// NOTE: The InferMeta Functions in this file are arranged in alphabetic order.
std::vector<DDim> GetMetaTensorsDim(const std::vector<MetaTensor*>& tensors);
void AdadeltaInferMeta(const MetaTensor& param,
......
paddle/phi/infermeta/nullary.h
浏览文件 @ 3898080e
......@@ -27,6 +27,8 @@ namespace phi {
// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
//
// The InferMeta Functions in this file are arranged in alphabetic order.
void CreateInferMeta(const ScalarArray& shape, DataType dtype, MetaTensor* out);
......
paddle/phi/infermeta/ternary.cc
浏览文件 @ 3898080e
......@@ -322,6 +322,83 @@ void NllLossRawInferMeta(const MetaTensor& input,
total_weight->set_dtype(input.dtype());
}
void RoiAlignInferMeta(const MetaTensor& x,
const MetaTensor& boxes,
paddle::optional<const MetaTensor&> boxes_num,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
MetaTensor* out,
MetaConfig config) {
auto input_dims = x.dims();
auto boxes_dims = boxes.dims();
if (boxes_num) {
auto boxes_num_dims = boxes_num->dims();
PADDLE_ENFORCE_EQ(
boxes_num_dims.size(),
1,
phi::errors::InvalidArgument("The size of RoisNum should be 1"
", but received size = %d",
boxes_num_dims.size()));
}
PADDLE_ENFORCE_EQ(input_dims.size(),
4,
phi::errors::InvalidArgument(
"The format of Input(X) in"
"RoIAlignOp is NCHW. And the rank of input must be 4. "
"But received rank = %d",
input_dims.size()));
PADDLE_ENFORCE_EQ(boxes_dims.size(),
2,
phi::errors::InvalidArgument("The rank of Input(ROIs) "
"in RoIAlignOp should be 2. "
"But the rank of RoIs is %d",
boxes_dims.size()));
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(boxes_dims[1],
4,
phi::errors::InvalidArgument(
"The second dimension "
"of Input(ROIs) should be 4. But received the "
"dimension = %d",
boxes_dims[1]));
}
PADDLE_ENFORCE_GT(pooled_height,
0,
phi::errors::InvalidArgument(
"The 'pooled_height' attribute in RoIAlignOp is "
"invalid. The height must be greater than 0. But "
"received 'pooled_height' = %d",
pooled_height));
PADDLE_ENFORCE_GT(pooled_width,
0,
phi::errors::InvalidArgument(
"The 'pooled_width' attribute in RoIAlignOp is "
"invalid. The width must be greater than 0. But "
"received 'pooled_width' = %d",
pooled_width));
PADDLE_ENFORCE_GT(spatial_scale,
0.0f,
phi::errors::InvalidArgument(
"The 'spatial_scale' attribute in RoIAlignOp is "
"invalid. The scale must be greater than 0. But "
"received 'spatial_scale' = %f",
spatial_scale));
auto out_dims = input_dims;
out_dims[0] = boxes_dims[0];
out_dims[1] = input_dims[1];
out_dims[2] = pooled_height;
out_dims[3] = pooled_width;
out->set_dims(out_dims);
out->set_dtype(x.dtype());
}
void ScatterInferMeta(const MetaTensor& x,
const MetaTensor& index,
const MetaTensor& updates,
......
paddle/phi/infermeta/ternary.h
浏览文件 @ 3898080e
......@@ -30,6 +30,8 @@ namespace phi {
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
//
// The InferMeta Functions in this file are arranged in alphabetic order.
void AccuracyInferMeta(const MetaTensor& out,
const MetaTensor& indice,
const MetaTensor& label,
......@@ -71,6 +73,17 @@ void NllLossRawInferMeta(const MetaTensor& input,
MetaTensor* total_weight,
MetaConfig config = MetaConfig());
void RoiAlignInferMeta(const MetaTensor& x,
const MetaTensor& boxes,
paddle::optional<const MetaTensor&> boxes_num,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
MetaTensor* out,
MetaConfig config = MetaConfig());
void ScatterInferMeta(const MetaTensor& x,
const MetaTensor& index,
const MetaTensor& updates,
......
paddle/phi/infermeta/unary.h
浏览文件 @ 3898080e
......@@ -31,6 +31,8 @@ class MetaConfig;
// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
//
// The InferMeta Functions in this file are arranged in alphabetic order.
void ArgMinMaxInferMeta(const MetaTensor& x,
int64_t axis,
......
paddle/phi/kernels/cpu/roi_align_grad_kernel.cc 0 → 100644
浏览文件 @ 3898080e
// Copyright (c) 2022 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.
#include "paddle/phi/kernels/roi_align_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
template <class T>
void bilinear_interpolate_gradient(const int height,
const int width,
T y,
T x,
const T out_grad_this_bin,
const T count,
T* batch_grad_data) {
int x_low, y_low, x_high, y_high;
T w1, w2, w3, w4;
if (y < -1.0 || y > height || x < -1.0 || x > width) {
w1 = w2 = w3 = w4 = 0;
x_low = x_high = y_low = y_high = -1;
return;
}
y = y <= 0 ? 0 : y;
x = x <= 0 ? 0 : x;
y_low = static_cast<int>(y);
x_low = static_cast<int>(x);
if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = static_cast<T>(y_low);
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = static_cast<T>(x_low);
} else {
x_high = x_low + 1;
}
T ly = y - y_low, lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
T diff1 = out_grad_this_bin * w1 / count;
T diff2 = out_grad_this_bin * w2 / count;
T diff3 = out_grad_this_bin * w3 / count;
T diff4 = out_grad_this_bin * w4 / count;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
*(batch_grad_data + y_low * width + x_low) += diff1;
*(batch_grad_data + y_low * width + x_high) += diff2;
*(batch_grad_data + y_high * width + x_low) += diff3;
*(batch_grad_data + y_high * width + x_high) += diff4;
}
}
template <typename T, typename Context>
void RoiAlignGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
const DenseTensor& out_grad,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
DenseTensor* dx) {
auto in_dims = x.dims();
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num = boxes.dims()[0];
if (!dx) {
return;
}
DenseTensor roi_batch_id_list = Empty<int>(dev_ctx, {rois_num});
int* box_batch_id_data = roi_batch_id_list.data<int>();
int boxes_batch_size;
if (boxes_num) {
boxes_batch_size = boxes_num->numel();
auto* boxes_num_data = boxes_num->data<int>();
int start = 0;
for (int n = 0; n < boxes_batch_size; ++n) {
for (int i = start; i < start + boxes_num_data[n]; ++i) {
box_batch_id_data[i] = n;
}
start += boxes_num_data[n];
}
} else {
auto boxes_lod = boxes.lod().back();
boxes_batch_size = boxes_lod.size() - 1;
for (int n = 0; n < boxes_batch_size; ++n) {
for (std::size_t i = boxes_lod[n]; i < boxes_lod[n + 1]; ++i) {
box_batch_id_data[i] = n;
}
}
}
dev_ctx.template Alloc<T>(dx);
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, dx, static_cast<T>(0));
int output_grad_size = out_grad.numel();
if ((!out_grad.IsInitialized()) || (output_grad_size <= 0)) {
return;
}
const T* boxes_data = boxes.data<T>();
const T* out_grad_data = out_grad.data<T>();
T* dx_data = dev_ctx.template Alloc<T>(dx);
auto in_stride = phi::stride(x.dims());
auto roi_stride = phi::stride(boxes.dims());
auto out_stride = phi::stride(out_grad.dims());
T roi_offset = aligned ? T(0.5) : 0;
for (int n = 0; n < rois_num; ++n) {
int box_batch_idx = box_batch_id_data[n];
T roi_xmin = boxes_data[0] * spatial_scale - roi_offset;
T roi_ymin = boxes_data[1] * spatial_scale - roi_offset;
T roi_xmax = boxes_data[2] * spatial_scale - roi_offset;
T roi_ymax = boxes_data[3] * spatial_scale - roi_offset;
T roi_width = roi_xmax - roi_xmin;
T roi_height = roi_ymax - roi_ymin;
roi_width = std::max(roi_width, static_cast<T>(1.));
roi_height = std::max(roi_height, static_cast<T>(1.));
if (!aligned) {
roi_width = std::max(roi_width, static_cast<T>(1.));
roi_height = std::max(roi_height, static_cast<T>(1.));
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
for (int c = 0; c < channels; ++c) {
T* batch_grad_data =
dx_data + box_batch_idx * in_stride[0] + c * in_stride[1];
const T* batch_out_grad_data =
out_grad_data + n * out_stride[0] + c * out_stride[1];
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
int pool_index = ph * pooled_width + pw;
T out_grad_this_bin = batch_out_grad_data[pool_index];
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_height / pooled_height);
int roi_bin_grid_w = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_width / pooled_width);
T count = roi_bin_grid_h * roi_bin_grid_w;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
const T y = roi_ymin + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h);
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T x = roi_xmin + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
bilinear_interpolate_gradient(height,
width,
y,
x,
out_grad_this_bin,
count,
batch_grad_data);
}
}
}
}
}
boxes_data += roi_stride[0];
}
}
} // namespace phi
PD_REGISTER_KERNEL(roi_align_grad,
CPU,
ALL_LAYOUT,
phi::RoiAlignGradKernel,
float,
double,
int) {}
paddle/phi/kernels/cpu/roi_align_kernel.cc
浏览文件 @ 3898080e
......@@ -179,7 +179,7 @@ void AvgPool(const std::vector<T>& interpolated_values,
}
template <typename T, typename Context>
void ROIAlignKernel(const Context& dev_ctx,
void RoiAlignKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
......@@ -315,4 +315,4 @@ void ROIAlignKernel(const Context& dev_ctx,
} // namespace phi
PD_REGISTER_KERNEL(
roi_align, CPU, ALL_LAYOUT, phi::ROIAlignKernel, float, double, int) {}
roi_align, CPU, ALL_LAYOUT, phi::RoiAlignKernel, float, double, int) {}
paddle/fluid/operators/roi_align_op.cu → paddle/phi/kernels/gpu/roi_align_grad_kernel.cu
浏览文件 @ 3898080e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
// Copyright (c) 2022 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.
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
#include "paddle/phi/kernels/roi_align_grad_kernel.h"
http://www.apache.org/licenses/LICENSE-2.0
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/backends/gpu/gpu_launch_config.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/math_function.h"
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 <vector>
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/roi_align_op.h"
#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
namespace phi {
static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaxinumNumBlocks = 4096;
......@@ -34,10 +36,18 @@ static inline int NumBlocks(const int N) {
}
template <class T>
__device__ void BilinearInterpolateGradient(const int height, const int width,
T y, T x, T* w1, T* w2, T* w3,
T* w4, int* x_low, int* x_high,
int* y_low, int* y_high) {
__device__ void BilinearInterpolateGradient(const int height,
const int width,
T y,
T x,
T* w1,
T* w2,
T* w3,
T* w4,
int* x_low,
int* x_high,
int* y_low,
int* y_high) {
if (y < -1.0 || y > height || x < -1.0 || x > width) {
return;
}
......@@ -66,12 +76,20 @@ __device__ void BilinearInterpolateGradient(const int height, const int width,
}
template <typename T>
__global__ void GPUROIAlignBackward(
const int nthreads, const T* input_rois, const T* out_grad,
const int num_rois, const float spatial_scale, const int channels,
const int height, const int width, const int pooled_height,
const int pooled_width, const int sampling_ratio, int* roi_batch_id_data,
T* input_grad, const bool continuous_coordinate) {
__global__ void GPURoiAlignBackward(const int nthreads,
const T* input_rois,
const T* out_grad,
const int num_rois,
const float spatial_scale,
const int channels,
const int height,
const int width,
const int pooled_height,
const int pooled_width,
const int sampling_ratio,
int* roi_batch_id_data,
T* input_grad,
const bool continuous_coordinate) {
CUDA_KERNEL_LOOP(i, nthreads) {
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
......@@ -119,109 +137,124 @@ __global__ void GPUROIAlignBackward(
static_cast<T>(roi_bin_grid_w);
T w1 = 0, w2 = 0, w3 = 0, w4 = 0;
int x_low = -1, x_high = -1, y_low = -1, y_high = -1;
BilinearInterpolateGradient(height, width, y, x, &w1, &w2, &w3, &w4,
&x_low, &x_high, &y_low, &y_high);
BilinearInterpolateGradient(height,
width,
y,
x,
&w1,
&w2,
&w3,
&w4,
&x_low,
&x_high,
&y_low,
&y_high);
T diff1 = out_grad_this_bin * w1 / count;
T diff2 = out_grad_this_bin * w2 / count;
T diff3 = out_grad_this_bin * w3 / count;
T diff4 = out_grad_this_bin * w4 / count;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
platform::CudaAtomicAdd(offset_input_grad + y_low * width + x_low,
diff1);
platform::CudaAtomicAdd(offset_input_grad + y_low * width + x_high,
diff2);
platform::CudaAtomicAdd(offset_input_grad + y_high * width + x_low,
diff3);
platform::CudaAtomicAdd(offset_input_grad + y_high * width + x_high,
diff4);
paddle::platform::CudaAtomicAdd(
offset_input_grad + y_low * width + x_low, diff1);
paddle::platform::CudaAtomicAdd(
offset_input_grad + y_low * width + x_high, diff2);
paddle::platform::CudaAtomicAdd(
offset_input_grad + y_high * width + x_low, diff3);
paddle::platform::CudaAtomicAdd(
offset_input_grad + y_high * width + x_high, diff4);
}
}
}
}
}
template <typename Place, typename T>
class GPUROIAlignGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<Tensor>("X");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* in_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
auto aligned = ctx.Attr<bool>("aligned");
int rois_num = rois->dims()[0];
int channels = in->dims()[1];
int height = in->dims()[2];
int width = in->dims()[3];
if (!in_grad) {
return;
}
Tensor roi_batch_id_list;
roi_batch_id_list.Resize({rois_num});
auto cplace = platform::CPUPlace();
int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
auto& dev_ctx = ctx.cuda_device_context();
auto gplace = ctx.GetPlace();
if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
int rois_batch_size = rois_num_t->numel();
std::vector<int> rois_num_list(rois_batch_size);
memory::Copy(cplace, rois_num_list.data(), gplace,
rois_num_t->data<int>(), sizeof(int) * rois_batch_size, 0);
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = start; i < start + rois_num_list[n]; ++i) {
roi_batch_id_data[i] = n;
}
start += rois_num_list[n];
}
} else {
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
template <typename T, typename Context>
void RoiAlignGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
const DenseTensor& out_grad,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
DenseTensor* dx) {
int rois_num = boxes.dims()[0];
int channels = x.dims()[1];
int height = x.dims()[2];
int width = x.dims()[3];
if (!dx) {
return;
}
DenseTensor box_batch_id_list;
box_batch_id_list.Resize({rois_num});
int* box_batch_size = dev_ctx.template HostAlloc<int>(&box_batch_id_list);
auto cplace = phi::CPUPlace();
auto gplace = dev_ctx.GetPlace();
if (boxes_num) {
int boxes_batch_size = boxes_num->numel();
std::vector<int> boxes_num_list(boxes_batch_size);
paddle::memory::Copy(cplace,
boxes_num_list.data(),
gplace,
boxes_num->data<int>(),
sizeof(int) * boxes_batch_size,
0);
int start = 0;
for (int n = 0; n < boxes_batch_size; ++n) {
for (size_t i = start; i < start + boxes_num_list[n]; ++i) {
box_batch_size[i] = n;
}
start += boxes_num_list[n];
}
auto roi_ptr =
memory::Alloc(dev_ctx, roi_batch_id_list.numel() * sizeof(int));
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
int bytes = roi_batch_id_list.numel() * sizeof(int);
memory::Copy(gplace, roi_id_data, cplace, roi_batch_id_data, bytes,
dev_ctx.stream());
in_grad->mutable_data<T>(ctx.GetPlace());
phi::funcs::SetConstant<Place, T> set_zero;
set_zero(dev_ctx, in_grad, static_cast<T>(0));
int output_grad_size = out_grad->numel();
int blocks = NumBlocks(output_grad_size);
int threads = kNumCUDAThreads;
if (output_grad_size > 0) {
GPUROIAlignBackward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
output_grad_size, rois->data<T>(), out_grad->data<T>(), rois_num,
spatial_scale, channels, height, width, pooled_height, pooled_width,
sampling_ratio, roi_id_data, in_grad->mutable_data<T>(ctx.GetPlace()),
aligned);
} else {
auto boxes_lod = boxes.lod().back();
int boxes_batch_size = boxes_lod.size() - 1;
for (int n = 0; n < boxes_batch_size; ++n) {
for (size_t i = boxes_lod[n]; i < boxes_lod[n + 1]; ++i) {
box_batch_size[i] = n;
}
}
}
};
auto roi_ptr =
paddle::memory::Alloc(dev_ctx, box_batch_id_list.numel() * sizeof(int));
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
int bytes = box_batch_id_list.numel() * sizeof(int);
paddle::memory::Copy(
gplace, roi_id_data, cplace, box_batch_size, bytes, dev_ctx.stream());
dev_ctx.template Alloc<T>(dx);
phi::funcs::SetConstant<Context, T> set_zero;
set_zero(dev_ctx, dx, static_cast<T>(0));
int output_grad_size = out_grad.numel();
int blocks = NumBlocks(output_grad_size);
int threads = kNumCUDAThreads;
if (output_grad_size > 0) {
GPURoiAlignBackward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
output_grad_size,
boxes.data<T>(),
out_grad.data<T>(),
rois_num,
spatial_scale,
channels,
height,
width,
pooled_height,
pooled_width,
sampling_ratio,
roi_id_data,
dx->data<T>(),
aligned);
}
}
} // namespace operators
} // namespace paddle
} // namespace phi
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
roi_align_grad,
ops::GPUROIAlignGradOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::GPUROIAlignGradOpKernel<paddle::platform::CUDADeviceContext, double>);
PD_REGISTER_KERNEL(
roi_align_grad, GPU, ALL_LAYOUT, phi::RoiAlignGradKernel, float, double) {}
paddle/phi/kernels/gpu/roi_align_kernel.cu
浏览文件 @ 3898080e
......@@ -71,7 +71,7 @@ __device__ T BilinearInterpolate(
}
template <class T>
__global__ void GPUROIAlignForward(const int nthreads,
__global__ void GPURoiAlignForward(const int nthreads,
const T* input_data,
const T* input_rois,
const float spatial_scale,
......@@ -137,7 +137,7 @@ __global__ void GPUROIAlignForward(const int nthreads,
}
template <typename T, typename Context>
void ROIAlignKernel(const Context& dev_ctx,
void RoiAlignKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
......@@ -233,7 +233,7 @@ void ROIAlignKernel(const Context& dev_ctx,
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
paddle::memory::Copy(
gplace, roi_id_data, cplace, roi_batch_id_data, bytes, dev_ctx.stream());
GPUROIAlignForward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
GPURoiAlignForward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
output_size,
x.data<T>(),
boxes.data<T>(),
......@@ -252,4 +252,4 @@ void ROIAlignKernel(const Context& dev_ctx,
} // namespace phi
PD_REGISTER_KERNEL(
roi_align, GPU, ALL_LAYOUT, phi::ROIAlignKernel, float, double) {}
roi_align, GPU, ALL_LAYOUT, phi::RoiAlignKernel, float, double) {}
paddle/phi/kernels/roi_align_grad_kernel.h 0 → 100644
浏览文件 @ 3898080e
// Copyright (c) 2022 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 "paddle/phi/core/dense_tensor.h"
#include "paddle/utils/optional.h"
namespace phi {
template <typename T, typename Context>
void RoiAlignGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
const DenseTensor& out_grad,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
DenseTensor* dx);
} // namespace phi
paddle/phi/kernels/roi_align_kernel.h
浏览文件 @ 3898080e
......@@ -20,7 +20,7 @@
namespace phi {
template <typename T, typename Context>
void ROIAlignKernel(const Context& dev_ctx,
void RoiAlignKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& boxes,
paddle::optional<const DenseTensor&> boxes_num,
......
paddle/phi/ops/compat/roi_align_sig.cc
浏览文件 @ 3898080e
......@@ -16,7 +16,7 @@
namespace phi {
KernelSignature ROIAlignOpArgumentMapping(const ArgumentMappingContext& ctx) {
KernelSignature RoiAlignOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("roi_align",
{"X", "ROIs", "RoisNum"},
{"pooled_height",
......@@ -27,6 +27,19 @@ KernelSignature ROIAlignOpArgumentMapping(const ArgumentMappingContext& ctx) {
{"Out"});
}
KernelSignature RoiAlignGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("roi_align_grad",
{"X", "ROIs", "RoisNum", GradVarName("Out")},
{"pooled_height",
"pooled_width",
"spatial_scale",
"sampling_ratio",
"aligned"},
{GradVarName("X")});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(roi_align, phi::ROIAlignOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(roi_align, phi::RoiAlignOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(roi_align_grad, phi::RoiAlignGradOpArgumentMapping);
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