Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
未验证 提交 feaf1e2d 编写于 作者: C chengduo 提交者： GitHub
浏览文件
操作

Merge pull request #5608 from chengduoZH/fix_pooling_function_parameter_order 

fix pooling functor parameter order
上级 d7319c22 21604977
显示空白变更内容
内联 并排
Showing with 278 addition and 272 deletion
paddle/operators/math/pooling.cc
浏览文件 @ feaf1e2d
......@@ -27,15 +27,15 @@ template <typename PoolProcess, typename T>
class Pool2dFunctor<platform::CPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_process) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process, framework::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 output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
......@@ -47,7 +47,7 @@ class Pool2dFunctor<platform::CPUPlace, PoolProcess, T> {
const int output_stride = output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -87,11 +87,12 @@ template <typename PoolProcess, class T>
class Pool2dGradFunctor<platform::CPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_grad_process) {
PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
......@@ -110,7 +111,7 @@ class Pool2dGradFunctor<platform::CPUPlace, PoolProcess, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -154,10 +155,11 @@ template <class T>
class MaxPool2dGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
......@@ -176,7 +178,7 @@ class MaxPool2dGradFunctor<platform::CPUPlace, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -240,17 +242,17 @@ template <typename PoolProcess, class T>
class Pool3dFunctor<platform::CPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_process) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
const int input_width = input.dims()[4];
const int output_channels = output.dims()[1];
const int output_depth = output.dims()[2];
const int output_height = output.dims()[3];
const int output_width = output.dims()[4];
const int output_channels = output->dims()[1];
const int output_depth = output->dims()[2];
const int output_height = output->dims()[3];
const int output_width = output->dims()[4];
const int ksize_depth = ksize[0];
const int ksize_height = ksize[1];
const int ksize_width = ksize[2];
......@@ -265,7 +267,7 @@ class Pool3dFunctor<platform::CPUPlace, PoolProcess, T> {
const int output_stride = output_depth * output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -315,11 +317,12 @@ template <typename PoolProcess, class T>
class Pool3dGradFunctor<platform::CPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_grad_process) {
PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -343,7 +346,7 @@ class Pool3dGradFunctor<platform::CPUPlace, PoolProcess, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -398,10 +401,11 @@ template <class T>
class MaxPool3dGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -425,7 +429,7 @@ class MaxPool3dGradFunctor<platform::CPUPlace, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -498,15 +502,15 @@ template <typename T>
class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask) {
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 output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
......@@ -517,8 +521,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
const int output_stride = output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* mask_data = mask.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -563,13 +567,13 @@ template <typename T>
class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const int batch_size = input_grad.dims()[0];
const int input_height = input_grad.dims()[2];
const int input_width = input_grad.dims()[3];
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input_grad->dims()[0];
const int input_height = input_grad->dims()[2];
const int input_width = input_grad->dims()[3];
const int output_channels = output_grad.dims()[1];
const int output_height = output_grad.dims()[2];
const int output_width = output_grad.dims()[3];
......@@ -578,7 +582,7 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) {
......@@ -612,17 +616,17 @@ template <typename T>
class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
const int input_width = input.dims()[4];
const int output_channels = output.dims()[1];
const int output_depth = output.dims()[2];
const int output_height = output.dims()[3];
const int output_width = output.dims()[4];
const int output_channels = output->dims()[1];
const int output_depth = output->dims()[2];
const int output_height = output->dims()[3];
const int output_width = output->dims()[4];
const int ksize_depth = ksize[0];
const int ksize_height = ksize[1];
const int ksize_width = ksize[2];
......@@ -636,8 +640,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
const int output_stride = output_depth * output_height * output_width;
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* mask_data = mask.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) {
......@@ -691,14 +695,14 @@ template <typename T>
class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const int batch_size = input_grad.dims()[0];
const int input_depth = input_grad.dims()[2];
const int input_height = input_grad.dims()[3];
const int input_width = input_grad.dims()[4];
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input_grad->dims()[0];
const int input_depth = input_grad->dims()[2];
const int input_height = input_grad->dims()[3];
const int input_width = input_grad->dims()[4];
const int output_channels = output_grad.dims()[1];
const int output_depth = output_grad.dims()[2];
const int output_height = output_grad.dims()[3];
......@@ -708,7 +712,7 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) {
......
paddle/operators/math/pooling.cu
浏览文件 @ feaf1e2d
......@@ -21,13 +21,13 @@ namespace math {
template <typename PoolProcess, typename T>
__global__ void KernelPool2D(const int nthreads, const T* input_data,
T* output_data, const int channels,
const int input_height, const int input_width,
const int output_height, const int output_width,
const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width,
const int padding_height, const int padding_width,
PoolProcess pool_process) {
const int channels, const int input_height,
const int input_width, const int output_height,
const int output_width, const int ksize_height,
const int ksize_width, const int stride_height,
const int stride_width, const int padding_height,
const int padding_width, PoolProcess pool_process,
T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -59,11 +59,11 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
template <typename PoolProcess, typename T>
__global__ void KernelPool2DGrad(
const int nthreads, const T* input_data, const T* output_data,
const T* output_grad, T* input_grad, const int channels,
const int input_height, const int input_width, const int output_height,
const int output_width, const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width, const int padding_height,
const int padding_width, PoolProcess pool_process) {
const T* output_grad, const int channels, const int input_height,
const int input_width, const int output_height, const int output_width,
const int ksize_height, const int ksize_width, const int stride_height,
const int stride_width, const int padding_height, const int padding_width,
PoolProcess pool_process, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -107,11 +107,11 @@ __global__ void KernelPool2DGrad(
template <typename T>
__global__ void KernelMaxPool2DGrad(
const int nthreads, const T* input_data, const T* output_data,
const T* output_grad, T* input_grad, const int channels,
const int input_height, const int input_width, const int output_height,
const int output_width, const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width, const int padding_height,
const int padding_width) {
const T* output_grad, const int channels, const int input_height,
const int input_width, const int output_height, const int output_width,
const int ksize_height, const int ksize_width, const int stride_height,
const int stride_width, const int padding_height, const int padding_width,
T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -158,16 +158,16 @@ template <typename PoolProcess, typename T>
class Pool2dFunctor<platform::GPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_process) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
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 output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
......@@ -176,7 +176,7 @@ class Pool2dFunctor<platform::GPUPlace, PoolProcess, T> {
const int padding_width = paddings[1];
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -187,11 +187,10 @@ class Pool2dFunctor<platform::GPUPlace, PoolProcess, T> {
PoolProcess,
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, input_data, output_data, input_channels,
input_height, input_width, output_height,
output_width, ksize_height, ksize_width,
stride_height, stride_width, padding_height,
padding_width, pool_process);
.stream()>>>(
nthreads, input_data, input_channels, input_height, input_width,
output_height, output_width, ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width, pool_process, output_data);
}
};
......@@ -204,11 +203,11 @@ template <typename PoolProcess, typename T>
class Pool2dGradFunctor<platform::GPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process) {
PoolProcess pool_process, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -225,7 +224,7 @@ class Pool2dGradFunctor<platform::GPUPlace, PoolProcess, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * input_channels * input_height * input_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -237,10 +236,10 @@ class Pool2dGradFunctor<platform::GPUPlace, PoolProcess, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, output_grad_data, input_grad_data,
input_channels, input_height, input_width, output_height, output_width,
ksize_height, ksize_width, stride_height, stride_width, padding_height,
padding_width, pool_process);
nthreads, input_data, output_data, output_grad_data, input_channels,
input_height, input_width, output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width, padding_height, padding_width,
pool_process, input_grad_data);
}
};
......@@ -253,10 +252,11 @@ template <typename T>
class MaxPool2dGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -274,7 +274,7 @@ class MaxPool2dGradFunctor<platform::GPUPlace, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -285,10 +285,10 @@ class MaxPool2dGradFunctor<platform::GPUPlace, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, output_grad_data, input_grad_data,
input_channels, input_height, input_width, output_height, output_width,
ksize_height, ksize_width, stride_height, stride_width, padding_height,
padding_width);
nthreads, input_data, output_data, output_grad_data, input_channels,
input_height, input_width, output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width, padding_height, padding_width,
input_grad_data);
}
};
......@@ -313,14 +313,16 @@ template class Pool2dGradFunctor<
platform::GPUPlace, paddle::operators::math::AvgPoolGrad<double>, double>;
template <typename PoolProcess, typename T>
__global__ void KernelPool3D(
const int nthreads, const T* input_data, T* output_data, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
PoolProcess pool_process) {
__global__ void KernelPool3D(const int nthreads, const T* input_data,
const int channels, const int input_depth,
const int input_height, const int input_width,
const int output_depth, const int output_height,
const int output_width, const int ksize_depth,
const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height,
const int stride_width, const int padding_depth,
const int padding_height, const int padding_width,
PoolProcess pool_process, T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -358,13 +360,13 @@ __global__ void KernelPool3D(
template <typename PoolProcess, typename T>
__global__ void KernelPool3DGrad(
const int nthreads, const T* input_data, const T* output_data,
const T* output_grad, T* input_grad, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
PoolProcess pool_process) {
const T* output_grad, const int channels, const int input_depth,
const int input_height, const int input_width, const int output_depth,
const int output_height, const int output_width, const int ksize_depth,
const int ksize_height, const int ksize_width, const int stride_depth,
const int stride_height, const int stride_width, const int padding_depth,
const int padding_height, const int padding_width, PoolProcess pool_process,
T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -422,13 +424,12 @@ __global__ void KernelPool3DGrad(
template <typename T>
__global__ void KernelMaxPool3DGrad(
const int nthreads, const T* input_data, const T* output_data,
const T* output_grad, T* input_grad, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height,
const int padding_width) {
const T* output_grad, const int channels, const int input_depth,
const int input_height, const int input_width, const int output_depth,
const int output_height, const int output_width, const int ksize_depth,
const int ksize_height, const int ksize_width, const int stride_depth,
const int stride_height, const int stride_width, const int padding_depth,
const int padding_height, const int padding_width, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -480,18 +481,18 @@ template <typename PoolProcess, class T>
class Pool3dFunctor<platform::GPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_process) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
const int input_width = input.dims()[4];
const int output_channels = output.dims()[1];
const int output_depth = output.dims()[2];
const int output_height = output.dims()[3];
const int output_width = output.dims()[4];
const int output_channels = output->dims()[1];
const int output_depth = output->dims()[2];
const int output_height = output->dims()[3];
const int output_width = output->dims()[4];
const int ksize_depth = ksize[0];
const int ksize_height = ksize[1];
const int ksize_width = ksize[2];
......@@ -503,7 +504,7 @@ class Pool3dFunctor<platform::GPUPlace, PoolProcess, T> {
const int padding_width = paddings[2];
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_depth * output_height *
output_width;
......@@ -516,11 +517,11 @@ class Pool3dFunctor<platform::GPUPlace, PoolProcess, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, input_channels, input_depth,
input_height, input_width, output_depth, output_height, output_width,
ksize_depth, ksize_height, ksize_width, stride_depth, stride_height,
stride_width, padding_depth, padding_height, padding_width,
pool_process);
nthreads, input_data, input_channels, input_depth, input_height,
input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width,
padding_depth, padding_height, padding_width, pool_process,
output_data);
}
};
......@@ -533,11 +534,11 @@ template <typename PoolProcess, class T>
class Pool3dGradFunctor<platform::GPUPlace, PoolProcess, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_process) {
PoolProcess pool_process, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -560,7 +561,7 @@ class Pool3dGradFunctor<platform::GPUPlace, PoolProcess, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads =
batch_size * input_channels * input_depth * input_height * input_width;
......@@ -573,11 +574,11 @@ class Pool3dGradFunctor<platform::GPUPlace, PoolProcess, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, output_grad_data, input_grad_data,
input_channels, input_depth, input_height, input_width, output_depth,
output_height, output_width, ksize_depth, ksize_height, ksize_width,
stride_depth, stride_height, stride_width, padding_depth,
padding_height, padding_width, pool_process);
nthreads, input_data, output_data, output_grad_data, input_channels,
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width, pool_process, input_grad_data);
}
};
......@@ -590,10 +591,11 @@ template <class T>
class MaxPool3dGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -616,7 +618,7 @@ class MaxPool3dGradFunctor<platform::GPUPlace, T> {
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_depth * output_height *
output_width;
......@@ -628,11 +630,11 @@ class MaxPool3dGradFunctor<platform::GPUPlace, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, output_grad_data, input_grad_data,
input_channels, input_depth, input_height, input_width, output_depth,
output_height, output_width, ksize_depth, ksize_height, ksize_width,
stride_depth, stride_height, stride_width, padding_depth,
padding_height, padding_width);
nthreads, input_data, output_data, output_grad_data, input_channels,
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width, input_grad_data);
}
};
......@@ -658,11 +660,11 @@ template class Pool3dGradFunctor<
template <typename T>
__global__ void KernelMaxPool2dWithIdx(
const int nthreads, const T* input_data, T* output_data, T* mask_data,
const int channels, const int input_height, const int input_width,
const int output_height, const int output_width, const int ksize_height,
const int ksize_width, const int stride_height, const int stride_width,
const int padding_height, const int padding_width) {
const int nthreads, const T* input_data, const int channels,
const int input_height, const int input_width, const int output_height,
const int output_width, const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width, const int padding_height,
const int padding_width, T* output_data, T* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -697,11 +699,11 @@ __global__ void KernelMaxPool2dWithIdx(
template <typename T>
__global__ void KernelMaxPool2DWithIdxGrad(
const int nthreads, T* input_grad, const T* output_grad, const T* mask_data,
const int nthreads, const T* output_grad, const T* mask_data,
const int channels, const int input_height, const int input_width,
const int output_height, const int output_width, const int ksize_height,
const int ksize_width, const int stride_height, const int stride_width,
const int padding_height, const int padding_width) {
const int padding_height, const int padding_width, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int w_offset = index % input_width;
......@@ -748,16 +750,16 @@ template <typename T>
class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
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 output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
......@@ -766,8 +768,8 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_width = paddings[1];
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* mask_data = mask.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -777,11 +779,10 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
KernelMaxPool2dWithIdx<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, input_data, output_data, mask_data,
input_channels, input_height, input_width,
output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width,
padding_height, padding_width);
.stream()>>>(
nthreads, input_data, input_channels, input_height, input_width,
output_height, output_width, ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width, output_data, mask_data);
}
};
......@@ -794,14 +795,14 @@ template <typename T>
class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const int batch_size = input_grad.dims()[0];
const int input_channels = input_grad.dims()[1];
const int input_height = input_grad.dims()[2];
const int input_width = input_grad.dims()[3];
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input_grad->dims()[0];
const int input_channels = input_grad->dims()[1];
const int input_height = input_grad->dims()[2];
const int input_width = input_grad->dims()[3];
const int output_height = output_grad.dims()[2];
const int output_width = output_grad.dims()[3];
const int ksize_height = ksize[0];
......@@ -813,7 +814,7 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
const T* mask_data = mask.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * input_channels * input_height * input_width;
int blocks = (nthreads + 1024 - 1) / 1024;
......@@ -823,11 +824,11 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
KernelMaxPool2DWithIdxGrad<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, input_grad_data, output_grad_data,
mask_data, input_channels, input_height,
input_width, output_height, output_width,
ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width);
.stream()>>>(nthreads, output_grad_data, mask_data,
input_channels, input_height, input_width,
output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width,
padding_height, padding_width, input_grad_data);
}
};
......@@ -838,13 +839,13 @@ template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, double>;
template <typename T>
__global__ void KernelMaxPool3DWithIdx(
const int nthreads, const T* input_data, T* output_data, T* mask_data,
const int channels, const int input_depth, const int input_height,
const int input_width, const int output_depth, const int output_height,
const int output_width, const int ksize_depth, const int ksize_height,
const int ksize_width, const int stride_depth, const int stride_height,
const int stride_width, const int padding_depth, const int padding_height,
const int padding_width) {
const int nthreads, const T* input_data, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
T* output_data, T* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -886,13 +887,13 @@ __global__ void KernelMaxPool3DWithIdx(
template <typename T>
__global__ void KernelMaxPool3DWithIdxGrad(
const int nthreads, T* input_grad, const T* output_grad, const T* mask,
const int channels, const int input_depth, const int input_height,
const int input_width, const int output_depth, const int output_height,
const int output_width, const int ksize_depth, const int ksize_height,
const int ksize_width, const int stride_depth, const int stride_height,
const int stride_width, const int padding_depth, const int padding_height,
const int padding_width) {
const int nthreads, const T* output_grad, const T* mask, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int w_offset = index % input_width;
......@@ -952,18 +953,18 @@ template <typename T>
class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
const int input_width = input.dims()[4];
const int output_channels = output.dims()[1];
const int output_depth = output.dims()[2];
const int output_height = output.dims()[3];
const int output_width = output.dims()[4];
const int output_channels = output->dims()[1];
const int output_depth = output->dims()[2];
const int output_height = output->dims()[3];
const int output_width = output->dims()[4];
const int ksize_depth = ksize[0];
const int ksize_height = ksize[1];
const int ksize_width = ksize[2];
......@@ -975,8 +976,8 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_width = paddings[2];
const T* input_data = input.data<T>();
T* output_data = output.mutable_data<T>(context.GetPlace());
T* mask_data = mask.mutable_data<T>(context.GetPlace());
T* output_data = output->mutable_data<T>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace());
int nthreads = batch_size * output_channels * output_depth * output_height *
output_width;
......@@ -988,11 +989,10 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, mask_data, input_channels,
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width);
nthreads, input_data, input_channels, input_depth, input_height,
input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width,
padding_depth, padding_height, padding_width, output_data, mask_data);
}
};
......@@ -1005,15 +1005,15 @@ template <typename T>
class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings) {
const int batch_size = input_grad.dims()[0];
const int input_channels = input_grad.dims()[1];
const int input_depth = input_grad.dims()[2];
const int input_height = input_grad.dims()[3];
const int input_width = input_grad.dims()[4];
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad) {
const int batch_size = input_grad->dims()[0];
const int input_channels = input_grad->dims()[1];
const int input_depth = input_grad->dims()[2];
const int input_height = input_grad->dims()[3];
const int input_width = input_grad->dims()[4];
const int output_depth = output_grad.dims()[2];
const int output_height = output_grad.dims()[3];
const int output_width = output_grad.dims()[4];
......@@ -1029,7 +1029,7 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
const T* output_grad_data = output_grad.data<T>();
const T* mask_data = mask.data<T>();
T* input_grad_data = input_grad.mutable_data<T>(context.GetPlace());
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads =
batch_size * input_channels * input_depth * input_height * input_width;
......@@ -1041,11 +1041,11 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_grad_data, output_grad_data, mask_data, input_channels,
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width);
nthreads, output_grad_data, mask_data, input_channels, input_depth,
input_height, input_width, output_depth, output_height, output_width,
ksize_depth, ksize_height, ksize_width, stride_depth, stride_height,
stride_width, padding_depth, padding_height, padding_width,
input_grad_data);
}
};
......
paddle/operators/math/pooling.h
浏览文件 @ feaf1e2d
......@@ -88,60 +88,62 @@ template <typename Place, typename PoolProcess, typename T>
class Pool2dFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_compute);
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_compute, framework::Tensor* output);
};
template <typename Place, typename PoolProcess, typename T>
class Pool2dGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_compute);
PoolProcess pool_compute, framework::Tensor* input_grad);
};
template <typename Place, class T>
class MaxPool2dGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad);
};
template <typename Place, typename PoolProcess, typename T>
class Pool3dFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
std::vector<int>& ksize, std::vector<int>& strides,
std::vector<int>& paddings, PoolProcess pool_compute);
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_compute, framework::Tensor* output);
};
template <typename Place, typename PoolProcess, typename T>
class Pool3dGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
PoolProcess pool_compute);
PoolProcess pool_compute, framework::Tensor* input_grad);
};
template <typename Place, class T>
class MaxPool3dGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& input_grad,
const framework::Tensor& input,
const framework::Tensor& output,
const framework::Tensor& output_grad, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad);
};
/*
......@@ -155,38 +157,38 @@ template <typename Place, typename T>
class MaxPool2dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask);
};
template <typename Place, typename T>
class MaxPool2dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad);
};
template <typename Place, typename T>
class MaxPool3dWithIndexFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor& output,
framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
const framework::Tensor& input, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* output, framework::Tensor* mask);
};
template <typename Place, typename T>
class MaxPool3dWithIndexGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
framework::Tensor& input_grad,
const framework::Tensor& output_grad,
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
std::vector<int>& strides, std::vector<int>& paddings,
framework::Tensor* input_grad);
};
} // namespace math
......
paddle/operators/pool_op.h
浏览文件 @ feaf1e2d
......@@ -75,16 +75,16 @@ class PoolKernel : public framework::OpKernel<T> {
Place, paddle::operators::math::MaxPool<T>, T>
pool2d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool2d_forward(context.device_context(), *in_x, *out, ksize, strides,
paddings, pool_process);
pool2d_forward(context.device_context(), *in_x, ksize, strides,
paddings, pool_process, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool2dFunctor<
Place, paddle::operators::math::AvgPool<T>, T>
pool2d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool2d_forward(context.device_context(), *in_x, *out, ksize, strides,
paddings, pool_process);
pool2d_forward(context.device_context(), *in_x, ksize, strides,
paddings, pool_process, out);
}
} break;
case 3: {
......@@ -93,15 +93,15 @@ class PoolKernel : public framework::OpKernel<T> {
Place, paddle::operators::math::MaxPool<T>, T>
pool3d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool3d_forward(context.device_context(), *in_x, *out, ksize, strides,
paddings, pool_process);
pool3d_forward(context.device_context(), *in_x, ksize, strides,
paddings, pool_process, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool3dFunctor<
Place, paddle::operators::math::AvgPool<T>, T>
pool3d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool3d_forward(context.device_context(), *in_x, *out, ksize, strides,
paddings, pool_process);
pool3d_forward(context.device_context(), *in_x, ksize, strides,
paddings, pool_process, out);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......@@ -142,30 +142,30 @@ class PoolGradKernel : public framework::OpKernel<T> {
if (pooling_type == "max") {
paddle::operators::math::MaxPool2dGradFunctor<Place, T>
pool2d_backward;
pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out,
*out_grad, ksize, strides, paddings);
pool2d_backward(context.device_context(), *in_x, *out, *out_grad,
ksize, strides, paddings, in_x_grad);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool2dGradFunctor<
Place, paddle::operators::math::AvgPoolGrad<T>, T>
pool2d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out,
*out_grad, ksize, strides, paddings, pool_process);
pool2d_backward(context.device_context(), *in_x, *out, *out_grad,
ksize, strides, paddings, pool_process, in_x_grad);
}
} break;
case 3: {
if (pooling_type == "max") {
paddle::operators::math::MaxPool3dGradFunctor<Place, T>
pool3d_backward;
pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out,
*out_grad, ksize, strides, paddings);
pool3d_backward(context.device_context(), *in_x, *out, *out_grad,
ksize, strides, paddings, in_x_grad);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool3dGradFunctor<
Place, paddle::operators::math::AvgPoolGrad<T>, T>
pool3d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out,
*out_grad, ksize, strides, paddings, pool_process);
pool3d_backward(context.device_context(), *in_x, *out, *out_grad,
ksize, strides, paddings, pool_process, in_x_grad);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......
paddle/operators/pool_with_index_op.h
浏览文件 @ feaf1e2d
......@@ -46,14 +46,14 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
case 2: {
paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T>
pool2d_forward;
pool2d_forward(context.device_context(), *in_x, *out, *mask, ksize,
strides, paddings);
pool2d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T>
pool3d_forward;
pool3d_forward(context.device_context(), *in_x, *out, *mask, ksize,
strides, paddings);
pool3d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask);
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
......@@ -89,14 +89,14 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
case 2: {
paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T>
pool2d_backward;
pool2d_backward(context.device_context(), *in_x_grad, *out_grad,
*mask, ksize, strides, paddings);
pool2d_backward(context.device_context(), *out_grad, *mask, ksize,
strides, paddings, in_x_grad);
} break;
case 3: {
paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T>
pool3d_backward;
pool3d_backward(context.device_context(), *in_x_grad, *out_grad,
*mask, ksize, strides, paddings);
pool3d_backward(context.device_context(), *out_grad, *mask, ksize,
strides, paddings, in_x_grad);
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或