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“2eae3616006ef1e1ff440211c5bb8c4399089318”上不存在“develop/doc/howto/cluster/multi_cluster/k8s_aws_en.html”
未验证 提交 f3c6d19c 编写于 作者: A Aurelius84 提交者： GitHub
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[OpAttr]ksize of pool2d support Tensor type of adaptive_avg_pool2d API (#45660) 

* [OpAttr]ksize of pool2d support Tensor type

* fix unittest

* add unittest
上级 4e3d222d
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Showing with 210 addition and 79 deletion
paddle/fluid/operators/pool_op.cc
浏览文件 @ f3c6d19c
...@@ -151,9 +151,8 @@ void Pool2dOpMaker::Make() { ...@@ -151,9 +151,8 @@ void Pool2dOpMaker::Make() {
"(vector<int>) The pooling window " "(vector<int>) The pooling window "
"size(height, width) of the pooling operator. " "size(height, width) of the pooling operator. "
"If global_pooling = true, ksize and paddings will " "If global_pooling = true, ksize and paddings will "
"be ignored."); // TODO(Chengduo): Add checker. "be ignored.")
// (Currently, .SupportTensor();
// TypedAttrChecker don't support vector type.)
AddAttr<bool>( AddAttr<bool>(
"global_pooling", "global_pooling",
"(bool) Whether to use the global pooling. " "(bool) Whether to use the global pooling. "
...@@ -371,9 +370,7 @@ void Pool3dOpMaker::Make() { ...@@ -371,9 +370,7 @@ void Pool3dOpMaker::Make() {
"(vector<int>) The pooling window size(depth, height, " "(vector<int>) The pooling window size(depth, height, "
"width) of pooling operator. " "width) of pooling operator. "
"If global_pooling = true, ksize and paddings will " "If global_pooling = true, ksize and paddings will "
"be ignored."); // TODO(Chengduo): Add checker. "be ignored.");
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>( AddAttr<bool>(
"global_pooling", "global_pooling",
"(bool) Whether to use the global pooling. " "(bool) Whether to use the global pooling. "
...@@ -554,13 +551,13 @@ namespace ops = paddle::operators; ...@@ -554,13 +551,13 @@ namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(pool2d, DECLARE_INFER_SHAPE_FUNCTOR(pool2d,
Pool2dInferShapeFunctor, Pool2dInferShapeFunctor,
PD_INFER_META(phi::PoolInferMeta)); PD_INFER_META(phi::Pool2DInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool2d_grad, DECLARE_INFER_SHAPE_FUNCTOR(pool2d_grad,
Pool2dGradInferShapeFunctor, Pool2dGradInferShapeFunctor,
PD_INFER_META(phi::PoolGradInferMeta)); PD_INFER_META(phi::UnchangedInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool2d_double_grad, DECLARE_INFER_SHAPE_FUNCTOR(pool2d_double_grad,
Pool2dDoubleGradInferShapeFunctor, Pool2dDoubleGradInferShapeFunctor,
PD_INFER_META(phi::PoolInferMeta)); PD_INFER_META(phi::Pool2DInferMeta));
REGISTER_OPERATOR( REGISTER_OPERATOR(
pool2d, pool2d,
...@@ -584,7 +581,7 @@ DECLARE_INFER_SHAPE_FUNCTOR(pool3d, ...@@ -584,7 +581,7 @@ DECLARE_INFER_SHAPE_FUNCTOR(pool3d,
PD_INFER_META(phi::PoolInferMeta)); PD_INFER_META(phi::PoolInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool3d_grad, DECLARE_INFER_SHAPE_FUNCTOR(pool3d_grad,
Pool3dGradInferShapeFunctor, Pool3dGradInferShapeFunctor,
PD_INFER_META(phi::PoolGradInferMeta)); PD_INFER_META(phi::UnchangedInferMeta));
REGISTER_OPERATOR( REGISTER_OPERATOR(
pool3d, pool3d,
......
paddle/phi/api/yaml/legacy_api.yaml
浏览文件 @ f3c6d19c
...@@ -1978,10 +1978,10 @@ ...@@ -1978,10 +1978,10 @@
backward : pixel_shuffle_grad backward : pixel_shuffle_grad
- api : pool2d - api : pool2d
args : (Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(out) output : Tensor(out)
infer_meta : infer_meta :
func : PoolInferMeta func : Pool2DInferMeta
kernel : kernel :
func : pool2d func : pool2d
use_gpudnn : true use_gpudnn : true
...@@ -1989,10 +1989,10 @@ ...@@ -1989,10 +1989,10 @@
# Used in adaptive_avg_pool2d API # Used in adaptive_avg_pool2d API
- api : pool2d_gpudnn_unused - api : pool2d_gpudnn_unused
args : (Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(out) output : Tensor(out)
infer_meta : infer_meta :
func : PoolInferMeta func : Pool2DInferMeta
kernel : kernel :
func : pool2d func : pool2d
use_gpudnn : false use_gpudnn : false
......
paddle/phi/api/yaml/legacy_backward.yaml
浏览文件 @ f3c6d19c
...@@ -1748,32 +1748,34 @@ ...@@ -1748,32 +1748,34 @@
func : pixel_shuffle_grad func : pixel_shuffle_grad
- backward_api : pool2d_double_grad - backward_api : pool2d_double_grad
forward : pool2d_grad(Tensor x, Tensor out, Tensor grad_out, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(grad_x) forward : pool2d_grad(Tensor x, Tensor out, Tensor grad_out, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(grad_x)
args : (Tensor grad_x_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor grad_x_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(grad_out_grad) output : Tensor(grad_out_grad)
infer_meta : infer_meta :
func : PoolInferMeta func : Pool2DInferMeta
kernel : kernel :
func : pool2d_double_grad func : pool2d_double_grad
use_gpudnn : true use_gpudnn : true
- backward_api : pool2d_grad - backward_api : pool2d_grad
forward : pool2d(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out) forward : pool2d(Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor x, Tensor out, Tensor out_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad) output : Tensor(x_grad)
infer_meta : infer_meta :
func : PoolGradInferMeta func : UnchangedInferMeta
param: [x]
kernel : kernel :
func : pool2d_grad func : pool2d_grad
use_gpudnn : true use_gpudnn : true
backward : pool2d_double_grad backward : pool2d_double_grad
- backward_api : pool2d_grad_gpudnn_unused - backward_api : pool2d_grad_gpudnn_unused
forward : pool2d_gpudnn_unused(Tensor x, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out) forward : pool2d_gpudnn_unused(Tensor x, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor x, Tensor out, Tensor out_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad) output : Tensor(x_grad)
infer_meta : infer_meta :
func : PoolGradInferMeta func : UnchangedInferMeta
param: [x]
kernel : kernel :
func : pool2d_grad func : pool2d_grad
use_gpudnn : false use_gpudnn : false
...@@ -1783,7 +1785,8 @@ ...@@ -1783,7 +1785,8 @@
args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) args : (Tensor x, Tensor out, Tensor out_grad, int[] kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad) output : Tensor(x_grad)
infer_meta : infer_meta :
func : PoolGradInferMeta func : UnchangedInferMeta
param: [x]
kernel : kernel :
func : pool3d_grad func : pool3d_grad
use_gpudnn : true use_gpudnn : true
......
paddle/phi/infermeta/backward.cc
浏览文件 @ f3c6d19c
...@@ -761,23 +761,6 @@ void PixelUnshuffleGradInferMeta(const MetaTensor& out_grad, ...@@ -761,23 +761,6 @@ void PixelUnshuffleGradInferMeta(const MetaTensor& out_grad,
x_grad->set_dtype(out_grad.dtype()); x_grad->set_dtype(out_grad.dtype());
} }
void PoolGradInferMeta(const MetaTensor& x,
const MetaTensor& out,
const MetaTensor& dout,
const std::vector<int>& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
MetaTensor* dx) {
dx->share_meta(x);
}
void PsroiPoolGradInferMeta(const MetaTensor& x, void PsroiPoolGradInferMeta(const MetaTensor& x,
const MetaTensor& rois, const MetaTensor& rois,
const MetaTensor& rois_num, const MetaTensor& rois_num,
......
paddle/phi/infermeta/backward.h
浏览文件 @ f3c6d19c
...@@ -319,21 +319,6 @@ void PsroiPoolGradInferMeta(const MetaTensor& x, ...@@ -319,21 +319,6 @@ void PsroiPoolGradInferMeta(const MetaTensor& x,
float spatial_scale, float spatial_scale,
MetaTensor* dx); MetaTensor* dx);
void PoolGradInferMeta(const MetaTensor& x,
const MetaTensor& out,
const MetaTensor& dout,
const std::vector<int>& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
MetaTensor* dx);
void RealAndImagGradInferMeta(const MetaTensor& out_grad, MetaTensor* dx); void RealAndImagGradInferMeta(const MetaTensor& out_grad, MetaTensor* dx);
void ReshapeDoubleGradInferMeta(const MetaTensor& out_grad, void ReshapeDoubleGradInferMeta(const MetaTensor& out_grad,
......
paddle/phi/infermeta/unary.cc
浏览文件 @ f3c6d19c
...@@ -2452,6 +2452,53 @@ void PNormInferMeta(const MetaTensor& x, ...@@ -2452,6 +2452,53 @@ void PNormInferMeta(const MetaTensor& x,
out->set_dtype(x.dtype()); out->set_dtype(x.dtype());
} }
void Pool2DInferMeta(const MetaTensor& x,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
MetaTensor* out,
MetaConfig config) {
const bool channel_last = (config.is_run_mkldnn_kernel == false) &&
(data_format == "NHWC" || data_format == "NDHWC");
if (!config.is_runtime && kernel_size.FromTensor()) {
auto x_dims = x.dims();
std::vector<int64_t> output_shape = std::move(phi::vectorize(x_dims));
// set dims of HW -1
output_shape[x_dims.size() - 2] = -1;
if (channel_last) { // for NHWC, NDHWC
output_shape[x_dims.size() - 3] = -1;
} else { // for NCHW
output_shape[x_dims.size() - 1] = -1;
}
out->set_dims(make_ddim(output_shape));
out->share_lod(x);
out->set_dtype(x.dtype());
} else {
std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PoolInferMeta(x,
kernel_size_val,
strides,
paddings,
ceil_mode,
exclusive,
data_format,
pooling_type,
global_pooling,
adaptive,
padding_algorithm,
out,
config);
}
}
void PoolInferMeta(const MetaTensor& x, void PoolInferMeta(const MetaTensor& x,
const std::vector<int>& kernel_size, const std::vector<int>& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
......
paddle/phi/infermeta/unary.h
浏览文件 @ f3c6d19c
...@@ -354,6 +354,20 @@ void PoolInferMeta(const MetaTensor& x, ...@@ -354,6 +354,20 @@ void PoolInferMeta(const MetaTensor& x,
MetaTensor* out, MetaTensor* out,
MetaConfig config = MetaConfig()); MetaConfig config = MetaConfig());
void Pool2DInferMeta(const MetaTensor& x,
const IntArray& kernel_size,
const std::vector<int>& strides,
const std::vector<int>& paddings,
bool ceil_mode,
bool exclusive,
const std::string& data_format,
const std::string& pooling_type,
bool global_pooling,
bool adaptive,
const std::string& padding_algorithm,
MetaTensor* out,
MetaConfig config = MetaConfig());
void QrInferMeta(const MetaTensor& x, void QrInferMeta(const MetaTensor& x,
const std::string& mode, const std::string& mode,
MetaTensor* q, MetaTensor* q,
......
paddle/phi/kernels/gpudnn/pool_grad_kernel.cu
浏览文件 @ f3c6d19c
...@@ -305,7 +305,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx, ...@@ -305,7 +305,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const DenseTensor& out, const DenseTensor& out,
const DenseTensor& dout, const DenseTensor& dout,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -316,11 +316,13 @@ void Pool2dGradGPUDNNKernel(const Context& ctx, ...@@ -316,11 +316,13 @@ void Pool2dGradGPUDNNKernel(const Context& ctx,
bool adaptive, bool adaptive,
const std::string& padding_algorithm, const std::string& padding_algorithm,
DenseTensor* dx) { DenseTensor* dx) {
std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PoolGradRawGPUDNNKernel<T, Context>(ctx, PoolGradRawGPUDNNKernel<T, Context>(ctx,
x, x,
out, out,
dout, dout,
kernel_size, kernel_size_val,
strides, strides,
paddings, paddings,
exclusive, exclusive,
...@@ -335,7 +337,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx, ...@@ -335,7 +337,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dDoubleGradGPUDNNKernel(const Context& ctx, void Pool2dDoubleGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
......
paddle/phi/kernels/gpudnn/pool_kernel.cu
浏览文件 @ f3c6d19c
...@@ -230,7 +230,7 @@ void PoolRawGPUDNNKernel(const Context& ctx, ...@@ -230,7 +230,7 @@ void PoolRawGPUDNNKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dGPUDNNKernel(const Context& ctx, void Pool2dGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -241,9 +241,11 @@ void Pool2dGPUDNNKernel(const Context& ctx, ...@@ -241,9 +241,11 @@ void Pool2dGPUDNNKernel(const Context& ctx,
bool adaptive, bool adaptive,
const std::string& padding_algorithm, const std::string& padding_algorithm,
DenseTensor* out) { DenseTensor* out) {
std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PoolRawGPUDNNKernel<T, Context>(ctx, PoolRawGPUDNNKernel<T, Context>(ctx,
x, x,
kernel_size, kernel_size_val,
strides, strides,
paddings, paddings,
exclusive, exclusive,
......
paddle/phi/kernels/impl/pool_grad_kernel_impl.h
浏览文件 @ f3c6d19c
...@@ -189,7 +189,7 @@ void Pool2dGradKernel(const Context& ctx, ...@@ -189,7 +189,7 @@ void Pool2dGradKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const DenseTensor& out, const DenseTensor& out,
const DenseTensor& dout, const DenseTensor& dout,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -200,11 +200,13 @@ void Pool2dGradKernel(const Context& ctx, ...@@ -200,11 +200,13 @@ void Pool2dGradKernel(const Context& ctx,
bool adaptive, bool adaptive,
const std::string& padding_algorithm, const std::string& padding_algorithm,
DenseTensor* dx) { DenseTensor* dx) {
std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PoolGradRawKernel<T, Context>(ctx, PoolGradRawKernel<T, Context>(ctx,
x, x,
out, out,
dout, dout,
kernel_size, kernel_size_val,
strides, strides,
paddings, paddings,
exclusive, exclusive,
...@@ -219,7 +221,7 @@ void Pool2dGradKernel(const Context& ctx, ...@@ -219,7 +221,7 @@ void Pool2dGradKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dDoubleGradKernel(const Context& ctx, void Pool2dDoubleGradKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
......
paddle/phi/kernels/impl/pool_kernel_impl.h
浏览文件 @ f3c6d19c
...@@ -223,7 +223,7 @@ void MaxPoolWithIndexRawKernel(const Context& ctx, ...@@ -223,7 +223,7 @@ void MaxPoolWithIndexRawKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dKernel(const Context& ctx, void Pool2dKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -234,9 +234,11 @@ void Pool2dKernel(const Context& ctx, ...@@ -234,9 +234,11 @@ void Pool2dKernel(const Context& ctx,
bool adaptive, bool adaptive,
const std::string& padding_algorithm, const std::string& padding_algorithm,
DenseTensor* out) { DenseTensor* out) {
std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PoolRawKernel<T, Context>(ctx, PoolRawKernel<T, Context>(ctx,
x, x,
kernel_size, kernel_size_val,
strides, strides,
paddings, paddings,
exclusive, exclusive,
......
paddle/phi/kernels/pool_grad_kernel.h
浏览文件 @ f3c6d19c
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h" #include "paddle/phi/core/dense_tensor.h"
namespace phi { namespace phi {
...@@ -26,7 +27,7 @@ void Pool2dGradKernel(const Context& ctx, ...@@ -26,7 +27,7 @@ void Pool2dGradKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const DenseTensor& out, const DenseTensor& out,
const DenseTensor& dout, const DenseTensor& dout,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -43,7 +44,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx, ...@@ -43,7 +44,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const DenseTensor& out, const DenseTensor& out,
const DenseTensor& dout, const DenseTensor& dout,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -58,7 +59,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx, ...@@ -58,7 +59,7 @@ void Pool2dGradGPUDNNKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dDoubleGradKernel(const Context& ctx, void Pool2dDoubleGradKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -73,7 +74,7 @@ void Pool2dDoubleGradKernel(const Context& ctx, ...@@ -73,7 +74,7 @@ void Pool2dDoubleGradKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dDoubleGradGPUDNNKernel(const Context& ctx, void Pool2dDoubleGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
......
paddle/phi/kernels/pool_kernel.h
浏览文件 @ f3c6d19c
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
#include <string> #include <string>
#include <vector> #include <vector>
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h" #include "paddle/phi/core/dense_tensor.h"
namespace phi { namespace phi {
...@@ -24,7 +25,7 @@ namespace phi { ...@@ -24,7 +25,7 @@ namespace phi {
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dKernel(const Context& ctx, void Pool2dKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
...@@ -39,7 +40,7 @@ void Pool2dKernel(const Context& ctx, ...@@ -39,7 +40,7 @@ void Pool2dKernel(const Context& ctx,
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dGPUDNNKernel(const Context& ctx, void Pool2dGPUDNNKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
bool ceil_mode, bool ceil_mode,
......
paddle/phi/kernels/xpu/pool_kernel.cc
浏览文件 @ f3c6d19c
...@@ -22,7 +22,7 @@ namespace phi { ...@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context> template <typename T, typename Context>
void Pool2dKernel(const Context& ctx, void Pool2dKernel(const Context& ctx,
const DenseTensor& x, const DenseTensor& x,
const std::vector<int>& kernel_size_t, const IntArray& kernel_size,
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings_t, const std::vector<int>& paddings_t,
bool ceil_mode, bool ceil_mode,
...@@ -36,7 +36,8 @@ void Pool2dKernel(const Context& ctx, ...@@ -36,7 +36,8 @@ void Pool2dKernel(const Context& ctx,
using XPUType = typename XPUTypeTrait<T>::Type; using XPUType = typename XPUTypeTrait<T>::Type;
std::vector<int> paddings(paddings_t); std::vector<int> paddings(paddings_t);
std::vector<int> kernel_size(kernel_size_t); std::vector<int> kernel_size_val(kernel_size.GetData().begin(),
kernel_size.GetData().end());
PADDLE_ENFORCE_EQ(kernel_size.size(), PADDLE_ENFORCE_EQ(kernel_size.size(),
2, 2,
......
python/paddle/fluid/layers/utils.py
浏览文件 @ f3c6d19c
...@@ -62,6 +62,9 @@ def convert_to_list(value, n, name, dtype=int): ...@@ -62,6 +62,9 @@ def convert_to_list(value, n, name, dtype=int):
raise ValueError("The " + name + "'s length must be " + str(n) + raise ValueError("The " + name + "'s length must be " + str(n) +
". Received: " + str(value)) ". Received: " + str(value))
for single_value in value_list: for single_value in value_list:
assert not isinstance(
single_value, Variable
), "Required numerical type with '%s', but received Tensor." % dtype
try: try:
dtype(single_value) dtype(single_value)
except (ValueError, TypeError): except (ValueError, TypeError):
......
python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py
浏览文件 @ f3c6d19c
...@@ -15,6 +15,7 @@ ...@@ -15,6 +15,7 @@
from __future__ import print_function from __future__ import print_function
from __future__ import division from __future__ import division
import os
import unittest import unittest
import numpy as np import numpy as np
...@@ -24,6 +25,8 @@ import paddle ...@@ -24,6 +25,8 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid import Program, program_guard from paddle.fluid import Program, program_guard
from test_attribute_var import UnittestBase
def adaptive_start_index(index, input_size, output_size): def adaptive_start_index(index, input_size, output_size):
return int(np.floor(index * input_size / output_size)) return int(np.floor(index * input_size / output_size))
...@@ -288,5 +291,81 @@ class TestAdaptiveAvgPool2DClassAPI(unittest.TestCase): ...@@ -288,5 +291,81 @@ class TestAdaptiveAvgPool2DClassAPI(unittest.TestCase):
assert np.allclose(out_5.numpy(), self.res_5_np) assert np.allclose(out_5.numpy(), self.res_5_np)
class TestOutputSizeTensor(UnittestBase):
def init_info(self):
self.shapes = [[1, 3, 6, 6]]
self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())
def test_static(self):
paddle.enable_static()
main_prog = Program()
starup_prog = Program()
with program_guard(main_prog, starup_prog):
fc = paddle.nn.Linear(6, 6)
x = paddle.randn(self.shapes[0])
x.stop_gradient = False
feat = fc(x) # [1,3,6,6]
out1, out2 = self.call_func(feat)
sgd = paddle.optimizer.SGD()
sgd.minimize(paddle.mean(out1 + out2))
self.assertTrue(self.var_prefix() in str(main_prog))
exe = paddle.static.Executor()
exe.run(starup_prog)
res = exe.run(fetch_list=[out1, out2])
np.testing.assert_allclose(res[0], res[1])
paddle.static.save_inference_model(self.save_path, [x],
[out1, out2], exe)
# Test for Inference Predictor
infer_outs = self.infer_prog()
np.testing.assert_array_equal(infer_outs[0].shape, (1, 3, 3, 3))
np.testing.assert_allclose(infer_outs[0], infer_outs[1])
def path_prefix(self):
return 'pool2d_tensor'
def var_prefix(self):
return "Vars["
def call_func(self, x):
# list[Tensor]
output_size = [paddle.assign([3]), paddle.assign([3])]
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
output_size=output_size)
return out1, out2
class TestOutputSizeListTensor(TestOutputSizeTensor):
def path_prefix(self):
return 'pool2d_tensors'
def call_func(self, x):
# list[int, Tensor]
output_size = [paddle.assign([3]), 3]
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
output_size=output_size)
return out1, out2
class TestOutputSizeListTensor2(TestOutputSizeTensor):
def path_prefix(self):
return 'pool2d_tensor2'
def call_func(self, x):
# A Tensor
output_size = paddle.assign([3, 3])
out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
output_size=output_size)
return out1, out2
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
python/paddle/nn/functional/pooling.py
浏览文件 @ f3c6d19c
...@@ -1469,11 +1469,20 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None): ...@@ -1469,11 +1469,20 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
output_size = utils.convert_to_list(output_size, 2, 'output_size') output_size = utils.convert_to_list(output_size, 2, 'output_size')
else: else:
output_size = list(output_size) output_size = list(output_size)
if output_size[0] == None: if output_size[0] is None:
output_size[0] = in_h output_size[0] = in_h
if output_size[1] == None: if output_size[1] is None:
output_size[1] = in_w output_size[1] = in_w
if _non_static_mode():
output_size = [
item.numpy().item(0) if isinstance(item, Variable) else item
for item in output_size
]
# output_size support Variable in static mode
elif utils._contain_var(output_size):
output_size = utils._convert_to_tensor_list(output_size)
if in_dygraph_mode(): if in_dygraph_mode():
return _C_ops.pool2d_gpudnn_unused(x, output_size, [1, 1], [0, 0], return _C_ops.pool2d_gpudnn_unused(x, output_size, [1, 1], [0, 0],
False, True, data_format, 'avg', False, True, data_format, 'avg',
...@@ -1585,11 +1594,11 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None): ...@@ -1585,11 +1594,11 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
output_size = utils.convert_to_list(output_size, 3, 'output_size') output_size = utils.convert_to_list(output_size, 3, 'output_size')
else: else:
output_size = list(output_size) output_size = list(output_size)
if output_size[0] == None: if output_size[0] is None:
output_size[0] = in_l output_size[0] = in_l
if output_size[1] == None: if output_size[1] is None:
output_size[1] = in_h output_size[1] = in_h
if output_size[2] == None: if output_size[2] is None:
output_size[2] = in_w output_size[2] = in_w
if in_dynamic_mode(): if in_dynamic_mode():
......
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