You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
233 lines
9.7 KiB
233 lines
9.7 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
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/operators/pool_op.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
int OutputSizePool(int input_size, int filter_size, int padding, int stride) {
|
|
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
|
|
return output_size;
|
|
}
|
|
|
|
void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput("Out"),
|
|
"Out(Output) of Pooling should not be null.");
|
|
|
|
auto in_x_dims = ctx->GetInputDim("X");
|
|
|
|
std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
|
|
std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
|
|
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
|
|
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
|
|
|
|
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
|
|
"Pooling intput should be 4-D or 5-D tensor.");
|
|
|
|
if (ctx->Attrs().Get<bool>("global_pooling")) {
|
|
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
|
|
for (size_t i = 0; i < ksize.size(); ++i) {
|
|
paddings[i] = 0;
|
|
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
|
|
}
|
|
}
|
|
|
|
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
|
|
"Input size and pooling size should be consistent.");
|
|
PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
|
|
"Strides size and pooling size should be the same.");
|
|
PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
|
|
"Paddings size and pooling size should be the same.");
|
|
|
|
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
|
|
for (size_t i = 0; i < ksize.size(); ++i) {
|
|
output_shape.push_back(
|
|
OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
|
|
}
|
|
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
|
|
}
|
|
|
|
void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
|
|
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
|
|
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
|
|
"Input(X@GRAD) should not be null.");
|
|
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
|
|
}
|
|
|
|
Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput(
|
|
"X",
|
|
"(Tensor) The input tensor of pooling operator. "
|
|
"The format of input tensor is NCHW, where N is batch size, C is the "
|
|
"number of channels, H is the height of the feature, "
|
|
"and W is the width of the feature.");
|
|
AddOutput("Out",
|
|
"(Tensor) The output tensor of pooling operator. "
|
|
"The format of output tensor is also NCHW, "
|
|
"where N is batch size, C is the number of channels, "
|
|
"H is the height of the feature, "
|
|
"and W is the width of the feature.");
|
|
|
|
AddAttr<std::string>("pooling_type",
|
|
"(string), pooling type, can be \"max\" for max-pooling "
|
|
"and \"avg\" for average-pooling.")
|
|
.InEnum({"max", "avg"});
|
|
AddAttr<std::vector<int>>("ksize",
|
|
"(vector<int>) The pooling window "
|
|
"size(height, width) of the pooling operator. "
|
|
"If global_pooling = true, ksize and paddings will "
|
|
"be ignored."); // TODO(Chengduo): Add checker.
|
|
// (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
AddAttr<bool>("global_pooling",
|
|
"(bool, default false) Whether to use the global pooling. "
|
|
"If global_pooling = true, ksize and paddings will be ignored.")
|
|
.SetDefault(false);
|
|
AddAttr<std::vector<int>>("strides",
|
|
"(vector<int>, default {1, 1}), strides(height, "
|
|
"width) of pooling operator.")
|
|
.SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
AddAttr<std::vector<int>>(
|
|
"paddings",
|
|
"(vector<int>, default {0,0}), paddings(height, width) of pooling "
|
|
"operator."
|
|
"If global_pooling = true, paddings and ksize will be ignored.")
|
|
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
|
|
AddComment(R"DOC(
|
|
Pool2d Operator.
|
|
|
|
The pooling2d operation calculates the output based on
|
|
the input, pooling_type and ksize, strides, paddings parameters.
|
|
Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
|
|
number of channels, H is the height of the feature, and W is the width of the feature.
|
|
Parameters(ksize, strides, paddings) are two elements.
|
|
These two elements represent height and width, respectively.
|
|
The input(X) size and output(Out) size may be different.
|
|
|
|
Example:
|
|
Input:
|
|
X shape: $(N, C, H_{in}, W_{in})$
|
|
Output:
|
|
Out shape: $(N, C, H_{out}, W_{out})$
|
|
Where
|
|
$$
|
|
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
|
|
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
|
|
$$
|
|
|
|
)DOC");
|
|
}
|
|
|
|
Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
|
|
: OpProtoAndCheckerMaker(proto, op_checker) {
|
|
AddInput("X",
|
|
"(Tensor) The input tensor of pooling operator. "
|
|
"The format of input tensor is NCDHW, where N is batch size, C is "
|
|
"the number of channels, and D, H and W is the depth, height and "
|
|
"width of "
|
|
"the feature, respectively.");
|
|
AddOutput("Out",
|
|
"(Tensor) The output tensor of pooling operator."
|
|
"The format of output tensor is also NCDHW, "
|
|
"where N is batch size, C is "
|
|
"the number of channels, and D, H and W is the depth, height and "
|
|
"width of the feature, respectively.");
|
|
|
|
AddAttr<std::string>("pooling_type",
|
|
"(string) Pooling type, can be \"max\" for max-pooling "
|
|
"and \"avg\" for average-pooling.")
|
|
.InEnum({"max", "avg"});
|
|
AddAttr<std::vector<int>>(
|
|
"ksize",
|
|
"(vector<int>) The pooling window size(depth, height, "
|
|
"width) of pooling operator. "
|
|
"If global_pooling = true, ksize and paddings will "
|
|
"be ignored."); // TODO(Chengduo): Add checker.
|
|
// (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
AddAttr<bool>(
|
|
"global_pooling",
|
|
"(bool, default false) Whether to use the global pooling. "
|
|
"If global_pooling = true, ksize and paddings wille be ignored.")
|
|
.SetDefault(false);
|
|
AddAttr<std::vector<int>>(
|
|
"strides",
|
|
"(vector<int>, default {1,1,1}) Strides(depth, height, "
|
|
"width) of the pooling operator.")
|
|
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
AddAttr<std::vector<int>>(
|
|
"paddings",
|
|
"(vector<int>, default {0,0,0}), paddings(depth, height, "
|
|
"width) of pooling operator. "
|
|
"If global_pooling = true, ksize and paddings will be ignored.")
|
|
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
|
|
// TypedAttrChecker don't support vector type.)
|
|
|
|
AddComment(R"DOC(
|
|
Pool3d Operator.
|
|
|
|
The pooling3d operation calculates the output based on
|
|
the input, pooling_type, ksize, strides, and paddings parameters.
|
|
Input(X) and output(Out) are in NCDHW format, where N is batch
|
|
size, C is the number of channels, and D, H and W are the depth, height and
|
|
width of the feature, respectively. Parameters(ksize, strides, paddings)
|
|
are three elements. These three elements represent depth, height and
|
|
width, respectively. The input(X) size and output(Out) size may be different.
|
|
|
|
Example:
|
|
Input:
|
|
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
|
|
Output:
|
|
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
|
|
Where
|
|
$$
|
|
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
|
|
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
|
|
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
|
|
$$
|
|
|
|
)DOC");
|
|
}
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
|
|
REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
|
|
ops::PoolOpGrad);
|
|
|
|
REGISTER_OP_CPU_KERNEL(
|
|
pool2d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
pool2d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>)
|
|
|
|
REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
|
|
ops::PoolOpGrad);
|
|
|
|
REGISTER_OP_CPU_KERNEL(
|
|
pool3d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
|
|
REGISTER_OP_CPU_KERNEL(
|
|
pool3d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
|
|
ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>);
|