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revert-4814-Add_sequence_project_op
chengduoZH 7 years ago
parent fcfce48421
commit 6c6474cbd8
3 changed files with 54 additions and 41 deletions
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15
paddle/operators/CMakeLists.txt
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@ -55,12 +55,20 @@ function(op_library TARGET)
set(pybind_flag 1)
endif()
# pool_op contains several operators
if ("${TARGET}" STREQUAL "pool_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
endif()
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
endif()
# activation_op contains several operators
if ("${TARGET}" STREQUAL "activation_op")
set(pybind_flag 1)
@ -75,13 +83,6 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(reduce_sum);\n")
endif()
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
endif()
# pybind USE_NO_KERNEL_OP
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")

23
paddle/operators/math/pooling.h
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@ -24,15 +24,16 @@ namespace math {
#define FLT_MAX \
__FLT_MAX__ // It might need to be placed in another file, but I'm still
// wondering where to put it
// wondering where to put it.
/*
* \brief Extracting simple operations from pooling.
* Both MaxPool and AvgPool need initial, compute and finalize operation.
* Both MaxPool and AvgPool need "initial", "compute" and "finalize"
* operation.
* MaxPool initializes temp variable to the negative maximum to find the
* maximum value in the pooling field.
* AvgPool initializes temp variable to the zero to accumulate all values
* in pool pooling, and takes the average.
* in pool pooling, and finally takes the average.
* MaxPoolGrad and AvgPoolGrad are gradient operations respectively.
*/
template <class T>
@ -72,17 +73,17 @@ class AvgPoolGrad {
/*
* \brief Getting pooling results, and calculating gradient.
*
* In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in
* NCDHW format.
* In pool2d, all tensors are in NCHW format. Where N is batch size, C is the
* number of channels, H and W is the height and width of feature.
* In pool3d, all tensors are in NCDHW format. Where N is batch size, C is the
* number of channels, D, H and W is the depth, height and width of feature.
*
* In max pooling, it is possible that the pooling region has multiple maximum
* elements.
* In this case, we should compute the gradient of the first maximum element.
* elements. In this case, we should compute the gradient of the first maximum
* element.
* This is different from average pooling. So we rewrite the max_pool_grad:
* MaxPool2dGradFunctor, MaxPool3dGradFunctor.
*
*/
template <typename Place, typename PoolProcess, typename T>
class Pool2dFunctor {
public:
@ -146,10 +147,9 @@ class MaxPool3dGradFunctor {
/*
* \brief Getting max pooling results and corresponding max index, and
* calculating gradient.
* In sub-sampling-pooling, it is necessary to know max element index.
* In up-sampling-pooling, it is necessary to know max element index.
* In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in
* NCDHW format.
*
*/
template <typename Place, typename T>
class MaxPool2dWithIndexFunctor {
@ -188,6 +188,7 @@ class MaxPool3dWithIndexGradFunctor {
const framework::Tensor& mask, std::vector<int>& ksize,
std::vector<int>& strides, std::vector<int>& paddings);
};
} // namespace math
} // namespace operators
} // namespace paddle

57
paddle/operators/pool_with_index_op.cc
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@ -34,7 +34,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Out(Output) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mask"),
"Out(Output) of Pooling should not be null.");
"Mask(Output) of Pooling should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
@ -52,13 +52,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
}
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Pooling intput size and pooling size should be consistent");
PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
"Pooling size size should be 2 elements. or 3 elements.");
"Intput size and pooling size should be consistent.");
PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
"strides size and pooling size should be the same.");
"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.");
"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) {
@ -76,11 +74,9 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of Pooling should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput(framework::GradVarName("X")),
"X@GRAD(Input@GRAD) of MaxPoolWithIndexOpGrad should not be null.");
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"));
}
};
@ -110,9 +106,10 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"Pooling size(height, width) of pooling operator."
"The pooling size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Add checker)
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"globalPooling",
"Whether to use the globalPooling."
@ -123,15 +120,21 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("strides",
"Strides(height, width) of pooling operator."
"Default {1,1}.")
.SetDefault({1, 1}); // TODO(Add checker)
.SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>("paddings",
"Paddings(height, width) of pooling operator."
"Default {0,0}.")
.SetDefault({0, 0}); // TODO(Add checker)
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddComment(R"DOC(
The maxPooling2d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
The maxPooling2d with index operation calculates the output and the mask
based on the input and ksize, strides, paddings parameters. Input(X) and
output(Out, Mask) are in NCHW format. Where N is batch size, C is the
number of channels, H and W is the height and width of feature.
Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
)DOC");
}
};
@ -162,9 +165,10 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"Pooling size(depth, height, width) of pooling operator."
"The pooling size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Add checker)
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"globalPooling",
"Whether to use the globalPooling."
@ -176,19 +180,26 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"strides",
"Strides(depth, height, width) of pooling operator."
"Default {1,1,1}.")
.SetDefault({1, 1, 1}); // TODO(Add checker)
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}.")
.SetDefault({0, 0, 0}); // TODO(Add checker)
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddComment(R"DOC(
The maxpooling3d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
The maxpooling3d with index operation calculates the output and the mask
based on the input and ksize, strides, paddings parameters.
Input(X) and output(Out, Mask) are in NCDHW format. Where N is batch
size, C is the number of channels, D, H and W is the depth, height and
width of feature. Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
)DOC");
}
};
} // namespace operators
} // namespace paddle

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