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Merge pull request #4676 from chengduoZH/fix_pool_op_doc_and_class_declarations

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Fix pool op doc and class declarations
revert-4814-Add_sequence_project_op
chengduo 8 years ago committed by GitHub
parent 2f17038428 4849fba7e9
commit e9325ea8b1
5 changed files with 243 additions and 160 deletions
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6
paddle/operators/CMakeLists.txt
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@ -112,7 +112,9 @@ set(DEPS_OPS
cond_op
cross_entropy_op
softmax_with_cross_entropy_op
sum_op)
sum_op
pool_op
pool_with_index_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
@ -121,6 +123,8 @@ op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})

6
paddle/operators/math/CMakeLists.txt
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@ -1,14 +1,16 @@
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator)
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator)
nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
else()
cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc DEPS cblas device_context operator)
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
cc_library(softmax SRCS softmax.cc DEPS operator)
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(vol2col SRCS vol2col.cc DEPS device_context)
endif()

310
paddle/operators/pool_op.cc
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File diff suppressed because it is too large Load Diff

28
paddle/operators/pool_op.h
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@ -24,6 +24,34 @@ namespace operators {
using Tensor = framework::Tensor;
class PoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
class PoolOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override;
};
class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Pool2dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
};
class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Pool3dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker);
};
template <typename Place, typename T>
class PoolKernel : public framework::OpKernel<T> {
public:

53
paddle/operators/pool_with_index_op.cc
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@ -43,7 +43,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
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");
"Pooling intput should be 4-D or 5-D tensor.");
if (ctx->Attrs().Get<bool>("globalPooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
@ -52,7 +52,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
}
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Intput size and pooling size should be consistent.");
"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(),
@ -74,6 +74,7 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Mask"), "Input(Mask) must 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.");
@ -88,17 +89,17 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"The input tensor of pooling operator. "
"(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 and W is the height and width of image.");
AddOutput("Out",
"The output tensor of pooling operator."
"(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 and W is the height and "
"width of image.");
AddOutput("Mask",
"The Mask tensor of pooling operator."
"(Tensor) The Mask tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is the number of channels, H and W "
"is the height and width of image."
@ -106,7 +107,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"The pooling size(height, width) of pooling operator."
"The pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
@ -118,13 +119,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"If globalPooling = true, ksize is ignored and need not be specified.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"Strides(height, width) of pooling operator."
"The strides(height, width) of pooling window."
"Default {1,1}.")
.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}.")
AddAttr<std::vector<int>>(
"paddings",
"The zero padding(height, width) size on both sides"
"Default {0,0}.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
@ -135,6 +137,17 @@ 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.
The input(X) size and output(Out, Mask) size may be different.
Example:
Input:
X shape: (N, C, H_in, W_in)
Output:
Out shape: (N, C, H_out, W_out)
Mask shape: (N, C, H_out, W_out)
where
H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
)DOC");
}
};
@ -146,18 +159,18 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"The input tensor of pooling operator. "
"(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, D, H and W is the depth, height and width of "
"image.");
AddOutput("Out",
"The output tensor of pooling operator."
"(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, D, H and W is the depth, height and "
"width of image.");
AddOutput("Mask",
"The Mask tensor of pooling operator."
"(Tensor) The Mask tensor of pooling operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is the number of channels, D, H and W "
"is the depth, height and width of image."
@ -165,7 +178,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>(
"ksize",
"The pooling size(depth, height, width) of pooling operator."
"The pooling window size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
@ -196,6 +209,18 @@ 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.
The input(X) size and output(Out, Mask) 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)
Mask shape: (N, C, D_out, H_out, W_out)
where
D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
W_out = (W_in - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
)DOC");
}
};

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