提交 67edd04a 编写于 作者: C chengduoZH

fix doc

上级 0f1d3af4
...@@ -40,8 +40,6 @@ class PoolOp : public framework::OperatorWithKernel { ...@@ -40,8 +40,6 @@ class PoolOp : public framework::OperatorWithKernel {
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides"); std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings"); std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg",
"pooling_type should be 'max' or 'avg'");
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5, 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");
...@@ -52,13 +50,11 @@ class PoolOp : public framework::OperatorWithKernel { ...@@ -52,13 +50,11 @@ class PoolOp : public framework::OperatorWithKernel {
} }
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U, PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Input size and Pooling size should be consistent."); "Input size and pooling size should be consistent.");
PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3,
"Pooling size should be 2 elements. or 3 elements.");
PADDLE_ENFORCE_EQ(ksize.size(), strides.size(), 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(), 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]}); std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) { for (size_t i = 0; i < ksize.size(); ++i) {
...@@ -75,10 +71,9 @@ class PoolOpGrad : public framework::OperatorWithKernel { ...@@ -75,10 +71,9 @@ class PoolOpGrad : public framework::OperatorWithKernel {
protected: protected:
void InferShape(framework::InferShapeContext *ctx) const override { void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
"X(Input) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input@Grad of Pooling should not be null."); "Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
} }
}; };
...@@ -94,17 +89,22 @@ class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -94,17 +89,22 @@ class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
"number of channels, H and W is the height and width of feature."); "number of channels, H and W is the height and width of feature.");
AddOutput("Out", AddOutput("Out",
"The output tensor of pooling operator." "The output tensor of pooling operator."
"The format of output tensor is also NCHW."); "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 feature.");
AddAttr<std::string>("poolingType", AddAttr<std::string>("poolingType",
"PoolingType of pooling operator." "PoolingType of pooling operator."
"Str constant equal to 'max' or 'avg'.") "Str constant equal to 'max' or 'avg'.")
.InEnum({"max", "avg"}); .InEnum({"max", "avg"});
AddAttr<std::vector<int>>( AddAttr<std::vector<int>>(
"ksize", "ksize",
"Pooling size(depth, height, width) of pooling operator." "The pooling size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be " "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>( AddAttr<bool>(
"globalPooling", "globalPooling",
"Whether to use the globalPooling." "Whether to use the globalPooling."
...@@ -114,15 +114,22 @@ class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -114,15 +114,22 @@ class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(false); .SetDefault(false);
AddAttr<std::vector<int>>("strides", AddAttr<std::vector<int>>("strides",
"Strides(height, width) of pooling operator." "Strides(height, width) of pooling operator."
"Default {1,1}") "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", AddAttr<std::vector<int>>("paddings",
"Paddings(height, width) of pooling operator." "Paddings(height, width) of pooling operator."
"Default {0,0}.") "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( AddComment(R"DOC(
The pooling2d operation calculates the output based on The pooling2d operation calculates the output based on
the input, poolingType and ksize, strides, paddings parameters. the input, poolingType 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 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"); )DOC");
} }
}; };
...@@ -131,25 +138,30 @@ class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -131,25 +138,30 @@ class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
public: public:
Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) { : OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", AddInput(
"The input tensor of pooling operator. " "X",
"The format of input tensor is NCDHW. Where N is batch size, C is " "The input tensor of pooling operator. "
"the " "The format of input tensor is NCDHW. Where N is batch size, C is "
"number of channels, D, H and W is the depth, height and width of " "the number of channels, D, H and W is the depth, height and width of "
"feature."); "feature.");
AddOutput("Out", AddOutput("Out",
"The output tensor of pooling operator." "The output tensor of pooling operator."
"The format of output tensor is also NCDHW."); "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 feature.");
AddAttr<std::string>("poolingType", AddAttr<std::string>("poolingType",
"PoolingType of pooling operator." "PoolingType of pooling operator."
"str constant equal to 'max' or 'avg'.") "Str constant equal to 'max' or 'avg'.")
.InEnum({"max", "avg"}); .InEnum({"max", "avg"});
AddAttr<std::vector<int>>( AddAttr<std::vector<int>>(
"ksize", "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 " "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>( AddAttr<bool>(
"globalPooling", "globalPooling",
"Whether to use the globalPooling." "Whether to use the globalPooling."
...@@ -161,15 +173,22 @@ class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -161,15 +173,22 @@ class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
"strides", "strides",
"Strides(depth, height, width) of pooling operator." "Strides(depth, height, width) of pooling operator."
"Default {1,1,1}.") "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>>( AddAttr<std::vector<int>>(
"paddings", "paddings",
"Paddings(depth, height, width) of pooling operator." "Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}.") "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( AddComment(R"DOC(
The pooling3d operation calculates the output based on The pooling3d operation calculates the output based on
the input, poolingType and ksize, strides, paddings parameters. the input, poolingType and ksize, strides, paddings parameters.
Input(X) and output(Out) 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"); )DOC");
} }
}; };
......
...@@ -28,7 +28,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel { ...@@ -28,7 +28,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
protected: protected:
void InferShape(framework::InferShapeContextBase *ctx) const override { void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of Pooling should not be null."); "X(Input) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), PADDLE_ENFORCE(ctx->HasOutput("Out"),
...@@ -52,7 +52,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel { ...@@ -52,7 +52,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
} }
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U, 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(), 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(), PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
...@@ -73,7 +73,8 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel { ...@@ -73,7 +73,8 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
protected: protected:
void InferShape(framework::InferShapeContextBase *ctx) const override { 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->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input(X@GRAD) should not be null."); "Input(X@GRAD) should not be null.");
......
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