diff --git a/paddle/operators/pool_op.cc b/paddle/operators/pool_op.cc index acc7e66c08016f2b3c36d88e1f701979e859cbac..25fd01844ba96108ce7b460f60930b020ff79b2c 100644 --- a/paddle/operators/pool_op.cc +++ b/paddle/operators/pool_op.cc @@ -22,108 +22,94 @@ int OutputSizePool(int input_size, int filter_size, int padding, int stride) { return output_size; } -class PoolOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContext *ctx) const override { - 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("poolingType"); - std::vector ksize = ctx->Attrs().Get>("ksize"); - std::vector strides = ctx->Attrs().Get>("strides"); - std::vector paddings = ctx->Attrs().Get>("paddings"); - - PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5, - "Pooling intput should be 4-D or 5-D"); - - if (ctx->Attrs().Get("globalPooling")) { - ksize.resize(static_cast(in_x_dims.size()) - 2); - for (size_t i = 0; i < ksize.size(); ++i) - ksize[i] = static_cast(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 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 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("poolingType"); + std::vector ksize = ctx->Attrs().Get>("ksize"); + std::vector strides = ctx->Attrs().Get>("strides"); + std::vector paddings = ctx->Attrs().Get>("paddings"); + + PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5, + "Pooling intput should be 4-D or 5-D"); + + if (ctx->Attrs().Get("globalPooling")) { + ksize.resize(static_cast(in_x_dims.size()) - 2); + for (size_t i = 0; i < ksize.size(); ++i) + ksize[i] = static_cast(in_x_dims[i + 2]); } -}; - -class PoolOpGrad : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContext *ctx) const override { - 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")); + + 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 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])); } -}; - -class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker { - public: - Pool2dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput( - "X", - "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 feature."); - AddOutput("Out", - "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 feature."); - - AddAttr("poolingType", - "PoolingType of pooling operator." - "Str constant equal to 'max' or 'avg'.") - .InEnum({"max", "avg"}); - - AddAttr>( - "ksize", - "The pooling 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.) - AddAttr( - "globalPooling", - "Whether to use the globalPooling." - "Bool constant equal to false or true." - "Default false." - "If globalPooling = true, ksize is ignored and need not be specified.") - .SetDefault(false); - AddAttr>("strides", - "Strides(height, width) of pooling operator." - "Default {1,1}.") - .SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently, - // TypedAttrChecker don't support vector type.) - AddAttr>("paddings", - "Paddings(height, width) of pooling operator." - "Default {0,0}.") - .SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently, - // TypedAttrChecker don't support vector type.) - - AddComment(R"DOC( + 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(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "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 feature."); + AddOutput("Out", + "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 feature."); + + AddAttr("poolingType", + "PoolingType of pooling operator." + "Str constant equal to 'max' or 'avg'.") + .InEnum({"max", "avg"}); + + AddAttr>( + "ksize", + "The pooling 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.) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>("strides", + "Strides(height, width) of pooling operator." + "Default {1,1}.") + .SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr>("paddings", + "Paddings(height, width) of pooling operator." + "Default {0,0}.") + .SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + + AddComment(R"DOC( The pooling2d operation calculates the output based on 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 @@ -131,58 +117,55 @@ 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"); - } -}; - -class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker { - public: - Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput( - "X", - "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 " - "feature."); - AddOutput("Out", - "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 feature."); - - AddAttr("poolingType", - "PoolingType of pooling operator." - "Str constant equal to 'max' or 'avg'.") - .InEnum({"max", "avg"}); - - AddAttr>( - "ksize", - "The pooling 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.) - AddAttr( - "globalPooling", - "Whether to use the globalPooling." - "Bool constant equal to false or true." - "Default false." - "If globalPooling = true, ksize is ignored and need not be specified.") - .SetDefault(false); - AddAttr>( - "strides", - "Strides(depth, height, width) of pooling operator." - "Default {1,1,1}.") - .SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently, - // TypedAttrChecker don't support vector type.) - AddAttr>( - "paddings", - "Paddings(depth, height, width) of pooling operator." - "Default {0,0,0}.") - .SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently, - // TypedAttrChecker don't support vector type.) - - AddComment(R"DOC( +} + +Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "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 " + "feature."); + AddOutput("Out", + "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 feature."); + + AddAttr("poolingType", + "PoolingType of pooling operator." + "Str constant equal to 'max' or 'avg'.") + .InEnum({"max", "avg"}); + + AddAttr>( + "ksize", + "The pooling 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.) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>("strides", + "Strides(depth, height, width) of pooling operator." + "Default {1,1,1}.") + .SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr>( + "paddings", + "Paddings(depth, height, width) of pooling operator." + "Default {0,0,0}.") + .SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + + AddComment(R"DOC( The pooling3d operation calculates the output based on the input, poolingType and ksize, strides, paddings parameters. Input(X) and output(Out) are in NCDHW format. Where N is batch @@ -190,8 +173,7 @@ 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 diff --git a/paddle/operators/pool_op.h b/paddle/operators/pool_op.h index c2bc358def42959f2cc8f61cb00436fae1b7514b..e5016d573dde0a9c8a90cddf14f68706b69fade5 100644 --- a/paddle/operators/pool_op.h +++ b/paddle/operators/pool_op.h @@ -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 class PoolKernel : public framework::OpKernel { public: