| /*! |
| * Copyright (c) 2015 by Contributors |
| * \file pooling_v1-inl.h |
| * \brief |
| * \author Bing Xu |
| */ |
| |
| #ifndef MXNET_OPERATOR_POOLING_V1_INL_H_ |
| #define MXNET_OPERATOR_POOLING_V1_INL_H_ |
| |
| #include <dmlc/logging.h> |
| #include <dmlc/parameter.h> |
| #include <mxnet/operator.h> |
| #include <algorithm> |
| #include <map> |
| #include <vector> |
| #include <string> |
| #include <utility> |
| #include "./operator_common.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| namespace pool_v1_enum { |
| enum PoolingV1OpInputs {kData}; |
| enum PoolingV1OpOutputs {kOut}; |
| enum PoolingV1OpType {kMaxPooling, kAvgPooling, kSumPooling}; |
| enum PoolingV1OpPadConventionType {kValid, kFull}; |
| } // namespace pool_v1_enum |
| |
| struct PoolingV1Param : public dmlc::Parameter<PoolingV1Param> { |
| TShape kernel; |
| TShape stride; |
| TShape pad; |
| int pool_type; |
| int pooling_convention; |
| bool global_pool; |
| DMLC_DECLARE_PARAMETER(PoolingV1Param) { |
| DMLC_DECLARE_FIELD(global_pool).set_default(false) |
| .describe("Ignore kernel size, do global pooling based on current input feature map. "); |
| |
| DMLC_DECLARE_FIELD(kernel) |
| .enforce_nonzero() |
| .describe("pooling kernel size: (y, x) or (d, y, x)"); |
| |
| DMLC_DECLARE_FIELD(pool_type) |
| .add_enum("max", pool_v1_enum::kMaxPooling) |
| .add_enum("avg", pool_v1_enum::kAvgPooling) |
| .add_enum("sum", pool_v1_enum::kSumPooling) |
| .describe("Pooling type to be applied."); |
| |
| DMLC_DECLARE_FIELD(pooling_convention).set_default(pool_v1_enum::kValid) |
| .add_enum("full", pool_v1_enum::kFull) |
| .add_enum("valid", pool_v1_enum::kValid) |
| .describe("Pooling convention to be applied."); |
| |
| DMLC_DECLARE_FIELD(stride).set_default(TShape()) |
| .enforce_nonzero() |
| .describe("stride: for pooling (y, x) or (d, y, x)"); |
| |
| DMLC_DECLARE_FIELD(pad).set_default(TShape()) |
| .describe("pad for pooling: (y, x) or (d, y, x)"); |
| } |
| }; |
| |
| template<typename xpu, typename Reducer, typename DType> |
| class PoolingV1Op : public Operator { |
| public: |
| explicit PoolingV1Op(PoolingV1Param p) { |
| this->param_ = p; |
| } |
| |
| virtual void Forward(const OpContext &ctx, |
| const std::vector<TBlob> &in_data, |
| const std::vector<OpReqType> &req, |
| const std::vector<TBlob> &out_data, |
| const std::vector<TBlob> &aux_args) { |
| using namespace mshadow; |
| using namespace mshadow::expr; |
| CHECK_EQ(in_data.size(), 1); |
| CHECK_EQ(out_data.size(), 1); |
| Stream<xpu> *s = ctx.get_stream<xpu>(); |
| if (param_.kernel.ndim() == 3) { |
| LOG(FATAL) << "3D kernel not implemented"; |
| } |
| Tensor<xpu, 4, DType> data = in_data[pool_v1_enum::kData].get<xpu, 4, DType>(s); |
| Tensor<xpu, 4, DType> out = out_data[pool_v1_enum::kOut].get<xpu, 4, DType>(s); |
| mshadow::Shape<2> out_shape = Shape2(out.shape_[2], out.shape_[3]); |
| if (param_.pool_type == pool_v1_enum::kMaxPooling |
| || param_.pool_type == pool_v1_enum::kSumPooling) { |
| Assign(out, |
| req[pool_v1_enum::kOut], |
| pool<Reducer>(pad(data, param_.pad[0], param_.pad[1]), |
| out_shape, |
| param_.global_pool ? data.shape_[2] : param_.kernel[0], |
| param_.global_pool ? data.shape_[3] : param_.kernel[1], |
| param_.global_pool ? 1 : param_.stride[0], |
| param_.global_pool ? 1 : param_.stride[1])); |
| } else if (param_.pool_type == pool_v1_enum::kAvgPooling) { |
| Assign(out, |
| req[pool_v1_enum::kOut], |
| scalar<DType>(1.0f / (param_.global_pool ? |
| data.shape_[2] * data.shape_[3] : |
| param_.kernel[0] * param_.kernel[1])) * \ |
| pool<Reducer>(pad(data, param_.pad[0], param_.pad[1]), |
| out_shape, |
| param_.global_pool ? data.shape_[2] : param_.kernel[0], |
| param_.global_pool ? data.shape_[3] : param_.kernel[1], |
| param_.global_pool ? 1 : param_.stride[0], |
| param_.global_pool ? 1 : param_.stride[1])); |
| } |
| } |
| |
| virtual void Backward(const OpContext &ctx, |
| const std::vector<TBlob> &out_grad, |
| const std::vector<TBlob> &in_data, |
| const std::vector<TBlob> &out_data, |
| const std::vector<OpReqType> &req, |
| const std::vector<TBlob> &in_grad, |
| const std::vector<TBlob> &aux_args) { |
| using namespace mshadow; |
| using namespace mshadow::expr; |
| CHECK_EQ(out_grad.size(), 1); |
| CHECK_EQ(in_data.size(), 1); |
| CHECK_EQ(out_data.size(), 1); |
| CHECK_EQ(req.size(), 1); |
| CHECK_EQ(in_grad.size(), 1); |
| // TODO(bing): remove pad (0,0) |
| if (param_.kernel.ndim() == 3) { |
| LOG(FATAL) << "3D kernel not implemented"; |
| } |
| Stream<xpu> *s = ctx.get_stream<xpu>(); |
| Tensor<xpu, 4, DType> grad = out_grad[pool_v1_enum::kOut].get<xpu, 4, DType>(s); |
| Tensor<xpu, 4, DType> data = in_data[pool_v1_enum::kData].get<xpu, 4, DType>(s); |
| Tensor<xpu, 4, DType> output_data = out_data[pool_v1_enum::kOut].get<xpu, 4, DType>(s); |
| Tensor<xpu, 4, DType> input_grad = in_grad[pool_v1_enum::kData].get<xpu, 4, DType>(s); |
| |
| mshadow::Shape<2> in_shape = Shape2(data.shape_[2], data.shape_[3]); |
| |
| if (param_.pool_type == pool_v1_enum::kMaxPooling |
| || param_.pool_type == pool_v1_enum::kSumPooling) { |
| Assign(input_grad, req[pool_v1_enum::kData], |
| crop(unpool<Reducer>(pad(data, param_.pad[0], param_.pad[1]), |
| pad(output_data, 0, 0), |
| pad(grad, 0, 0), |
| param_.global_pool ? in_shape[0] : param_.kernel[0], |
| param_.global_pool ? in_shape[1] : param_.kernel[1], |
| param_.global_pool ? 1 : param_.stride[0], |
| param_.global_pool ? 1 : param_.stride[1]), |
| in_shape, |
| param_.pad[0], |
| param_.pad[1])); |
| } else if (param_.pool_type == pool_v1_enum::kAvgPooling) { |
| Assign(input_grad, req[pool_v1_enum::kData], |
| scalar<DType>(1.0f / (param_.global_pool ? |
| data.shape_[2] * data.shape_[3] : |
| param_.kernel[0] * param_.kernel[1])) * \ |
| crop(unpool<Reducer>(pad(data, param_.pad[0], param_.pad[1]), |
| pad(output_data, 0, 0), |
| pad(grad, 0, 0), |
| param_.global_pool ? in_shape[0] : param_.kernel[0], |
| param_.global_pool ? in_shape[1] : param_.kernel[1], |
| param_.global_pool ? 1 : param_.stride[0], |
| param_.global_pool ? 1 : param_.stride[1]), |
| in_shape, |
| param_.pad[0], |
| param_.pad[1])); |
| } |
| } |
| |
| private: |
| PoolingV1Param param_; |
| }; // class PoolingV1Op |
| |
| template<typename xpu> |
| Operator* CreateOp(PoolingV1Param param, int dtype); |
| |
| |
| #if DMLC_USE_CXX11 |
| class PoolingV1Prop : public OperatorProperty { |
| public: |
| void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override { |
| using namespace mshadow; |
| param_.Init(kwargs); |
| if (param_.kernel.ndim() == 2) { |
| if (param_.stride.ndim() == 0) param_.stride = Shape2(1, 1); |
| if (param_.pad.ndim() == 0) param_.pad = Shape2(0, 0); |
| } else { |
| CHECK_EQ(param_.kernel.ndim(), 3U) << param_.kernel.ndim() << "D pooling not supported"; |
| if (param_.stride.ndim() == 0) param_.stride = Shape3(1, 1, 1); |
| if (param_.pad.ndim() == 0) param_.pad = Shape3(0, 0, 0); |
| } |
| CHECK_EQ(param_.stride.ndim(), param_.kernel.ndim()) |
| << "stride and kernel should have the same length"; |
| CHECK_EQ(param_.pad.ndim(), param_.kernel.ndim()) |
| << "pad and kernel should have the same length"; |
| } |
| |
| std::map<std::string, std::string> GetParams() const override { |
| return param_.__DICT__(); |
| } |
| |
| bool InferShape(std::vector<TShape> *in_shape, |
| std::vector<TShape> *out_shape, |
| std::vector<TShape> *aux_shape) const override { |
| CHECK_EQ(in_shape->size(), 1U); |
| const TShape &dshape = (*in_shape)[0]; |
| CHECK_GE(dshape.ndim(), 4U) << "Pooling: Input data should be 4D in (batch, channel, y, x) " |
| << "Or 5D in (batch, channel, d, y, x)"; |
| TShape oshape = dshape; |
| if (dshape.ndim() == 0) return false; |
| if (param_.kernel.ndim() == 2) { |
| CHECK_EQ(dshape.ndim(), 4) << "Pooling: Input data should be 4D in (batch, channel, y, x)"; |
| if (param_.global_pool) { |
| oshape[2] = 1; |
| oshape[3] = 1; |
| } else { |
| CHECK(param_.kernel[0] <= dshape[2] + 2 * param_.pad[0]) |
| << "kernel size (" << param_.kernel[0] << ") exceeds input (" << dshape[2] |
| << " padded to " << (dshape[2] + 2*param_.pad[0]) << ")"; |
| CHECK(param_.kernel[1] <= dshape[3] + 2 * param_.pad[1]) |
| << "kernel size (" << param_.kernel[1] << ") exceeds input (" << dshape[3] |
| << " padded to " << (dshape[3] + 2*param_.pad[1]) << ")"; |
| if (param_.pooling_convention == pool_v1_enum::kValid) { |
| oshape[2] = 1 + (dshape[2] + 2 * param_.pad[0] - param_.kernel[0]) / |
| param_.stride[0]; |
| oshape[3] = 1 + (dshape[3] + 2 * param_.pad[1] - param_.kernel[1]) / |
| param_.stride[1]; |
| } else { |
| oshape[2] = 1 + static_cast<int>(ceil(static_cast<float>( |
| dshape[2] + 2 * param_.pad[0] - |
| param_.kernel[0]) / param_.stride[0])); |
| oshape[3] = 1 + static_cast<int>(ceil(static_cast<float>( |
| dshape[3] + 2 * param_.pad[1] - |
| param_.kernel[1]) / param_.stride[1])); |
| } |
| } |
| out_shape->clear(); |
| out_shape->push_back(oshape); |
| } else if (param_.kernel.ndim() == 3) { |
| CHECK_EQ(dshape.ndim(), 5) << "Pooling: Input data should be 5D in (batch, channel, d, y, x)"; |
| CHECK_LE(param_.kernel[0], dshape[2] + 2 * param_.pad[0]) << "kernel size exceeds input"; |
| CHECK_LE(param_.kernel[1], dshape[3] + 2 * param_.pad[1]) << "kernel size exceeds input"; |
| CHECK_LE(param_.kernel[2], dshape[4] + 2 * param_.pad[2]) << "kernel size exceeds input"; |
| if (param_.global_pool) { |
| oshape[2] = 1; |
| oshape[3] = 1; |
| oshape[4] = 1; |
| } else { |
| if (param_.pooling_convention == pool_v1_enum::kValid) { |
| oshape[2] = 1 + (dshape[2] + 2 * param_.pad[0] - param_.kernel[0]) / |
| param_.stride[0]; |
| oshape[3] = 1 + (dshape[3] + 2 * param_.pad[1] - param_.kernel[1]) / |
| param_.stride[1]; |
| oshape[4] = 1 + (dshape[4] + 2 * param_.pad[2] - param_.kernel[2]) / |
| param_.stride[2]; |
| } else { |
| oshape[2] = 1 + static_cast<int>(ceil(static_cast<float>( |
| dshape[2] + 2 * param_.pad[0] - |
| param_.kernel[0]) / param_.stride[0])); |
| oshape[3] = 1 + static_cast<int>(ceil(static_cast<float>( |
| dshape[3] + 2 * param_.pad[1] - |
| param_.kernel[1]) / param_.stride[1])); |
| oshape[4] = 1 + static_cast<int>(ceil(static_cast<float>( |
| dshape[4] + 2 * param_.pad[2] - |
| param_.kernel[2]) / param_.stride[2])); |
| } |
| } |
| |
| out_shape->clear(); |
| out_shape->push_back(oshape); |
| } |
| return true; |
| } |
| |
| bool InferType(std::vector<int> *in_type, |
| std::vector<int> *out_type, |
| std::vector<int> *aux_type) const override { |
| CHECK_EQ(in_type->size(), 1); |
| int dtype = (*in_type)[0]; |
| |
| if (dtype == -1) { |
| LOG(FATAL) << "Input type to pooling is not specified."; |
| return false; |
| } |
| |
| out_type->clear(); |
| out_type->push_back(dtype); |
| return true; |
| } |
| |
| OperatorProperty* Copy() const override { |
| PoolingV1Prop *prop_sym = new PoolingV1Prop(); |
| prop_sym->param_ = this->param_; |
| return prop_sym; |
| } |
| |
| std::string TypeString() const override { |
| return "Pooling_v1"; |
| } |
| |
| std::vector<int> DeclareBackwardDependency( |
| const std::vector<int> &out_grad, |
| const std::vector<int> &in_data, |
| const std::vector<int> &out_data) const override { |
| return {out_grad[pool_v1_enum::kOut], in_data[pool_v1_enum::kData], |
| out_data[pool_v1_enum::kOut]}; |
| } |
| |
| std::vector<std::pair<int, void*> > BackwardInplaceOption( |
| const std::vector<int> &out_grad, |
| const std::vector<int> &in_data, |
| const std::vector<int> &out_data, |
| const std::vector<void*> &in_grad) const override { |
| #if MXNET_USE_CUDNN == 1 |
| return {}; |
| #else |
| return {{in_data[pool_v1_enum::kData], in_grad[pool_v1_enum::kData]}}; |
| #endif |
| } |
| |
| Operator* CreateOperator(Context ctx) const override { |
| LOG(FATAL) << "Not Implemented."; |
| return NULL; |
| } |
| |
| Operator* CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape, |
| std::vector<int> *in_type) const override; |
| |
| private: |
| PoolingV1Param param_; |
| }; // class PoolingV1Prop |
| #endif // DMLC_USE_CXX11 |
| } // namespace op |
| } // namespace mxnet |
| |
| #endif // MXNET_OPERATOR_POOLING_V1_INL_H_ |
