| /*! |
| * Copyright (c) 2015 by Contributors |
| * \file upsampling_nearest.cc |
| * \brief |
| * \author Bing Xu |
| */ |
| |
| #include "./upsampling-inl.h" |
| #include <nnvm/op_attr_types.h> |
| #include "./deconvolution-inl.h" |
| |
| namespace mxnet { |
| namespace op { |
| template<> |
| Operator *CreateOp<cpu>(UpSamplingParam param, int dtype) { |
| Operator *op = NULL; |
| MSHADOW_REAL_TYPE_SWITCH(dtype, DType, { |
| if (param.sample_type == up_enum::kNearest) { |
| op = new UpSamplingNearestOp<cpu, DType>(param); |
| } else if (param.sample_type == up_enum::kBilinear) { |
| DeconvolutionParam p = DeconvolutionParam(); |
| int kernel = 2 * param.scale - param.scale % 2; |
| int stride = param.scale; |
| int pad = static_cast<int>(ceil((param.scale - 1) / 2.)); |
| p.workspace = param.workspace; |
| p.num_group = param.num_filter; |
| p.num_filter = param.num_filter; |
| p.no_bias = true; |
| int shape[] = {1, 1}; |
| shape[0] = shape[1] = kernel; |
| p.kernel = TShape(shape, shape + 2); |
| shape[0] = shape[1] = stride; |
| p.stride = TShape(shape, shape + 2); |
| shape[0] = shape[1] = pad; |
| p.pad = TShape(shape, shape + 2); |
| shape[0] = shape[1] = 0; |
| p.target_shape = TShape(shape, shape + 2); |
| op = new DeconvolutionOp<cpu, DType>(p); |
| } else { |
| LOG(FATAL) << "Unknown sample type"; |
| } |
| }); |
| return op; |
| } |
| |
| Operator* UpSamplingProp::CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape, |
| std::vector<int> *in_type) const { |
| std::vector<TShape> out_shape, aux_shape; |
| std::vector<int> out_type, aux_type; |
| CHECK(InferType(in_type, &out_type, &aux_type)); |
| CHECK(InferShape(in_shape, &out_shape, &aux_shape)); |
| DO_BIND_DISPATCH(CreateOp, param_, in_type->at(0)); |
| } |
| |
| DMLC_REGISTER_PARAMETER(UpSamplingParam); |
| |
| MXNET_REGISTER_OP_PROPERTY(UpSampling, UpSamplingProp) |
| .describe("Perform nearest neighboor/bilinear up sampling to inputs") |
| .add_argument("data", "Symbol[]", "Array of tensors to upsample") |
| .add_arguments(UpSamplingParam::__FIELDS__()) |
| .set_key_var_num_args("num_args"); |
| |
| NNVM_REGISTER_OP(UpSampling) |
| .set_attr<nnvm::FSetInputVarAttrOnCompose>("FSetInputVarAttrOnCompose", |
| [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) { |
| if (var->attrs.dict.find("__init__") != var->attrs.dict.end()) return; |
| if (index == 1) { |
| var->attrs.dict["__init__"] = "[\"bilinear\", {}]"; |
| } |
| }); |
| } // namespace op |
| } // namespace mxnet |