/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, * software distributed under the License is distributed on an * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY * KIND, either express or implied. See the License for the * specific language governing permissions and limitations * under the License. */ /*! * \file upsampling.cc * \brief Property def of upsampling operators. */ #include <tvm/operation.h> #include <tvm/expr.h> #include <nnvm/layout.h> #include <nnvm/compiler/op_attr_types.h> #include <nnvm/op.h> #include <nnvm/node.h> #include <nnvm/op_attr_types.h> #include <nnvm/top/nn.h> #include "nn_common.h" #include "../op_common.h" #include "../elemwise_op_common.h" #include "topi/elemwise.h" #include "topi/transform.h" #include "topi/nn/upsampling.h" namespace nnvm { namespace top { using tvm::Expr; using tvm::Array; using tvm::Tensor; using nnvm::compiler::FTVMCompute; DMLC_REGISTER_PARAMETER(UpSamplingParam); inline bool UpSamplingInferShape(const nnvm::NodeAttrs& attrs, std::vector<TShape>* in_shape, std::vector<TShape>* out_shape) { static const Layout kNCHW("NCHW"); const UpSamplingParam& param = nnvm::get<UpSamplingParam>(attrs.parsed); CHECK_EQ(in_shape->size(), 1U); CHECK_EQ(out_shape->size(), 1U); TShape dshape = (*in_shape)[0]; if (dshape.ndim() == 0) return false; dshape = ConvertLayout(dshape, param.layout, kNCHW); TShape oshape = dshape; oshape[2] = oshape[2] * param.scale; oshape[3] = oshape[3] * param.scale; oshape = ConvertLayout(oshape, kNCHW, param.layout); NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, oshape); return true; } inline bool UpsamplingLayout(const NodeAttrs& attrs, std::vector<Layout> *in_layouts, const std::vector<Layout> *last_in_layouts, std::vector<Layout> *out_layouts) { const UpSamplingParam& param = nnvm::get<UpSamplingParam>(attrs.parsed); CHECK_EQ(in_layouts->size(), 1U); CHECK_EQ(out_layouts->size(), 1U); const Layout layout(param.layout); NNVM_ASSIGN_LAYOUT(*in_layouts, 0, layout); NNVM_ASSIGN_LAYOUT(*out_layouts, 0, layout); return true; } NNVM_REGISTER_OP(upsampling) .describe(R"(Perform upsampling to input array with nearest neighbour or bilinear interpolation. - **data**: data is 4D array of shape (batch_size, channels, in_height, in_width) for NCHW (batch_size, in_height, in_width, channels) for NHWC - **out**: Output is 4D array of shape for layout NCHW (batch_size, channels, in_height*scale, in_width*scale) for layout NHWC (batch_size, in_height*scale, in_width*scale, channels) )" NNVM_ADD_FILELINE) .add_argument("data", "4D Tensor", "Input data.") .add_arguments(UpSamplingParam::__FIELDS__()) .set_attr_parser(ParamParser<UpSamplingParam>) .set_attr<FGetAttrDict>("FGetAttrDict", ParamGetAttrDict<UpSamplingParam>) .set_attr<FInferShape>("FInferShape", UpSamplingInferShape) .set_attr<FInferType>("FInferType", ElemwiseType<1, 1>) .set_attr<FCorrectLayout>("FCorrectLayout", UpsamplingLayout) .set_num_outputs(1) .set_num_inputs(1) .set_attr<FTVMCompute>( "FTVMCompute", [](const NodeAttrs& attrs, const Array<Tensor>& inputs, const Array<Tensor>& out_info) { const UpSamplingParam& param = nnvm::get<UpSamplingParam>(attrs.parsed); Array<Expr> oshape; if (param.layout == "NCHW") { oshape.push_back(out_info[0]->shape[2]); oshape.push_back(out_info[0]->shape[3]); } else { oshape.push_back(out_info[0]->shape[1]); oshape.push_back(out_info[0]->shape[2]); } return Array<Tensor>{ topi::nn::upsampling(inputs[0], oshape, param.layout, param.method)}; }) .set_support_level(2); } // namespace top } // namespace nnvm