upsampling.cc

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 * to you under the Apache License, Version 2.0 (the
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 * specific language governing permissions and limitations
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/*!
 *  Copyright (c) 2017 by Contributors
 * \file upsampling.cc
 * \brief Property def of upsampling operators.
 */
#include <tvm/tvm.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