src/operator/tensor/histogram.cc - master - githubmirror/incubator-mxnet

src/operator/tensor/histogram.cc
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mozga
[master][clang-format] Re-format cc. .h. .cu files; cond. (#20704)
19 ноя 2021, 11:27
19 ноя 2021, 11:271add250
/*
 * 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 histogram.cc
 * \brief CPU implementation of histogram operator
 */
#include "./histogram-inl.h"

namespace mxnet {
namespace op {

struct ComputeBinKernel {
  template <typename DType>
  MSHADOW_XINLINE static void Map(index_t i,
                                  const DType* in_data,
                                  const DType* bin_bounds,
                                  int* bin_indices,
                                  int bin_cnt,
                                  double min,
                                  double max) {
    DType data = in_data[i];
    int target = -1;
    if (data >= min && data <= max) {
      target = (data - min) * bin_cnt / (max - min);
      target = mshadow_op::minimum::Map(bin_cnt - 1, target);
      target -= (data < bin_bounds[target]) ? 1 : 0;
      target += ((data >= bin_bounds[target + 1]) && (target != bin_cnt - 1)) ? 1 : 0;
    }
    bin_indices[i] = target;
  }

  template <typename DType>
  MSHADOW_XINLINE static void Map(index_t i,
                                  const DType* in_data,
                                  int* bin_indices,
                                  const DType* bin_bounds,
                                  int num_bins) {
    DType data = in_data[i];
    int target = -1;
    if (data >= bin_bounds[0] && data <= bin_bounds[num_bins]) {
      target = 0;
      while ((data - bin_bounds[target]) >= 0) {
        target += 1;
      }
      target = mshadow_op::minimum::Map(target - 1, num_bins - 1);
    }
    bin_indices[i] = target;
  }
};

template <typename CType>
void ComputeHistogram(const int* bin_indices, CType* out_data, size_t input_size) {
  for (size_t i = 0; i < input_size; ++i) {
    int target = bin_indices[i];
    if (target >= 0) {
      out_data[target] += 1;
    }
  }
}

template <>
void HistogramForwardImpl<cpu>(const OpContext& ctx,
                               const TBlob& in_data,
                               const TBlob& bin_bounds,
                               const TBlob& out_data,
                               const TBlob& out_bins) {
  using namespace mshadow;
  using namespace mxnet_op;
  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
  Tensor<cpu, 1, int> bin_indices =
      ctx.requested[0].get_space_typed<cpu, 1, int>(Shape1(in_data.Size()), s);
  const int bin_cnt = out_data.Size();

  MSHADOW_TYPE_SWITCH(in_data.type_flag_, DType, {
    Kernel<ComputeBinKernel, cpu>::Launch(s,
                                          in_data.Size(),
                                          in_data.dptr<DType>(),
                                          bin_indices.dptr_,
                                          bin_bounds.dptr<DType>(),
                                          bin_cnt);
    Kernel<op_with_req<mshadow_op::identity, kWriteTo>, cpu>::Launch(
        s, bin_bounds.Size(), out_bins.dptr<DType>(), bin_bounds.dptr<DType>());
  });
  MSHADOW_TYPE_SWITCH(out_data.type_flag_, CType, {
    Kernel<set_zero, cpu>::Launch(s, bin_cnt, out_data.dptr<CType>());
    ComputeHistogram(bin_indices.dptr_, out_data.dptr<CType>(), in_data.Size());
  });
}

template <>
void HistogramForwardImpl<cpu>(const OpContext& ctx,
                               const TBlob& in_data,
                               const TBlob& out_data,
                               const TBlob& out_bins,
                               const int bin_cnt,
                               const double min,
                               const double max) {
  using namespace mshadow;
  using namespace mxnet_op;
  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
  Tensor<cpu, 1, int> bin_indices =
      ctx.requested[0].get_space_typed<cpu, 1, int>(Shape1(in_data.Size()), s);

  MSHADOW_TYPE_SWITCH(in_data.type_flag_, DType, {
    Kernel<FillBinBoundsKernel, cpu>::Launch(
        s, bin_cnt + 1, out_bins.dptr<DType>(), bin_cnt, min, max);
    Kernel<ComputeBinKernel, cpu>::Launch(s,
                                          in_data.Size(),
                                          in_data.dptr<DType>(),
                                          out_bins.dptr<DType>(),
                                          bin_indices.dptr_,
                                          bin_cnt,
                                          min,
                                          max);
  });
  MSHADOW_TYPE_SWITCH(out_data.type_flag_, CType, {
    Kernel<set_zero, cpu>::Launch(s, bin_cnt, out_data.dptr<CType>());
    ComputeHistogram(bin_indices.dptr_, out_data.dptr<CType>(), in_data.Size());
  });
}

DMLC_REGISTER_PARAMETER(HistogramParam);

NNVM_REGISTER_OP(_histogram)
    .add_alias("_npi_histogram")
    .describe(R"code(This operators implements the histogram function.

Example::
  x = [[0, 1], [2, 2], [3, 4]]
  histo, bin_edges = histogram(data=x, bin_bounds=[], bin_cnt=5, range=(0,5))
  histo = [1, 1, 2, 1, 1]
  bin_edges = [0., 1., 2., 3., 4.]
  histo, bin_edges = histogram(data=x, bin_bounds=[0., 2.1, 3.])
  histo = [4, 1]

)code" ADD_FILELINE)
    .set_attr_parser(ParamParser<HistogramParam>)
    .set_num_inputs([](const NodeAttrs& attrs) {
      const HistogramParam& params = nnvm::get<HistogramParam>(attrs.parsed);
      return params.bin_cnt.has_value() ? 1 : 2;
    })
    .set_num_outputs(2)
    .set_attr<nnvm::FListInputNames>("FListInputNames",
                                     [](const NodeAttrs& attrs) {
                                       const HistogramParam& params =
                                           nnvm::get<HistogramParam>(attrs.parsed);
                                       return params.bin_cnt.has_value() ?
                                                  std::vector<std::string>{"data"} :
                                                  std::vector<std::string>{"data", "bins"};
                                     })
    .set_attr<FResourceRequest>("FResourceRequest",
                                [](const NodeAttrs& attrs) {
                                  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
                                })
    .set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
    .set_attr<mxnet::FInferShape>("FInferShape", HistogramOpShape)
    .set_attr<nnvm::FInferType>("FInferType", HistogramOpType)
    .set_attr<FCompute>("FCompute<cpu>", HistogramOpForward<cpu>)
    .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
    .add_argument("bins", "NDArray-or-Symbol", "Input ndarray")
    .add_arguments(HistogramParam::__FIELDS__());

}  // namespace op
}  // namespace mxnet