fc_inference.cc

#include "caffe2/operators/fc_inference.h"

namespace caffe2 {
std::vector<TensorShape> FCShapeInference(
    const OperatorDef& def,
    const vector<TensorShape>& in,
    bool pretransposed_weight) {
  vector<TensorShape> out(1);
  ArgumentHelper helper(def);

  auto axis = helper.GetSingleArgument<int32_t>("axis", 1);
  const auto canonical_axis = canonical_axis_index_(axis, in[0].dims().size());
  auto axis_w = helper.GetSingleArgument<int32_t>("axis_w", 1);
  const int canonical_axis_w =
      canonical_axis_index_(axis_w, in[1].dims().size());
  const int N = pretransposed_weight
      ? size_from_dim_(canonical_axis_w, GetDimsVector(in[1]))
      : size_to_dim_(canonical_axis_w, GetDimsVector(in[1]));

  vector<int> y_shape(in[0].dims().begin(), in[0].dims().end());
  CAFFE_ENFORCE_LE(canonical_axis + 1, y_shape.size());
  y_shape.resize(canonical_axis + 1);
  y_shape[canonical_axis] = N;
  out[0] = CreateTensorShape(y_shape, in[0].data_type());
  return out;
}

OpSchema::Cost CostInferenceForFC(
    const OperatorDef& def,
    const vector<TensorShape>& in) {
  struct OpSchema::Cost c;
  ArgumentHelper helper(def);

  auto axis = helper.GetSingleArgument<int32_t>("axis", 1);
  const auto canonical_axis = canonical_axis_index_(axis, in[0].dims().size());
  const int M = size_to_dim_(canonical_axis, GetDimsVector(in[0]));
  const int K = size_from_dim_(canonical_axis, GetDimsVector(in[0]));
  auto axis_w = helper.GetSingleArgument<int32_t>("axis_w", 1);
  const int canonical_axis_w =
      canonical_axis_index_(axis_w, in[1].dims().size());
  const int N = size_to_dim_(canonical_axis_w, GetDimsVector(in[1]));
  c.flops = 2 * K * M * N + M * N;
  c.bytes_moved = M * N * sizeof(float);
  c.params_bytes = (K * N + N) * sizeof(float);
  return c;
}
} // namespace caffe2