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);

  if (in[0].unknown_shape() || in[1].unknown_shape()) {
    out[0].set_unknown_shape(true);
    return out;
  }

  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 int64_t N = pretransposed_weight
      ? size_from_dim_(canonical_axis_w, GetDimsVector(in[1]))
      : size_to_dim_(canonical_axis_w, GetDimsVector(in[1]));

  vector<int64_t> 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,
    bool pretransposed_weight) {
  CAFFE_ENFORCE_EQ(in.size(), 3, "FC requires three inputs");
  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 uint64_t M = size_to_dim_(canonical_axis, GetDimsVector(in[0]));
  const uint64_t 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 uint64_t N = pretransposed_weight
      ? size_from_dim_(canonical_axis_w, GetDimsVector(in[1]))
      : size_to_dim_(canonical_axis_w, GetDimsVector(in[1]));

  const auto& X = in[0];
  c.flops = M * N * (2 * K + 1);
  c.bytes_read = (K * (M + N) + N) * sizeof(X.data_type());
  c.bytes_written = M * N * sizeof(X.data_type());
  c.params_bytes = (K * N + N) * sizeof(X.data_type());
  return c;
}
} // namespace caffe2