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fc_inference.cc
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fc_inference.cc
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#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