caffe2/operators/elementwise_logical_ops.cc

/**
 * Copyright (c) 2016-present, Facebook, Inc.
 *
 * Licensed 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.
 */

#include "caffe2/operators/elementwise_logical_ops.h"

namespace caffe2 {
namespace {

REGISTER_CPU_OPERATOR(Where, WhereOp<CPUContext>);

// Input: C, X, Y, output: Z
OPERATOR_SCHEMA(Where)
    .NumInputs(3)
    .NumOutputs(1)
    .AllowInplace({{1, 2}})
    .IdenticalTypeAndShapeOfInput(1)
    .SetDoc(R"DOC(
Operator Where takes three input data (Tensor<bool>, Tensor<T>, Tensor<T>) and
produces one output data (Tensor<T>) where z = c ? x : y is applied elementwise.
)DOC")
    .Input(0, "C", "input tensor containing booleans")
    .Input(1, "X", "input tensor")
    .Input(2, "Y", "input tensor")
    .Output(0, "Z", "output tensor");

SHOULD_NOT_DO_GRADIENT(Where);

REGISTER_CPU_OPERATOR(IsMemberOf, IsMemberOfOp<CPUContext>);

// Input: X, output: Y
OPERATOR_SCHEMA(IsMemberOf)
    .NumInputs(1)
    .NumOutputs(1)
    .TensorInferenceFunction(
        [](const OperatorDef&, const vector<TensorShape>& input_types) {
          vector<TensorShape> out(1);
          out[0] = input_types[0];
          out[0].set_data_type(TensorProto_DataType::TensorProto_DataType_BOOL);
          return out;
        })
    .Arg("value", "Declare one value for the membership test.")
    .Arg(
        "dtype",
        "The data type for the elements of the output tensor."
        "Strictly must be one of the types from DataType enum in TensorProto.")
    .SetDoc(R"DOC(
IsMemberOf takes input data (Tensor<T>) and a list of values as argument, and
produces one output data (Tensor<bool>) where the function `f(x) = x in values`,
is applied to the data tensor elementwise.
)DOC")
    .Input(0, "X", "Input tensor of any shape")
    .Output(0, "Y", "Output tensor (same size as X containing booleans)");

SHOULD_NOT_DO_GRADIENT(IsMemberOf);

} // namespace

template <>
std::unordered_set<int32_t>& IsMemberOfValueHolder::get<int32_t>() {
  return int32_values_;
}

template <>
std::unordered_set<int64_t>& IsMemberOfValueHolder::get<int64_t>() {
  return int64_values_;
}

template <>
std::unordered_set<bool>& IsMemberOfValueHolder::get<bool>() {
  return bool_values_;
}

template <>
std::unordered_set<string>& IsMemberOfValueHolder::get<string>() {
  return string_values_;
}

} // namespace caffe2