cxx11_tensor_argmax.cpp

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Eugene Brevdo <ebrevdo@google.com>
//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
#include "main.h"
 
#include <Eigen/CXX11/Tensor>
 
using Eigen::Tensor;
using Eigen::array;
using Eigen::Tuple;
 
template <int DataLayout>
static void test_simple_index_tuples()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
 
  Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7);
  index_tuples = tensor.index_tuples();
 
  for (DenseIndex n = 0; n < 2*3*5*7; ++n) {
    const Tuple<DenseIndex, float>& v = index_tuples.coeff(n);
    VERIFY_IS_EQUAL(v.first, n);
    VERIFY_IS_EQUAL(v.second, tensor.coeff(n));
  }
}
 
template <int DataLayout>
static void test_index_tuples_dim()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
 
  Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7);
 
  index_tuples = tensor.index_tuples();
 
  for (Eigen::DenseIndex n = 0; n < tensor.size(); ++n) {
    const Tuple<DenseIndex, float>& v = index_tuples(n); //(i, j, k, l);
    VERIFY_IS_EQUAL(v.first, n);
    VERIFY_IS_EQUAL(v.second, tensor(n));
  }
}
 
template <int DataLayout>
static void test_argmax_tuple_reducer()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
 
  Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7);
  index_tuples = tensor.index_tuples();
 
  Tensor<Tuple<DenseIndex, float>, 0, DataLayout> reduced;
  DimensionList<DenseIndex, 4> dims;
  reduced = index_tuples.reduce(
      dims, internal::ArgMaxTupleReducer<Tuple<DenseIndex, float> >());
 
  Tensor<float, 0, DataLayout> maxi = tensor.maximum();
 
  VERIFY_IS_EQUAL(maxi(), reduced(0).second);
 
  array<DenseIndex, 3> reduce_dims;
  for (int d = 0; d < 3; ++d) reduce_dims[d] = d;
  Tensor<Tuple<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7);
  reduced_by_dims = index_tuples.reduce(
      reduce_dims, internal::ArgMaxTupleReducer<Tuple<DenseIndex, float> >());
 
  Tensor<float, 1, DataLayout> max_by_dims = tensor.maximum(reduce_dims);
 
  for (int l = 0; l < 7; ++l) {
    VERIFY_IS_EQUAL(max_by_dims(l), reduced_by_dims(l).second);
  }
}
 
template <int DataLayout>
static void test_argmin_tuple_reducer()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
 
  Tensor<Tuple<DenseIndex, float>, 4, DataLayout> index_tuples(2,3,5,7);
  index_tuples = tensor.index_tuples();
 
  Tensor<Tuple<DenseIndex, float>, 0, DataLayout> reduced;
  DimensionList<DenseIndex, 4> dims;
  reduced = index_tuples.reduce(
      dims, internal::ArgMinTupleReducer<Tuple<DenseIndex, float> >());
 
  Tensor<float, 0, DataLayout> mini = tensor.minimum();
 
  VERIFY_IS_EQUAL(mini(), reduced(0).second);
 
  array<DenseIndex, 3> reduce_dims;
  for (int d = 0; d < 3; ++d) reduce_dims[d] = d;
  Tensor<Tuple<DenseIndex, float>, 1, DataLayout> reduced_by_dims(7);
  reduced_by_dims = index_tuples.reduce(
      reduce_dims, internal::ArgMinTupleReducer<Tuple<DenseIndex, float> >());
 
  Tensor<float, 1, DataLayout> min_by_dims = tensor.minimum(reduce_dims);
 
  for (int l = 0; l < 7; ++l) {
    VERIFY_IS_EQUAL(min_by_dims(l), reduced_by_dims(l).second);
  }
}
 
template <int DataLayout>
static void test_simple_argmax()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
  tensor(0,0,0,0) = 10.0;
 
  Tensor<DenseIndex, 0, DataLayout> tensor_argmax;
 
  tensor_argmax = tensor.argmax();
 
  VERIFY_IS_EQUAL(tensor_argmax(0), 0);
 
  tensor(1,2,4,6) = 20.0;
 
  tensor_argmax = tensor.argmax();
 
  VERIFY_IS_EQUAL(tensor_argmax(0), 2*3*5*7 - 1);
}
 
template <int DataLayout>
static void test_simple_argmin()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  tensor.setRandom();
  tensor = (tensor + tensor.constant(0.5)).log();
  tensor(0,0,0,0) = -10.0;
 
  Tensor<DenseIndex, 0, DataLayout> tensor_argmin;
 
  tensor_argmin = tensor.argmin();
 
  VERIFY_IS_EQUAL(tensor_argmin(0), 0);
 
  tensor(1,2,4,6) = -20.0;
 
  tensor_argmin = tensor.argmin();
 
  VERIFY_IS_EQUAL(tensor_argmin(0), 2*3*5*7 - 1);
}
 
template <int DataLayout>
static void test_argmax_dim()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  std::vector<int> dims {2, 3, 5, 7};
 
  for (int dim = 0; dim < 4; ++dim) {
    tensor.setRandom();
    tensor = (tensor + tensor.constant(0.5)).log();
 
    Tensor<DenseIndex, 3, DataLayout> tensor_argmax;
    array<DenseIndex, 4> ix;
    for (int i = 0; i < 2; ++i) {
      for (int j = 0; j < 3; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 7; ++l) {
            ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
            if (ix[dim] != 0) continue;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
            tensor(ix) = 10.0;
          }
        }
      }
    }
 
    tensor_argmax = tensor.argmax(dim);
 
    VERIFY_IS_EQUAL(tensor_argmax.size(),
                    ptrdiff_t(2*3*5*7 / tensor.dimension(dim)));
    for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) {
      // Expect max to be in the first index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_argmax.data()[n], 0);
    }
 
    for (int i = 0; i < 2; ++i) {
      for (int j = 0; j < 3; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 7; ++l) {
            ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
            if (ix[dim] != tensor.dimension(dim) - 1) continue;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
            tensor(ix) = 20.0;
          }
        }
      }
    }
 
    tensor_argmax = tensor.argmax(dim);
 
    VERIFY_IS_EQUAL(tensor_argmax.size(),
                    ptrdiff_t(2*3*5*7 / tensor.dimension(dim)));
    for (ptrdiff_t n = 0; n < tensor_argmax.size(); ++n) {
      // Expect max to be in the last index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_argmax.data()[n], tensor.dimension(dim) - 1);
    }
  }
}
 
template <int DataLayout>
static void test_argmin_dim()
{
  Tensor<float, 4, DataLayout> tensor(2,3,5,7);
  std::vector<int> dims {2, 3, 5, 7};
 
  for (int dim = 0; dim < 4; ++dim) {
    tensor.setRandom();
    tensor = (tensor + tensor.constant(0.5)).log();
 
    Tensor<DenseIndex, 3, DataLayout> tensor_argmin;
    array<DenseIndex, 4> ix;
    for (int i = 0; i < 2; ++i) {
      for (int j = 0; j < 3; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 7; ++l) {
            ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
            if (ix[dim] != 0) continue;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
            tensor(ix) = -10.0;
          }
        }
      }
    }
 
    tensor_argmin = tensor.argmin(dim);
 
    VERIFY_IS_EQUAL(tensor_argmin.size(),
                    ptrdiff_t(2*3*5*7 / tensor.dimension(dim)));
    for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) {
      // Expect min to be in the first index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_argmin.data()[n], 0);
    }
 
    for (int i = 0; i < 2; ++i) {
      for (int j = 0; j < 3; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 7; ++l) {
            ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
            if (ix[dim] != tensor.dimension(dim) - 1) continue;
            // suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
            tensor(ix) = -20.0;
          }
        }
      }
    }
 
    tensor_argmin = tensor.argmin(dim);
 
    VERIFY_IS_EQUAL(tensor_argmin.size(),
                    ptrdiff_t(2*3*5*7 / tensor.dimension(dim)));
    for (ptrdiff_t n = 0; n < tensor_argmin.size(); ++n) {
      // Expect min to be in the last index of the reduced dimension
      VERIFY_IS_EQUAL(tensor_argmin.data()[n], tensor.dimension(dim) - 1);
    }
  }
}
 
EIGEN_DECLARE_TEST(cxx11_tensor_argmax)
{
  CALL_SUBTEST(test_simple_index_tuples<RowMajor>());
  CALL_SUBTEST(test_simple_index_tuples<ColMajor>());
  CALL_SUBTEST(test_index_tuples_dim<RowMajor>());
  CALL_SUBTEST(test_index_tuples_dim<ColMajor>());
  CALL_SUBTEST(test_argmax_tuple_reducer<RowMajor>());
  CALL_SUBTEST(test_argmax_tuple_reducer<ColMajor>());
  CALL_SUBTEST(test_argmin_tuple_reducer<RowMajor>());
  CALL_SUBTEST(test_argmin_tuple_reducer<ColMajor>());
  CALL_SUBTEST(test_simple_argmax<RowMajor>());
  CALL_SUBTEST(test_simple_argmax<ColMajor>());
  CALL_SUBTEST(test_simple_argmin<RowMajor>());
  CALL_SUBTEST(test_simple_argmin<ColMajor>());
  CALL_SUBTEST(test_argmax_dim<RowMajor>());
  CALL_SUBTEST(test_argmax_dim<ColMajor>());
  CALL_SUBTEST(test_argmin_dim<RowMajor>());
  CALL_SUBTEST(test_argmin_dim<ColMajor>());
}