UserBatchTensor.cxx

// Copyright 2023, UChicago Argonne, LLC
// All Rights Reserved
// Software Name: NEML2 -- the New Engineering material Model Library, version 2
// By: Argonne National Laboratory
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#include "neml2/tensors/user_tensors/UserBatchTensor.h"
#include "neml2/base/Sequence.h"
 
namespace neml2
{
register_NEML2_object_alias(UserBatchTensor, "BatchTensor");
 
OptionSet
UserBatchTensor::expected_options()
{
  OptionSet options = UserTensor::expected_options();
  options.doc() = "Construct a BatchTensor from a vector of values. The vector will be reshaped "
                  "according to the specified batch and base shapes.";
 
  options.set<Sequence<Real>>("values");
  options.set("values").doc() = "Values in this (flattened) tensor";
 
  options.set<TorchShape>("batch_shape") = {};
  options.set("batch_shape").doc() = "Batch shape";
 
  options.set<TorchShape>("base_shape") = {};
  options.set("base_shape").doc() = "Base shape";
 
  return options;
}
 
UserBatchTensor::UserBatchTensor(const OptionSet & options)
  : BatchTensor(BatchTensor::empty(options.get<TorchShape>("batch_shape"),
                                   options.get<TorchShape>("base_shape"),
                                   default_tensor_options())),
    UserTensor(options)
{
  auto vals = options.get<Sequence<Real>>("values").vec();
  auto flat = torch::tensor(vals, default_tensor_options());
  if (vals.size() == size_t(this->base_storage()))
    this->index_put_({torch::indexing::Ellipsis}, flat.reshape(this->base_sizes()));
  else if (vals.size() == size_t(utils::storage_size(this->sizes())))
    this->index_put_({torch::indexing::Ellipsis}, flat.reshape(this->sizes()));
  else
    neml_assert(false,
                "Number of values ",
                vals.size(),
                " must equal to either the base storage size ",
                this->base_storage(),
                " or the total storage size ",
                utils::storage_size(this->sizes()));
}
} // namespace neml2