Skip to content

dataloader2 is forced to copy data #12

@coreyjadams

Description

@coreyjadams

7% of execution time to read batches of data with dataloader2 is spent copying data. When trying to not copy data, an error occurs:

Traceback (most recent call last):
  File "bin/resnet3d.py", line 79, in <module>
    main()
  File "bin/resnet3d.py", line 53, in main
    trainer.initialize(io_only=True)
  File "/home/cadams/DLP3/NEXT_SparseEventID/src/utils/trainercore.py", line 155, in initialize
    self._initialize_io()
  File "/home/cadams/DLP3/NEXT_SparseEventID/src/utils/trainercore.py", line 82, in _initialize_io
    self._larcv_interface.prepare_manager('primary', io_config, FLAGS.MINIBATCH_SIZE, data_keys)
  File "/home/cadams/DLP3/dlp/lib/python2.7/site-packages/larcv-3.0a1-py2.7-linux-x86_64.egg/larcv/larcv_interface.py", line 90, in prepare_manager
    self.next(mode)
  File "/home/cadams/DLP3/dlp/lib/python2.7/site-packages/larcv-3.0a1-py2.7-linux-x86_64.egg/larcv/larcv_interface.py", line 116, in next
    self._dataloaders[mode].next(store_event_ids=True, store_entries=True)
  File "/home/cadams/DLP3/dlp/lib/python2.7/site-packages/larcv-3.0a1-py2.7-linux-x86_64.egg/larcv/dataloader2.py", line 257, in next
    storage.set_data(next_storage_id, batch_data)
  File "/home/cadams/DLP3/dlp/lib/python2.7/site-packages/larcv-3.0a1-py2.7-linux-x86_64.egg/larcv/dataloader2.py", line 66, in set_data
    self._npy_data = larcv.as_ndarray(larcv_batchdata.data())
NotImplementedError: Wrong number or type of arguments for overloaded function 'as_ndarray'.
  Possible C/C++ prototypes are:
    larcv3::as_ndarray(std::vector< short,std::allocator< short > > const &)
    larcv3::as_ndarray(std::vector< unsigned short,std::allocator< unsigned short > > const &)
    larcv3::as_ndarray(std::vector< long long,std::allocator< long long > > const &)
    larcv3::as_ndarray(std::vector< unsigned long long,std::allocator< unsigned long long > > const &)
    larcv3::as_ndarray(larcv3::Image2D const &)

It looks like there is some work to do to fix this, but it would give a moderate boost to io performance and data pipelining.

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Type

    No type

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions