CIDEr scorer.

Wrap a list to a torch Dataset. It produces elements of the list as data.

The user-facing class that provides metric storage functionalities. In the future we may add support for storing / logging other types of da

Map a function over the elements in a dataset. Args: dataset: a dataset where map function is applied. map_func: a callable

Multi-head attention layer with Dropout and Layer Normalization.

Position-wise feed forward layer

Same as :class:`Checkpointer`, but is able to handle models in xmodaler model zoo, and apply conversions for legacy models.

Runs basic tokenization (punctuation splitting, lower casing, etc.).

The same as `fvcore.common.config.CfgNode`, but different in: 1. Use unsafe yaml loading by default. Note that this may lead to arbit

Print **common** metrics to the terminal, including iteration time, ETA, memory, all losses, and the learning rate. It also applies smoot

Write scalars to a json file. It saves scalars as one json per line (instead of a big json) for easy parsing. Examples parsing such a j

Multi-head attention layer with Dropout and Layer Normalization.

useful for distributed training No-Ops

Wrap an object to make it more picklable, note that it uses heavy weight serialization libraries that are slower than pickle. It's best t

Scaled dot-product attention

Scaled dot-product attention with memory

Write all scalars to a tensorboard file.

Runs WordPiece tokenization.

A hook which runs `torch.autograd.profiler.profile`. Examples: :: hooks.AutogradProfiler( lambda trainer: trainer.i

r""" Constructs a BertTokenizer. :class:`~pytorch_transformers.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiec

Create a hook using callback functions provided by the user.

Main Class to compute the CIDEr metric

A large bulk of rename, before public release.

A trainer with default training logic. It does the following: 1. Create a :class:`DefaultTrainer` using model, optimizer, dataloader

Run an evaluation function periodically, and at the end of training. It is executed every ``eval_period`` iterations and after the last iter

Base class for writers that obtain events from :class:`EventStorage` and process them.

Fix LR

Base class for hooks that can be registered with :class:`TrainerBase`. Each hook can implement 4 methods. The way they are called is demonst

Track the time spent for each iteration (each run_step call in the trainer). Print a summary in the end of training. This hook uses the