sparkocr.transformers.readers.mixed_iterator#

Classes#

Attributes#

class MixedDataset(dataframes, size, max_patches: int = 2048, max_length: int = 48, split: str = 'train', ignore_id: int = -100, task_start_token: str = '', prompt_end_token: str = None, sort_json_key: bool = True, repo_id: str = '')#

Bases: torch.utils.data.IterableDataset

An iterable Dataset.

All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream.

All subclasses should overwrite __iter__(), which would return an iterator of samples in this dataset.

When a subclass is used with DataLoader, each item in the dataset will be yielded from the DataLoader iterator. When num_workers > 0, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. get_worker_info(), when called in a worker process, returns information about the worker. It can be used in either the dataset’s __iter__() method or the DataLoader ‘s worker_init_fn option to modify each copy’s behavior.

Example 1: splitting workload across all workers in __iter__():

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         worker_info = torch.utils.data.get_worker_info()
...         if worker_info is None:  # single-process data loading, return the full iterator
...             iter_start = self.start
...             iter_end = self.end
...         else:  # in a worker process
...             # split workload
...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
...             worker_id = worker_info.id
...             iter_start = self.start + worker_id * per_worker
...             iter_end = min(iter_start + per_worker, self.end)
...         return iter(range(iter_start, iter_end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]

>>> # Mult-process loading with two worker processes
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
[3, 4, 5, 6]

Example 2: splitting workload across all workers using worker_init_fn:

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         return iter(range(self.start, self.end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>>
>>> # Directly doing multi-process loading yields duplicate data
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 3, 4, 4, 5, 5, 6, 6]

>>> # Define a `worker_init_fn` that configures each dataset copy differently
>>> def worker_init_fn(worker_id):
...     worker_info = torch.utils.data.get_worker_info()
...     dataset = worker_info.dataset  # the dataset copy in this worker process
...     overall_start = dataset.start
...     overall_end = dataset.end
...     # configure the dataset to only process the split workload
...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
...     worker_id = worker_info.id
...     dataset.start = overall_start + worker_id * per_worker
...     dataset.end = min(dataset.start + per_worker, overall_end)
...

>>> # Mult-process loading with the custom `worker_init_fn`
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]

dataframes#

df_iterators#

df_sizes#

gt_token_sequences = []#

ignore_id = -100#

max_length = 48#

max_patches = 2048#

processor#

produced = 0#

produced_distribution#

prompt_end_token = ''#

prompt_end_token_id#

size#

sort_json_key = True#

split = 'train'#

task_start_token = ''#

total_weights#

weights#

add_tokens(list_of_tokens: List[str])#

Add special tokens to tokenizer and resize the token embeddings of the decoder

process_input_row(example)#

added_tokens = []#