Source code for espnet2.samplers.sorted_batch_sampler

import logging
from typing import Iterator, Tuple

from typeguard import check_argument_types

from espnet2.fileio.read_text import load_num_sequence_text
from espnet2.samplers.abs_sampler import AbsSampler


[docs]class SortedBatchSampler(AbsSampler): """BatchSampler with sorted samples by length. Args: batch_size: shape_file: sort_in_batch: 'descending', 'ascending' or None. sort_batch: """ def __init__( self, batch_size: int, shape_file: str, sort_in_batch: str = "descending", sort_batch: str = "ascending", drop_last: bool = False, ): assert check_argument_types() assert batch_size > 0 self.batch_size = batch_size self.shape_file = shape_file self.sort_in_batch = sort_in_batch self.sort_batch = sort_batch self.drop_last = drop_last # utt2shape: (Length, ...) # uttA 100,... # uttB 201,... utt2shape = load_num_sequence_text(shape_file, loader_type="csv_int") if sort_in_batch == "descending": # Sort samples in descending order (required by RNN) keys = sorted(utt2shape, key=lambda k: -utt2shape[k][0]) elif sort_in_batch == "ascending": # Sort samples in ascending order keys = sorted(utt2shape, key=lambda k: utt2shape[k][0]) else: raise ValueError( f"sort_in_batch must be either one of " f"ascending, descending, or None: {sort_in_batch}" ) if len(keys) == 0: raise RuntimeError(f"0 lines found: {shape_file}") # Apply max(, 1) to avoid 0-batches N = max(len(keys) // batch_size, 1) if not self.drop_last: # Split keys evenly as possible as. Note that If N != 1, # the these batches always have size of batch_size at minimum. self.batch_list = [ keys[i * len(keys) // N : (i + 1) * len(keys) // N] for i in range(N) ] else: self.batch_list = [ tuple(keys[i * batch_size : (i + 1) * batch_size]) for i in range(N) ] if len(self.batch_list) == 0: logging.warning(f"{shape_file} is empty") if sort_in_batch != sort_batch: if sort_batch not in ("ascending", "descending"): raise ValueError( f"sort_batch must be ascending or descending: {sort_batch}" ) self.batch_list.reverse() if len(self.batch_list) == 0: raise RuntimeError("0 batches") def __repr__(self): return ( f"{self.__class__.__name__}(" f"N-batch={len(self)}, " f"batch_size={self.batch_size}, " f"shape_file={self.shape_file}, " f"sort_in_batch={self.sort_in_batch}, " f"sort_batch={self.sort_batch})" ) def __len__(self): return len(self.batch_list) def __iter__(self) -> Iterator[Tuple[str, ...]]: return iter(self.batch_list)