Source code for espnet2.samplers.num_elements_batch_sampler

from typing import Iterator, List, Tuple, Union

import numpy as np
from typeguard import typechecked

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


[docs]class NumElementsBatchSampler(AbsSampler): @typechecked def __init__( self, batch_bins: int, shape_files: Union[Tuple[str, ...], List[str]], min_batch_size: int = 1, sort_in_batch: str = "descending", sort_batch: str = "ascending", drop_last: bool = False, padding: bool = True, ): assert batch_bins > 0 if sort_batch != "ascending" and sort_batch != "descending": raise ValueError( f"sort_batch must be ascending or descending: {sort_batch}" ) if sort_in_batch != "descending" and sort_in_batch != "ascending": raise ValueError( f"sort_in_batch must be ascending or descending: {sort_in_batch}" ) self.batch_bins = batch_bins self.shape_files = shape_files self.sort_in_batch = sort_in_batch self.sort_batch = sort_batch self.drop_last = drop_last # utt2shape: (Length, ...) # uttA 100,... # uttB 201,... utt2shapes = [ load_num_sequence_text(s, loader_type="csv_int") for s in shape_files ] first_utt2shape = utt2shapes[0] for s, d in zip(shape_files, utt2shapes): if set(d) != set(first_utt2shape): raise RuntimeError( f"keys are mismatched between {s} != {shape_files[0]}" ) # Sort samples in ascending order # (shape order should be like (Length, Dim)) keys = sorted(first_utt2shape, key=lambda k: first_utt2shape[k][0]) if len(keys) == 0: raise RuntimeError(f"0 lines found: {shape_files[0]}") if padding: # If padding case, the feat-dim must be same over whole corpus, # therefore the first sample is referred feat_dims = [np.prod(d[keys[0]][1:]) for d in utt2shapes] else: feat_dims = None # Decide batch-sizes batch_sizes = [] current_batch_keys = [] for key in keys: current_batch_keys.append(key) # shape: (Length, dim1, dim2, ...) if padding: for d, s in zip(utt2shapes, shape_files): if tuple(d[key][1:]) != tuple(d[keys[0]][1:]): raise RuntimeError( "If padding=True, the " f"feature dimension must be unified: {s}", ) bins = sum( len(current_batch_keys) * sh[key][0] * d for sh, d in zip(utt2shapes, feat_dims) ) else: bins = sum( np.prod(d[k]) for k in current_batch_keys for d in utt2shapes ) if bins > batch_bins and len(current_batch_keys) >= min_batch_size: batch_sizes.append(len(current_batch_keys)) current_batch_keys = [] else: if len(current_batch_keys) != 0 and ( not self.drop_last or len(batch_sizes) == 0 ): batch_sizes.append(len(current_batch_keys)) if len(batch_sizes) == 0: # Maybe we can't reach here raise RuntimeError("0 batches") # If the last batch-size is smaller than minimum batch_size, # the samples are redistributed to the other mini-batches if len(batch_sizes) > 1 and batch_sizes[-1] < min_batch_size: for i in range(batch_sizes.pop(-1)): batch_sizes[-(i % len(batch_sizes)) - 1] += 1 if not self.drop_last: # Bug check assert sum(batch_sizes) == len(keys), f"{sum(batch_sizes)} != {len(keys)}" # Set mini-batch self.batch_list = [] iter_bs = iter(batch_sizes) bs = next(iter_bs) minibatch_keys = [] for key in keys: minibatch_keys.append(key) if len(minibatch_keys) == bs: if sort_in_batch == "descending": minibatch_keys.reverse() elif sort_in_batch == "ascending": # Key are already sorted in ascending pass else: raise ValueError( "sort_in_batch must be ascending" f" or descending: {sort_in_batch}" ) self.batch_list.append(tuple(minibatch_keys)) minibatch_keys = [] try: bs = next(iter_bs) except StopIteration: break if sort_batch == "ascending": pass elif sort_batch == "descending": self.batch_list.reverse() else: raise ValueError( f"sort_batch must be ascending or descending: {sort_batch}" ) def __repr__(self): return ( f"{self.__class__.__name__}(" f"N-batch={len(self)}, " f"batch_bins={self.batch_bins}, " 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)