import logging from typing import Any from typing import Dict from typing import Iterator from typing import List from typing import Sequence from typing import Tuple from typing import Union import numpy as np import torch from typeguard import check_argument_types from espnet2.iterators.abs_iter_factory import AbsIterFactory from espnet2.iterators.sequence_iter_factory import SequenceIterFactory from espnet2.samplers.abs_sampler import AbsSampler class ChunkIterFactory(AbsIterFactory): """Creates chunks from a sequence Examples: >>> batches = [["id1"], ["id2"], ...] >>> batch_size = 128 >>> chunk_length = 1000 >>> iter_factory = ChunkIterFactory(dataset, batches, batch_size, chunk_length) >>> it = iter_factory.build_iter(epoch) >>> for ids, batch in it: ... ... - The number of mini-batches are varied in each epochs and we can't get the number in advance because IterFactory doesn't be given to the length information. - Since the first reason, "num_iters_per_epoch" can't be implemented for this iterator. Instead of it, "num_samples_per_epoch" is implemented. """ def __init__( self, dataset, batch_size: int, batches: Union[AbsSampler, Sequence[Sequence[Any]]], chunk_length: Union[int, str], chunk_shift_ratio: float = 0.5, num_cache_chunks: int = 1024, num_samples_per_epoch: int = None, seed: int = 0, shuffle: bool = False, num_workers: int = 0, collate_fn=None, pin_memory: bool = False, ): assert check_argument_types() assert all(len(x) == 1 for x in batches), "batch-size must be 1" self.per_sample_iter_factory = SequenceIterFactory( dataset=dataset, batches=batches, num_iters_per_epoch=num_samples_per_epoch, seed=seed, shuffle=shuffle, num_workers=num_workers, collate_fn=collate_fn, pin_memory=pin_memory, ) self.num_cache_chunks = max(num_cache_chunks, batch_size) if isinstance(chunk_length, str): if len(chunk_length) == 0: raise ValueError("e.g. 5,8 or 3-5: but got empty string") self.chunk_lengths = [] for x in chunk_length.split(","): try: sps = list(map(int, x.split("-"))) except ValueError: raise ValueError(f"e.g. 5,8 or 3-5: but got {chunk_length}") if len(sps) > 2: raise ValueError(f"e.g. 5,8 or 3-5: but got {chunk_length}") elif len(sps) == 2: # Append all numbers between the range into the candidates self.chunk_lengths += list(range(sps[0], sps[1] + 1)) else: self.chunk_lengths += [sps[0]] else: # Single candidates: Fixed chunk length self.chunk_lengths = [chunk_length] self.chunk_shift_ratio = chunk_shift_ratio self.batch_size = batch_size self.seed = seed self.shuffle = shuffle def build_iter( self, epoch: int, shuffle: bool = None, ) -> Iterator[Tuple[List[str], Dict[str, torch.Tensor]]]: per_sample_loader = self.per_sample_iter_factory.build_iter(epoch, shuffle) if shuffle is None: shuffle = self.shuffle state = np.random.RandomState(epoch + self.seed) # NOTE(kamo): # This iterator supports multiple chunk lengths and # keep chunks for each lenghts here until collecting specified numbers cache_chunks_dict = {} cache_id_list_dict = {} for ids, batch in per_sample_loader: # Must be per-sample-loader assert len(ids) == 1, f"Must be per-sample-loader: {len(ids)}" assert all(len(x) == 1 for x in batch.values()) # Get keys of sequence data sequence_keys = [] for key in batch: if key + "_lengths" in batch: sequence_keys.append(key) # Remove lengths data and get the first sample batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} id_ = ids[0] for key in sequence_keys: if len(batch[key]) != len(batch[sequence_keys[0]]): raise RuntimeError( f"All sequences must has same length: " f"{len(batch[key])} != {len(batch[sequence_keys[0]])}" ) L = len(batch[sequence_keys[0]]) # Select chunk length chunk_lengths = [lg for lg in self.chunk_lengths if lg < L] if len(chunk_lengths) == 0: logging.warning( f"The length of '{id_}' is {L}, but it is shorter than " f"any candidates of chunk-length: {self.chunk_lengths}" ) continue W = int(state.choice(chunk_lengths, 1)) cache_id_list = cache_id_list_dict.setdefault(W, []) cache_chunks = cache_chunks_dict.setdefault(W, {}) # Shift width to the next chunk S = int(W * self.chunk_shift_ratio) # Number of chunks N = (L - W) // S + 1 if shuffle: Z = state.randint(0, (L - W) % S + 1) else: Z = 0 # Split a sequence into chunks. # Note that the marginal frames divided by chunk length are discarded for k, v in batch.items(): if k not in cache_chunks: cache_chunks[k] = [] if k in sequence_keys: # Shift chunks with overlapped length for data augmentation cache_chunks[k] += [v[Z + i * S : Z + i * S + W] for i in range(N)] else: # If not sequence, use whole data instead of chunk cache_chunks[k] += [v for _ in range(N)] cache_id_list += [id_ for _ in range(N)] if len(cache_id_list) > self.num_cache_chunks: cache_id_list, cache_chunks = yield from self._generate_mini_batches( cache_id_list, cache_chunks, shuffle, state, ) cache_id_list_dict[W] = cache_id_list cache_chunks_dict[W] = cache_chunks else: for W in cache_id_list_dict: cache_id_list = cache_id_list_dict.setdefault(W, []) cache_chunks = cache_chunks_dict.setdefault(W, {}) yield from self._generate_mini_batches( cache_id_list, cache_chunks, shuffle, state, ) def _generate_mini_batches( self, id_list: List[str], batches: Dict[str, List[torch.Tensor]], shuffle: bool, state: np.random.RandomState, ): if shuffle: indices = np.arange(0, len(id_list)) state.shuffle(indices) batches = {k: [v[i] for i in indices] for k, v in batches.items()} id_list = [id_list[i] for i in indices] bs = self.batch_size while len(id_list) >= bs: # Make mini-batch and yield yield ( id_list[:bs], {k: torch.stack(v[:bs], 0) for k, v in batches.items()}, ) id_list = id_list[bs:] batches = {k: v[bs:] for k, v in batches.items()} return id_list, batches