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
[docs]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
[docs] 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 lengths 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