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"""Data loading and sampling utils for distributed training."""
import hashlib
import json
import logging
import pickle
# from collections.abc import Iterable
from copy import deepcopy
from pathlib import Path
from timeit import default_timer
import numpy as np
import torch
# from lightning import LightningDataModule
from torch.utils.data import (
BatchSampler,
ConcatDataset,
DataLoader,
Dataset,
DistributedSampler,
)
from typing import Optional, Iterable
from .data import CTCData
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def cache_class(cachedir=None):
"""A simple file cache for CTCData."""
def make_hashable(obj):
if isinstance(obj, tuple | list):
return tuple(make_hashable(e) for e in obj)
elif isinstance(obj, Path):
return obj.as_posix()
elif isinstance(obj, dict):
return tuple(sorted((k, make_hashable(v)) for k, v in obj.items()))
else:
return obj
def hash_args_kwargs(*args, **kwargs):
hashable_args = tuple(make_hashable(arg) for arg in args)
hashable_kwargs = make_hashable(kwargs)
combined_serialized = json.dumps(
[hashable_args, hashable_kwargs], sort_keys=True
)
hash_obj = hashlib.sha256(combined_serialized.encode())
return hash_obj.hexdigest()
if cachedir is None:
return CTCData
else:
cachedir = Path(cachedir)
def _wrapped(*args, **kwargs):
h = hash_args_kwargs(*args, **kwargs)
cachedir.mkdir(exist_ok=True, parents=True)
cache_file = cachedir / f"{h}.pkl"
if cache_file.exists():
logger.info(f"Loading cached dataset from {cache_file}")
with open(cache_file, "rb") as f:
return pickle.load(f)
else:
c = CTCData(*args, **kwargs)
logger.info(f"Saving cached dataset to {cache_file}")
pickle.dump(c, open(cache_file, "wb"))
return c
return _wrapped
class BalancedBatchSampler(BatchSampler):
"""samples batch indices such that the number of objects in each batch is balanced
(so to reduce the number of paddings in the batch).
"""
def __init__(
self,
dataset: torch.utils.data.Dataset,
batch_size: int,
n_pool: int = 10,
num_samples: Optional[int] = None,
weight_by_ndivs: bool = False,
weight_by_dataset: bool = False,
drop_last: bool = False,
):
"""Setting n_pool =1 will result in a regular random batch sampler.
weight_by_ndivs: if True, the probability of sampling an element is proportional to the number of divisions
weight_by_dataset: if True, the probability of sampling an element is inversely proportional to the length of the dataset
"""
if isinstance(dataset, CTCData):
self.n_objects = dataset.n_objects
self.n_divs = np.array(dataset.n_divs)
self.n_sizes = np.ones(len(dataset)) * len(dataset)
elif isinstance(dataset, ConcatDataset):
self.n_objects = tuple(n for d in dataset.datasets for n in d.n_objects)
self.n_divs = np.array(tuple(n for d in dataset.datasets for n in d.n_divs))
self.n_sizes = np.array(
tuple(len(d) for d in dataset.datasets for _ in range(len(d)))
)
else:
raise NotImplementedError(
f"BalancedBatchSampler: Unknown dataset type {type(dataset)}"
)
assert len(self.n_objects) == len(self.n_divs) == len(self.n_sizes)
self.batch_size = batch_size
self.n_pool = n_pool
self.drop_last = drop_last
self.num_samples = num_samples
self.weight_by_ndivs = weight_by_ndivs
self.weight_by_dataset = weight_by_dataset
logger.debug(f"{weight_by_ndivs=}")
logger.debug(f"{weight_by_dataset=}")
def get_probs(self, idx):
idx = np.array(idx)
if self.weight_by_ndivs:
probs = 1 + np.sqrt(self.n_divs[idx])
else:
probs = np.ones(len(idx))
if self.weight_by_dataset:
probs = probs / (self.n_sizes[idx] + 1e-6)
probs = probs / (probs.sum() + 1e-10)
return probs
def sample_batches(self, idx: Iterable[int]):
# we will split the indices into pools of size n_pool
num_samples = self.num_samples if self.num_samples is not None else len(idx)
# sample from the indices with replacement and given probabilites
idx = np.random.choice(idx, num_samples, replace=True, p=self.get_probs(idx))
n_pool = min(
self.n_pool * self.batch_size,
(len(idx) // self.batch_size) * self.batch_size,
)
batches = []
for i in range(0, len(idx), n_pool):
# the indices in the pool are sorted by their number of objects
idx_pool = idx[i : i + n_pool]
idx_pool = sorted(idx_pool, key=lambda i: self.n_objects[i])
# such that we can create batches where each element has a similar number of objects
jj = np.arange(0, len(idx_pool), self.batch_size)
np.random.shuffle(jj)
for j in jj:
# dont drop_last, as this leads to a lot of lightning problems....
# if j + self.batch_size > len(idx_pool): # assume drop_last=True
# continue
batch = idx_pool[j : j + self.batch_size]
batches.append(batch)
return batches
def __iter__(self):
idx = np.arange(len(self.n_objects))
batches = self.sample_batches(idx)
return iter(batches)
def __len__(self):
if self.num_samples is not None:
return self.num_samples // self.batch_size
else:
return len(self.n_objects) // self.batch_size
class BalancedDistributedSampler(DistributedSampler):
def __init__(
self,
dataset: Dataset,
batch_size: int,
n_pool: int,
num_samples: int,
weight_by_ndivs: bool = False,
weight_by_dataset: bool = False,
*args,
**kwargs,
) -> None:
super().__init__(dataset=dataset, *args, drop_last=True, **kwargs)
self._balanced_batch_sampler = BalancedBatchSampler(
dataset,
batch_size=batch_size,
n_pool=n_pool,
num_samples=max(1, num_samples // self.num_replicas),
weight_by_ndivs=weight_by_ndivs,
weight_by_dataset=weight_by_dataset,
)
def __len__(self) -> int:
if self.num_samples is not None:
return self._balanced_batch_sampler.num_samples
else:
return super().__len__()
def __iter__(self):
indices = list(super().__iter__())
batches = self._balanced_batch_sampler.sample_batches(indices)
for batch in batches:
yield from batch
# class BalancedDataModule(LightningDataModule):
# def __init__(
# self,
# input_train: list,
# input_val: list,
# cachedir: str,
# augment: int,
# distributed: bool,
# dataset_kwargs: dict,
# sampler_kwargs: dict,
# loader_kwargs: dict,
# ):
# super().__init__()
# self.input_train = input_train
# self.input_val = input_val
# self.cachedir = cachedir
# self.augment = augment
# self.distributed = distributed
# self.dataset_kwargs = dataset_kwargs
# self.sampler_kwargs = sampler_kwargs
# self.loader_kwargs = loader_kwargs
# def prepare_data(self):
# """Loads and caches the datasets if not already done.
# Running on the main CPU process.
# """
# CTCData = cache_class(self.cachedir)
# datasets = dict()
# for split, inps in zip(
# ("train", "val"),
# (self.input_train, self.input_val),
# ):
# logger.info(f"Loading {split.upper()} data")
# start = default_timer()
# datasets[split] = torch.utils.data.ConcatDataset(
# CTCData(
# root=Path(inp),
# augment=self.augment if split == "train" else 0,
# **self.dataset_kwargs,
# )
# for inp in inps
# )
# logger.info(
# f"Loaded {len(datasets[split])} {split.upper()} samples (in"
# f" {(default_timer() - start):.1f} s)\n\n"
# )
# del datasets
# def setup(self, stage: str):
# CTCData = cache_class(self.cachedir)
# self.datasets = dict()
# for split, inps in zip(
# ("train", "val"),
# (self.input_train, self.input_val),
# ):
# logger.info(f"Loading {split.upper()} data")
# start = default_timer()
# self.datasets[split] = torch.utils.data.ConcatDataset(
# CTCData(
# root=Path(inp),
# augment=self.augment if split == "train" else 0,
# **self.dataset_kwargs,
# )
# for inp in inps
# )
# logger.info(
# f"Loaded {len(self.datasets[split])} {split.upper()} samples (in"
# f" {(default_timer() - start):.1f} s)\n\n"
# )
# def train_dataloader(self):
# loader_kwargs = self.loader_kwargs.copy()
# if self.distributed:
# sampler = BalancedDistributedSampler(
# self.datasets["train"],
# **self.sampler_kwargs,
# )
# batch_sampler = None
# else:
# sampler = None
# batch_sampler = BalancedBatchSampler(
# self.datasets["train"],
# **self.sampler_kwargs,
# )
# if not loader_kwargs["batch_size"] == batch_sampler.batch_size:
# raise ValueError(
# f"Batch size in loader_kwargs ({loader_kwargs['batch_size']}) and sampler_kwargs ({batch_sampler.batch_size}) must match"
# )
# del loader_kwargs["batch_size"]
# loader = DataLoader(
# self.datasets["train"],
# sampler=sampler,
# batch_sampler=batch_sampler,
# **loader_kwargs,
# )
# return loader
# def val_dataloader(self):
# val_loader_kwargs = deepcopy(self.loader_kwargs)
# val_loader_kwargs["persistent_workers"] = False
# val_loader_kwargs["num_workers"] = 1
# return DataLoader(
# self.datasets["val"],
# shuffle=False,
# **val_loader_kwargs,
# )
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