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#
# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
# This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style).
#
"""Miscellaneous utility functions."""
import fnmatch
import functools
import glob
import importlib
import itertools as itt
import os
import re
import sys
import warnings
from typing import Any, Callable, Iterable, Iterator, Union
import braceexpand
import numpy as np
enforce_security = bool(int(os.environ.get("WDS_SECURE", "0")))
def glob_with_braces(pattern):
"""Apply glob to patterns with braces by pre-expanding the braces.
Args:
pattern (str): The glob pattern with braces.
Returns:
list: A list of file paths matching the expanded pattern.
"""
expanded = braceexpand.braceexpand(pattern)
return [f for pat in expanded for f in glob.glob(pat)]
def fnmatch_with_braces(filename, pattern):
"""Apply fnmatch to patterns with braces by pre-expanding the braces.
Args:
filename (str): The filename to match against.
pattern (str): The pattern with braces to match.
Returns:
bool: True if the filename matches any of the expanded patterns, False otherwise.
"""
expanded = braceexpand.braceexpand(pattern)
for pat in expanded:
if fnmatch.fnmatch(filename, pat):
return True
return any(fnmatch.fnmatch(filename, pat) for pat in expanded)
def make_seed(*args):
"""Generate a seed value from the given arguments.
Args:
*args: Variable length argument list to generate the seed from.
Returns:
int: A 31-bit positive integer seed value.
"""
seed = 0
for arg in args:
seed = (seed * 31 + hash(arg)) & 0x7FFFFFFF
return seed
def is_iterable(obj):
"""Check if an object is iterable (excluding strings and bytes).
Args:
obj: The object to check for iterability.
Returns:
bool: True if the object is iterable (excluding strings and bytes), False otherwise.
"""
if isinstance(obj, str):
return False
if isinstance(obj, bytes):
return False
if isinstance(obj, list):
return True
if isinstance(obj, Iterable):
return True
if isinstance(obj, Iterator):
return True
return False
class PipelineStage:
"""Base class for pipeline stages."""
def invoke(self, *args, **kw):
"""Invoke the pipeline stage.
Args:
*args: Variable length argument list.
**kw: Arbitrary keyword arguments.
Raises:
NotImplementedError: This method should be implemented by subclasses.
"""
raise NotImplementedError
def identity(x: Any) -> Any:
"""Return the argument as is.
Args:
x (Any): The input value.
Returns:
Any: The input value unchanged.
"""
return x
def safe_eval(s: str, expr: str = "{}"):
"""Evaluate the given expression more safely.
Args:
s (str): The string to evaluate.
expr (str, optional): The expression format. Defaults to "{}".
Returns:
Any: The result of the evaluation.
Raises:
ValueError: If the input string contains illegal characters.
"""
if re.sub("[^A-Za-z0-9_]", "", s) != s:
raise ValueError(f"safe_eval: illegal characters in: '{s}'")
return eval(expr.format(s))
def lookup_sym(sym: str, modules: list):
"""Look up a symbol in a list of modules.
Args:
sym (str): The symbol to look up.
modules (list): A list of module names to search in.
Returns:
Any: The found symbol, or None if not found.
"""
for mname in modules:
module = importlib.import_module(mname, package="webdataset")
result = getattr(module, sym, None)
if result is not None:
return result
return None
def repeatedly0(loader: Iterator, nepochs: int = sys.maxsize, nbatches: int = sys.maxsize):
"""Repeatedly returns batches from a DataLoader.
Args:
loader (Iterator): The data loader to yield batches from.
nepochs (int, optional): Number of epochs to repeat. Defaults to sys.maxsize.
nbatches (int, optional): Number of batches per epoch. Defaults to sys.maxsize.
Yields:
Any: Batches from the data loader.
"""
for _ in range(nepochs):
yield from itt.islice(loader, nbatches)
def guess_batchsize(batch: Union[tuple, list]):
"""Guess the batch size by looking at the length of the first element in a tuple.
Args:
batch (Union[tuple, list]): The batch to guess the size of.
Returns:
int: The guessed batch size.
"""
return len(batch[0])
def repeatedly(
source: Iterator,
nepochs: int = None,
nbatches: int = None,
nsamples: int = None,
batchsize: Callable[..., int] = guess_batchsize,
):
"""Repeatedly yield samples from an iterator.
Args:
source (Iterator): The source iterator to yield samples from.
nepochs (int, optional): Number of epochs to repeat. Defaults to None.
nbatches (int, optional): Number of batches to yield. Defaults to None.
nsamples (int, optional): Number of samples to yield. Defaults to None.
batchsize (Callable[..., int], optional): Function to guess batch size. Defaults to guess_batchsize.
Yields:
Any: Samples from the source iterator.
"""
epoch = 0
batch = 0
total = 0
while True:
for sample in source:
yield sample
batch += 1
if nbatches is not None and batch >= nbatches:
return
if nsamples is not None:
total += guess_batchsize(sample)
if total >= nsamples:
return
epoch += 1
if nepochs is not None and epoch >= nepochs:
return
def pytorch_worker_info(group=None): # sourcery skip: use-contextlib-suppress
"""Return node and worker info for PyTorch and some distributed environments.
Args:
group (optional): The process group for distributed environments. Defaults to None.
Returns:
tuple: A tuple containing (rank, world_size, worker, num_workers).
"""
rank = 0
world_size = 1
worker = 0
num_workers = 1
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
else:
try:
import torch.distributed
if torch.distributed.is_available() and torch.distributed.is_initialized():
group = group or torch.distributed.group.WORLD
rank = torch.distributed.get_rank(group=group)
world_size = torch.distributed.get_world_size(group=group)
except ModuleNotFoundError:
pass
if "WORKER" in os.environ and "NUM_WORKERS" in os.environ:
worker = int(os.environ["WORKER"])
num_workers = int(os.environ["NUM_WORKERS"])
else:
try:
import torch.utils.data
worker_info = torch.utils.data.get_worker_info()
if worker_info is not None:
worker = worker_info.id
num_workers = worker_info.num_workers
except ModuleNotFoundError:
pass
return rank, world_size, worker, num_workers
def pytorch_worker_seed(group=None):
"""Compute a distinct, deterministic RNG seed for each worker and node.
Args:
group (optional): The process group for distributed environments. Defaults to None.
Returns:
int: A deterministic RNG seed.
"""
rank, world_size, worker, num_workers = pytorch_worker_info(group=group)
return rank * 1000 + worker
def deprecated(arg=None):
if callable(arg):
# The decorator was used without arguments
func = arg
reason = None
else:
# The decorator was used with arguments
func = None
reason = arg
def decorator(func):
@functools.wraps(func)
def new_func(*args, **kwargs):
msg = f"Call to deprecated function {func.__name__}."
if reason is not None:
msg += " Reason: " + reason
warnings.warn(
msg,
category=DeprecationWarning,
stacklevel=2,
)
return func(*args, **kwargs)
return new_func
if func is None:
# The decorator was used with arguments
return decorator
else:
# The decorator was used without arguments
return decorator(func)
class ObsoleteException(Exception):
pass
def obsolete(func=None, *, reason=None):
if func is None:
return functools.partial(obsolete, reason=reason)
@functools.wraps(func)
def new_func(*args, **kwargs):
if not int(os.environ.get("ALLOW_OBSOLETE", "0")):
msg = f"Call to obsolete function {func.__name__}. Set env ALLOW_OBSOLETE=1 to permit."
if reason is not None:
msg += " Reason: " + reason
raise ObsoleteException(msg)
return func(*args, **kwargs)
return new_func
def compute_sample_weights(n_w_pairs):
ns = np.array([p[0] for p in n_w_pairs])
ws = np.array([p[1] for p in n_w_pairs])
weighted = ns * ws
ps = weighted / np.amax(weighted)
return ps
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