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Source code for ignite.utils

import collections.abc as collections
import logging
import random
from typing import Any, Callable, Optional, Tuple, Type, Union

import torch

__all__ = ["convert_tensor", "apply_to_tensor", "apply_to_type", "to_onehot", "setup_logger"]


[docs]def convert_tensor( input_: Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes], device: Optional[Union[str, torch.device]] = None, non_blocking: bool = False, ) -> Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes]: """Move tensors to relevant device.""" def _func(tensor: torch.Tensor) -> torch.Tensor: return tensor.to(device=device, non_blocking=non_blocking) if device is not None else tensor return apply_to_tensor(input_, _func)
[docs]def apply_to_tensor( input_: Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes], func: Callable ) -> Union[torch.Tensor, collections.Sequence, collections.Mapping, str, bytes]: """Apply a function on a tensor or mapping, or sequence of tensors. """ return apply_to_type(input_, torch.Tensor, func)
[docs]def apply_to_type( input_: Union[Any, collections.Sequence, collections.Mapping, str, bytes], input_type: Union[Type, Tuple[Type[Any], Any]], func: Callable, ) -> Union[Any, collections.Sequence, collections.Mapping, str, bytes]: """Apply a function on a object of `input_type` or mapping, or sequence of objects of `input_type`. """ if isinstance(input_, input_type): return func(input_) elif isinstance(input_, (str, bytes)): return input_ elif isinstance(input_, collections.Mapping): return type(input_)({k: apply_to_type(sample, input_type, func) for k, sample in input_.items()}) elif isinstance(input_, tuple) and hasattr(input_, "_fields"): # namedtuple return type(input_)(*(apply_to_type(sample, input_type, func) for sample in input_)) elif isinstance(input_, collections.Sequence): return type(input_)([apply_to_type(sample, input_type, func) for sample in input_]) else: raise TypeError(("input must contain {}, dicts or lists; found {}".format(input_type, type(input_))))
[docs]def to_onehot(indices: torch.Tensor, num_classes: int) -> torch.Tensor: """Convert a tensor of indices of any shape `(N, ...)` to a tensor of one-hot indicators of shape `(N, num_classes, ...) and of type uint8. Output's device is equal to the input's device`. """ onehot = torch.zeros(indices.shape[0], num_classes, *indices.shape[1:], dtype=torch.uint8, device=indices.device) return onehot.scatter_(1, indices.unsqueeze(1), 1)
[docs]def setup_logger( name: Optional[str] = None, level: int = logging.INFO, format: str = "%(asctime)s %(name)s %(levelname)s: %(message)s", filepath: Optional[str] = None, distributed_rank: Optional[int] = None, ) -> logging.Logger: """Setups logger: name, level, format etc. Args: name (str, optional): new name for the logger. If None, the standard logger is used. level (int): logging level, e.g. CRITICAL, ERROR, WARNING, INFO, DEBUG format (str): logging format. By default, `%(asctime)s %(name)s %(levelname)s: %(message)s` filepath (str, optional): Optional logging file path. If not None, logs are written to the file. distributed_rank (int, optional): Optional, rank in distributed configuration to avoid logger setup for workers. If None, distributed_rank is initialized to the rank of process. Returns: logging.Logger For example, to improve logs readability when training with a trainer and evaluator: .. code-block:: python from ignite.utils import setup_logger trainer = ... evaluator = ... trainer.logger = setup_logger("trainer") evaluator.logger = setup_logger("evaluator") trainer.run(data, max_epochs=10) # Logs will look like # 2020-01-21 12:46:07,356 trainer INFO: Engine run starting with max_epochs=5. # 2020-01-21 12:46:07,358 trainer INFO: Epoch[1] Complete. Time taken: 00:5:23 # 2020-01-21 12:46:07,358 evaluator INFO: Engine run starting with max_epochs=1. # 2020-01-21 12:46:07,358 evaluator INFO: Epoch[1] Complete. Time taken: 00:01:02 # ... """ logger = logging.getLogger(name) # don't propagate to ancestors # the problem here is to attach handlers to loggers # should we provide a default configuration less open ? if name is not None: logger.propagate = False # Remove previous handlers if logger.hasHandlers(): for h in list(logger.handlers): logger.removeHandler(h) formatter = logging.Formatter(format) if distributed_rank is None: import ignite.distributed as idist distributed_rank = idist.get_rank() if distributed_rank > 0: logger.addHandler(logging.NullHandler()) else: logger.setLevel(level) ch = logging.StreamHandler() ch.setLevel(level) ch.setFormatter(formatter) logger.addHandler(ch) if filepath is not None: fh = logging.FileHandler(filepath) fh.setLevel(level) fh.setFormatter(formatter) logger.addHandler(fh) return logger
def manual_seed(seed: int) -> None: """Setup random state from a seed for `torch`, `random` and optionally `numpy` (if can be imported). Args: seed (int): Random state seed """ random.seed(seed) torch.manual_seed(seed) try: import numpy as np np.random.seed(seed) except ImportError: pass

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 08/16/2022, 6:38:31 AM.

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