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Source code for ignite.utils

import os
import collections.abc as collections
import logging

import torch


[docs]def convert_tensor(input_, device=None, non_blocking=False): """Move tensors to relevant device.""" def _func(tensor): return tensor.to(device=device, non_blocking=non_blocking) if device else tensor return apply_to_tensor(input_, _func)
[docs]def apply_to_tensor(input_, func): """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_, input_type, func): """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 {k: apply_to_type(sample, input_type, func) for k, sample in input_.items()} elif isinstance(input_, collections.Sequence): return [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, num_classes): """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, level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s: %(message)s", filepath=None, distributed_rank=0): """Setups logger: name, level, format etc. Args: name (str): new name for the logger. 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. 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) if distributed_rank > 0: return logger logger.setLevel(level) # Remove previous handlers if logger.hasHandlers(): for h in list(logger.handlers): logger.removeHandler(h) formatter = logging.Formatter(format) 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

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 05/04/2022, 8:31:40 PM.

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