A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is the most important.
PEP 8 -- Style Guide for Python Code
一个需要注意的地方是，PEP 8 的代码规范并不是绝对的，项目内的一致性要优先于 PEP 8 的规范。OpenMMLab 各个项目都在 setup.cfg 设定了一些代码规范的设置，请遵照这些设置。一个例子是在 PEP 8 中有如下一个例子：
# Correct:hypot2 = x*x + y*y# Wrong:hypot2 = x * x + y * y
"""A one line summary of the module or program, terminated by a period.Leave one blank line. The rest of this docstring should contain anoverall description of the module or program. Optionally, it may alsocontain a brief description of exported classes and functions and/or usageexamples.Typical usage example:foo = ClassFoo()bar = foo.FunctionBar()"""
classBaseRunner(metaclass=ABCMeta):"""The base class of Runner, a training helper for PyTorch. All subclasses should implement the following APIs: - ``run()`` - ``train()`` - ``val()`` - ``save_checkpoint()`` Args: model (:obj:`torch.nn.Module`): The model to be run. batch_processor (callable, optional): A callable method that process a data batch. The interface of this method should be ``batch_processor(model, data, train_mode) -> dict``. Defaults to None. optimizer (dict | :obj:`torch.optim.Optimizer` | None): It can be either an optimizer (in most cases) or a dict of optimizers (in models that requires more than one optimizer, e.g., GAN). Defaults to None. work_dir (str, optional): The working directory to save checkpoints and logs. Defaults to None. logger (:obj:`logging.Logger`): Logger used during training. Defaults to None. (The default value is just for backward compatibility) meta (dict, optional): A dict records some import information such as environment info and seed, which will be logged in logger hook. Defaults to None. max_epochs (int, optional): Total training epochs. Defaults to None. max_iters (int, optional): Total training iterations. Defaults to None. """def__init__(self,model,batch_processor=None,optimizer=None,work_dir=None,logger=None,meta=None,max_iters=None,max_epochs=None): ...
# 参考实现# This func is modified from `detectron2# <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
# 复制代码# This code was copied from the `ubelt # library<https://github.com/Erotemic/ubelt>`_.# 引用论文 & 添加公式classLabelSmoothLoss(nn.Module):r"""Intializer for the label smoothed cross entropy loss. Refers to `Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`_. This decreases gap between output scores and encourages generalization. Labels provided to forward can be one-hot like vectors (NxC) or class indices (Nx1). And this accepts linear combination of one-hot like labels from mixup or cutmix except multi-label task. Args: label_smooth_val (float): The degree of label smoothing. num_classes (int, optional): Number of classes. Defaults to None. mode (str): Refers to notes, Options are "original", "classy_vision", "multi_label". Defaults to "classy_vision". reduction (str): The method used to reduce the loss. Options are "none", "mean" and "sum". Defaults to 'mean'. loss_weight (float): Weight of the loss. Defaults to 1.0. Note: if the ``mode`` is "original", this will use the same label smooth method as the original paper as: .. math:: (1-\epsilon)\delta_{k, y} + \frac{\epsilon}{K} where :math:`\epsilon` is the ``label_smooth_val``, :math:`K` is the ``num_classes`` and :math:`\delta_{k,y}` is Dirac delta, which equals 1 for k=y and 0 otherwise. if the ``mode`` is "classy_vision", this will use the same label smooth method as the `facebookresearch/ClassyVision <https://github.com/facebookresearch/ClassyVision/blob/main/classy_vision/losses/label_smoothing_loss.py>`_ repo as:
.. math:: \frac{\delta_{k, y} + \epsilon/K}{1+\epsilon} if the ``mode`` is "multi_label", this will accept labels from multi-label task and smoothing them as: .. math:: (1-2\epsilon)\delta_{k, y} + \epsilon
defimport_modules_from_strings(imports,allow_failed_imports=False):"""Import modules from the given list of strings. Args: imports (list | str | None): The given module names to be imported. allow_failed_imports (bool): If True, the failed imports will return None. Otherwise, an ImportError is raise. Defaults to False. Returns: List[module] | module | None: The imported modules. All these three lines in docstring will be compiled into the same line in readthedocs. Examples:>>> osp, sys = import_modules_from_strings(... ['os.path', 'sys'])>>> import os.path as osp_>>> import sys as sys_>>> assert osp == osp_>>> assert sys == sys_ """ ...
classCheckpointHook(Hook):"""Save checkpoints periodically. Args:. ... out_dir (str, optional): The root directory to save checkpoints. If not specified, ``runner.work_dir`` will be used by default. If specified, the ``out_dir`` will be the concatenation of ``out_dir`` and the last level directory of ``runner.work_dir``. Defaults to None. `Changed in version 1.3.15.` ... file_client_args (dict, optional): Arguments to instantiate a FileClient. See :class:`mmcv.fileio.FileClient` for details. Defaults to None. `New in version 1.3.15.` Warning: Before v1.3.15, the ``out_dir`` argument indicates the path where the checkpoint is stored. However, in v1.3.15 and later, ``out_dir`` indicates the root directory and the final path to save checkpoint is the concatenation of out_dir and the last level directory of ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A" and the value of ``runner.work_dir`` is "/path/of/B", then the final path will be "/path/of/A/B".
如果参数或返回值里带有需要展开描述字段的 dict，则应该采用如下格式：
deffunc(x):r""" Args: x (None): A dict with 2 keys, ``padded_targets``, and ``targets``. - | ``targets`` (list[Tensor]): A list of tensors. Each tensor has the shape of :math:`(T_i)`. Each element is the index of a character. - | ``padded_targets`` (Tensor): A tensor of shape :math:`(N)`. Each item is the length of a word. Returns: dict: A dict with 2 keys, ``padded_targets``, and ``targets``. - | ``targets`` (list[Tensor]): A list of tensors. Each tensor has the shape of :math:`(T_i)`. Each element is the index of a character. - | ``padded_targets`` (Tensor): A tensor of shape :math:`(N)`. Each item is the length of a word. """return x
# We use a weighted dictionary search to find out where i is in# the array. We extrapolate position based on the largest num# in the array and the array size and then do binary search to# get the exact number.if i & (i-1) ==0:# True if i is 0 or a power of 2.
# Wrong:# Now go through the b array and make sure whenever i occurs# the next element is i+1# Wrong:if i & (i-1) ==0:# True if i bitwise and i-1 is 0.
# `_reversed_padding_repeated_twice` is the padding to be passed to# `F.pad` if needed (e.g., for non-zero padding types that are# implemented as two ops: padding + conv). `F.pad` accepts paddings in# reverse order than the dimension.self._reversed_padding_repeated_twice =_reverse_repeat_tuple(self.padding, 2)
# self.build_func will be set with the following priority:# 1. build_func# 2. parent.build_func# 3. build_from_cfgif build_func isNone:if parent isnotNone: self.build_func = parent.build_funcelse: self.build_func = build_from_cfgelse: self.build_func = build_func
def_save_ckpt(checkpoint,file):# The 1.6 release of PyTorch switched torch.save to use a new# zipfile-based file format. It will cause RuntimeError when a# checkpoint was saved in high version (PyTorch version>=1.6.0) but# loaded in low version (PyTorch version<1.6.0). More details at# https://github.com/open-mmlab/mmpose/issues/904ifdigit_version(TORCH_VERSION)>=digit_version('1.6.0'): torch.save(checkpoint, file, _use_new_zipfile_serialization=False)else: torch.save(checkpoint, file)
from typing import TypeVar, ListT =TypeVar('T')# Can be anythingA =TypeVar('A', str, bytes)# Must be str or bytesdefrepeat(x: T,n:int) -> List[T]:"""Return a list containing n references to x."""return [x]*ndeflongest(x: A,y: A) -> A:"""Return the longest of two strings."""return x iflen(x)>=len(y)else y
