checkpoint.py

# Copyright (c) OpenMMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory

import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo

import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.dist_utils import get_dist_info

ENV_MMCV_HOME = 'MMCV_HOME'
ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
DEFAULT_CACHE_DIR = '~/.cache'


def load_state_dict(module, state_dict, strict=False, logger=None):
    """Load state_dict to a module.

    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
    Default value for ``strict`` is set to ``False`` and the message for
    param mismatch will be shown even if strict is False.

    Args:
        module (Module): Module that receives the state_dict.
        state_dict (OrderedDict): Weights.
        strict (bool): whether to strictly enforce that the keys
            in :attr:`state_dict` match the keys returned by this module's
            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
        logger (:obj:`logging.Logger`, optional): Logger to log the error
            message. If not specified, print function will be used.
    """
    unexpected_keys = []
    all_missing_keys = []
    err_msg = []

    metadata = getattr(state_dict, '_metadata', None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    # use _load_from_state_dict to enable checkpoint version control
    def load(module, prefix=''):
        # recursively check parallel module in case that the model has a
        # complicated structure, e.g., nn.Module(nn.Module(DDP))
        if is_module_wrapper(module):
            module = module.module
        local_metadata = {} if metadata is None else metadata.get(
            prefix[:-1], {})
        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
                                     all_missing_keys, unexpected_keys,
                                     err_msg)
        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + '.')

    load(module)
    load = None  # break load->load reference cycle

    # ignore "num_batches_tracked" of BN layers
    missing_keys = [
        key for key in all_missing_keys if 'num_batches_tracked' not in key
    ]

    if unexpected_keys:
        err_msg.append('unexpected key in source '
                       f'state_dict: {", ".join(unexpected_keys)}\n')
    if missing_keys:
        err_msg.append(
            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')

    rank, _ = get_dist_info()
    if len(err_msg) > 0 and rank == 0:
        err_msg.insert(
            0, 'The model and loaded state dict do not match exactly\n')
        err_msg = '\n'.join(err_msg)
        if strict:
            raise RuntimeError(err_msg)
        elif logger is not None:
            logger.warning(err_msg)
        else:
            print(err_msg)




class CheckpointLoader:
    """A general checkpoint loader to manage all schemes."""

    _schemes = {}

    @classmethod
    def _register_scheme(cls, prefixes, loader, force=False):
        if isinstance(prefixes, str):
            prefixes = [prefixes]
        else:
            assert isinstance(prefixes, (list, tuple))
        for prefix in prefixes:
            if (prefix not in cls._schemes) or force:
                cls._schemes[prefix] = loader
            else:
                raise KeyError(
                    f'{prefix} is already registered as a loader backend, '
                    'add "force=True" if you want to override it')
        # sort, longer prefixes take priority
        cls._schemes = OrderedDict(
            sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True))

    @classmethod
    def register_scheme(cls, prefixes, loader=None, force=False):
        """Register a loader to CheckpointLoader.

        This method can be used as a normal class method or a decorator.

        Args:
            prefixes (str or list[str] or tuple[str]):
            The prefix of the registered loader.
            loader (function, optional): The loader function to be registered.
                When this method is used as a decorator, loader is None.
                Defaults to None.
            force (bool, optional): Whether to override the loader
                if the prefix has already been registered. Defaults to False.
        """

        if loader is not None:
            cls._register_scheme(prefixes, loader, force=force)
            return

        def _register(loader_cls):
            cls._register_scheme(prefixes, loader_cls, force=force)
            return loader_cls

        return _register

    @classmethod
    def _get_checkpoint_loader(cls, path):
        """Finds a loader that supports the given path. Falls back to the local
        loader if no other loader is found.

        Args:
            path (str): checkpoint path

        Returns:
            loader (function): checkpoint loader
        """

        for p in cls._schemes:
            if path.startswith(p):
                return cls._schemes[p]

    @classmethod
    def load_checkpoint(cls, filename, map_location=None, logger=None):
        """load checkpoint through URL scheme path.

        Args:
            filename (str): checkpoint file name with given prefix
            map_location (str, optional): Same as :func:`torch.load`.
                Default: None
            logger (:mod:`logging.Logger`, optional): The logger for message.
                Default: None

        Returns:
            dict or OrderedDict: The loaded checkpoint.
        """

        checkpoint_loader = cls._get_checkpoint_loader(filename)
        class_name = checkpoint_loader.__name__
        mmcv.print_log(
            f'load checkpoint from {class_name[10:]} path: {filename}', logger)
        return checkpoint_loader(filename, map_location)



def _load_checkpoint(filename, map_location=None, logger=None):
    """Load checkpoint from somewhere (modelzoo, file, url).

    Args:
        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
            details.
        map_location (str, optional): Same as :func:`torch.load`.
           Default: None.
        logger (:mod:`logging.Logger`, optional): The logger for error message.
           Default: None

    Returns:
        dict or OrderedDict: The loaded checkpoint. It can be either an
           OrderedDict storing model weights or a dict containing other
           information, which depends on the checkpoint.
    """
    return CheckpointLoader.load_checkpoint(filename, map_location, logger)


def load_checkpoint(model,
                    filename,
                    map_location=None,
                    strict=False,
                    logger=None,
                    revise_keys=[(r'^module\.', '')]):
    """Load checkpoint from a file or URI.

    Args:
        model (Module): Module to load checkpoint.
        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
            details.
        map_location (str): Same as :func:`torch.load`.
        strict (bool): Whether to allow different params for the model and
            checkpoint.
        logger (:mod:`logging.Logger` or None): The logger for error message.
        revise_keys (list): A list of customized keywords to modify the
            state_dict in checkpoint. Each item is a (pattern, replacement)
            pair of the regular expression operations. Default: strip
            the prefix 'module.' by [(r'^module\\.', '')].

    Returns:
        dict or OrderedDict: The loaded checkpoint.
    """
    checkpoint = _load_checkpoint(filename, map_location, logger)
    # OrderedDict is a subclass of dict
    if not isinstance(checkpoint, dict):
        raise RuntimeError(
            f'No state_dict found in checkpoint file {filename}')
    # get state_dict from checkpoint
    import pdb; pdb.set_trace()
    if 'state_dict' in checkpoint:
        state_dict = checkpoint['state_dict']
    elif 'model' in checkpoint:
        state_dict = checkpoint['model']
    else:
        state_dict = checkpoint

    # strip prefix of state_dict
    metadata = getattr(state_dict, '_metadata', OrderedDict())
    for p, r in revise_keys:
        state_dict = OrderedDict(
            {re.sub(p, r, k): v
             for k, v in state_dict.items()})
    # Keep metadata in state_dict
    state_dict._metadata = metadata

    # load state_dict
    load_state_dict(model, state_dict, strict, logger)
    return checkpoint