80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
|
import collections
|
||
|
|
from itertools import repeat
|
||
|
|
from typing import List, Dict, Any
|
||
|
|
|
||
|
|
__all__ = ['consume_prefix_in_state_dict_if_present']
|
||
|
|
|
||
|
|
|
||
|
|
def _ntuple(n, name="parse"):
|
||
|
|
def parse(x):
|
||
|
|
if isinstance(x, collections.abc.Iterable):
|
||
|
|
return tuple(x)
|
||
|
|
return tuple(repeat(x, n))
|
||
|
|
|
||
|
|
parse.__name__ = name
|
||
|
|
return parse
|
||
|
|
|
||
|
|
|
||
|
|
_single = _ntuple(1, "_single")
|
||
|
|
_pair = _ntuple(2, "_pair")
|
||
|
|
_triple = _ntuple(3, "_triple")
|
||
|
|
_quadruple = _ntuple(4, "_quadruple")
|
||
|
|
|
||
|
|
|
||
|
|
def _reverse_repeat_tuple(t, n):
|
||
|
|
r"""Reverse the order of `t` and repeat each element for `n` times.
|
||
|
|
|
||
|
|
This can be used to translate padding arg used by Conv and Pooling modules
|
||
|
|
to the ones used by `F.pad`.
|
||
|
|
"""
|
||
|
|
return tuple(x for x in reversed(t) for _ in range(n))
|
||
|
|
|
||
|
|
|
||
|
|
def _list_with_default(out_size: List[int], defaults: List[int]) -> List[int]:
|
||
|
|
import torch
|
||
|
|
if isinstance(out_size, (int, torch.SymInt)):
|
||
|
|
return out_size
|
||
|
|
if len(defaults) <= len(out_size):
|
||
|
|
raise ValueError(
|
||
|
|
f"Input dimension should be at least {len(out_size) + 1}"
|
||
|
|
)
|
||
|
|
return [
|
||
|
|
v if v is not None else d for v, d in zip(out_size, defaults[-len(out_size) :])
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def consume_prefix_in_state_dict_if_present(
|
||
|
|
state_dict: Dict[str, Any], prefix: str
|
||
|
|
) -> None:
|
||
|
|
r"""Strip the prefix in state_dict in place, if any.
|
||
|
|
|
||
|
|
..note::
|
||
|
|
Given a `state_dict` from a DP/DDP model, a local model can load it by applying
|
||
|
|
`consume_prefix_in_state_dict_if_present(state_dict, "module.")` before calling
|
||
|
|
:meth:`torch.nn.Module.load_state_dict`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
state_dict (OrderedDict): a state-dict to be loaded to the model.
|
||
|
|
prefix (str): prefix.
|
||
|
|
"""
|
||
|
|
keys = list(state_dict.keys())
|
||
|
|
for key in keys:
|
||
|
|
if key.startswith(prefix):
|
||
|
|
newkey = key[len(prefix) :]
|
||
|
|
state_dict[newkey] = state_dict.pop(key)
|
||
|
|
|
||
|
|
# also strip the prefix in metadata if any.
|
||
|
|
if hasattr(state_dict, "_metadata"):
|
||
|
|
keys = list(state_dict._metadata.keys())
|
||
|
|
for key in keys:
|
||
|
|
# for the metadata dict, the key can be:
|
||
|
|
# '': for the DDP module, which we want to remove.
|
||
|
|
# 'module': for the actual model.
|
||
|
|
# 'module.xx.xx': for the rest.
|
||
|
|
if len(key) == 0:
|
||
|
|
continue
|
||
|
|
# handling both, 'module' case and 'module.' cases
|
||
|
|
if key == prefix.replace('.', '') or key.startswith(prefix):
|
||
|
|
newkey = key[len(prefix) :]
|
||
|
|
state_dict._metadata[newkey] = state_dict._metadata.pop(key)
|