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9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from abc import ABCMeta, abstractmethod
import torch.nn as nn
from models.utils import load_checkpoint
class BaseBackbone(nn.Module, metaclass=ABCMeta):
"""Base backbone.
This class defines the basic functions of a backbone. Any backbone that
inherits this class should at least define its own `forward` function.
"""
def init_weights(self, pretrained=None, patch_padding="pad", part_features=None):
"""Init backbone weights.
Args:
pretrained (str | None): If pretrained is a string, then it
initializes backbone weights by loading the pretrained
checkpoint. If pretrained is None, then it follows default
initializer or customized initializer in subclasses.
"""
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(
self,
pretrained,
strict=False,
logger=logger,
patch_padding=patch_padding,
part_features=part_features,
)
elif pretrained is None:
# use default initializer or customized initializer in subclasses
pass
else:
raise TypeError(
"pretrained must be a str or None." f" But received {type(pretrained)}."
)
@abstractmethod
def forward(self, x):
"""Forward function.
Args:
x (Tensor | tuple[Tensor]): x could be a torch.Tensor or a tuple of
torch.Tensor, containing input data for forward computation.
"""
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