|
|
|
|
|
from typing import Optional
|
|
|
|
|
|
from mmengine.fileio import load
|
|
|
from mmengine.logging import MMLogger
|
|
|
from mmengine.runner.checkpoint import _load_checkpoint_with_prefix
|
|
|
from torch import Tensor, nn
|
|
|
|
|
|
from mmaction.registry import MODELS
|
|
|
from mmaction.utils import ConfigType, get_str_type
|
|
|
from .base import BaseHead
|
|
|
|
|
|
|
|
|
@MODELS.register_module()
|
|
|
class UniFormerHead(BaseHead):
|
|
|
"""Classification head for UniFormer. supports loading pretrained
|
|
|
Kinetics-710 checkpoint to fine-tuning on other Kinetics dataset.
|
|
|
|
|
|
A pytorch implement of: `UniFormerV2: Spatiotemporal
|
|
|
Learning by Arming Image ViTs with Video UniFormer
|
|
|
<https://arxiv.org/abs/2211.09552>`
|
|
|
|
|
|
Args:
|
|
|
num_classes (int): Number of classes to be classified.
|
|
|
in_channels (int): Number of channels in input feature.
|
|
|
loss_cls (dict or ConfigDict): Config for building loss.
|
|
|
Defaults to `dict(type='CrossEntropyLoss')`.
|
|
|
dropout_ratio (float): Probability of dropout layer.
|
|
|
Defaults to : 0.0.
|
|
|
channel_map (str, optional): Channel map file to selecting
|
|
|
channels from pretrained head with extra channels.
|
|
|
Defaults to None.
|
|
|
init_cfg (dict or ConfigDict, optional): Config to control the
|
|
|
initialization. Defaults to
|
|
|
``[
|
|
|
dict(type='TruncNormal', layer='Linear', std=0.01)
|
|
|
]``.
|
|
|
kwargs (dict, optional): Any keyword argument to be used to initialize
|
|
|
the head.
|
|
|
"""
|
|
|
|
|
|
def __init__(self,
|
|
|
num_classes: int,
|
|
|
in_channels: int,
|
|
|
loss_cls: ConfigType = dict(type='CrossEntropyLoss'),
|
|
|
dropout_ratio: float = 0.0,
|
|
|
channel_map: Optional[str] = None,
|
|
|
init_cfg: Optional[dict] = dict(
|
|
|
type='TruncNormal', layer='Linear', std=0.02),
|
|
|
**kwargs) -> None:
|
|
|
super().__init__(
|
|
|
num_classes, in_channels, loss_cls, init_cfg=init_cfg, **kwargs)
|
|
|
self.channel_map = channel_map
|
|
|
self.dropout_ratio = dropout_ratio
|
|
|
|
|
|
if self.dropout_ratio != 0:
|
|
|
self.dropout = nn.Dropout(p=self.dropout_ratio)
|
|
|
else:
|
|
|
self.dropout = None
|
|
|
self.fc_cls = nn.Linear(self.in_channels, self.num_classes)
|
|
|
|
|
|
def _select_channels(self, stact_dict):
|
|
|
selected_channels = load(self.channel_map)
|
|
|
for key in stact_dict:
|
|
|
stact_dict[key] = stact_dict[key][selected_channels]
|
|
|
|
|
|
def init_weights(self) -> None:
|
|
|
"""Initiate the parameters from scratch."""
|
|
|
if get_str_type(self.init_cfg['type']) == 'Pretrained':
|
|
|
assert self.channel_map is not None, \
|
|
|
'load cls_head weights needs to specify the channel map file'
|
|
|
logger = MMLogger.get_current_instance()
|
|
|
pretrained = self.init_cfg['checkpoint']
|
|
|
logger.info(f'load pretrained model from {pretrained}')
|
|
|
state_dict = _load_checkpoint_with_prefix(
|
|
|
'cls_head.', pretrained, map_location='cpu')
|
|
|
self._select_channels(state_dict)
|
|
|
msg = self.load_state_dict(state_dict, strict=False)
|
|
|
logger.info(msg)
|
|
|
else:
|
|
|
super().init_weights()
|
|
|
|
|
|
def forward(self, x: Tensor, **kwargs) -> Tensor:
|
|
|
"""Defines the computation performed at every call.
|
|
|
|
|
|
Args:
|
|
|
x (Tensor): The input data.
|
|
|
|
|
|
Returns:
|
|
|
Tensor: The classification scores for input samples.
|
|
|
"""
|
|
|
|
|
|
if self.dropout is not None:
|
|
|
x = self.dropout(x)
|
|
|
|
|
|
cls_score = self.fc_cls(x)
|
|
|
|
|
|
return cls_score
|
|
|
|
