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# Copyright (c) OpenMMLab. All rights reserved.
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.
"""
# [N, in_channels]
if self.dropout is not None:
x = self.dropout(x)
# [N, in_channels]
cls_score = self.fc_cls(x)
# [N, num_classes]
return cls_score
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