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Source code for mmaction.models.heads.uniformer_head

# 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


[docs]@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]
[docs] 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()
[docs] 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