Source code for mmaction.models.heads.tsn_head

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.model.weight_init import normal_init
from torch import Tensor, nn

from mmaction.registry import MODELS
from mmaction.utils import ConfigType, get_str_type
from .base import AvgConsensus, BaseHead

[docs]@MODELS.register_module() class TSNHead(BaseHead): """Class head for TSN. 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. Default: dict(type='CrossEntropyLoss'). spatial_type (str or ConfigDict): Pooling type in spatial dimension. Default: 'avg'. consensus (dict): Consensus config dict. dropout_ratio (float): Probability of dropout layer. Default: 0.4. init_std (float): Std value for Initiation. Default: 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'), spatial_type: str = 'avg', consensus: ConfigType = dict(type='AvgConsensus', dim=1), dropout_ratio: float = 0.4, init_std: float = 0.01, **kwargs) -> None: super().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs) self.spatial_type = spatial_type self.dropout_ratio = dropout_ratio self.init_std = init_std consensus_ = consensus.copy() consensus_type = consensus_.pop('type') if get_str_type(consensus_type) == 'AvgConsensus': self.consensus = AvgConsensus(**consensus_) else: self.consensus = None if self.spatial_type == 'avg': # use `nn.AdaptiveAvgPool2d` to adaptively match the in_channels. self.avg_pool = nn.AdaptiveAvgPool2d((1, 1)) else: self.avg_pool = None 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)
[docs] def init_weights(self) -> None: """Initiate the parameters from scratch.""" normal_init(self.fc_cls, std=self.init_std)
[docs] def forward(self, x: Tensor, num_segs: int, **kwargs) -> Tensor: """Defines the computation performed at every call. Args: x (Tensor): The input data. num_segs (int): Number of segments into which a video is divided. Returns: Tensor: The classification scores for input samples. """ # [N * num_segs, in_channels, 7, 7] if self.avg_pool is not None: if isinstance(x, tuple): shapes = [y.shape for y in x] assert 1 == 0, f'x is tuple {shapes}' x = self.avg_pool(x) # [N * num_segs, in_channels, 1, 1] x = x.reshape((-1, num_segs) + x.shape[1:]) # [N, num_segs, in_channels, 1, 1] x = self.consensus(x) # [N, 1, in_channels, 1, 1] x = x.squeeze(1) # [N, in_channels, 1, 1] if self.dropout is not None: x = self.dropout(x) # [N, in_channels, 1, 1] x = x.view(x.size(0), -1) # [N, in_channels] cls_score = self.fc_cls(x) # [N, num_classes] return cls_score
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