Source code for mmaction.models.heads.tsm_head

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

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

[docs]@MODELS.register_module() class TSMHead(BaseHead): """Class head for TSM. Args: num_classes (int): Number of classes to be classified. in_channels (int): Number of channels in input feature. num_segments (int): Number of frame segments. Default: 8. loss_cls (dict or ConfigDict): Config for building loss. Default: dict(type='CrossEntropyLoss') spatial_type (str): Pooling type in spatial dimension. Default: 'avg'. consensus (dict or ConfigDict): Consensus config dict. dropout_ratio (float): Probability of dropout layer. Default: 0.4. init_std (float): Std value for Initiation. Default: 0.01. is_shift (bool): Indicating whether the feature is shifted. Default: True. temporal_pool (bool): Indicating whether feature is temporal pooled. Default: False. kwargs (dict, optional): Any keyword argument to be used to initialize the head. """ def __init__(self, num_classes: int, in_channels: int, num_segments: int = 8, loss_cls: ConfigType = dict(type='CrossEntropyLoss'), spatial_type: str = 'avg', consensus: ConfigType = dict(type='AvgConsensus', dim=1), dropout_ratio: float = 0.8, init_std: float = 0.001, is_shift: bool = True, temporal_pool: bool = False, **kwargs) -> None: super().__init__(num_classes, in_channels, loss_cls, **kwargs) self.spatial_type = spatial_type self.dropout_ratio = dropout_ratio self.num_segments = num_segments self.init_std = init_std self.is_shift = is_shift self.temporal_pool = temporal_pool consensus_ = consensus.copy() consensus_type = consensus_.pop('type') if consensus_type == 'AvgConsensus': self.consensus = AvgConsensus(**consensus_) else: self.consensus = 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) if self.spatial_type == 'avg': # use `nn.AdaptiveAvgPool2d` to adaptively match the in_channels. self.avg_pool = nn.AdaptiveAvgPool2d(1) else: self.avg_pool = None
[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): Useless in TSMHead. By default, `num_segs` is equal to `clip_len * num_clips * num_crops`, which is automatically generated in Recognizer forward phase and useless in TSM models. The `self.num_segments` we need is a hyper parameter to build TSM models. Returns: Tensor: The classification scores for input samples. """ # [N * num_segs, in_channels, 7, 7] if self.avg_pool is not None: x = self.avg_pool(x) # [N * num_segs, in_channels, 1, 1] x = torch.flatten(x, 1) # [N * num_segs, in_channels] if self.dropout is not None: x = self.dropout(x) # [N * num_segs, num_classes] cls_score = self.fc_cls(x) if self.is_shift and self.temporal_pool: # [2 * N, num_segs // 2, num_classes] cls_score = cls_score.view((-1, self.num_segments // 2) + cls_score.size()[1:]) else: # [N, num_segs, num_classes] cls_score = cls_score.view((-1, self.num_segments) + cls_score.size()[1:]) # [N, 1, num_classes] cls_score = self.consensus(cls_score) # [N, num_classes] return cls_score.squeeze(1)
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