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# 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, get_str_type
from .base import AvgConsensus, BaseHead
@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 get_str_type(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
def init_weights(self) -> None:
"""Initiate the parameters from scratch."""
normal_init(self.fc_cls, std=self.init_std)
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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