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# 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
@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)
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): 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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