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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Union
import torch
import torch.nn as nn
from mmaction.registry import MODELS
from .base import BaseHead
@MODELS.register_module()
class GCNHead(BaseHead):
"""The classification head for GCN.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of channels in input feature.
loss_cls (dict): Config for building loss.
Defaults to ``dict(type='CrossEntropyLoss')``.
dropout (float): Probability of dropout layer. Defaults to 0.
init_cfg (dict or list[dict]): Config to control the initialization.
Defaults to ``dict(type='Normal', layer='Linear', std=0.01)``.
"""
def __init__(self,
num_classes: int,
in_channels: int,
loss_cls: Dict = dict(type='CrossEntropyLoss'),
dropout: float = 0.,
average_clips: str = 'prob',
init_cfg: Union[Dict, List[Dict]] = dict(
type='Normal', layer='Linear', std=0.01),
**kwargs) -> None:
super().__init__(
num_classes,
in_channels,
loss_cls=loss_cls,
average_clips=average_clips,
init_cfg=init_cfg,
**kwargs)
self.dropout_ratio = dropout
if self.dropout_ratio != 0:
self.dropout = nn.Dropout(p=self.dropout_ratio)
else:
self.dropout = None
self.pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(self.in_channels, self.num_classes)
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
"""Forward features from the upstream network.
Args:
x (torch.Tensor): Features from the upstream network.
Returns:
torch.Tensor: Classification scores with shape (B, num_classes).
"""
N, M, C, T, V = x.shape
x = x.view(N * M, C, T, V)
x = self.pool(x)
x = x.view(N, M, C)
x = x.mean(dim=1)
assert x.shape[1] == self.in_channels
if self.dropout is not None:
x = self.dropout(x)
cls_scores = self.fc(x)
return cls_scores
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