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Source code for mmaction.models.heads.gcn_head

# 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


[docs]@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)
[docs] 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