Source code for mmaction.models.heads.slowfast_head

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

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 BaseHead

[docs]@MODELS.register_module() class SlowFastHead(BaseHead): """The classification head for SlowFast. 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): Pooling type in spatial dimension. Default: 'avg'. dropout_ratio (float): Probability of dropout layer. Default: 0.8. 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', dropout_ratio: float = 0.8, init_std: float = 0.01, **kwargs) -> None: super().__init__(num_classes, in_channels, loss_cls, **kwargs) self.spatial_type = spatial_type self.dropout_ratio = dropout_ratio self.init_std = init_std if self.dropout_ratio != 0: self.dropout = nn.Dropout(p=self.dropout_ratio) else: self.dropout = None self.fc_cls = nn.Linear(in_channels, num_classes) if self.spatial_type == 'avg': self.avg_pool = nn.AdaptiveAvgPool3d((1, 1, 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: Tuple[Tensor], **kwargs) -> None: """Defines the computation performed at every call. Args: x (tuple[torch.Tensor]): The input data. Returns: Tensor: The classification scores for input samples. """ # ([N, channel_slow, T1, H, W], [(N, channel_fast, T2, H, W)]) x_slow, x_fast = x # ([N, channel_slow, 1, 1, 1], [N, channel_fast, 1, 1, 1]) x_slow = self.avg_pool(x_slow) x_fast = self.avg_pool(x_fast) # [N, channel_fast + channel_slow, 1, 1, 1] x =, x_slow), dim=1) if self.dropout is not None: x = self.dropout(x) # [N x C] x = x.view(x.size(0), -1) # [N x num_classes] cls_score = self.fc_cls(x) return cls_score
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