import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..registry import HEADS
from .base import BaseHead
[docs]@HEADS.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): 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,
in_channels,
loss_cls=dict(type='CrossEntropyLoss'),
spatial_type='avg',
dropout_ratio=0.8,
init_std=0.01,
**kwargs):
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):
"""Initiate the parameters from scratch."""
normal_init(self.fc_cls, std=self.init_std)
[docs] def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The classification scores for input samples.
"""
# ([N, channel_fast, T, H, W], [(N, channel_slow, T, H, W)])
x_fast, x_slow = x
# ([N, channel_fast, 1, 1, 1], [N, channel_slow, 1, 1, 1])
x_fast = self.avg_pool(x_fast)
x_slow = self.avg_pool(x_slow)
# [N, channel_fast + channel_slow, 1, 1, 1]
x = torch.cat((x_slow, x_fast), 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