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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
from .base import BaseHead
@MODELS.register_module()
class X3DHead(BaseHead):
"""Classification head for I3D.
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: 0.5.
init_std (float): Std value for Initiation. Default: 0.01.
fc1_bias (bool): If the first fc layer has bias. Default: False.
"""
def __init__(self,
num_classes: int,
in_channels: int,
loss_cls: ConfigType = dict(type='CrossEntropyLoss'),
spatial_type: str = 'avg',
dropout_ratio: float = 0.5,
init_std: float = 0.01,
fc1_bias: bool = False,
**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.in_channels = in_channels
self.mid_channels = 2048
self.num_classes = num_classes
self.fc1_bias = fc1_bias
self.fc1 = nn.Linear(
self.in_channels, self.mid_channels, bias=self.fc1_bias)
self.fc2 = nn.Linear(self.mid_channels, self.num_classes)
self.relu = nn.ReLU()
self.pool = None
if self.spatial_type == 'avg':
self.pool = nn.AdaptiveAvgPool3d((1, 1, 1))
elif self.spatial_type == 'max':
self.pool = nn.AdaptiveMaxPool3d((1, 1, 1))
else:
raise NotImplementedError
def init_weights(self) -> None:
"""Initiate the parameters from scratch."""
normal_init(self.fc1, std=self.init_std)
normal_init(self.fc2, std=self.init_std)
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, in_channels, T, H, W]
assert self.pool is not None
x = self.pool(x)
# [N, in_channels, 1, 1, 1]
# [N, in_channels, 1, 1, 1]
x = x.view(x.shape[0], -1)
# [N, in_channels]
x = self.fc1(x)
# [N, 2048]
x = self.relu(x)
if self.dropout is not None:
x = self.dropout(x)
cls_score = self.fc2(x)
# [N, num_classes]
return cls_score
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