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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
from mmengine.model.weight_init import constant_init, trunc_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 MViTHead(BaseHead):
"""Classification head for Multi-scale ViT.
A PyTorch implement of : `MViTv2: Improved Multiscale Vision Transformers
for Classification and Detection <https://arxiv.org/abs/2112.01526>`_
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.
Defaults to `dict(type='CrossEntropyLoss')`.
dropout_ratio (float): Probability of dropout layer. Defaults to 0.5.
init_std (float): Std value for Initiation. Defaults to 0.02.
init_scale (float): Scale factor for Initiation parameters.
Defaults to 1.
with_cls_token (bool): Whether the backbone output feature with
cls_token. Defaults to True.
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'),
dropout_ratio: float = 0.5,
init_std: float = 0.02,
init_scale: float = 1.0,
with_cls_token: bool = True,
**kwargs) -> None:
super().__init__(num_classes, in_channels, loss_cls, **kwargs)
self.init_std = init_std
self.init_scale = init_scale
self.dropout_ratio = dropout_ratio
self.with_cls_token = with_cls_token
if self.dropout_ratio != 0:
self.dropout = nn.Dropout(p=self.dropout_ratio)
else:
self.dropout = None
self.fc_cls = nn.Linear(self.in_channels, self.num_classes)
def init_weights(self) -> None:
"""Initiate the parameters from scratch."""
trunc_normal_init(self.fc_cls.weight, std=self.init_std)
constant_init(self.fc_cls.bias, 0.02)
self.fc_cls.weight.data.mul_(self.init_scale)
self.fc_cls.bias.data.mul_(self.init_scale)
def pre_logits(self, feats: Tuple[List[Tensor]]) -> Tensor:
"""The process before the final classification head.
The input ``feats`` is a tuple of list of tensor, and each tensor is
the feature of a backbone stage.
"""
if self.with_cls_token:
_, cls_token = feats[-1]
return cls_token
else:
patch_token = feats[-1]
return patch_token.mean(dim=(2, 3, 4))
def forward(self, x: Tuple[List[Tensor]], **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tuple[List[Tensor]]): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
x = self.pre_logits(x)
if self.dropout is not None:
x = self.dropout(x)
# [N, in_channels]
cls_score = self.fc_cls(x)
# [N, num_classes]
return cls_score
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