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from typing import List, Tuple
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from mmengine.model.weight_init import constant_init, trunc_normal_init
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from torch import Tensor, nn
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from mmaction.registry import MODELS
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from mmaction.utils import ConfigType
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from .base import BaseHead
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@MODELS.register_module()
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class MViTHead(BaseHead):
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"""Classification head for Multi-scale ViT.
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A PyTorch implement of : `MViTv2: Improved Multiscale Vision Transformers
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for Classification and Detection <https://arxiv.org/abs/2112.01526>`_
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Args:
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num_classes (int): Number of classes to be classified.
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in_channels (int): Number of channels in input feature.
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loss_cls (dict or ConfigDict): Config for building loss.
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Defaults to `dict(type='CrossEntropyLoss')`.
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dropout_ratio (float): Probability of dropout layer. Defaults to 0.5.
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init_std (float): Std value for Initiation. Defaults to 0.02.
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init_scale (float): Scale factor for Initiation parameters.
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Defaults to 1.
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with_cls_token (bool): Whether the backbone output feature with
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cls_token. Defaults to True.
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kwargs (dict, optional): Any keyword argument to be used to initialize
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the head.
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"""
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def __init__(self,
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num_classes: int,
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in_channels: int,
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loss_cls: ConfigType = dict(type='CrossEntropyLoss'),
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dropout_ratio: float = 0.5,
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init_std: float = 0.02,
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init_scale: float = 1.0,
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with_cls_token: bool = True,
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**kwargs) -> None:
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super().__init__(num_classes, in_channels, loss_cls, **kwargs)
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self.init_std = init_std
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self.init_scale = init_scale
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self.dropout_ratio = dropout_ratio
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self.with_cls_token = with_cls_token
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if self.dropout_ratio != 0:
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self.dropout = nn.Dropout(p=self.dropout_ratio)
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else:
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self.dropout = None
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self.fc_cls = nn.Linear(self.in_channels, self.num_classes)
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def init_weights(self) -> None:
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"""Initiate the parameters from scratch."""
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trunc_normal_init(self.fc_cls.weight, std=self.init_std)
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constant_init(self.fc_cls.bias, 0.02)
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self.fc_cls.weight.data.mul_(self.init_scale)
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self.fc_cls.bias.data.mul_(self.init_scale)
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def pre_logits(self, feats: Tuple[List[Tensor]]) -> Tensor:
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"""The process before the final classification head.
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The input ``feats`` is a tuple of list of tensor, and each tensor is
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the feature of a backbone stage.
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"""
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if self.with_cls_token:
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_, cls_token = feats[-1]
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return cls_token
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else:
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patch_token = feats[-1]
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return patch_token.mean(dim=(2, 3, 4))
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def forward(self, x: Tuple[List[Tensor]], **kwargs) -> Tensor:
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"""Defines the computation performed at every call.
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Args:
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x (Tuple[List[Tensor]]): The input data.
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Returns:
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Tensor: The classification scores for input samples.
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"""
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x = self.pre_logits(x)
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if self.dropout is not None:
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x = self.dropout(x)
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cls_score = self.fc_cls(x)
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return cls_score
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