66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Tuple
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import torch
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import torch.nn as nn
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from mmcv.cnn import build_activation_layer
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from mmpretrain.registry import MODELS
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from .cls_head import ClsHead
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@MODELS.register_module()
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class VigClsHead(ClsHead):
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"""The classification head for Vision GNN.
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Args:
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num_classes (int): Number of categories excluding the background
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category.
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in_channels (int): Number of channels in the input feature map.
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hidden_dim (int): The number of middle channels. Defaults to 1024.
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act_cfg (dict): The config of activation function.
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Defaults to ``dict(type='GELU')``.
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dropout (float): The dropout rate.
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loss (dict): Config of classification loss. Defaults to
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``dict(type='CrossEntropyLoss', loss_weight=1.0)``.
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init_cfg (dict, optional): the config to control the initialization.
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Defaults to None.
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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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hidden_dim: int = 1024,
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act_cfg: dict = dict(type='GELU'),
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dropout: float = 0.,
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**kwargs):
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super().__init__(**kwargs)
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self.fc1 = nn.Linear(in_channels, hidden_dim)
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self.bn = nn.BatchNorm1d(hidden_dim)
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self.act = build_activation_layer(act_cfg)
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self.drop = nn.Dropout(dropout)
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self.fc2 = nn.Linear(hidden_dim, num_classes)
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def pre_logits(self, feats: Tuple[torch.Tensor]) -> torch.Tensor:
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"""The process before the final classification head.
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The input ``feats`` is a tuple of tensor, and each tensor is the
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feature of a stage_blocks stage. In ``VigClsHead``, we just obtain the
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feature of the last stage.
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"""
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feats = feats[-1]
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feats = self.fc1(feats)
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feats = self.bn(feats)
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feats = self.act(feats)
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feats = self.drop(feats)
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return feats
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def forward(self, feats: Tuple[torch.Tensor]) -> torch.Tensor:
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"""The forward process."""
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pre_logits = self.pre_logits(feats)
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# The final classification head.
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cls_score = self.fc2(pre_logits)
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return cls_score
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