88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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from collections import OrderedDict
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import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import build_activation_layer
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from mmcv.cnn.utils.weight_init import trunc_normal_
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from mmcv.runner import Sequential
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from ..builder import HEADS
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from .cls_head import ClsHead
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@HEADS.register_module()
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class VisionTransformerClsHead(ClsHead):
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"""Vision Transformer classifier head.
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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): Number of the dimensions for hidden layer. Only
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available during pre-training. Default None.
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act_cfg (dict): The activation config. Only available during
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pre-training. Defaults to Tanh.
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"""
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def __init__(self,
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num_classes,
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in_channels,
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hidden_dim=None,
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act_cfg=dict(type='Tanh'),
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init_cfg=dict(type='Constant', layer='Linear', val=0),
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*args,
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**kwargs):
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super(VisionTransformerClsHead, self).__init__(
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init_cfg=init_cfg, *args, **kwargs)
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self.in_channels = in_channels
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self.num_classes = num_classes
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self.hidden_dim = hidden_dim
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self.act_cfg = act_cfg
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if self.num_classes <= 0:
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raise ValueError(
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f'num_classes={num_classes} must be a positive integer')
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self._init_layers()
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def _init_layers(self):
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if self.hidden_dim is None:
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layers = [('head', nn.Linear(self.in_channels, self.num_classes))]
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else:
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layers = [
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('pre_logits', nn.Linear(self.in_channels, self.hidden_dim)),
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('act', build_activation_layer(self.act_cfg)),
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('head', nn.Linear(self.hidden_dim, self.num_classes)),
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]
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self.layers = Sequential(OrderedDict(layers))
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def init_weights(self):
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super(VisionTransformerClsHead, self).init_weights()
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# Modified from ClassyVision
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if hasattr(self.layers, 'pre_logits'):
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# Lecun norm
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trunc_normal_(
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self.layers.pre_logits.weight,
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std=math.sqrt(1 / self.layers.pre_logits.in_features))
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nn.init.zeros_(self.layers.pre_logits.bias)
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def simple_test(self, x):
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"""Test without augmentation."""
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x = x[-1]
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_, cls_token = x
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cls_score = self.layers(cls_token)
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if isinstance(cls_score, list):
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cls_score = sum(cls_score) / float(len(cls_score))
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pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
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return self.post_process(pred)
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def forward_train(self, x, gt_label, **kwargs):
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x = x[-1]
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_, cls_token = x
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cls_score = self.layers(cls_token)
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losses = self.loss(cls_score, gt_label, **kwargs)
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return losses
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