104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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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.utils.weight_init import trunc_normal_
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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 ConformerHead(ClsHead):
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"""Linear 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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init_cfg (dict | optional): The extra init config of layers.
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Defaults to use dict(type='Normal', layer='Linear', std=0.01).
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"""
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def __init__(
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self,
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num_classes,
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in_channels, # [conv_dim, trans_dim]
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init_cfg=dict(type='Normal', layer='Linear', std=0.01),
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*args,
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**kwargs):
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super(ConformerHead, self).__init__(init_cfg=None, *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.init_cfg = init_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.conv_cls_head = nn.Linear(self.in_channels[0], num_classes)
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self.trans_cls_head = nn.Linear(self.in_channels[1], num_classes)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def init_weights(self):
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super(ConformerHead, self).init_weights()
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if (isinstance(self.init_cfg, dict)
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and self.init_cfg['type'] == 'Pretrained'):
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# Suppress default init if use pretrained model.
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return
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else:
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self.apply(self._init_weights)
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def simple_test(self, x):
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"""Test without augmentation."""
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if isinstance(x, tuple):
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x = x[-1]
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assert isinstance(x,
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list) # There are two outputs in the Conformer model
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conv_cls_score = self.conv_cls_head(x[0])
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tran_cls_score = self.trans_cls_head(x[1])
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cls_score = conv_cls_score + tran_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):
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if isinstance(x, tuple):
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x = x[-1]
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assert isinstance(x, list) and len(x) == 2, \
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'There should be two outputs in the Conformer model'
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conv_cls_score = self.conv_cls_head(x[0])
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tran_cls_score = self.trans_cls_head(x[1])
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losses = self.loss([conv_cls_score, tran_cls_score], gt_label)
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return losses
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def loss(self, cls_score, gt_label):
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num_samples = len(cls_score[0])
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losses = dict()
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# compute loss
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loss = sum([
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self.compute_loss(score, gt_label, avg_factor=num_samples) /
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len(cls_score) for score in cls_score
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])
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if self.cal_acc:
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# compute accuracy
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acc = self.compute_accuracy(cls_score[0] + cls_score[1], gt_label)
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assert len(acc) == len(self.topk)
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losses['accuracy'] = {
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f'top-{k}': a
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for k, a in zip(self.topk, acc)
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}
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losses['loss'] = loss
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return losses
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