52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import torch
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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 normal_init
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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 LinearClsHead(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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"""
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def __init__(self, num_classes, in_channels, *args, **kwargs):
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super(LinearClsHead, self).__init__(*args, **kwargs)
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self.in_channels = in_channels
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self.num_classes = num_classes
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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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self.fc = nn.Linear(self.in_channels, self.num_classes)
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def init_weights(self):
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normal_init(self.fc, mean=0, std=0.01, bias=0)
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def simple_test(self, img):
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"""Test without augmentation."""
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cls_score = self.fc(img)
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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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if torch.onnx.is_in_onnx_export():
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return pred
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pred = list(pred.detach().cpu().numpy())
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return pred
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def forward_train(self, x, gt_label):
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cls_score = self.fc(x)
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losses = self.loss(cls_score, gt_label)
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return losses
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