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Source code for mmcls.models.heads.linear_head

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F

from ..builder import HEADS
from .cls_head import ClsHead

[docs]@HEADS.register_module() class LinearClsHead(ClsHead): """Linear classifier head. Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. init_cfg (dict | optional): The extra init config of layers. Defaults to use dict(type='Normal', layer='Linear', std=0.01). """ def __init__(self, num_classes, in_channels, init_cfg=dict(type='Normal', layer='Linear', std=0.01), *args, **kwargs): super(LinearClsHead, self).__init__(init_cfg=init_cfg, *args, **kwargs) self.in_channels = in_channels self.num_classes = num_classes if self.num_classes <= 0: raise ValueError( f'num_classes={num_classes} must be a positive integer') self.fc = nn.Linear(self.in_channels, self.num_classes) def pre_logits(self, x): if isinstance(x, tuple): x = x[-1] return x
[docs] def simple_test(self, x, softmax=True, post_process=True): """Inference without augmentation. Args: x (tuple[Tensor]): The input features. Multi-stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be ``(num_samples, in_channels)``. softmax (bool): Whether to softmax the classification score. post_process (bool): Whether to do post processing the inference results. It will convert the output to a list. Returns: Tensor | list: The inference results. - If no post processing, the output is a tensor with shape ``(num_samples, num_classes)``. - If post processing, the output is a multi-dimentional list of float and the dimensions are ``(num_samples, num_classes)``. """ x = self.pre_logits(x) cls_score = self.fc(x) if softmax: pred = ( F.softmax(cls_score, dim=1) if cls_score is not None else None) else: pred = cls_score if post_process: return self.post_process(pred) else: return pred
def forward_train(self, x, gt_label, **kwargs): x = self.pre_logits(x) cls_score = self.fc(x) losses = self.loss(cls_score, gt_label, **kwargs) return losses
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