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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

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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