mirror of https://github.com/JDAI-CV/fast-reid.git
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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import torch.nn.functional as F
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from fastreid.utils.events import get_event_storage
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class CrossEntropyLoss(object):
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"""
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A class that stores information and compute losses about outputs of a Baseline head.
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"""
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def __init__(self, cfg):
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self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
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self._eps = cfg.MODEL.LOSSES.CE.EPSILON
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self._alpha = cfg.MODEL.LOSSES.CE.ALPHA
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self._scale = cfg.MODEL.LOSSES.CE.SCALE
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self._topk = (1,)
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def _log_accuracy(self, pred_class_logits, gt_classes):
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"""
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Log the accuracy metrics to EventStorage.
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"""
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bsz = pred_class_logits.size(0)
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maxk = max(self._topk)
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_, pred_class = pred_class_logits.topk(maxk, 1, True, True)
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pred_class = pred_class.t()
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correct = pred_class.eq(gt_classes.view(1, -1).expand_as(pred_class))
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ret = []
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for k in self._topk:
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correct_k = correct[:k].view(-1).float().sum(dim=0, keepdim=True)
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ret.append(correct_k.mul_(1. / bsz))
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storage = get_event_storage()
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storage.put_scalar("cls_accuracy", ret[0])
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def __call__(self, pred_class_logits, _, gt_classes):
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"""
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Compute the softmax cross entropy loss for box classification.
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Returns:
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scalar Tensor
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"""
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self._log_accuracy(pred_class_logits, gt_classes)
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if self._eps >= 0:
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smooth_param = self._eps
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else:
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# adaptive lsr
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soft_label = F.softmax(pred_class_logits, dim=1)
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smooth_param = self._alpha * soft_label[torch.arange(soft_label.size(0)), gt_classes].unsqueeze(1)
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log_probs = F.log_softmax(pred_class_logits, dim=1)
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with torch.no_grad():
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targets = torch.ones_like(log_probs)
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targets *= smooth_param / (self._num_classes - 1)
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targets.scatter_(1, gt_classes.data.unsqueeze(1), (1 - smooth_param))
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loss = (-targets * log_probs).mean(0).sum()
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return {
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"loss_cls": loss * self._scale,
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}
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