mirror of https://github.com/JDAI-CV/fast-reid.git
63 lines
1.8 KiB
Python
63 lines
1.8 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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def log_accuracy(pred_class_logits, gt_classes, topk=(1,)):
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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(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 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 cross_entropy_loss(pred_class_outputs, gt_classes, eps, alpha=0.2):
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num_classes = pred_class_outputs.size(1)
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if eps >= 0:
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smooth_param = eps
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else:
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# Adaptive label smooth regularization
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soft_label = F.softmax(pred_class_outputs, dim=1)
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smooth_param = alpha * soft_label[torch.arange(soft_label.size(0)), gt_classes].unsqueeze(1)
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log_probs = F.log_softmax(pred_class_outputs, 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 / (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).sum(dim=1)
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"""
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# confidence penalty
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conf_penalty = 0.3
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probs = F.softmax(pred_class_logits, dim=1)
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entropy = torch.sum(-probs * log_probs, dim=1)
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loss = torch.clamp_min(loss - conf_penalty * entropy, min=0.)
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"""
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with torch.no_grad():
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non_zero_cnt = max(loss.nonzero(as_tuple=False).size(0), 1)
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loss = loss.sum() / non_zero_cnt
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return loss
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