Junhao Liang
bb6cd5149a
* fix bug in fix_BN * gaitgl OUMVLP support. * update ./doc/3.advance_usage.md Cross-Dataset Evalution & Data Agumentation * update config * update docs.3 * update docs.3 * add loss doc and gather input decorator * refine the create model doc * support rearrange directory of unzipped OUMVLP * fix some bugs in loss_aggregator.py * refine docs and little fix * add oumvlp pretreatment description * pretreatment dataset fix oumvlp description * add gaitgl oumvlp result * assert gaitgl input size * add pipeline * update the readme. * update pipeline and readme * Corrigendum. * add logo and remove path * update new logo * Update README.md * modify logo size Co-authored-by: 12131100 <12131100@mail.sustech.edu.cn> Co-authored-by: noahshen98 <77523610+noahshen98@users.noreply.github.com> Co-authored-by: Noah <595311942@qq.com>
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import torch
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import torch.nn.functional as F
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from .base import BaseLoss
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class CrossEntropyLoss(BaseLoss):
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def __init__(self, scale=2**4, label_smooth=True, eps=0.1, loss_term_weights=1.0, log_accuracy=False):
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super(CrossEntropyLoss, self).__init__()
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self.scale = scale
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self.label_smooth = label_smooth
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self.eps = eps
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self.log_accuracy = log_accuracy
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self.loss_term_weights = loss_term_weights
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def forward(self, logits, labels):
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"""
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logits: [n, p, c]
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labels: [n]
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"""
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logits = logits.permute(1, 0, 2).contiguous() # [n, p, c] -> [p, n, c]
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p, _, c = logits.size()
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log_preds = F.log_softmax(logits * self.scale, dim=-1) # [p, n, c]
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one_hot_labels = self.label2one_hot(
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labels, c).unsqueeze(0).repeat(p, 1, 1) # [p, n, c]
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loss = self.compute_loss(log_preds, one_hot_labels)
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self.info.update({'loss': loss.detach().clone()})
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if self.log_accuracy:
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pred = logits.argmax(dim=-1) # [p, n]
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accu = (pred == labels.unsqueeze(0)).float().mean()
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self.info.update({'accuracy': accu})
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return loss, self.info
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def compute_loss(self, predis, labels):
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softmax_loss = -(labels * predis).sum(-1) # [p, n]
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losses = softmax_loss.mean(-1)
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if self.label_smooth:
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smooth_loss = - predis.mean(dim=-1) # [p, n]
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smooth_loss = smooth_loss.mean() # [p]
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smooth_loss = smooth_loss * self.eps
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losses = smooth_loss + losses * (1. - self.eps)
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return losses
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def label2one_hot(self, label, class_num):
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label = label.unsqueeze(-1)
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batch_size = label.size(0)
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device = label.device
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return torch.zeros(batch_size, class_num).to(device).scatter(1, label, 1)
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