66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
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import json
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import numpy as np
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def rank_score(ranks):
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# prepare the dataset
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len_samples = len(ranks)
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hits10 = [0] * len_samples
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hits5 = [0] * len_samples
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hits1 = [0] * len_samples
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mrr = []
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for idx, rank in enumerate(ranks):
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if rank <= 10:
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hits10[idx] = 1.
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if rank <= 5:
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hits5[idx] = 1.
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if rank <= 1:
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hits1[idx] = 1.
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mrr.append(1./rank)
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return np.mean(hits10), np.mean(hits5), np.mean(hits1), np.mean(mrr)
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def acc(logits, labels):
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preds = np.argmax(logits, axis=-1)
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return (preds == labels).mean()
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import torch.nn as nn
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import torch
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class LabelSmoothSoftmaxCEV1(nn.Module):
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'''
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This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
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'''
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def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
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super(LabelSmoothSoftmaxCEV1, self).__init__()
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self.lb_smooth = lb_smooth
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self.reduction = reduction
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self.lb_ignore = ignore_index
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self.log_softmax = nn.LogSoftmax(dim=1)
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def forward(self, logits, label):
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'''
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args: logits: tensor of shape (N, C, H, W)
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args: label: tensor of shape(N, H, W)
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'''
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# overcome ignored label
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with torch.no_grad():
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num_classes = logits.size(1)
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label = label.clone().detach()
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ignore = label == self.lb_ignore
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n_valid = (ignore == 0).sum()
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label[ignore] = 0
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lb_pos, lb_neg = 1. - self.lb_smooth, self.lb_smooth / num_classes
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label = torch.empty_like(logits).fill_(
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lb_neg).scatter_(1, label.unsqueeze(1), lb_pos).detach()
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logs = self.log_softmax(logits)
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loss = -torch.sum(logs * label, dim=1)
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loss[ignore] = 0
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if self.reduction == 'mean':
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loss = loss.sum() / n_valid
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if self.reduction == 'sum':
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loss = loss.sum()
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return loss
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