33 lines
870 B
Python
33 lines
870 B
Python
import torch
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import torch.nn.functional as F
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def ClsScoreRegression(cls_scores, GT_label, batch_size):
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"""
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Multi-class cross-entropy loss
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Inputs:
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- cls_scores: Predicted class scores, of shape (M, C).
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- GT_label: GT class labels, of shape (M,).
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Outputs:
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- cls_score_loss: Torch scalar
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"""
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cls_loss = F.cross_entropy(cls_scores, GT_label, \
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reduction='sum') * 1. / batch_size
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return cls_loss
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def BboxRegression(offsets, GT_offsets, batch_size):
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""""
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Use SmoothL1 loss as in Faster R-CNN
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Inputs:
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- offsets: Predicted box offsets, of shape (M, 4)
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- GT_offsets: GT box offsets, of shape (M, 4)
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Outputs:
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- bbox_reg_loss: Torch scalar
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"""
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bbox_reg_loss = F.smooth_l1_loss(offsets, GT_offsets, reduction='sum') * 1. / batch_size
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return bbox_reg_loss |