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