大家好，又见面了，我是你们的朋友全栈君。
详细解读一下OHEM的实现代码：
def ohem_loss(
batch_size, cls_pred, cls_target, loc_pred, loc_target, smooth_l1_sigma=1.0
):
"""
Arguments:
batch_size (int): number of sampled rois for bbox head training
loc_pred (FloatTensor): [R, 4], location of positive rois
loc_target (FloatTensor): [R, 4], location of positive rois
pos_mask (FloatTensor): [R], binary mask for sampled positive rois
cls_pred (FloatTensor): [R, C]
cls_target (LongTensor): [R]
Returns:
cls_loss, loc_loss (FloatTensor)
"""
ohem_cls_loss = F.cross_entropy(cls_pred, cls_target, reduction='none', ignore_index=-1)
ohem_loc_loss = smooth_l1_loss(loc_pred, loc_target, sigma=smooth_l1_sigma, reduce=False)
#这里先暂存下正常的分类loss和回归loss
loss = ohem_cls_loss + ohem_loc_loss
#然后对分类和回归loss求和
sorted_ohem_loss, idx = torch.sort(loss, descending=True)
#再对loss进行降序排列
keep_num = min(sorted_ohem_loss.size()[0], batch_size)
#得到需要保留的loss数量
if keep_num < sorted_ohem_loss.size()[0]:
#这句的作用是如果保留数目小于现有loss总数，则进行筛选保留，否则全部保留
keep_idx_cuda = idx[:keep_num]
#保留到需要keep的数目
ohem_cls_loss = ohem_cls_loss[keep_idx_cuda]
ohem_loc_loss = ohem_loc_loss[keep_idx_cuda]
#分类和回归保留相同的数目
cls_loss = ohem_cls_loss.sum() / keep_num
loc_loss = ohem_loc_loss.sum() / keep_num
#然后分别对分类和回归loss求均值
return cls_loss, loc_loss
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