Tutorial 6: Customize Losses¶
MMDetection provides users with different loss functions. But the default configuration may be not applicable for different datasets or models, so users may want to modify a specific loss to adapt the new situation.
This tutorial first elaborate the computation pipeline of losses, then give some instructions about how to modify each step. The modification can be categorized as tweaking and weighting.
Computation pipeline of a loss¶
Given the input prediction and target, as well as the weights, a loss function maps the input tensor to the final loss scalar. The mapping can be divided into five steps:
Set the sampling method to sample positive and negative samples.
Get element-wise or sample-wise loss by the loss kernel function.
Weighting the loss with a weight tensor element-wisely.
Reduce the loss tensor to a scalar.
Weighting the loss with a scalar.
Set sampling method (step 1)¶
For some loss functions, sampling strategies are needed to avoid imbalance between positive and negative samples.
For example, when using
CrossEntropyLoss in RPN head, we need to set
train_cfg=dict( rpn=dict( sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False))
For some other losses which have positive and negative sample balance mechanism such as Focal Loss, GHMC, and QualityFocalLoss, the sampler is no more necessary.
Tweaking a loss is more related with step 2, 4, 5, and most modifications can be specified in the config. Here we take Focal Loss (FL) as an example. The following code sniper are the construction method and config of FL respectively, they are actually one to one correspondence.
@LOSSES.register_module() class FocalLoss(nn.Module): def __init__(self, use_sigmoid=True, gamma=2.0, alpha=0.25, reduction='mean', loss_weight=1.0):
loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0)
Tweaking hyper-parameters (step 2)¶
beta are two hyper-parameters in the Focal Loss. Say if we want to change the value of
gamma to be 1.5 and
alpha to be 0.5, then we can specify them in the config as follows:
loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=1.5, alpha=0.5, loss_weight=1.0)
Tweaking the way of reduction (step 3)¶
The default way of reduction is
mean for FL. Say if we want to change the reduction from
sum, we can specify it in the config as follows:
loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0, reduction='sum')
Tweaking loss weight (step 5)¶
The loss weight here is a scalar which controls the weight of different losses in multi-task learning, e.g. classification loss and regression loss. Say if we want to change to loss weight of classification loss to be 0.5, we can specify it in the config as follows:
loss_cls=dict( type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.5)
Weighting loss (step 3)¶
Weighting loss means we re-weight the loss element-wisely. To be more specific, we multiply the loss tensor with a weight tensor which has the same shape. As a result, different entries of the loss can be scaled differently, and so called element-wisely.
The loss weight varies across different models and highly context related, but overall there are two kinds of loss weights,
label_weights for classification loss and
bbox_weights for bbox regression loss. You can find them in the
get_target method of the corresponding head. Here we take ATSSHead as an example, which inherit AnchorHead but overwrite its
get_targets method which yields different
class ATSSHead(AnchorHead): ... def get_targets(self, anchor_list, valid_flag_list, gt_bboxes_list, img_metas, gt_bboxes_ignore_list=None, gt_labels_list=None, label_channels=1, unmap_outputs=True):