KeyKy/focal-loss-1

Focal loss for Dense Object Detection

focal-loss

The code is unofficial version for focal loss for Dense Object Detection. https://arxiv.org/abs/1708.02002

this is implementtd using mxnet python layer.

usage

Assue that you have put the focal_loss.py in your operator path

you can use:

from your_operators.focal_loss import *

cls_prob = mx.sym.Custom(op_type='FocalLoss', name = 'cls_prob', data = cls_score, labels = label, alpha =0.25, gamma= 2)

focal loss with softmax on kitti(10 cls)

this is my experiments on kitti 10 cls, the performance on hard cls is great!!

method@0.7	car	van	Truck	cyclist	pedestrian	person_sitting	tram	misc	dontcare
base line(faster rcnn + ohem(1:2))	0.7892	0.7462	0.8465	0.623	0.4254	0.1374	0.5035	0.5007	0.1329
faster rcnn + focal loss with softmax	0.797	0.874	0.8959	0.7914	0.5700	0.2806	0.7884	0.7052	0.1433

note!!

very important!!!

in my experiment, i have to use the strategy in paper section 3.3.

LIKE:

Uder such an initialization, in the presence of class imbalance, the loss due to the frequent class can dominate total loss and cause instability in early training.

##AND YOU CAN TRY MY INSTEAD STRATEGY:

train the model using the classical softmax for several times (for examples 3 in kitti dataset)

choose a litti learning rate:

and the traing loss will work well:

now focal loss with softmax work well

focal loss value is not used in focal_loss.py, becayse we should forward the cls_pro in this layer, the major task of focal_loss.py is to backward the focal loss gradient.

the focal loss vale should be calculated in metric.py and use normalization in it.

and this layer is not support use_ignore

for example :

class RCNNLogLossMetric(mx.metric.EvalMetric):
    def __init__(self, cfg):
        super(RCNNLogLossMetric, self).__init__('RCNNLogLoss')
        self.e2e = cfg.TRAIN.END2END
        self.ohem = cfg.TRAIN.ENABLE_OHEM
        self.pred, self.label = get_rcnn_names(cfg)

    def update(self, labels, preds):
        pred = preds[self.pred.index('rcnn_cls_prob')]
        if self.ohem or self.e2e:
            label = preds[self.pred.index('rcnn_label')]
        else:
            label = labels[self.label.index('rcnn_label')]

        last_dim = pred.shape[-1]
        pred = pred.asnumpy().reshape(-1, last_dim)
        label = label.asnumpy().reshape(-1,).astype('int32')

        # filter with keep_inds
        keep_inds = np.where(label != -1)[0]
        label = label[keep_inds]
        cls = pred[keep_inds, label]

        cls += 1e-14
        gamma = 2
        alpha = 0.25

        cls_loss = alpha*(-1.0 * np.power(1 - cls, gamma) * np.log(cls))

        cls_loss = np.sum(cls_loss)/len(label)
        #print cls_loss
        self.sum_metric += cls_loss
        self.num_inst += label.shape[0]

the value must like

forward value

backward gradient value

you can check the gradient value in your debug(if need). By the way

this is my derivation about backward, if it has mistake, please note to me.

softmax activation:

cross entropy with softmax

Focal loss with softmax