objdet_losssplitchannel.py

from keras import backend as K

def my_loss( y_true, y_pred ):

    loss_conf, loss_bbox, loss_cls = K.variable(value=0), K.variable(value=0), K.variable(value=0)
    for i in range(y_pred.shape[-2]):
        true_bbox, true_conf, true_cls = y_true[..., i,:4], y_true[..., i,4], y_true[..., i,5:] 
        pred_bbox, pred_conf, pred_cls = y_pred[..., i,:4], y_pred[..., i,4], y_pred[..., i,5:] 
        pred_conf = K.sigmoid(pred_conf)
        pred_bbox = K.sigmoid(pred_bbox)
        pred_cls  = K.softmax(pred_cls) 

        loss_conf = loss_conf + K.binary_crossentropy(true_conf, pred_conf) 
        loss_bbox = loss_bbox + true_conf*K.mean( (true_bbox-pred_bbox) ** 2 ) # mse
        loss_cls  = loss_cls  + true_conf*K.categorical_crossentropy(true_cls, pred_cls)
        
    return loss_conf + loss_bbox + loss_cls

model.compile(loss=my_loss, optimizer='adam')