def focal_loss(pred, y, alpha=0.25, gamma=2): r"""Compute focal loss for predictions. Multi-labels Focal loss formula: FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p) ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor. Args: pred: A float tensor of shape [batch_size, num_anchors, num_classes] representing the predicted logits for each class y: A float tensor of shape [batch_size, num_anchors, num_classes] representing one-hot encoded classification targets alpha: A scalar tensor for focal loss alpha hyper-parameter gamma: A scalar tensor for focal loss gamma hyper-parameter Returns: loss: A (scalar) tensor representing the value of the loss function """ zeros = tf.zeros_like(pred, dtype=pred.dtype) # For positive prediction, only need consider front part loss, back part is 0; # target_tensor > zeros <=> z=1, so positive coefficient = z - p. pos_p_sub = tf.where(y > zeros, y - pred, zeros) # positive sample 寻找正样本，并进行填充 # For negative prediction, only need consider back part loss, front part is 0; # target_tensor > zeros <=> z=1, so negative coefficient = 0. neg_p_sub = tf.where(y > zeros, zeros, pred) # negative sample 寻找负样本，并进行填充 per_entry_cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(pred, 1e-8, 1.0)) \ - (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - pred, 1e-8, 1.0)) return tf.reduce_sum(per_entry_cross_ent)
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