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)
