keras lifted loss

keras_lifted_loss.py

#!/usr/bin/env python

"""
    keras_lifted_loss.py
"""

from keras import backend as K

def lifted_loss(margin=1):
    """
      Lifted loss, per "Deep Metric Learning via Lifted Structured Feature Embedding" by Song et al
      Implemented in `keras`
        
      See also the `pytorch` implementation at: https://gist.github.com/bkj/565c5e145786cfd362cffdbd8c089cf4
    """
    def f(target, score):
        
        # Compute mask (-1 for different class, 1 for same class, 0 for diagonal)
        mask = (2 * K.equal(0, target - K.reshape(target, (-1, 1))) - 1)
        mask = (mask - K.eye(score.shape[0]))
        
        # Compute distance between rows
        mag  = (score ** 2).sum(axis=-1)
        mag  = K.tile(mag, (mag.shape[0], 1))
        dist = (mag + mag.T - 2 * score.dot(score.T))
        dist = K.sqrt(K.maximum(0, dist))
        
        # Negative component (points from different class should be far)
        l_n = K.sum((K.exp(margin - dist) * K.equal(mask, -1)), axis=-1)
        l_n = K.tile(l_n, (score.shape[0], 1))
        l_n = K.log(l_n + K.transpose(l_n))
        l_n = l_n * K.equal(mask, 1)
        
        # Positive component (points from same class should be close)
        l_p = dist * K.equal(mask, 1)
        
        loss  = K.sum((K.maximum(0, l_n + l_p) ** 2))
        n_pos = K.sum(K.equal(mask, 1))
        loss /= (2 * n_pos)
        
        return loss
        
    return f

# --

if __name__ == "__main__":
    import numpy as np
    np.random.seed(123)
    
    score = np.random.uniform(0, 1, (20, 3))
    target = np.random.choice(range(3), 20)
    
    print lifted_loss(1)(target, score).eval()

Hello Ben,

I'm trying to solve image similarity problem and I'm wondering what is score parameter here?
Standard loss function expect target value and predicted value, but in triplet target is ignored.
Do you use some kind of pre calculated value as score?

@sstolpovskiy
score is the representations(embedding) of every sample. Following code is to computer the L2-distance matric between every sample.
mag = (score ** 2).sum(axis=-1)
mag = K.tile(mag, (mag.shape[0], 1))
dist = (mag + mag.T - 2 * score.dot(score.T))

@sstolpovskiy
Just realized that it's 1 and half years ago you asked question. I guess you already figure it out.
Lift structured loss needs the 'labels' because it needs computer loss basing on all other sample for each sample, and it needs the label to generate a label matrix( size=(label_size, label_size).
The triplet loss is kind of easy to computer loss, because it only needs to involve (a,p,n) pair. The order of (a,p,n) is a kind of label and it's enough to computer the loss