Selecting semi-hard examples, possible bug?

[guyov1]

Hello,
In the tripletselect layer, we aim to choose semi-hard negative examples. meaning, negative examples that are within a margin alpha comparing to the positive example.
In your custom layer, you sort both a_p and a_n examples so that the highest distance is first.
Don't we want the highest a_p distance and the smallest a_n distance so they might violate the semi-hard definition?

The following code with my suggested fix:

` archor_feature = bottom[0].data[0]

    for i in range(self.triplet):

        positive_feature = bottom[0].data[i+self.triplet]
        a_p = archor_feature - positive_feature
        ap = np.dot(a_p,a_p)
        aps[i+self.triplet] = ap
        aps = sorted(aps.items(), key = lambda d: d[1], reverse = True)

    for i in range(self.triplet):

        negative_feature = bottom[0].data[i+self.triplet*2]
        a_n = archor_feature - negative_feature
        an = np.dot(a_n,a_n)
        ans[i+self.triplet*2] = an
    #ans = sorted(ans.items(), key = lambda d: d[1], reverse = True)  # guyn - seems to me like a bug, we want the lowest distance to be first
    ans = sorted(ans.items(), key = lambda d: d[1], reverse = False)`

What do you think?
Guy