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Caffe Python layer for Contrastive Loss
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import caffe | |
import numpy as np | |
import os | |
import sys | |
# Author: Axel Angel, copyright 2015, license GPLv3. | |
class OwnContrastiveLossLayer(caffe.Layer): | |
def setup(self, bottom, top): | |
# check input pair | |
if len(bottom) != 3: | |
raise Exception("Need two inputs to compute distance.") | |
def reshape(self, bottom, top): | |
# check input dimensions match | |
if bottom[0].count != bottom[1].count: | |
raise Exception("Inputs must have the same dimension.") | |
# difference is shape of inputs | |
self.diff = np.zeros(bottom[0].num, dtype=np.float32) | |
self.dist_sq = np.zeros(bottom[0].num, dtype=np.float32) | |
self.zeros = np.zeros(bottom[0].num) | |
self.m = 1.0 | |
# loss output is scalar | |
top[0].reshape(1) | |
def forward(self, bottom, top): | |
GW1 = bottom[0].data | |
GW2 = bottom[1].data | |
Y = bottom[2].data | |
loss = 0.0 | |
self.diff = GW1 - GW2 | |
self.dist_sq = np.sum(self.diff**2, axis=1) | |
losses = Y * self.dist_sq \ | |
+ (1-Y) * np.max([self.zeros, self.m - self.dist_sq], axis=0) | |
loss = np.sum(losses) | |
top[0].data[0] = loss / 2.0 / bottom[0].num | |
def backward(self, top, propagate_down, bottom): | |
Y = bottom[2].data | |
disClose = np.where(self.m - self.dist_sq > 0.0, 1.0, 0.0) | |
for i, sign in enumerate([ +1, -1 ]): | |
if propagate_down[i]: | |
alphas = np.where(Y > 0, +1.0, -1.0) * sign * top[0].diff[0] / bottom[i].num | |
facts = ((1-Y) * disClose + Y) * alphas | |
bottom[i].diff[...] = np.array([facts, facts]).T * self.diff |
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