train_svms.py 文件源码

def _get_feature_scale(self, num_images=100):
        TARGET_NORM = 20.0 # Magic value from traditional R-CNN
        _t = Timer()
        roidb = self.imdb.roidb
        total_norm = 0.0
        count = 0.0
        inds = npr.choice(xrange(self.imdb.num_images), size=num_images,
                          replace=False)
        for i_, i in enumerate(inds):
            im = cv2.imread(self.imdb.image_path_at(i))
            if roidb[i]['flipped']:
                im = im[:, ::-1, :]
            _t.tic()
            scores, boxes = im_detect(self.net, im, roidb[i]['boxes'])
            _t.toc()
            feat = self.net.blobs[self.layer].data
            total_norm += np.sqrt((feat ** 2).sum(axis=1)).sum()
            count += feat.shape[0]
            print('{}/{}: avg feature norm: {:.3f}'.format(i_ + 1, num_images,
                                                           total_norm / count))

        return TARGET_NORM * 1.0 / (total_norm / count)