Python mxnet.ndarray.equal() Examples

The following are 3 code examples of mxnet.ndarray.equal(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mxnet.ndarray , or try the search function .
Example #1
Source File: voc_segmentation.py    From cascade_rcnn_gluon with Apache License 2.0 6 votes vote down vote up 
def batch_intersection_union(output, target, nclass):
    """mIoU"""
    # inputs are NDarray, output 4D, target 3D
    predict = F.argmax(output, 1)
    target = target.astype(predict.dtype)
    mini = 1
    maxi = nclass
    nbins = nclass
    predict = predict.asnumpy() + 1
    target = target.asnumpy() + 1

    predict = predict * (target > 0).astype(predict.dtype)
    #intersection = predict * (F.equal(predict, target)).astype(predict.dtype)
    intersection = predict * (predict == target)
    # areas of intersection and union
    area_inter, _ = np.histogram(intersection, bins=nbins, range=(mini, maxi))
    area_pred, _ = np.histogram(predict, bins=nbins, range=(mini, maxi))
    area_lab, _ = np.histogram(target, bins=nbins, range=(mini, maxi))
    area_union = area_pred + area_lab - area_inter
    return area_inter, area_union
Example #2
Source File: __init__.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def array_equal(a, b):
    return nd.equal(a, b).asnumpy().all()
Example #3
Source File: custom_layers.py    From d-SNE with Apache License 2.0 5 votes vote down vote up 
def hybrid_forward(self, F, fts, ys, ftt, yt):
        """
        Semantic Alignment Loss
        :param F: Function
        :param yt: label for the target domain [N]
        :param ftt: features for the target domain [N, K]
        :param ys: label for the source domain [M]
        :param fts: features for the source domain [M, K]
        :return:
        """
        if self._fn:
            # Normalize ft
            fts = F.L2Normalization(fts, mode='instance')
            ftt = F.L2Normalization(ftt, mode='instance')

        fts_rpt = F.broadcast_to(fts.expand_dims(axis=0), shape=(self._bs_tgt, self._bs_src, self._embed_size))
        ftt_rpt = F.broadcast_to(ftt.expand_dims(axis=1), shape=(self._bs_tgt, self._bs_src, self._embed_size))

        dists = F.sum(F.square(ftt_rpt - fts_rpt), axis=2)

        yt_rpt = F.broadcast_to(yt.expand_dims(axis=1), shape=(self._bs_tgt, self._bs_src)).astype('int32')
        ys_rpt = F.broadcast_to(ys.expand_dims(axis=0), shape=(self._bs_tgt, self._bs_src)).astype('int32')

        y_same = F.equal(yt_rpt, ys_rpt).astype('float32')
        y_diff = F.not_equal(yt_rpt, ys_rpt).astype('float32')

        intra_cls_dists = dists * y_same
        inter_cls_dists = dists * y_diff

        max_dists = F.max(dists, axis=1, keepdims=True)
        max_dists = F.broadcast_to(max_dists, shape=(self._bs_tgt, self._bs_src))
        revised_inter_cls_dists = F.where(y_same, max_dists, inter_cls_dists)

        max_intra_cls_dist = F.max(intra_cls_dists, axis=1)
        min_inter_cls_dist = F.min(revised_inter_cls_dists, axis=1)

        loss = F.relu(max_intra_cls_dist - min_inter_cls_dist + self._margin)

        return loss