objectives.py 文件源码

 复制代码def iou_loss(p, t):
    # print "pass"
    tp, tt = p.reshape((p.shape[0], 2, 2)), t.reshape((t.shape[0], 2, 2))
    overlaps_t0 = T.maximum(tp[:, 0, :], tt[:, 0, :])
    overlaps_t1 = T.minimum(tp[:, 1, :], tt[:, 1, :])
    intersection = overlaps_t1 - overlaps_t0
    bool_overlap = T.min(intersection, axis=1) > 0
    intersection = intersection[:, 0] * intersection[:, 1]
    intersection = T.maximum(intersection, np.float32(0.))
    dims_p = tp[:, 1, :] - tp[:, 0, :]
    areas_p = dims_p[:, 0] * dims_p[:, 1]
    dims_t = tt[:, 1, :] - tt[:, 0, :]
    areas_t = dims_t[:, 0] * dims_t[:, 1]
    union = areas_p + areas_t - intersection
    loss = 1. - T.minimum(
        T.exp(T.log(T.abs_(intersection)) -
              T.log(T.abs_(union) + np.float32(1e-5))),
        np.float32(1.)
    )
    # return loss
    return T.mean(loss)