objectives.py 文件源码

def iou_loss_val(p, t):
    tp, tt = p.reshape((p.shape[0], 2, 2)), t.reshape((t.shape[0], 2, 2))
    overlaps = np.zeros_like(tp, dtype=np.float32)
    overlaps[:, 0, :] = np.maximum(tp[:, 0, :], tt[:, 0, :])
    overlaps[:, 1, :] = np.minimum(tp[:, 1, :], tt[:, 1, :])
    intersection = overlaps[:, 1, :] - overlaps[:, 0, :]
    bool_overlap = np.min(intersection, axis=1) > 0
    intersection = intersection[:, 0] * intersection[:, 1]
    intersection = np.maximum(intersection, 0.)
    # print "bool", bool_overlap
    # print "Int", intersection
    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
    # print "un", union
    loss = 1. - np.minimum(
        np.exp(np.log(np.abs(intersection)) - np.log(np.abs(union) + 1e-5)),
        1.
    )
    # print loss
    return np.mean(loss)