helper.py 文件源码

def get_mean_error(ret_nodes, nodes, assumedNodesPresent, trueNodesPresent):
    '''
    Computes average displacement error
    Parameters
    ==========

    ret_nodes : A tensor of shape pred_length x numNodes x 2
    Contains the predicted positions for the nodes

    nodes : A tensor of shape pred_length x numNodes x 2
    Contains the true positions for the nodes

    nodesPresent : A list of lists, of size pred_length
    Each list contains the nodeIDs of the nodes present at that time-step

    Returns
    =======

    Error : Mean euclidean distance between predicted trajectory and the true trajectory
    '''
    pred_length = ret_nodes.size()[0]
    error = torch.zeros(pred_length).cuda()
    counter = 0

    for tstep in range(pred_length):
        counter = 0
        for nodeID in assumedNodesPresent:

            if nodeID not in trueNodesPresent[tstep]:
                continue

            pred_pos = ret_nodes[tstep, nodeID, :]
            true_pos = nodes[tstep, nodeID, :]

            error[tstep] += torch.norm(pred_pos - true_pos, p=2)
            counter += 1

        if counter != 0:
            error[tstep] = error[tstep] / counter

    return torch.mean(error)