7b34113cc3b5529a127bd02667de9de0b5b75df9,models/dist_model.py,,score_jnd_dataset,#,301

Before Change

    gts = []

    for data in tqdm(data_loader.load_data(), desc=name):
        ds+=func(data["p0"],data["p1"]).tolist()
        gts+=data["same"].cpu().numpy().flatten().tolist()

    sames = np.array(gts)
    ds = np.array(ds)

    sorted_inds = np.argsort(ds)
    ds_sorted = ds[sorted_inds]
    sames_sorted = sames[sorted_inds]

    TPs = np.cumsum(sames_sorted)
    FPs = np.cumsum(1-sames_sorted)
    FNs = np.sum(sames_sorted)-TPs

    precs = TPs/(TPs+FPs)
    recs = TPs/(TPs+FNs)
    score = util.voc_ap(recs,precs)

    return(score, dict(ds=ds,sames=sames))

After Change

    gts = []

    for data in tqdm(data_loader.load_data(), desc=name):
        ds+=func(data["p0"],data["p1"]).data.cpu().numpy().tolist()
        gts+=data["same"].cpu().numpy().flatten().tolist()

    sames = np.array(gts)
    ds = np.array(ds)

    sorted_inds = np.argsort(ds)
    ds_sorted = ds[sorted_inds]
    sames_sorted = sames[sorted_inds]

    TPs = np.cumsum(sames_sorted)
    FPs = np.cumsum(1-sames_sorted)
    FNs = np.sum(sames_sorted)-TPs

    precs = TPs/(TPs+FPs)
    recs = TPs/(TPs+FNs)
    score = util.voc_ap(recs,precs)

    return(score, dict(ds=ds,sames=sames))

Instances