def energyevalmix()

in ebm_sandbox.py [0:0]

• 61 lines of code
• 11 McCabe index (conditional complexity)

def energyevalmix(dataloader, test_dataloader, target_vars, sess):
    X = target_vars['X']
    Y_GT = target_vars['Y_GT']
    energy = target_vars['energy']

    if FLAGS.svhnmix:
        dataset = Svhn(train=False)
        test_dataloader_val = DataLoader(dataset, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, shuffle=True, drop_last=False)
        test_iter = iter(test_dataloader_val)
    elif FLAGS.cifar100mix:
        dataset = Cifar100(train=False)
        test_dataloader_val = DataLoader(dataset, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, shuffle=True, drop_last=False)
        test_iter = iter(test_dataloader_val)
    elif FLAGS.texturemix:
        dataset = Textures()
        test_dataloader_val = DataLoader(dataset, batch_size=FLAGS.batch_size, num_workers=FLAGS.data_workers, shuffle=True, drop_last=False)
        test_iter = iter(test_dataloader_val)

    probs = []
    labels = []
    negs = []
    pos = []
    for data_corrupt, data, label_gt in tqdm(test_dataloader):
        data = data.numpy()
        data_corrupt = data_corrupt.numpy()
        if FLAGS.svhnmix:
            _, data_mix, _ = test_iter.next()
        elif FLAGS.cifar100mix:
            _, data_mix, _ = test_iter.next()
        elif FLAGS.texturemix:
            _, data_mix, _ = test_iter.next()
        elif FLAGS.randommix:
            data_mix = np.random.randn(FLAGS.batch_size, 32, 32, 3) * 0.5 + 0.5
        else:
            data_idx = np.concatenate([np.arange(1, data.shape[0]), [0]])
            data_other = data[data_idx]
            data_mix = (data + data_other) / 2

        data_mix = data_mix[:data.shape[0]]

        if FLAGS.cclass:
            # It's unfair to take a random class
            label_gt= np.tile(np.eye(10), (data.shape[0], 1, 1))
            label_gt = label_gt.reshape(data.shape[0] * 10, 10)
            data_mix = np.tile(data_mix[:, None, :, :, :], (1, 10, 1, 1, 1))
            data = np.tile(data[:, None, :, :, :], (1, 10, 1, 1, 1))

            data_mix = data_mix.reshape(-1, 32, 32, 3)
            data = data.reshape(-1, 32, 32, 3)


        feed_dict = {X: data, Y_GT: label_gt}
        feed_dict_neg = {X: data_mix, Y_GT: label_gt}

        pos_energy = sess.run([energy], feed_dict)[0]
        neg_energy = sess.run([energy], feed_dict_neg)[0]

        if FLAGS.cclass:
            pos_energy = pos_energy.reshape(-1, 10).min(axis=1)
            neg_energy = neg_energy.reshape(-1, 10).min(axis=1)

        probs.extend(list(-1*pos_energy))
        probs.extend(list(-1*neg_energy))
        pos.extend(list(-1*pos_energy))
        negs.extend(list(-1*neg_energy))
        labels.extend([1]*pos_energy.shape[0])
        labels.extend([0]*neg_energy.shape[0])

    pos, negs = np.array(pos), np.array(negs)
    np.save("pos.npy", pos)
    np.save("neg.npy", negs)
    auroc = sk.roc_auc_score(labels, probs)
    print("Roc score of {}".format(auroc))