0557b8abe6df2bae50bbda83478762ab96ec25ba,examples/semantic_segmentation/eval_semantic_segmentation_multi.py,,main,#,15

Before Change

        model.predict, it, hook=ProgressHook(len(dataset)), comm=comm)
    // Delete an iterator of images to save memory usage.
    del in_values
    pred_labels, = out_values
    gt_labels, = rest_values

    result = eval_semantic_segmentation(pred_labels, gt_labels)

    for iu, label_name in zip(result["iou"], label_names):
        print("{:>23} : {:.4f}".format(label_name, iu))
    print("=" * 34)
    print("{:>23} : {:.4f}".format("mean IoU", result["miou"]))
    print("{:>23} : {:.4f}".format(
        "Class average accuracy", result["mean_class_accuracy"]))

After Change

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--dataset", choices=("cityscapes", "ade20k", "camvid", "voc"))
    parser.add_argument("--model", choices=sorted(models.keys()))
    parser.add_argument("--pretrained-model")
    parser.add_argument("--batchsize", type=int)
    parser.add_argument("--input-size", type=int, default=None)

Instances