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if __name__ == '__main__':
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FLAGS = get_args()
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# print summary of what we will do
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print("*" * 80)
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print("INTERFACE:")
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print("Eval What:", FLAGS.eval_type)
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print("Data: ", FLAGS.dataset)
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print("Predictions: ", FLAGS.predictions)
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print("Sequences: ", FLAGS.sequences)
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print("Config: ", FLAGS.datacfg)
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print("Limit: ", FLAGS.limit)
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print("*" * 80)
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print("Opening data config file %s" % FLAGS.datacfg)
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DATA = yaml.safe_load(open(FLAGS.datacfg, 'r'))
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# get number of interest classes, and the label mappings
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class_strings = DATA["labels"]
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class_ignore = DATA["learning_ignore"]
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class_inv_remap = DATA["learning_map_inv"]
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nr_classes = len(class_inv_remap)
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data_config = FLAGS.datacfg
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DATA = yaml.safe_load(open(data_config, 'r'))
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remap_dict = DATA["learning_map"]
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max_key = max(remap_dict.keys())
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remap_lut = np.zeros((max_key + 100), dtype=np.int32)
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remap_lut[list(remap_dict.keys())] = list(remap_dict.values())
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# create evaluator
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ignore = []
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for cl, ign in class_ignore.items():
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if ign:
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x_cl = int(cl)
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ignore.append(x_cl)
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print("Ignoring xentropy class ", x_cl, " in IoU evaluation")
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# create evaluator
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# create evaluator
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evaluators = []
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for i in range(len(DISTANCES)):
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evaluators.append(iouEval(nr_classes, ignore))
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evaluators[i].reset()
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# get label paths
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if FLAGS.eval_type == "sub":
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label_names = load_label(FLAGS.dataset, FLAGS.sequences, "labels", "npy")
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else:
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label_names = load_label(FLAGS.dataset, FLAGS.sequences, "labels", "label")
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py_names = load_label(FLAGS.dataset, FLAGS.sequences, "velodyne", "bin")
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# py_names = py_names[0:len(py_names):4]
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# get predictions paths
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pred_names = load_label(FLAGS.predictions, FLAGS.sequences, "predictions", "label")
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# label_names = label_names[0:len(label_names):4]
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print(len(label_names), len(pred_names))
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assert(len(label_names) == len(pred_names))
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print("Evaluating sequences")
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N = len(label_names)
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# open each file, get the tensor, and make the iou comparison
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for i in tqdm(range(N)):
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label_file = label_names[i]
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pred_file = pred_names[i]
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points_file = py_names[i]
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# open label
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if FLAGS.eval_type == "sub":
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label = np.load(label_file)
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label = label.reshape((-1)) # reshape to vector
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else:
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label = np.fromfile(label_file, dtype=np.int32)
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label = label.reshape((-1))
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sem_label = label & 0xFFFF # semantic label in lower half
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inst_label = label >> 16 # instance id in upper half
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assert ((sem_label + (inst_label << 16) == label).all())
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label = remap_lut[sem_label]
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if FLAGS.limit is not None:
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label = label[:FLAGS.limit] # limit to desired length
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# open prediction
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pred = np.fromfile(pred_file, dtype=np.int32)
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pred = pred.reshape((-1)) # reshape to vector
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pred = remap_lut[pred]
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if FLAGS.limit is not None:
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pred = pred[:FLAGS.limit] # limit to desired length
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# add single scan to evaluation
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xyzr = np.fromfile(points_file, dtype=np.float32)
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xyzr = xyzr.reshape((-1, 4))
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points = xyzr[:, 0:3]
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if FLAGS.limit is not None:
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