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Fix issues #609, #649 and correct RandLA-Net preprocessing #680

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Fix issues #609, #649 and correct RandLA-Net preprocessing #680

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7 changes: 5 additions & 2 deletions ml3d/datasets/augment/augmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,11 @@ def recenter(self, data, cfg):
if not cfg:
return data
dim = cfg.get('dim', [0, 1, 2])
data[:, dim] = data[:, dim] - data.mean(0)[dim]
return data
data_float64 = data.astype(np.float64)
mean_vals = data_float64[:, dim].mean(0)
data_float64[:, dim] -= mean_vals

Copilot AI Feb 22, 2026

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Remove trailing whitespace on this empty line to maintain consistent code formatting.

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return data_float64.astype(np.float32)

def normalize(self, pc, feat, cfg):
"""Normalize pointcloud and/or features.
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7 changes: 5 additions & 2 deletions ml3d/datasets/customdataset.py
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Expand Up @@ -220,10 +220,13 @@ def save_test_result(self, results, attr):
make_dir(path)

pred = results['predict_labels']
pred = np.array(self.label_to_names[pred])
if isinstance(pred, np.ndarray):
pred_names = np.vectorize(lambda x: self.label_to_names[int(x)])(pred)

Copilot AI Feb 22, 2026

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Using np.vectorize with a lambda function is inefficient for large arrays as it's essentially a Python loop. Consider using array indexing directly with pred_names = np.array([self.label_to_names[int(x)] for x in pred]) or converting label_to_names to a numpy array and using advanced indexing like np.array(self.label_to_names)[pred.astype(int)].

Suggested change
pred_names = np.vectorize(lambda x: self.label_to_names[int(x)])(pred)
flat_pred = pred.ravel()
pred_names = np.array(
[self.label_to_names[int(x)] for x in flat_pred]
).reshape(pred.shape)

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else:
pred_names = self.label_to_names[int(pred)]

store_path = join(path, name + '.npy')
np.save(store_path, pred)
np.save(store_path, pred_names)


DATASET._register_module(Custom3D)
2 changes: 1 addition & 1 deletion ml3d/torch/models/randlanet.py
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Expand Up @@ -191,7 +191,7 @@ def transform(self, data, attr, min_possibility_idx=None):
if 'normalize' in augment_cfg:
val_augment_cfg['normalize'] = augment_cfg.pop('normalize')

self.augmenter.augment(pc, feat, label, val_augment_cfg, seed=rng)
pc, feat, label = self.augmenter.augment(pc, feat, label, val_augment_cfg, seed=rng)

if attr['split'] in ['training', 'train']:
pc, feat, label = self.augmenter.augment(pc,
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6 changes: 3 additions & 3 deletions ml3d/torch/pipelines/semantic_segmentation.py
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Expand Up @@ -257,14 +257,14 @@ def run_test(self):
self.metric_test.update(valid_scores, valid_labels)
log.info(f"Accuracy : {self.metric_test.acc()}")
log.info(f"IoU : {self.metric_test.iou()}")
log.info(
f"Overall Testing Accuracy : {self.metric_test.acc()[-1]}, mIoU : {self.metric_test.iou()[-1]}"
)
dataset.save_test_result(inference_result, attr)
# Save only for the first batch
if 'test' in record_summary and 'test' not in self.summary:
self.summary['test'] = self.get_3d_summary(
results, inputs['data'], 0, save_gt=False)
log.info(
f"Overall Testing Accuracy : {self.metric_test.acc()[-1]}, mIoU : {self.metric_test.iou()[-1]}"
)

log.info("Finished testing")

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