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HuguesTHOMAS
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@ -504,7 +504,8 @@ class SemanticKittiDataset(PointCloudDataset):
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# > Drop: We can drop even more points. Random choice could be faster without replace=False
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# > reproj: No reprojection needed
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# > Augment: See which data agment we want at test time
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# > input: MAIN BOTTLENECK. We need to see if we can do faster, maybe with some parallelisation
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# > input: MAIN BOTTLENECK. We need to see if we can do faster, maybe with some parallelisation. neighbors
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# and subsampling accelerated with lidar frame order
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return [self.config.num_layers] + input_list
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@ -887,9 +888,6 @@ class SemanticKittiSampler(Sampler):
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if breaking:
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break
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# TODO: Compute the percentile np.percentile?
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# TODO: optionnally show a plot of the in_points histogram?
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self.dataset.max_in_p = int(np.percentile(all_lengths, 100*untouched_ratio))
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if verbose:
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@ -1379,7 +1377,7 @@ def debug_class_w(dataset, loader):
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i = 0
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counts = np.zeros((0, dataset.num_classes,), dtype=np.int64)
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counts = np.zeros((dataset.num_classes,), dtype=np.int64)
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s = '{:^6}|'.format('step')
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for c in dataset.label_names:
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@ -1393,12 +1391,12 @@ def debug_class_w(dataset, loader):
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# count labels
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new_counts = np.bincount(batch.labels)
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counts[:new_counts.shape[0]] += new_counts.astype(np.int64)
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# Update proportions
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proportions = 1000 * counts / np.sum(counts)
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print(proportions)
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s = '{:^6d}|'.format(i)
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for pp in proportions:
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s += '{:^6.1f}'.format(pp)
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@ -117,8 +117,6 @@ class KPCNN(nn.Module):
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:return: loss
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"""
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# TODO: Ignore unclassified points in loss for segmentation architecture
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# Cross entropy loss
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self.output_loss = self.criterion(outputs, labels)
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@ -1464,6 +1464,8 @@ def SemanticKittiFirst(old_result_limit):
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logs_names = ['R=5.0_dl=0.04',
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'R=5.0_dl=0.08',
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'R=10.0_dl=0.08',
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'R=10.0_dl=0.08_weigths',
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'R=10.0_dl=0.08_sqrt_weigths',
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'test']
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logs_names = np.array(logs_names[:len(logs)])
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@ -1481,7 +1483,6 @@ if __name__ == '__main__':
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######################################################
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# TODO: test deformable on S3DIS to see of fitting loss works
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# TODO: GOOOO SemanticKitti for wednesday at least have a timing to give to them
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# TODO: try class weights on S3DIS (very low weight for beam)
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# Old result limit
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@ -178,7 +178,7 @@ class S3DISConfig(Config):
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augment_noise = 0.001
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augment_color = 0.8
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# The way we balance segmentation loss TODO: implement and test 'class' and 'batch' modes
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# The way we balance segmentation loss
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# > 'none': Each point in the whole batch has the same contribution.
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# > 'class': Each class has the same contribution (points are weighted according to class balance)
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# > 'batch': Each cloud in the batch has the same contribution (points are weighted according cloud sizes)
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@ -180,7 +180,24 @@ class SemanticKittiConfig(Config):
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augment_color = 0.8
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# Choose weights for class (used in segmentation loss). Empty list for no weights
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class_w = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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# class proportion for R=10.0 and dl=0.08 (first is unlabeled)
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# 19.1 48.9 0.5 1.1 5.6 3.6 0.7 0.6 0.9 193.2 17.7 127.4 6.7 132.3 68.4 283.8 7.0 78.5 3.3 0.8
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#
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#
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# Inverse of proportion * 20
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# class_w = [0.409, 40.000, 18.182, 3.571, 5.556, 28.571, 33.333, 22.222, 0.104,
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# 1.130, 0.157, 2.985, 0.151, 0.292, 0.070, 2.857, 0.255, 6.061, 25.000]
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# Inverse of proportion *20 capped (0.1 < X < 10)
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# class_w = [0.409, 10.000, 10.000, 3.571, 5.556, 10.000, 10.000, 10.000, 0.104,
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# 1.130, 0.157, 2.985, 0.151, 0.292, 0.100, 2.857, 0.255, 6.061, 10.000]
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# Inverse of proportion *20 then sqrt
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class_w = [0.639529479, 6.32455532, 4.264014327, 1.889822365, 2.357022604, 5.345224838,
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5.773502692, 4.714045208, 0.321744726, 1.062988007, 0.396214426, 1.727736851,
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0.388807896, 0.54073807, 0.265465937, 1.690308509, 0.504754465, 2.46182982, 5]
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# Do we nee to save convergence
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saving = True
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@ -283,7 +300,7 @@ if __name__ == '__main__':
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# debug_timing(training_dataset, training_loader)
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# debug_timing(test_dataset, test_loader)
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debug_class_w(training_dataset, training_loader)
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# debug_class_w(training_dataset, training_loader)
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print('\nModel Preparation')
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print('*****************')
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@ -316,6 +333,3 @@ if __name__ == '__main__':
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print('Forcing exit now')
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os.kill(os.getpid(), signal.SIGINT)
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# TODO: Create a function debug_class_weights that shows class distribution in input sphere. Use that as
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# indication for the class weights during training
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