Initial commit

datasets/SemanticKitti.py

@@ -504,7 +504,8 @@ class SemanticKittiDataset(PointCloudDataset):
             #  > Drop: We can drop even more points. Random choice could be faster without replace=False
             #  > reproj: No reprojection needed
             #  > Augment: See which data agment we want at test time
-            #  > input: MAIN BOTTLENECK. We need to see if we can do faster, maybe with some parallelisation
+            #  > input: MAIN BOTTLENECK. We need to see if we can do faster, maybe with some parallelisation. neighbors
+            #           and subsampling accelerated with lidar frame order
 
         return [self.config.num_layers] + input_list
 
@@ -887,9 +888,6 @@ class SemanticKittiSampler(Sampler):
                 if breaking:
                     break
 
-            # TODO: Compute the percentile np.percentile?
-            # TODO: optionnally show a plot of the in_points histogram?
-
             self.dataset.max_in_p = int(np.percentile(all_lengths, 100*untouched_ratio))
 
             if verbose:
@@ -1379,7 +1377,7 @@ def debug_class_w(dataset, loader):
 
     i = 0
 
-    counts = np.zeros((0, dataset.num_classes,), dtype=np.int64)
+    counts = np.zeros((dataset.num_classes,), dtype=np.int64)
 
     s = '{:^6}|'.format('step')
     for c in dataset.label_names:
@@ -1393,12 +1391,12 @@ def debug_class_w(dataset, loader):
 
             # count labels
             new_counts = np.bincount(batch.labels)
+
             counts[:new_counts.shape[0]] += new_counts.astype(np.int64)
 
             # Update proportions
             proportions = 1000 * counts / np.sum(counts)
 
-            print(proportions)
             s = '{:^6d}|'.format(i)
             for pp in proportions:
                 s += '{:^6.1f}'.format(pp)

models/architectures.py

@@ -117,8 +117,6 @@ class KPCNN(nn.Module):
         :return: loss
         """
 
-        # TODO: Ignore unclassified points in loss for segmentation architecture
-
         # Cross entropy loss
         self.output_loss = self.criterion(outputs, labels)
 

plot_convergence.py

@@ -1464,6 +1464,8 @@ def SemanticKittiFirst(old_result_limit):
     logs_names = ['R=5.0_dl=0.04',
                   'R=5.0_dl=0.08',
                   'R=10.0_dl=0.08',
+                  'R=10.0_dl=0.08_weigths',
+                  'R=10.0_dl=0.08_sqrt_weigths',
                   'test']
 
     logs_names = np.array(logs_names[:len(logs)])
@@ -1481,7 +1483,6 @@ if __name__ == '__main__':
     ######################################################
 
     # TODO: test deformable on S3DIS to see of fitting loss works
-    # TODO: GOOOO SemanticKitti for wednesday at least have a timing to give to them
     # TODO: try class weights on S3DIS (very low weight for beam)
 
     # Old result limit

train_S3DIS.py

@@ -178,7 +178,7 @@ class S3DISConfig(Config):
     augment_noise = 0.001
     augment_color = 0.8
 
-    # The way we balance segmentation loss TODO: implement and test 'class' and 'batch' modes
+    # The way we balance segmentation loss
     #   > 'none': Each point in the whole batch has the same contribution.
     #   > 'class': Each class has the same contribution (points are weighted according to class balance)
     #   > 'batch': Each cloud in the batch has the same contribution (points are weighted according cloud sizes)

train_SemanticKitti.py

@@ -180,7 +180,24 @@ class SemanticKittiConfig(Config):
     augment_color = 0.8
 
     # Choose weights for class (used in segmentation loss). Empty list for no weights
-    class_w = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    # class proportion for R=10.0 and dl=0.08 (first is unlabeled)
+    # 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
+    #
+    #
+
+    # Inverse of proportion * 20
+    # class_w = [0.409, 40.000, 18.182, 3.571, 5.556, 28.571, 33.333, 22.222, 0.104,
+    #            1.130, 0.157, 2.985, 0.151, 0.292, 0.070, 2.857, 0.255, 6.061, 25.000]
+
+    # Inverse of proportion *20 capped (0.1 < X < 10)
+    # class_w = [0.409, 10.000, 10.000, 3.571, 5.556, 10.000, 10.000, 10.000, 0.104,
+    #            1.130, 0.157, 2.985, 0.151, 0.292, 0.100, 2.857, 0.255, 6.061, 10.000]
+
+    # Inverse of proportion *20 then sqrt
+    class_w = [0.639529479, 6.32455532, 4.264014327, 1.889822365, 2.357022604, 5.345224838,
+               5.773502692, 4.714045208, 0.321744726, 1.062988007, 0.396214426, 1.727736851,
+               0.388807896, 0.54073807, 0.265465937, 1.690308509, 0.504754465, 2.46182982, 5]
+
 
     # Do we nee to save convergence
     saving = True
@@ -283,7 +300,7 @@ if __name__ == '__main__':
 
     # debug_timing(training_dataset, training_loader)
     # debug_timing(test_dataset, test_loader)
-    debug_class_w(training_dataset, training_loader)
+    # debug_class_w(training_dataset, training_loader)
 
     print('\nModel Preparation')
     print('*****************')
@@ -316,6 +333,3 @@ if __name__ == '__main__':
 
     print('Forcing exit now')
     os.kill(os.getpid(), signal.SIGINT)
-
-    # TODO: Create a function debug_class_weights that shows class distribution in input sphere. Use that as
-    #  indication for the class weights during training