fix missing updates

src/data/data.py

@@ -812,10 +812,11 @@ def semantic_segmentation_oracle(
 
         # Performance evaluation
         from src.metrics import ConfusionMatrix
-        metric = ConfusionMatrix(num_classes, *metric_args, **metric_kwargs)
-        metric(pred.cpu(), target.cpu())
+        cm = ConfusionMatrix(num_classes, *metric_args, **metric_kwargs)
+        cm(pred.cpu(), target.cpu())
+        metrics = cm.all_metrics()
 
-        return metric.miou(), metric.iou(), metric.oa(), metric.macc()
+        return metrics
 
     def instance_segmentation_oracle(self, *metric_args, **metric_kwargs):
         """Compute the oracle performance for instance segmentation.
@@ -929,7 +930,7 @@ def from_data_list(cls, data_list, follow_batch=None, exclude_keys=None):
         # and 'obj' to a proper InstanceBatch.
         # Note we will need to do the same in `get_example` to avoid
         # breaking PyG Batch mechanisms
-        if batch.is_super:
+        if batch.is_super and isinstance(batch.sub, Cluster):
             batch.sub = ClusterBatch.from_list(batch.sub)
         if batch.obj is not None:
             batch.obj = InstanceBatch.from_list(batch.obj)

src/data/instance.py

@@ -752,10 +752,11 @@ def semantic_segmentation_oracle(
 
         # Performance evaluation
         from src.metrics import ConfusionMatrix
-        metric = ConfusionMatrix(num_classes, *metric_args, **metric_kwargs)
-        metric(pred.cpu(), target.cpu())
+        cm = ConfusionMatrix(num_classes, *metric_args, **metric_kwargs)
+        cm(pred.cpu(), target.cpu())
+        metrics = cm.all_metrics()
 
-        return metric.miou(), metric.iou(), metric.oa(), metric.macc()
+        return metrics
 
     def oracle(self, num_classes):
         """Compute the oracle predictions for instance and panoptic

src/data/nag.py

@@ -88,6 +88,16 @@ def num_points(self):
         """Number of points/nodes in the lower-level graph."""
         return [d.num_points for d in self] if self.num_levels > 0 else 0
 
+    @property
+    def level_ratios(self):
+        """Ratios of number of nodes between consecutive partition
+        levels. This can be useful for investigating how much each
+        partition level 'compresses' the previous one.
+        """
+        return {
+            f"|P_{i}| / |P_{i+1}|": self.num_points[i] / self.num_points[i + 1]
+            for i in range(self.num_levels - 1)}
+
     def to_list(self):
         """Return the Data list"""
         return self._list

src/datamodules/base.py

@@ -182,21 +182,10 @@ def setup(self, stage=None):
     def set_transforms(self):
         """Parse in self.hparams in search for '*transform*' keys and
         instantiate the corresponding transforms.
-
-        Credit: https://github.com/torch-points3d/torch-points3d
         """
-        for key_name in self.hparams.keys():
-            if "transform" in key_name:
-                name = key_name.replace("transforms", "transform")
-                params = getattr(self.hparams, key_name, None)
-                if params is None:
-                    continue
-                try:
-                    transform = instantiate_transforms(params)
-                except Exception:
-                    log.exception(f"Error trying to create {name}, {params}")
-                    continue
-                setattr(self, name, transform)
+        t_dict = instantiate_datamodule_transforms(self.hparams, log=log)
+        for key, transform in t_dict.items():
+            setattr(self, key, transform)
 
     def check_tta_conflicts(self):
         """Make sure the transforms are Test-Time Augmentation-friendly

src/eval.py

@@ -16,7 +16,7 @@
 # - automatically loads environment variables from ".env" file if exists
 #
 # how it works:
-# - the line above recursively searches for either ".git" or "pyproject.toml" in present
+# - the line above recursively searches for either ".git" or "README.md" in present
 #   and parent dirs, to determine the project root dir
 # - adds root dir to the PYTHONPATH (if `pythonpath=True`), so this file can be run from
 #   any place without installing project as a package
@@ -49,7 +49,8 @@
 # Registering the "eval" resolver allows for advanced config
 # interpolation with arithmetic operations:
 # https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html
-OmegaConf.register_new_resolver("eval", eval)
+if not OmegaConf.has_resolver('eval'):
+    OmegaConf.register_new_resolver('eval', eval)
 
 log = utils.get_pylogger(__name__)
 

src/metrics/semantic.py

@@ -4,6 +4,7 @@
 import numpy as np
 from torchmetrics.classification import MulticlassConfusionMatrix
 from torch_scatter import scatter_add
+from src.metrics.mean_average_precision import BaseMetricResults
 
 
 log = logging.getLogger(__name__)
@@ -12,6 +13,17 @@
 __all__ = ['ConfusionMatrix']
 
 
+class SemanticMetricResults(BaseMetricResults):
+    """Class to wrap the final metric results for Semantic Segmentation.
+    """
+    __slots__ = (
+        'oa',
+        'macc',
+        'miou',
+        'iou_per_class',
+        'seen_class')
+
+
 class ConfusionMatrix(MulticlassConfusionMatrix):
     """TorchMetrics's MulticlassConfusionMatrix but tailored to our
     needs. In particular, new methods allow computing OA, mAcc, mIoU
@@ -231,6 +243,22 @@ def print_metrics(self, class_names=None):
                 continue
             print(f'  {c:<13}: {iou:0.2f}')
 
+    def all_metrics(self, as_percent=True):
+        """Helper to return all important metrics, stored in a
+        `SemanticMetricResults` object   print the OA, mAcc, mIoU and per-class IoU.
+
+        :param as_percent: bool
+            If True, the returned metric is expressed in [0, 100]
+        """
+        metrics = SemanticMetricResults()
+        metrics.oa = self.oa(as_percent=as_percent)
+        metrics.macc = self.macc(as_percent=as_percent)
+        metrics.miou = self.miou(as_percent=as_percent)
+        iou, seen = self.iou(as_percent=as_percent)
+        metrics.iou_per_class = iou
+        metrics.seen_class = seen
+        return metrics
+
 
 def save_confusion_matrix(cm, path2save, ordered_names):
     import seaborn as sns