Server-side histogram binning + grid/sort perf fixes

weightslab/examples/PyTorch/ws-classification/main.py

@@ -89,7 +89,7 @@ def _build_filepath_mapping(self):
         # For each index, construct a meaningful filepath
         # MNIST doesn't have original individual files, so we create virtual paths
         for idx in range(len(self.mnist)):
-            if self.max_samples is not None and idx >= self.max_samples:
+            if self.max_samples != None and idx >= self.max_samples:
                 break
             label = self.mnist.targets[idx].item() if hasattr(self.mnist.targets[idx], 'item') else self.mnist.targets[idx]
             split = 'train' if self.train else 'test'
@@ -105,7 +105,7 @@ def _build_filepath_mapping(self):
             self.filepaths[idx] = virtual_path
 
     def __len__(self):
-        if self.max_samples is not None:
+        if self.max_samples != None:
             return min(len(self.mnist), self.max_samples)
         return len(self.mnist)
 
@@ -371,15 +371,15 @@ def test(loader, model, criterion_mlt, metric_mlt, device, test_loader_len):
     # Setup clean progress bar with custom format
     if tqdm_display:
         train_range = tqdm.tqdm(
-            range(training_steps_to_do) if training_steps_to_do is not None else itertools.count(),
+            range(training_steps_to_do) if training_steps_to_do != None else itertools.count(),
             desc="Training",
             bar_format="{desc}: {n}/{total} [{elapsed}<{remaining}, {rate_fmt}] {bar} | {postfix}",
             ncols=140,
             position=0,
             leave=True
         )
     else:
-        train_range = range(training_steps_to_do) if training_steps_to_do is not None else itertools.count()
+        train_range = range(training_steps_to_do) if training_steps_to_do != None else itertools.count()
 
     # ================
     # Training Loop

weightslab/examples/PyTorch/ws-detection/utils/data.py

@@ -131,7 +131,7 @@ def __init__(
             val_set = set(all_imgs[::k])
             selected = [f for f in all_imgs if f not in val_set]
 
-        selected = selected[:max_samples] if max_samples is not None else selected
+        selected = selected[:max_samples] if max_samples != None else selected
 
         self.images = []
         self.masks = []

weightslab/examples/PyTorch/ws-segmentation/utils/data.py

@@ -51,7 +51,7 @@ def __init__(
             for f in os.listdir(img_dir)
             if f.lower().endswith((".jpg", ".jpeg", ".png"))
         ]
-        image_files = sorted(set(image_files))[:max_samples] if max_samples is not None else sorted(set(image_files))  # Optionally limit number of samples for faster testing
+        image_files = sorted(set(image_files))[:max_samples] if max_samples != None else sorted(set(image_files))  # Optionally limit number of samples for faster testing
 
         self.images = []
         self.masks = []

weightslab/examples/Ultralytics/ws-detection/main.py

@@ -64,7 +64,7 @@ def main():
         trainer=WLAwareTrainer,
         data=data_root,
         imgsz=image_size,
-        epochs=1000 if max_steps is None else max(1, int(max_steps)),
+        epochs=1000 if max_steps == None else max(1, int(max_steps)),
         device=device,
         project=project, name=name,  # → UL save_dir → WL logger log_dir/name
         resume=False,

weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py

@@ -116,7 +116,7 @@ def __init__(
         else:
             val_set = set(frames[::k])
             selected = [f for f in frames if f not in val_set]
-        self.frames = selected[:max_samples] if max_samples is not None else selected
+        self.frames = selected[:max_samples] if max_samples != None else selected
 
     def __len__(self):
         return len(self.frames)

weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py

@@ -446,7 +446,7 @@ def __init__(
         else:
             val_set = set(frames[::k])
             selected = [f for f in frames if f not in val_set]
-        self.frames = selected[:max_samples] if max_samples is not None else selected
+        self.frames = selected[:max_samples] if max_samples != None else selected
 
         if len(self.frames) == 0:
             raise RuntimeError(f"No LiDAR frames found (source={source}, root={root})")

weightslab/proto/experiment_service.proto

@@ -426,6 +426,8 @@ message HistogramResponse {
   string message = 2;
   int64 total_rows = 3;  // rows in the view that were binned
   repeated HistogramBin bins = 4;
+}
+
 // --- Metadata retrieval (separated from GetDataSamples) ---
 message GetMetaDataRequest {
   int32 start_index = 1;        // grid slice start (current view order)

weightslab/proto/experiment_service_pb2_grpc.py

@@ -5,7 +5,7 @@
 
 from weightslab.proto import experiment_service_pb2 as weightslab_dot_proto_dot_experiment__service__pb2
 
-GRPC_GENERATED_VERSION = '1.81.1'
+GRPC_GENERATED_VERSION = '1.76.0'
 GRPC_VERSION = grpc.__version__
 _version_not_supported = False
 
@@ -25,7 +25,7 @@
     )
 
 
-class ExperimentServiceStub:
+class ExperimentServiceStub(object):
     """Missing associated documentation comment in .proto file."""
 
     def __init__(self, channel):
@@ -78,6 +78,7 @@ def __init__(self, channel):
                 '/ExperimentService/GetHistogram',
                 request_serializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramRequest.SerializeToString,
                 response_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramResponse.FromString,
+                _registered_method=True)
         self.GetMetaData = channel.unary_unary(
                 '/ExperimentService/GetMetaData',
                 request_serializer=weightslab_dot_proto_dot_experiment__service__pb2.GetMetaDataRequest.SerializeToString,
@@ -145,7 +146,7 @@ def __init__(self, channel):
                 _registered_method=True)
 
 
-class ExperimentServiceServicer:
+class ExperimentServiceServicer(object):
     """Missing associated documentation comment in .proto file."""
 
     def GetLatestLoggerData(self, request, context):
@@ -199,6 +200,11 @@ def GetDataSamples(self, request, context):
 
     def GetHistogram(self, request, context):
         """Server-side histogram binning of one metadata/signal column.
+        """
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
     def GetMetaData(self, request, context):
         """Metadata-only retrieval (dataframe columns). Returns every metadata column
         name for the WHOLE dataset, the current grid slice's per-sample metadata, and
@@ -332,6 +338,7 @@ def add_ExperimentServiceServicer_to_server(servicer, server):
                     servicer.GetHistogram,
                     request_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramRequest.FromString,
                     response_serializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramResponse.SerializeToString,
+            ),
             'GetMetaData': grpc.unary_unary_rpc_method_handler(
                     servicer.GetMetaData,
                     request_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.GetMetaDataRequest.FromString,
@@ -405,7 +412,7 @@ def add_ExperimentServiceServicer_to_server(servicer, server):
 
 
  # This class is part of an EXPERIMENTAL API.
-class ExperimentService:
+class ExperimentService(object):
     """Missing associated documentation comment in .proto file."""
 
     @staticmethod
@@ -626,7 +633,6 @@ def GetDataSamples(request,
 
     @staticmethod
     def GetHistogram(request,
-    def GetMetaData(request,
             target,
             options=(),
             channel_credentials=None,
@@ -642,6 +648,30 @@ def GetMetaData(request,
             '/ExperimentService/GetHistogram',
             weightslab_dot_proto_dot_experiment__service__pb2.HistogramRequest.SerializeToString,
             weightslab_dot_proto_dot_experiment__service__pb2.HistogramResponse.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def GetMetaData(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(
+            request,
+            target,
             '/ExperimentService/GetMetaData',
             weightslab_dot_proto_dot_experiment__service__pb2.GetMetaDataRequest.SerializeToString,
             weightslab_dot_proto_dot_experiment__service__pb2.GetMetaDataResponse.FromString,