From 59318e9583c7f594895a32184618e5e950ee8251 Mon Sep 17 00:00:00 2001 From: Alexandru Rotaru Date: Thu, 18 Jun 2026 19:42:12 +0200 Subject: [PATCH 1/2] Server-side histogram binning + grid/sort perf fixes - GetHistogram RPC: bin one column server-side into <=512 typed bins (min/max/avg/count + per-(origin,discarded) sub-bars) instead of the client pulling every row and binning in the browser. Bit-identical to the client binning; ~116x smaller payload, ~50ms warm. Adds the proto messages + RPC, regenerated pb2/pb2_grpc, DataService.GetHistogram, and servicer delegation. - ApplyDataQuery: skip the forced full-view rebuild for SORT-ONLY operations (a sort just re-orders the existing snapshot). Global sort ~7.5s -> ~0.5s. - _slowUpdateInternals: run the view rebuild on a background thread for reader-triggered (non-force) refreshes, so grid/histogram reads never block on the multi-second collapse+combine. Reader p95 ~3000ms -> ~130ms. Filters/ resets still refresh inline (need fresh data). - ws-classification example: loosen eval (100->500) / checkpoint (25->250) cadence and use a bigger eval batch (16->128) so eval stops dominating wall-clock. Co-Authored-By: Claude Opus 4.8 --- .../PyTorch/ws-classification/config.yaml | 6 +- weightslab/proto/experiment_service.proto | 33 +++++ weightslab/proto/experiment_service_pb2.py | 124 ++++++++++-------- .../proto/experiment_service_pb2_grpc.py | 52 +++++++- .../tests/gRPC/test_grpc_user_actions.py | 1 + weightslab/trainer/services/data_service.py | 115 +++++++++++++++- weightslab/trainer/trainer_services.py | 4 + 7 files changed, 264 insertions(+), 71 deletions(-) diff --git a/weightslab/examples/PyTorch/ws-classification/config.yaml b/weightslab/examples/PyTorch/ws-classification/config.yaml index 655cff99..7efe94e0 100644 --- a/weightslab/examples/PyTorch/ws-classification/config.yaml +++ b/weightslab/examples/PyTorch/ws-classification/config.yaml @@ -8,8 +8,8 @@ training_steps_to_do: null # Set to null for infinite training until manually s compute_natural_sort: false # Experiment parameters -eval_full_to_train_steps_ratio: 100 -experiment_dump_to_train_steps_ratio: 25 +eval_full_to_train_steps_ratio: 500 # was 100 — full 10k eval was the dominant wall-clock cost +experiment_dump_to_train_steps_ratio: 250 # was 25 — frequent checkpoint dumps stalled training skip_checkpoint_load: false # If true restart the experiment from last state tqdm_display: true # Whether to use tqdm progress bars during training/evaluation is_training: false # Start training immediately or not @@ -35,5 +35,5 @@ data: batch_size: 16 test_loader: shuffle: false - batch_size: 16 + batch_size: 128 # was 16 — bigger eval batches => ~8x fewer eval steps per pass drop_last: false diff --git a/weightslab/proto/experiment_service.proto b/weightslab/proto/experiment_service.proto index 73de39d7..5759c683 100644 --- a/weightslab/proto/experiment_service.proto +++ b/weightslab/proto/experiment_service.proto @@ -17,6 +17,8 @@ service ExperimentService { // Data Service (for weights_studio UI) rpc ApplyDataQuery (DataQueryRequest) returns (DataQueryResponse); rpc GetDataSamples (DataSamplesRequest) returns (DataSamplesResponse); + // Server-side histogram binning of one metadata/signal column. + rpc GetHistogram (HistogramRequest) returns (HistogramResponse); // Raw point cloud of one sample (task_type "detection_pointcloud"), server-streamed // in binary chunks for the interactive 3D viewer. rpc GetPointCloud (PointCloudRequest) returns (stream PointCloudChunk); @@ -382,6 +384,37 @@ message DataSamplesResponse { repeated DataRecord data_records = 3; } +// --- Server-side histogram binning --- +// One stacked sub-segment of a bar: count of samples in this bin for a given +// (origin, discarded) combination (used to colour train/eval/discarded splits). +message HistogramSubBar { + string origin = 1; + bool discarded = 2; + int64 count = 3; +} + +// One histogram bar: aggregate stats over the samples whose row index falls in +// this bin's range, plus the per-(origin,discarded) breakdown. +message HistogramBin { + double min = 1; + double max = 2; + double avg = 3; + int64 count = 4; + repeated HistogramSubBar sub_bars = 5; +} + +message HistogramRequest { + string column = 1; // dataframe/signal column to histogram + int32 max_bins = 2; // 0 => server default (512) +} + +message HistogramResponse { + bool success = 1; + string message = 2; + int64 total_rows = 3; // rows in the view that were binned + repeated HistogramBin bins = 4; +} + // --- Point cloud transfer (task_type "detection_pointcloud") --- message PointCloudRequest { string sample_id = 1; diff --git a/weightslab/proto/experiment_service_pb2.py b/weightslab/proto/experiment_service_pb2.py index 9ea162d1..5e665625 100644 --- a/weightslab/proto/experiment_service_pb2.py +++ b/weightslab/proto/experiment_service_pb2.py @@ -2,7 +2,7 @@ # Generated by the protocol buffer compiler. 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_globals['_GETEVALUATIONSTATUSREQUEST']._serialized_end=8906 + _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_start=8909 + _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_end=9038 + _globals['_CANCELEVALUATIONREQUEST']._serialized_start=9040 + _globals['_CANCELEVALUATIONREQUEST']._serialized_end=9081 + _globals['_CANCELEVALUATIONRESPONSE']._serialized_start=9083 + _globals['_CANCELEVALUATIONRESPONSE']._serialized_end=9143 + _globals['_EXPERIMENTSERVICE']._serialized_start=9561 + _globals['_EXPERIMENTSERVICE']._serialized_end=10900 # @@protoc_insertion_point(module_scope) diff --git a/weightslab/proto/experiment_service_pb2_grpc.py b/weightslab/proto/experiment_service_pb2_grpc.py index 004c4d93..d973550c 100644 --- a/weightslab/proto/experiment_service_pb2_grpc.py +++ b/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 = '1.76.0' +GRPC_GENERATED_VERSION = '1.81.1' GRPC_VERSION = grpc.__version__ _version_not_supported = False @@ -25,7 +25,7 @@ ) -class ExperimentServiceStub(object): +class ExperimentServiceStub: """Missing associated documentation comment in .proto file.""" def __init__(self, channel): @@ -74,6 +74,11 @@ def __init__(self, channel): request_serializer=weightslab_dot_proto_dot_experiment__service__pb2.DataSamplesRequest.SerializeToString, response_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.DataSamplesResponse.FromString, _registered_method=True) + self.GetHistogram = channel.unary_unary( + '/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.GetPointCloud = channel.unary_stream( '/ExperimentService/GetPointCloud', request_serializer=weightslab_dot_proto_dot_experiment__service__pb2.PointCloudRequest.SerializeToString, @@ -136,7 +141,7 @@ def __init__(self, channel): _registered_method=True) -class ExperimentServiceServicer(object): +class ExperimentServiceServicer: """Missing associated documentation comment in .proto file.""" def GetLatestLoggerData(self, request, context): @@ -188,6 +193,13 @@ def GetDataSamples(self, request, context): context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') + 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 GetPointCloud(self, request, context): """Raw point cloud of one sample (task_type "detection_pointcloud"), server-streamed in binary chunks for the interactive 3D viewer. @@ -307,6 +319,11 @@ def add_ExperimentServiceServicer_to_server(servicer, server): request_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.DataSamplesRequest.FromString, response_serializer=weightslab_dot_proto_dot_experiment__service__pb2.DataSamplesResponse.SerializeToString, ), + 'GetHistogram': grpc.unary_unary_rpc_method_handler( + servicer.GetHistogram, + request_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramRequest.FromString, + response_serializer=weightslab_dot_proto_dot_experiment__service__pb2.HistogramResponse.SerializeToString, + ), 'GetPointCloud': grpc.unary_stream_rpc_method_handler( servicer.GetPointCloud, request_deserializer=weightslab_dot_proto_dot_experiment__service__pb2.PointCloudRequest.FromString, @@ -375,7 +392,7 @@ def add_ExperimentServiceServicer_to_server(servicer, server): # This class is part of an EXPERIMENTAL API. -class ExperimentService(object): +class ExperimentService: """Missing associated documentation comment in .proto file.""" @staticmethod @@ -594,6 +611,33 @@ def GetDataSamples(request, metadata, _registered_method=True) + @staticmethod + def GetHistogram(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/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 GetPointCloud(request, target, diff --git a/weightslab/tests/gRPC/test_grpc_user_actions.py b/weightslab/tests/gRPC/test_grpc_user_actions.py index 05bfc639..349abfbd 100644 --- a/weightslab/tests/gRPC/test_grpc_user_actions.py +++ b/weightslab/tests/gRPC/test_grpc_user_actions.py @@ -280,6 +280,7 @@ def _make_real_data_service(self): # the first call proceeds (mirrors DataService.__init__). ds._update_done = threading.Event() ds._update_done.set() + ds._refresh_in_flight = threading.Lock() # mirrors __init__: bg view-refresh guard ds._df_manager = df_manager ds._all_datasets_df = df.copy() ds._compute_natural_sort = False diff --git a/weightslab/trainer/services/data_service.py b/weightslab/trainer/services/data_service.py index b12495c3..ddaccf6f 100755 --- a/weightslab/trainer/services/data_service.py +++ b/weightslab/trainer/services/data_service.py @@ -237,6 +237,10 @@ def __init__(self, ctx): self._update_lock = threading.Lock() self._update_done = threading.Event() self._update_done.set() # "done" initially so the very first call proceeds + # Guard so a non-force (reader-triggered) view refresh runs in the BACKGROUND + # at most once at a time — readers never pay the rebuild cost (they read the + # current snapshot; the bg thread swaps in fresh data when ready). + self._refresh_in_flight = threading.Lock() self._df_manager = get_dataframe() # init references to the context components @@ -2526,6 +2530,17 @@ def _watched_lock(self, lock_name: str = "_lock"): # ------------------------------------------------------------------ # Main update method # ------------------------------------------------------------------ + def _bg_view_refresh(self) -> None: + """Background view rebuild for reader-triggered (non-force) refreshes. Runs the + real rebuild+swap via force=True OFF the request path, then releases the guard so + a later stale read can trigger another. Never raises into a request.""" + try: + self._slowUpdateInternals(force=True) + except Exception: + logger.exception("[ViewRefresh] background view refresh failed") + finally: + self._refresh_in_flight.release() + def _slowUpdateInternals(self, force: bool = False, reset_view: bool = False) -> None: """Update the internal dataframe view with the latest data from the manager. @@ -2555,7 +2570,24 @@ def _slowUpdateInternals(self, force: bool = False, reset_view: bool = False) -> current_time - self._last_internals_update_time <= 10: return - # --- Try to become the single updater --- + # --- Non-force (reader-triggered) refresh: run it in the BACKGROUND --- + # The view is stale, but a reader (grid/histogram/periodic fetch) must NOT block + # on the multi-second collapse+combine rebuild. Kick a single background refresh + # (the WL-ViewRefresh thread calls force=True, which does the real rebuild+atomic + # swap below) and return immediately — the caller reads the current (last-completed) + # snapshot. If a refresh is already running, just return; the next fetch sees the swap. + if not force: + if self._refresh_in_flight.acquire(blocking=False): + try: + threading.Thread( + target=self._bg_view_refresh, name="WL-ViewRefresh", daemon=True + ).start() + except Exception: + self._refresh_in_flight.release() # never leak the guard + logger.exception("[ViewRefresh] failed to start background refresh") + return + + # --- Try to become the single updater (force path: rebuild inline) --- t_wait_start = time.time() acquired = self._update_lock.acquire(blocking=False) @@ -3262,11 +3294,17 @@ def ApplyDataQuery(self, request, context): # Apply operations with lock with self._watched_lock("_lock[ApplyDataQuery/ops]"): - # If this is JUST a view-sort for a specific page, DO NOT force an internal refresh - # as that wipes out the existing slice/sort state before we try to modify the next slice. - is_only_view_sort = len(operations) == 1 and operations[0].get("function") == "df.sort_view_slice" - if not is_only_view_sort: - self._slowUpdateInternals(force=True) # Refresh internals before applying Agent operations + # Skip the forced full-view rebuild for SORT-ONLY operations. Sorting just + # re-orders the existing snapshot, so a fresh collapse+combine (hundreds of + # ms on large views, and — being lock-held — contends with the training + # thread for multi-second stalls) is unnecessary. Filters/edits still refresh + # so they operate on the latest data. The view is frozen on direct queries + # anyway (_is_filtered=True), so it wasn't auto-refreshing mid-sort regardless. + _SORT_FUNCS = {"df.sort_values", "df.sort_index", "df.sort_view_slice"} + is_sort_only = bool(operations) and all( + op.get("function") in _SORT_FUNCS for op in operations) + if not is_sort_only: + self._slowUpdateInternals(force=True) # Refresh internals before applying non-sort operations # Work on a copy to allow concurrent readers to see a consistent state df = self._all_datasets_df # Remove copy because memory waste and slowdown @@ -3440,6 +3478,71 @@ def GetDataSamples(self, request, context): data_records=[] ) + def GetHistogram(self, request, context): + """Server-side histogram binning of one column (typed RPC). + + Bins the current all-data view by ROW ORDER into <= max_bins equal- + population bins; each bin carries {min,max,avg,count} over its finite + values plus a per-(origin,discarded) sub-bar breakdown. Returns typed + HistogramBin messages (no DataStat name-encoding). Empty bins are emitted + with count=0 and NaN stats so the client's positional bars stay aligned. + """ + try: + column = request.column or "" + max_bins = int(request.max_bins) if request.max_bins > 0 else 512 + df = getattr(self, "_all_datasets_df", None) + if df is None or df.empty: + return pb2.HistogramResponse( + success=False, message="empty dataframe view", total_rows=0, bins=[]) + df = safe_reset_index(df) + n = len(df) + if column not in df.columns: + return pb2.HistogramResponse( + success=False, message=f"column '{column}' not in view", + total_rows=n, bins=[]) + + bars = max(1, min(n, max_bins)) + vals = pd.to_numeric(df[column], errors="coerce").to_numpy() + origin = (df["origin"].astype(str).to_numpy() if "origin" in df.columns + else np.full(n, "")) + disc = (df["discarded"].astype(bool).to_numpy() if "discarded" in df.columns + else np.zeros(n, bool)) + edges = (np.arange(bars + 1) * n) // bars + bin_of_row = np.searchsorted(edges, np.arange(n), side="right") - 1 + fin = np.isfinite(vals) + gf = pd.DataFrame({"b": bin_of_row[fin], "v": vals[fin], + "o": origin[fin], "d": disc[fin]}) + stats = gf.groupby("b")["v"].agg(["min", "max", "mean", "count"]) + sub_by_bin = {} + for (b, d, o), c in gf.groupby(["b", "d", "o"]).size().items(): + sub_by_bin.setdefault(int(b), []).append( + pb2.HistogramSubBar(origin=str(o), discarded=bool(d), count=int(c))) + have = stats.index.to_numpy() + mn, mx, av, cn = (stats["min"].to_numpy(), stats["max"].to_numpy(), + stats["mean"].to_numpy(), stats["count"].to_numpy()) + pos = {int(b): i for i, b in enumerate(have)} + _nan = float("nan") + bins = [] + for b in range(bars): + i = pos.get(b) + if i is None: + bins.append(pb2.HistogramBin( + min=_nan, max=_nan, avg=_nan, count=0, sub_bars=[])) + else: + bins.append(pb2.HistogramBin( + min=float(mn[i]), max=float(mx[i]), avg=float(av[i]), + count=int(cn[i]), sub_bars=sub_by_bin.get(b, []))) + logger.info("[HistBin] column=%s rows=%d bins=%d", column, n, len(bins)) + return pb2.HistogramResponse( + success=True, + message=f"histogram {column}: {len(bins)} bins from {n} rows", + total_rows=n, bins=bins) + except Exception as e: + logger.error("Error in GetHistogram: %s", str(e), exc_info=True) + return pb2.HistogramResponse( + success=False, message=f"histogram failed: {str(e)}", + total_rows=0, bins=[]) + # Streamed chunk size for GetPointCloud (raw float32 bytes per message). _POINT_CLOUD_CHUNK_BYTES = 1 << 20 # 1 MiB diff --git a/weightslab/trainer/trainer_services.py b/weightslab/trainer/trainer_services.py index dfe1ad42..f6d6b040 100644 --- a/weightslab/trainer/trainer_services.py +++ b/weightslab/trainer/trainer_services.py @@ -334,6 +334,10 @@ def GetDataSamples(self, request, context): logger.debug(f"\nExperimentServiceServicer.GetDataSamples({request})") return self._exp_service.data_service.GetDataSamples(request, context) + def GetHistogram(self, request, context): + logger.debug(f"\nExperimentServiceServicer.GetHistogram({request})") + return self._exp_service.data_service.GetHistogram(request, context) + def GetPointCloud(self, request, context): logger.debug(f"\nExperimentServiceServicer.GetPointCloud({request})") # Server-streaming RPC: delegate the generator directly. From 5bcd3d6733585e04472a96b1dd277c0cfd67cd8f Mon Sep 17 00:00:00 2001 From: GuillaumePELLUET Date: Mon, 22 Jun 2026 11:42:46 +0200 Subject: [PATCH 2/2] Fix proto files and is/is not ValueProxy from ledgers to ==/!= --- .../PyTorch/ws-classification/main.py | 8 +- .../PyTorch/ws-detection/utils/data.py | 2 +- .../PyTorch/ws-segmentation/utils/data.py | 2 +- .../examples/Ultralytics/ws-detection/main.py | 2 +- .../ws-2d-lidar-detection/utils/data.py | 2 +- .../ws-3d-lidar-detection/utils/data.py | 2 +- weightslab/proto/experiment_service.proto | 2 + weightslab/proto/experiment_service_pb2.py | 247 +++++------------- .../proto/experiment_service_pb2_grpc.py | 40 ++- 9 files changed, 116 insertions(+), 191 deletions(-) diff --git a/weightslab/examples/PyTorch/ws-classification/main.py b/weightslab/examples/PyTorch/ws-classification/main.py index 8f2ec0d0..8dc53e78 100644 --- a/weightslab/examples/PyTorch/ws-classification/main.py +++ b/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) @@ -371,7 +371,7 @@ 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, @@ -379,7 +379,7 @@ def test(loader, model, criterion_mlt, metric_mlt, device, test_loader_len): 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 diff --git a/weightslab/examples/PyTorch/ws-detection/utils/data.py b/weightslab/examples/PyTorch/ws-detection/utils/data.py index 93e60dd1..c4823197 100644 --- a/weightslab/examples/PyTorch/ws-detection/utils/data.py +++ b/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 = [] diff --git a/weightslab/examples/PyTorch/ws-segmentation/utils/data.py b/weightslab/examples/PyTorch/ws-segmentation/utils/data.py index 864c6853..28ba4dde 100644 --- a/weightslab/examples/PyTorch/ws-segmentation/utils/data.py +++ b/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 = [] diff --git a/weightslab/examples/Ultralytics/ws-detection/main.py b/weightslab/examples/Ultralytics/ws-detection/main.py index d2ba1a61..ea1876e3 100644 --- a/weightslab/examples/Ultralytics/ws-detection/main.py +++ b/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, diff --git a/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py b/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py index 10e19736..fd544bf3 100644 --- a/weightslab/examples/Usecases/ws-2d-lidar-detection/utils/data.py +++ b/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) diff --git a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py b/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py index 77df470b..45a15a0a 100644 --- a/weightslab/examples/Usecases/ws-3d-lidar-detection/utils/data.py +++ b/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})") diff --git a/weightslab/proto/experiment_service.proto b/weightslab/proto/experiment_service.proto index 0165a56b..770965fd 100644 --- a/weightslab/proto/experiment_service.proto +++ b/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) diff --git a/weightslab/proto/experiment_service_pb2.py b/weightslab/proto/experiment_service_pb2.py index d1f48342..6a521ac8 100644 --- a/weightslab/proto/experiment_service_pb2.py +++ b/weightslab/proto/experiment_service_pb2.py @@ -2,7 +2,7 @@ # Generated by the protocol buffer compiler. 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_globals['_CANCELEVALUATIONREQUEST']._serialized_start=9040 - _globals['_CANCELEVALUATIONREQUEST']._serialized_end=9081 - _globals['_CANCELEVALUATIONRESPONSE']._serialized_start=9083 - _globals['_CANCELEVALUATIONRESPONSE']._serialized_end=9143 - _globals['_EXPERIMENTSERVICE']._serialized_start=9561 - _globals['_EXPERIMENTSERVICE']._serialized_end=10900 _globals['_SAVECHECKPOINTOPERATION']._serialized_start=2469 _globals['_SAVECHECKPOINTOPERATION']._serialized_end=2545 _globals['_TRAINERCOMMAND']._serialized_start=2548 @@ -250,52 +135,60 @@ _globals['_DATARECORD']._serialized_end=7277 _globals['_DATASAMPLESRESPONSE']._serialized_start=7279 _globals['_DATASAMPLESRESPONSE']._serialized_end=7369 - _globals['_GETMETADATAREQUEST']._serialized_start=7371 - _globals['_GETMETADATAREQUEST']._serialized_end=7458 - _globals['_GETMETADATARESPONSE']._serialized_start=7461 - _globals['_GETMETADATARESPONSE']._serialized_end=7614 - _globals['_POINTCLOUDREQUEST']._serialized_start=7616 - 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_globals['_CHANGEAGENTMODELRESPONSE']._serialized_start=8482 - _globals['_CHANGEAGENTMODELRESPONSE']._serialized_end=8542 - _globals['_GETAGENTMODELSREQUEST']._serialized_start=8544 - _globals['_GETAGENTMODELSREQUEST']._serialized_end=8567 - _globals['_GETAGENTMODELSRESPONSE']._serialized_start=8569 - _globals['_GETAGENTMODELSRESPONSE']._serialized_end=8643 - _globals['_RESETAGENTRESPONSE']._serialized_start=8645 - _globals['_RESETAGENTRESPONSE']._serialized_end=8699 - _globals['_RESTORECHECKPOINTREQUEST']._serialized_start=8701 - _globals['_RESTORECHECKPOINTREQUEST']._serialized_end=8752 - _globals['_RESTORECHECKPOINTRESPONSE']._serialized_start=8754 - _globals['_RESTORECHECKPOINTRESPONSE']._serialized_end=8815 - _globals['_TRIGGEREVALUATIONREQUEST']._serialized_start=8817 - _globals['_TRIGGEREVALUATIONREQUEST']._serialized_end=8899 - _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_start=8901 - _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_end=8962 - 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_globals['_HISTOGRAMRESPONSE']._serialized_end=7702 + _globals['_GETMETADATAREQUEST']._serialized_start=7704 + _globals['_GETMETADATAREQUEST']._serialized_end=7791 + _globals['_GETMETADATARESPONSE']._serialized_start=7794 + _globals['_GETMETADATARESPONSE']._serialized_end=7947 + _globals['_POINTCLOUDREQUEST']._serialized_start=7949 + _globals['_POINTCLOUDREQUEST']._serialized_end=8023 + _globals['_POINTCLOUDCHUNK']._serialized_start=8026 + _globals['_POINTCLOUDCHUNK']._serialized_end=8217 + _globals['_DATAEDITSREQUEST']._serialized_start=8220 + _globals['_DATAEDITSREQUEST']._serialized_end=8440 + _globals['_DATAEDITSRESPONSE']._serialized_start=8442 + _globals['_DATAEDITSRESPONSE']._serialized_end=8495 + _globals['_DATASPLITSRESPONSE']._serialized_start=8497 + _globals['_DATASPLITSRESPONSE']._serialized_end=8555 + _globals['_AGENTHEALTHRESPONSE']._serialized_start=8557 + _globals['_AGENTHEALTHRESPONSE']._serialized_end=8614 + _globals['_INITIALIZEAGENTREQUEST']._serialized_start=8616 + _globals['_INITIALIZEAGENTREQUEST']._serialized_end=8710 + _globals['_INITIALIZEAGENTRESPONSE']._serialized_start=8712 + _globals['_INITIALIZEAGENTRESPONSE']._serialized_end=8771 + _globals['_CHANGEAGENTMODELREQUEST']._serialized_start=8773 + _globals['_CHANGEAGENTMODELREQUEST']._serialized_end=8813 + _globals['_CHANGEAGENTMODELRESPONSE']._serialized_start=8815 + _globals['_CHANGEAGENTMODELRESPONSE']._serialized_end=8875 + _globals['_GETAGENTMODELSREQUEST']._serialized_start=8877 + _globals['_GETAGENTMODELSREQUEST']._serialized_end=8900 + _globals['_GETAGENTMODELSRESPONSE']._serialized_start=8902 + _globals['_GETAGENTMODELSRESPONSE']._serialized_end=8976 + _globals['_RESETAGENTRESPONSE']._serialized_start=8978 + _globals['_RESETAGENTRESPONSE']._serialized_end=9032 + _globals['_RESTORECHECKPOINTREQUEST']._serialized_start=9034 + _globals['_RESTORECHECKPOINTREQUEST']._serialized_end=9085 + _globals['_RESTORECHECKPOINTRESPONSE']._serialized_start=9087 + _globals['_RESTORECHECKPOINTRESPONSE']._serialized_end=9148 + _globals['_TRIGGEREVALUATIONREQUEST']._serialized_start=9150 + _globals['_TRIGGEREVALUATIONREQUEST']._serialized_end=9232 + _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_start=9234 + _globals['_TRIGGEREVALUATIONRESPONSE']._serialized_end=9295 + _globals['_GETEVALUATIONSTATUSREQUEST']._serialized_start=9297 + _globals['_GETEVALUATIONSTATUSREQUEST']._serialized_end=9325 + _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_start=9328 + _globals['_GETEVALUATIONSTATUSRESPONSE']._serialized_end=9457 + _globals['_CANCELEVALUATIONREQUEST']._serialized_start=9459 + _globals['_CANCELEVALUATIONREQUEST']._serialized_end=9500 + _globals['_CANCELEVALUATIONRESPONSE']._serialized_start=9502 + _globals['_CANCELEVALUATIONRESPONSE']._serialized_end=9562 + _globals['_EXPERIMENTSERVICE']._serialized_start=9980 + _globals['_EXPERIMENTSERVICE']._serialized_end=11377 # @@protoc_insertion_point(module_scope) diff --git a/weightslab/proto/experiment_service_pb2_grpc.py b/weightslab/proto/experiment_service_pb2_grpc.py index ba9759d1..e75d391c 100644 --- a/weightslab/proto/experiment_service_pb2_grpc.py +++ b/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,