add: multi batch setting

frigate/app.py

@@ -4,6 +4,7 @@ import multiprocessing as mp
 import os
 import secrets
 import shutil
+import threading
 from multiprocessing import Queue
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Optional
@@ -101,7 +102,9 @@ class FrigateApp:
         self.processes: dict[str, int] = {}
         self.embeddings: Optional[EmbeddingsContext] = None
         self.region_grids: dict[str, list[list[dict[str, int]]]] = {}
-        self.frame_manager = SharedMemoryFrameManager()
+        self.frame_manager = SharedMemoryFrameManager(
+            frame_shape=(config.model.height, config.model.width, 3)
+        )
         self.config = config
 
     def ensure_dirs(self) -> None:
@@ -359,9 +362,12 @@ class FrigateApp:
             try:
                 largest_frame = max(
                     [
-                        det.model.height * det.model.width * 3
+                        (
-                        if det.model is not None
+                            det.model.height * det.model.width * 3 * det.model.max_batch
-                        else 320
+                            + 8
+                            if det.model is not None
+                            else 320
+                        )
                         for det in self.config.detectors.values()
                     ]
                 )
@@ -375,7 +381,9 @@ class FrigateApp:
 
             try:
                 shm_out = UntrackedSharedMemory(
-                    name=f"out-{name}", create=True, size=20 * 6 * 4
+                    name=f"out-{name}",
+                    create=True,
+                    size=20 * 6 * 4 * self.config.model.max_batch + 8,
                 )
             except FileExistsError:
                 shm_out = UntrackedSharedMemory(name=f"out-{name}")

frigate/detectors/detector_config.py

@@ -47,6 +47,7 @@ class ModelConfig(BaseModel):
     labelmap_path: Optional[str] = Field(
         None, title="Label map for custom object detector."
     )
+    max_batch: int = Field(default=4, title="Max batch size.")
     width: int = Field(default=320, title="Object detection model input width.")
     height: int = Field(default=320, title="Object detection model input height.")
     labelmap: Dict[int, str] = Field(

frigate/object_detection.py

@@ -18,7 +18,7 @@ from frigate.detectors.detector_config import (
     InputTensorEnum,
 )
 from frigate.util.builtin import EventsPerSecond, load_labels
-from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
+from frigate.util.image import SharedMemoryFrameManager, SharedMemoryResultManager
 from frigate.util.services import listen
 
 logger = logging.getLogger(__name__)
@@ -111,23 +111,19 @@ def run_detector(
     signal.signal(signal.SIGINT, receiveSignal)
 
     frame_manager = SharedMemoryFrameManager()
+    result_manager = SharedMemoryResultManager()
     object_detector = LocalObjectDetector(detector_config=detector_config)
 
-    outputs = {}
     for name in out_events.keys():
-        out_shm = UntrackedSharedMemory(name=f"out-{name}", create=False)
+        result_manager.create(name=f"out-{name}")
-        out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
-        outputs[name] = {"shm": out_shm, "np": out_np}
 
     while not stop_event.is_set():
         try:
             connection_id = detection_queue.get(timeout=1)
         except queue.Empty:
             continue
-        input_frame = frame_manager.get(
+
-            connection_id,
+        input_frame = frame_manager.get_frame(connection_id)
-            (1, detector_config.model.height, detector_config.model.width, 3),
-        )
 
         if input_frame is None:
             logger.warning(f"Failed to get frame {connection_id} from SHM")
@@ -138,7 +134,8 @@ def run_detector(
         detections = object_detector.detect_raw(input_frame)
         duration = datetime.datetime.now().timestamp() - start.value
         frame_manager.close(connection_id)
-        outputs[connection_id]["np"][:] = detections[:]
+
+        result_manager.write_result(f"out-{connection_id}", detections)
         out_events[connection_id].set()
         start.value = 0.0
 
@@ -198,21 +195,27 @@ class ObjectDetectProcess:
 
 
 class RemoteObjectDetector:
-    def __init__(self, name, labels, detection_queue, event, model_config, stop_event):
+    def __init__(
+        self,
+        name,
+        labels,
+        detection_queue,
+        event,
+        model_config,
+        stop_event,
+        frame_manager: SharedMemoryFrameManager,
+    ):
         self.labels = labels
         self.name = name
         self.fps = EventsPerSecond()
         self.detection_queue = detection_queue
         self.event = event
         self.stop_event = stop_event
-        self.shm = UntrackedSharedMemory(name=self.name, create=False)
+        self.frame_manager = frame_manager
-        self.np_shm = np.ndarray(
+
-            (1, model_config.height, model_config.width, 3),
+        self.result_manager = SharedMemoryResultManager(
-            dtype=np.uint8,
+            max_frame=model_config.max_batch
-            buffer=self.shm.buf,
         )
-        self.out_shm = UntrackedSharedMemory(name=f"out-{self.name}", create=False)
-        self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
 
     def detect(self, tensor_input, threshold=0.4):
         detections = []
@@ -221,7 +224,8 @@ class RemoteObjectDetector:
             return detections
 
         # copy input to shared memory
-        self.np_shm[:] = tensor_input[:]
+        self.frame_manager.write_frame(self.name, tensor_input)
+
         self.event.clear()
         self.detection_queue.put(self.name)
         result = self.event.wait(timeout=5.0)
@@ -230,15 +234,23 @@ class RemoteObjectDetector:
         if result is None:
             return detections
 
-        for d in self.out_np_shm:
+        batch_result_np = self.result_manager.get_result(f"out-{self.name}")
-            if d[1] < threshold:
+
-                break
+        if not isinstance(batch_result_np, np.ndarray):
-            detections.append(
+            return detections
-                (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
+
-            )
+        for result_np in batch_result_np:
+            tmp_detections = []
+            for d in result_np:
+                if d[1] < threshold:
+                    break
+                tmp_detections.append(
+                    (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
+                )
+            detections.append(tmp_detections)
         self.fps.update()
         return detections
 
     def cleanup(self):
-        self.shm.unlink()
+        self.result_manager.cleanup()
-        self.out_shm.unlink()
+        self.frame_manager.cleanup()

frigate/video.py

@@ -9,6 +9,7 @@ import threading
 import time
 
 import cv2
+import numpy as np
 from setproctitle import setproctitle
 
 from frigate.camera import CameraMetrics, PTZMetrics
@@ -503,14 +504,20 @@ def track_camera(
         name=config.name,
         ptz_metrics=ptz_metrics,
     )
+
+    frame_manager = SharedMemoryFrameManager(max_frame=model_config.max_batch)
     object_detector = RemoteObjectDetector(
-        name, labelmap, detection_queue, result_connection, model_config, stop_event
+        name,
+        labelmap,
+        detection_queue,
+        result_connection,
+        model_config,
+        stop_event,
+        frame_manager,
     )
 
     object_tracker = NorfairTracker(config, ptz_metrics)
 
-    frame_manager = SharedMemoryFrameManager()
-
     # create communication for region grid updates
     requestor = InterProcessRequestor()
 
@@ -549,35 +556,53 @@ def detect(
     object_detector,
     frame,
     model_config: ModelConfig,
-    region,
+    regions,
     objects_to_track,
     object_filters,
+    multi_batch: bool = False,
 ):
-    tensor_input = create_tensor_input(frame, model_config, region)
+    if multi_batch:
+        tensor_list = []
+        for i, region in enumerate(regions):
+            if i > model_config.max_batch:
+                logger.info(f"batch is too large, skipping")
+                break
+            tensor_list.append(create_tensor_input(frame, model_config, region))
+        tensor_input = np.concatenate(tensor_list, axis=0)
+    else:
+        tensor_input = create_tensor_input(frame, model_config, regions)
 
+    region_detections_list = object_detector.detect(tensor_input)
     detections = []
-    region_detections = object_detector.detect(tensor_input)
+    if not multi_batch:
-    for d in region_detections:
+        region_detections_list = [region_detections_list]
-        box = d[2]
+        regions = [regions]
-        size = region[2] - region[0]
-        x_min = int(max(0, (box[1] * size) + region[0]))
-        y_min = int(max(0, (box[0] * size) + region[1]))
-        x_max = int(min(detect_config.width - 1, (box[3] * size) + region[0]))
-        y_max = int(min(detect_config.height - 1, (box[2] * size) + region[1]))
 
-        # ignore objects that were detected outside the frame
+    for region_detections, region in zip(region_detections_list, regions):
-        if (x_min >= detect_config.width - 1) or (y_min >= detect_config.height - 1):
+        for d in region_detections:
-            continue
+            box = d[2]
+            size = region[2] - region[0]
+            x_min = int(max(0, (box[1] * size) + region[0]))
+            y_min = int(max(0, (box[0] * size) + region[1]))
+            x_max = int(min(detect_config.width - 1, (box[3] * size) + region[0]))
+            y_max = int(min(detect_config.height - 1, (box[2] * size) + region[1]))
+
+            # ignore objects that were detected outside the frame
+            if (x_min >= detect_config.width - 1) or (
+                y_min >= detect_config.height - 1
+            ):
+                continue
+
+            width = x_max - x_min
+            height = y_max - y_min
+            area = width * height
+            ratio = width / max(1, height)
+            det = (d[0], d[1], (x_min, y_min, x_max, y_max), area, ratio, region)
+            # apply object filters
+            if is_object_filtered(det, objects_to_track, object_filters):
+                continue
+            detections.append(det)
 
-        width = x_max - x_min
-        height = y_max - y_min
-        area = width * height
-        ratio = width / max(1, height)
-        det = (d[0], d[1], (x_min, y_min, x_max, y_max), area, ratio, region)
-        # apply object filters
-        if is_object_filtered(det, objects_to_track, object_filters):
-            continue
-        detections.append(det)
     return detections
 
 
@@ -618,6 +643,8 @@ def process_frames(
     attributes_map = model_config.attributes_map
     all_attributes = model_config.all_attributes
 
+    multi_batch = model_config.max_batch > 1
+
     # remove license_plate from attributes if this camera is a dedicated LPR cam
     if camera_config.type == CameraTypeEnum.lpr:
         modified_attributes_map = model_config.attributes_map.copy()
@@ -813,18 +840,32 @@ def process_frames(
                 if obj["id"] in stationary_object_ids
             ]
 
-            for region in regions:
+            if multi_batch and len(regions) > 0:
                 detections.extend(
                     detect(
                         detect_config,
                         object_detector,
                         frame,
                         model_config,
-                        region,
+                        regions,
                         objects_to_track,
                         object_filters,
+                        multi_batch,
                     )
                 )
+            else:
+                for region in regions:
+                    detections.extend(
+                        detect(
+                            detect_config,
+                            object_detector,
+                            frame,
+                            model_config,
+                            region,
+                            objects_to_track,
+                            object_filters,
+                        )
+                    )
 
             consolidated_detections = reduce_detections(frame_shape, detections)