fix inf,-inf with 2 concurrency

machine-learning/app/sessions/rknn.py

@@ -6,7 +6,7 @@ from typing import Any, List
 import numpy as np
 import onnxruntime as ort
 from numpy.typing import NDArray
-from rknnlite.api import RKNNLite
+from rknn.rknnpool import rknnPoolExecutor
 
 
 from app.models.constants import SUPPORTED_PROVIDERS
@@ -14,38 +14,40 @@ from app.schemas import SessionNode
 
 from ..config import log, settings
 
+def runInfrence(rknn_lite, input):
+    outputs = rknn_lite.inference(inputs=[input], data_format='nchw')
+
+    return outputs
 
 class RknnSession:
     def __init__(self, model_path: Path | str):
+
         self.model_path = Path(model_path)
-
         self.ort_model_path = str(self.model_path).replace(".rknn", ".onnx")
+        log.info(f"Loading RKNN model from {self.model_path} with {1 if 'textual' in str(self.model_path) else 2} threads.")
+        self.rknnpool = rknnPoolExecutor(
+            rknnModel=self.model_path.as_posix(),
+            TPEs= 1 if 'textual' in str(self.model_path) else 2,
+            func=runInfrence)
 
-        self.rknn = RKNNLite()
-
-        log.info(f"Loading RKNN model from {self.model_path}")
 
         self.ort_session = ort.InferenceSession(
             self.ort_model_path,
         )
+        self.inputs = self.ort_session.get_inputs()
+        self.outputs = self.ort_session.get_outputs()
 
-        # ret = self.rknn.load_onnx(self.model_path)
+        del self.ort_session
-        print('--> Load RKNN model')
-        ret = self.rknn.load_rknn(self.model_path.as_posix())
-        if ret != 0:
-            raise RuntimeError("Failed to load RKNN model")
 
-        ret = self.rknn.init_runtime()
+
-        if ret != 0:
+    def __del__(self):
-             raise RuntimeError("Failed to initialize RKNN runtime")
+        self.rknnpool.release()
 
     def get_inputs(self) -> list[SessionNode]:
-        inputs: list[SessionNode] = self.ort_session.get_inputs()
+        return self.inputs
-        return inputs
 
     def get_outputs(self) -> list[SessionNode]:
-        outputs: list[SessionNode] = self.ort_session.get_outputs()
+        return self.outputs
-        return outputs
 
     def run(
         self,
@@ -55,15 +57,7 @@ class RknnSession:
     ):
 
         input_data = [np.ascontiguousarray(v) for v in input_feed.values()][0]
-
+        self.rknnpool.put(input_data)
-
+        outputs = self.rknnpool.get()[0]
-        # log.info(f"Running inference on RKNN model")
-        outputs = self.rknn.inference(inputs=[input_data], data_format='nchw')
-
-        # log.info("inputs:")
-        # log.info(input_data)
-        # log.info("outputs:")
-        # log.info(outputs)
-        # log.info("RKNN END")
         return outputs
 

machine-learning/rknn/rknnpool.py

@@ -0,0 +1,53 @@
+# This code is from leafqycc/rknn-multi-threaded
+# Following Apache License 2.0
+
+from queue import Queue
+from rknnlite.api import RKNNLite
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+
+def initRKNN(rknnModel="./rknnModel/yolov5s.rknn", id=0):
+    rknn_lite = RKNNLite()
+    ret = rknn_lite.load_rknn(rknnModel)
+    if ret != 0:
+        print("Load RKNN rknnModel failed")
+        exit(ret)
+    ret = rknn_lite.init_runtime()
+    if ret != 0:
+        print("Init runtime environment failed")
+        exit(ret)
+    print(rknnModel, "\t\tdone")
+    return rknn_lite
+
+
+def initRKNNs(rknnModel="./rknnModel/yolov5s.rknn", TPEs=1):
+    rknn_list = []
+    for i in range(TPEs):
+        rknn_list.append(initRKNN(rknnModel, i % 3))
+    return rknn_list
+
+
+class rknnPoolExecutor():
+    def __init__(self, rknnModel, TPEs, func):
+        self.TPEs = TPEs
+        self.queue = Queue()
+        self.rknnPool = initRKNNs(rknnModel, TPEs)
+        self.pool = ThreadPoolExecutor(max_workers=TPEs)
+        self.func = func
+        self.num = 0
+
+    def put(self, frame):
+        self.queue.put(self.pool.submit(
+            self.func, self.rknnPool[self.num % self.TPEs], frame))
+        self.num += 1
+
+    def get(self):
+        if self.queue.empty():
+            return None, False
+        fut = self.queue.get()
+        return fut.result(), True
+
+    def release(self):
+        self.pool.shutdown()
+        for rknn_lite in self.rknnPool:
+            rknn_lite.release()