Upload demo_qnn.py

model_farm_yolov5n_qcs8550_qnn2.16_int8_aidlite/python/demo_qnn.py

@@ -284,9 +284,13 @@ def main():
         
         if result != 0:
             print("interpreter set_input_tensor() failed")
-        stride8 = interpreter.get_output_tensor(0)
-        stride16 = interpreter.get_output_tensor(1)
-        stride32 = interpreter.get_output_tensor(2)
+    
+    out1 = interpreter.get_output_tensor(0)
+    out2 = interpreter.get_output_tensor(1)
+    out3 = interpreter.get_output_tensor(2)
+    
+    output  = [out1,out2,out3]
+    output = sorted(output,key=len)
    
 
     result = interpreter.destory()
@@ -303,9 +307,9 @@ def main():
     ##  后处理
     stride = [8, 16, 32]
     yolo_head = Detect(OBJ_CLASS_NUM, anchors, stride, MODEL_SIZE)
-    validCount0 = stride8.reshape(*output_shapes[2]).transpose(0, 3, 1, 2)
-    validCount1 = stride16.reshape(*output_shapes[1]).transpose(0, 3, 1, 2)
-    validCount2 = stride32.reshape(*output_shapes[0]).transpose(0, 3, 1, 2)
+    validCount0 = output[2].reshape(*output_shapes[2]).transpose(0, 3, 1, 2)
+    validCount1 = output[1].reshape(*output_shapes[1]).transpose(0, 3, 1, 2)
+    validCount2 = output[0].reshape(*output_shapes[0]).transpose(0, 3, 1, 2)
     pred = yolo_head([validCount0, validCount1, validCount2])
     det_pred = detect_postprocess(pred, frame.shape, [MODEL_SIZE, MODEL_SIZE, 3], conf_thres=0.5, iou_thres=0.45)
     det_pred[np.isnan(det_pred)] = 0.0