# Load ONNX model print('--> Loading model') ret = rknn.load_onnx(model=ONNX_MODEL) if ret != 0: print('Load yolov5 failed!') exit(ret) print('done') # Build model print('--> Building model') ret = rknn.build(do_quantization=False, dataset=DATASET) if ret != 0: print(...
如果是BGR将会被#调整为BGRrknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2')print('done')#Load tensorflow modelret = rknn.load_onnx(model ='./centerface_1088_1920.onnx')ifret !=0:print('Load model failed!') exit(ret)print('done')#Build modelprint('--> Buildi...
构造完模型之后,用下面这三行代码来检查模型正确性、把模型以文本形式输出、存储到一个 “.onnx” 文件里。这里用 onnx.checker.check_model 来检查模型是否满足 ONNX 标准是必要的,因为无论模型是否满足标准,ONNX 都允许我们用 onnx.save 存储模型 onnx.checker.check_model(model) print(model) onnx.save(...
rknn.config(reorder_channel='1 2 0', optimization_level=3, target_platform = 'rk3399pro',output_optimize=1)print('--> Loading model') ret = rknn.load_onnx(model=ONNX_MODEL, input_size_list=[1, 3, 224, 398])if ret != 0: print('Load model failed!') exit(ret) print('...
CRNN(self.opt.input_height,1,self.class_num,128,export_onnx=True) self.__load_weights(...
load: model: ./best.onnxinputs: in_0: shape: [3, 640, 640] mean_values: [0, 0, 0] std_values: [255, 255, 255] img_type: RGBoutputs: export_pre_compile_path: ./model_cvt/RV1109_1126/best_RV1109_1126_u8_precompile.rknn —-> Create RKNN object—-> Seting RKNN config—-...
ret=rknn.load_onnx(model='model.onnx') ifret!= 0: print('Load ONNX model failed!') exit(ret) print('done') #print('done') #Buildmodel print('--> Building model') ret=rknn.build(do_quantization=False) ifret!= 0: print('Build RKNN model failed!') ...
# Load ONNX model ret = rknn.load_onnx(model=ONNX_MODEL, outputs=['/model.28/Mul_2_output_0', '/model.28 /Sigmoid_output_0']) 需要注意的是,这里得到的输出只是模型的输出(两个四维张量),并非检测结果!为了得到有用的检测框和类别信息,还需要手动进行后处理操作。这里我们将 outputs 的维度输...
rknn-toolkit-master\rknn-toolkit-master\examples\onnx\yolov5目录下修改test.py 默认的平台是rk1808,修改target_platform为rv1109和rv1126,如果只是写rv1109应该也可以。 rknn.config(reorder_channel='0 1 2', mean_values=[[0,0,0]], std_values=[[255,255,255]], ...