config(mean_values=[[0, 0, 0]], std_values=[ [255, 255, 255]], target_platform=platform) print('done') # Load model print('--> Loading model') ret = rknn.load_onnx(model=model_path) if ret != 0: print('Load model failed!') exit(ret) print('done') # Build model print...
rknn.config(mean_values=[[123.675, 116.28, 103.53]], std_values=[[58.82, 58.82, 58.82]], reorder_channel='0 1 2') print('done') 复制代码
mean_values=[[0,0,0]], std_values=[[255,255,255]], optimization_level=3, target_platform=['rv1109'], output_optimize=1, quantize_input_node=QUANTIZE_ON) 修改rknn.init_runtime函数,设置平台为rv1109,device_id可以不填,那个是PC模拟挂载多个板卡才需要指定。 ret = rknn.init_runtime('rv110...
rknn.config(reorder_channel='0 1 2', mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], optimization_level=3, target_platform = 'rv1126', output_optimize=1, quantize_input_node=QUANTIZE_ON) print('done') # Load ONNX model print('--> Loading model') ret = rknn.load_o...
height、width,--inputs是模型输入节点的名称，--mean-values是图片预处理时的mean和std，--source-...
# 239行 rknn = RKNN(verbose=True) 替换为 rknn = RKNN() # 243行 rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128]) 替换为 rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], target_platform='rk3588') # 272行 ret = rknn.init_runtime(...
mean_values=[[0, 0, 0]],std_values=[[255, 255, 255]],optimization_level=3,target_platform = 'rk1808',output_optimize=1)ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)轉檔都沒有問題,但是使用一張題圖片做quantization 跟使用2000張圖片做quantization...
rknn.config(batch_size=1, mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], reorder_channel='0 1 2', target_platform=[platform], force_builtin_perm=add_perm, output_optimize=1) print('done') # Loadtensorflowmodel
mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], optimization_level=3, target_platform='rv1126', output_optimize=1, quantize_input_node=QUANTIZE_ON)print('done') # Load ONNX modelprint('--> Loading model') ret = rknn.load_onnx(model=ONNX_MODEL,outputs=['output','405...
if__name__=='__main__':model_path,platform,do_quant,output_path=parse_arg()# Create RKNN objectrknn=RKNN(verbose=False)# Pre-process configprint('--> Config model')rknn.config(mean_values=[[0,0,0]],std_values=[[255,255,255]],target_platform=platform)print('done')# Load modelpr...