if (RKNN_TENSOR_NHWC == input_attrs[0].fmt) { m_in_height = input_attrs[0].dims[1]; m_in_width = input_attrs[0].dims[2]; m_in_channel = input_attrs[0].dims[3]; } else if (RKNN_TENSOR_NCHW == input_attrs[0].fmt) { m_in_height = input_attrs[0].dims[2]; m_in_...
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('rv1109') 激活当创建虚拟环境: conda activate rknn 切换到对应的目录下,然后执行: python test.py 可以看到RKNN model已经...
# target_platform='rv1109', quantize_input_node= QUANTIZE_ON, batch_size = 200, output_optimize=1, force_builtin_perm=_force_builtin_perm ) print('done') # Load PT model print('--> Loading model') ret = rknn.load_pytorch(model=pt_model, input_size_list=[[3,416, 416]]) if ...
input_node = model.graph.input[0] # 修改输入尺寸 input_node.type.tensor_type.shape.dim[0].dim_value = H # 设定输入高度H input_node.type.tensor_type.shape.dim[1].dim_value = W # 设定输入宽度W input_node.type.tensor_type.shape.dim[2].dim_value = C # 设定输入通道数C # 保存修改...
quantize_input_node: False merge_dequant_layer_and_output_node: False force_builtin_perm: False reorder_channel: 0 1 2export_rknn: export_path: ./model_cvt/RV1109_1126/best_RV1109_1126_u8.rknnverbose: Falsedataset: ./../../../../../datasets/COCO/VOC_dataset_1.txtquantize: Truebui...
[255, 255, 255]], optimization_level=3, target_platform = 'rv1109', quantize_input_node= QUANTIZE_ON, output_optimize=1, force_builtin_perm=_force_builtin_perm) print('done') # Load ONNX model print('--> Loading model') ret = rknn.load_pytorch(model=PT_MODEL, input_size_list=...
= opt self.input_w,self.input_h = opt.input_size self.QUANTIZE_ON = True s...
inputs=['images'], input_size_list=[[1,3,640,640]], outputs=[ '/model.22/Mul_5_output_0', '/model.22/Split_1_output_1', ] ) rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET, rknn_batch_size=1) rknn.export_rknn(RKNN_MODEL) ...
node.op.axes = [axis_map[axis] for axis in node.op.axes] 1. 2. 3. 4. 5. 6. 7. 8. 模型量化 snpe-onnx-to-dlc的默认输出是未量化的模型。 这意味着所有网络参数都保留在原始ONNX模型中的32位浮点表示中。 要将模型量化为8位固定点, 请注意,要使用snpe-dlc-quantize进行量化的模型必须将其...
So the inputs of inference should be set to correct size. We also need to process the returned outputs on post processing. 2 6 http://t.rock-chips.com Return 0: Build successfully value -1: Build failed The sample code is as follows: # Build and quantize RKNN model ret = rknn....