How to use MaixPy3 nn to load custom models

[Protozzzer]

I have some problem with understanding how to use own models with MaixPy3 nn on Sipeed MAIX-II Dock(V831). In your example scripts is presented resnet model, and it works well. I want to load into sipeed other cnn model, for example yolo3 or mobilenet_ssd for object detection and some other models, that have different formats of popular frameworks( tflite, keras h5, tensorflow pb). In your example model is presented via 2 files: resnet.bin and resnet.param. How can i convert my files of models (h5, or pb, or tflite) into this representation to load with nn lib? Maybe there are some open-source scripts for this purpose? I cant find some way(scripts) to create resnet.param. In this file was written some params, that i dont understand. I will be glad your feedback!

[ar055]

I subscribe to the question. How to train and convert your own network models? Ok found https://www.maixhub.com/modelConvert Will only online conversion be possible?

[mchuv]

Hi, I have the same question, i've tested the demos and it works, but there is no information how to use custom network models. In this page: https://www.maixhub.com/ModelConvertHelp.html There are instructions to create a ZIP file but not how to create the .bin and .params files.

I hope some body could help us, because Maix-II looks very good and a looks like true replacement for raspberry pi 4 and maybe Jetson Nano for some cases, for simple image processing.

[Neutree]

the doc is not ready yet, but you can read this post( wriiten with Chinese so you may need to use google translate ) https://neucrack.com/p/358

[thepian]

It seems that the blog post has translation disabled. Not appearing on Chrome & Safari

[mchuv]

the doc is not ready yet, but you can read this post( wriiten with Chinese so you may need to use google translate ) https://neucrack.com/p/358

Thank you very much.

Google tranlator doesn't work, but the source code is in english. By the way the neural networks are made with Tourch, not Tensorflow , with Maix-I the kformat uses tflite as source.

To help others like me here is de summary: The example works with PyTourch first you musta create a model, second translate it to ONNX, and finally translate the ONNX to NCNN.

This are the codes of the functions:

def torch_to_onnx(net, input_shape, out_name="out/model.onnx", input_names=["input0"], output_names=["output0"], device="cpu"): batch_size = 1 if len(input_shape) == 3: x = torch.randn(batch_size, input_shape[0], input_shape[1], input_shape[2], dtype=torch.float32, requires_grad=True).to(device) elif len(input_shape) == 1: x = torch.randn(batch_size, input_shape[0], dtype=torch.float32, requires_grad=False).to(device) else: raise Exception("not support input shape")

print("input shape:", x.shape)
# torch.onnx._export(net, x, "out/conv0.onnx", export_params=True)
torch.onnx.export(net, x, out_name, export_params=True, input_names = input_names, output_names=output_names)
onnx_out="out/resnet_1000.onnx"
ncnn_out_param = "out/resnet_1000.param"
ncnn_out_bin = "out/resnet_1000.bin"
input_img = filename

def onnx_to_ncnn(input_shape, onnx="out/model.onnx", ncnn_param="out/conv0.param", ncnn_bin = "out/conv0.bin"): import os # onnx2ncnn tool compiled from ncnn/tools/onnx, and in the buld dir cmd = f"onnx2ncnn {onnx} {ncnn_param} {ncnn_bin}" os.system(cmd) with open(ncnn_param) as f: content = f.read().split("\n") if len(input_shape) == 1: content[2] += " 0={}".format(input_shape[0]) else: content[2] += " 0={} 1={} 2={}".format(input_shape[2], input_shape[1], input_shape[0]) content = "\n".join(content) with open(ncnn_param, "w") as f: f.write(content)

First you must have the model and the input shape:

model = xxxxxx. # Code to crate a custom model input_shape = (3, 224, 224). # Input Shape of my custom model onnx_out = "model.onnx" ncnn_out = "conv0.param" ncnn_out_bin = "conv0.bin"

Then you have to call the functions

torch_to_onnx(model, input_shape, onnx_out, device="cuda:0") onnx_to_ncnn(input_shape, onnx=onnx_out, ncnn_param=ncnn_out_param, ncnn_bin=ncnn_out_bin)

[diazGT94]

Which model should I used for transfer learning for my custom detection model. I tried with Detectron2 and Torchvision an I succed in getting my NCNN files but when I try tu upload them to the online converter they exceed the 100 MB constrain.