How to convert custom .pth Model to .onnx?

[endh1337]

Hey, first of all thanks for sharing your great work!

As described in the docs I trained a custom model based on the U²Net architecture to remove specific backgrounds and the results were fine but it seems like you cut off custom model support in 3b18bad. Are you planing to add this again in future? Could you please give an insight how you converted the u2net .pth-models to the .onnx ones?

Thanks!

[danielgatis]

try this:
https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html

[endh1337]

Wow! Thanks for your fast and also helpful response!

I converted my custom model via

import io
import numpy as np
import torch.onnx
from model import U2NET

torch_model = U2NET(3,1)
model_path = "<pathToStateDict>.pth"
batch_size = 1

torch_model.load_state_dict(torch.load(model_path))
torch_model.eval()

x = torch.randn(batch_size, 3, 320, 320, requires_grad=True)
torch_out = torch_model(x)

torch.onnx.export(torch_model, x, "model.onnx", export_params=True, opset_version=11, do_constant_folding=True, input_names = ['input'], output_names = ['output'], dynamic_axes = {'input' : {0: 'batch_size'}, 'output': {0: 'batch_size'}})

And now it's working again with rembg!

[suri199507]

Wow! Thanks for your fast and also helpful response!

I converted my custom model via

import io
import numpy as np
import torch.onnx
from model import U2NET

torch_model = U2NET(3,1)
model_path = "<pathToStateDict>.pth"
batch_size = 1

torch_model.load_state_dict(torch.load(model_path))
torch_model.eval()

x = torch.randn(batch_size, 3, 320, 320, requires_grad=True)
torch_out = torch_model(x)

torch.onnx.export(torch_model, x, "model.onnx", export_params=True, opset_version=11, do_constant_folding=True, input_names = ['input'], output_names = ['output'], dynamic_axes = {'input' : {0: 'batch_size'}, 'output': {0: 'batch_size'}})

And now it's working again with rembg!
@endh1337
How did you train the model with custom images, and How did you get this from model import U2NET

[danielgatis]

Hi @endh1337,
It would be wonderful if you could describe your steps to training models

[endh1337]

Hey sorry @danielgatis and @suri199507, latest events got me quite busy

First of all, I'm a newbie to python and machine learning and stuff, I'm just crawling the web for information I can use to achieve some specific background/object removal tasks. So please don't judge this non professional response 😅

TL;DR

1. Create your own dataset like for example the DUTS-TR dataset
2. Checkout and install the U²Net-Repository
3. (optional) get it to work with the DUTS-TR dataset so you can get see by yourself how this works
4. Change hardcoded references and stuff for use with DUTS-TR dataset in u2net_train.py to work with your own dataset (file system references etc,)
5. If you know what you are doing, change the training parameters and loss function - I did not know, so I left it like it was
6. Maybe extend your training data by mirroring and rotating your images and masks
7. run python u2net_train.py and leave it running until your auto saved results in saved_models satisfy you
8. Convert it to onnx like I described early
9. Use it with rembg :)

Long answer (no in-depth guide!)

The U²Net-Repository was originally trained on the DUTS-TR dataset which is a set of imagery and their counterpart masks. So you have images like

DUTS-TR/DUTS-TR-Image/ILSVRC2012_test_00000022.jpg

and their couterpart mask DUTS-TR/DUTS-TR-Mask/ILSVRC2012_test_00000022.png

which is according to the resources I found the ground truth binary mask (i guess this means only black and white) of what element of the image should be segmentated.

So at first, u need to create your own dataset like DUTS-TR and mask the objects you want to segmentate in white and leave the background / parts you want to be removed by RemBG black. By the way, RemBG does not only work for background removal, you can train a U²Net model to also segmentate a specific part of the image you want to be removed (leave it black in the mask and all the surroundings white. You can change this behavior, but by default you have one directory with the images (.jpg) and another directory containting only the masks (same name like it's original image but .png extension)

I cloned the U²Net-Repository and made a few changes in it's u2net_train.py-file (like changing the model name and directories for the train data because references to them were quiet hard coded). Because U²Net was originally trained with the DUTS-TR dataset, you'll need to change some file system references to your own dataset. Here an example out of u2net_train.py

model_name = 'u2net' #'u2netp'

data_dir = os.path.join(os.getcwd(), 'train_data' + os.sep)
tra_image_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'im_aug' + os.sep)
tra_label_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'gt_aug' + os.sep)

If you change the model_name some other references won't work as well, so you need to make some adjustments like switching from

# ------- 3. define model --------
# define the net
if(model_name=='u2net'):
    net = U2NET(3, 1)
elif(model_name=='u2netp'):
    net = U2NETP(3,1)

to

# ------- 3. define model --------
net = U2NET(3, 1)

You will need to change some other parts I won't describe here e.g auto saving models, cuda support etc.. Crawl The U2net Issues for more information. If you (are different than me and you) know what you are doing, you can adjust the model parameters like the loss function in u2net_train.py or others in model\u2net.py. Anyways, I left them like they were and got nevertheless good results, although my dataset has nothing to do with "salient object detection".

After fixing all the errors occuring while executing python u2net_train.py (almost every error/warning was due to switching file system references/directories etc.), you can leave it running for some time (I trained my dataset almost 2 weeks to get satisfiable results). Your model/weights will be saved after a certain count of iterations which you can then use to test your weights on test images in u2net_test.py.

After that you convert it to .onnx like described before and it works wonderful with rembg.

To @suri199507

and How did you get this from model import U2NET

The script to convert the .pth-weights and model to onnx I posted before just sits in the same cloned repository of U²Net, so it imports the models out of model/u2net.py. As far as i know, pytorch needs this information besides the saved model weights (the .pth file) to interprete whats going on and process an .onnx based model.

---

My response is messier than I thought it'll be. Hope its helpful anyways

[940smiley]

Hey sorry @danielgatis and @suri199507, latest events got me quite busy

First of all, I'm a newbie to python and machine learning and stuff, I'm just crawling the web for information I can use to achieve some specific background/object removal tasks. So please don't judge this non professional response 😅

TL;DR

1. Create your own dataset like for example the DUTS-TR dataset
2. Checkout and install the U²Net-Repository
3. (optional) get it to work with the DUTS-TR dataset so you can get see by yourself how this works
4. Change hardcoded references and stuff for use with DUTS-TR dataset in u2net_train.py to work with your own dataset (file system references etc,)
5. If you know what you are doing, change the training parameters and loss function - I did not know, so I left it like it was
6. Maybe extend your training data by mirroring and rotating your images and masks
7. run python u2net_train.py and leave it running until your auto saved results in saved_models satisfy you
8. Convert it to onnx like I described early
9. Use it with rembg :)

Long answer (no in-depth guide!)

The U²Net-Repository was originally trained on the DUTS-TR dataset which is a set of imagery and their counterpart masks. So you have images like

DUTS-TR/DUTS-TR-Image/ILSVRC2012_test_00000022.jpg

and their couterpart mask DUTS-TR/DUTS-TR-Mask/ILSVRC2012_test_00000022.png

which is according to the resources I found the ground truth binary mask (i guess this means only black and white) of what element of the image should be segmentated.

So at first, u need to create your own dataset like DUTS-TR and mask the objects you want to segmentate in white and leave the background / parts you want to be removed by RemBG black. By the way, RemBG does not only work for background removal, you can train a U²Net model to also segmentate a specific part of the image you want to be removed (leave it black in the mask and all the surroundings white. You can change this behavior, but by default you have one directory with the images (.jpg) and another directory containting only the masks (same name like it's original image but .png extension)

I cloned the U²Net-Repository and made a few changes in it's u2net_train.py-file (like changing the model name and directories for the train data because references to them were quiet hard coded). Because U²Net was originally trained with the DUTS-TR dataset, you'll need to change some file system references to your own dataset. Here an example out of u2net_train.py

model_name = 'u2net' #'u2netp'

data_dir = os.path.join(os.getcwd(), 'train_data' + os.sep)
tra_image_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'im_aug' + os.sep)
tra_label_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'gt_aug' + os.sep)

If you change the model_name some other references won't work as well, so you need to make some adjustments like switching from

# ------- 3. define model --------
# define the net
if(model_name=='u2net'):
    net = U2NET(3, 1)
elif(model_name=='u2netp'):
    net = U2NETP(3,1)

to

# ------- 3. define model --------
net = U2NET(3, 1)

You will need to change some other parts I won't describe here e.g auto saving models, cuda support etc.. Crawl The U2net Issues for more information. If you (are different than me and you) know what you are doing, you can adjust the model parameters like the loss function in u2net_train.py or others in model\u2net.py. Anyways, I left them like they were and got nevertheless good results, although my dataset has nothing to do with "salient object detection".

After fixing all the errors occuring while executing python u2net_train.py (almost every error/warning was due to switching file system references/directories etc.), you can leave it running for some time (I trained my dataset almost 2 weeks to get satisfiable results). Your model/weights will be saved after a certain count of iterations which you can then use to test your weights on test images in u2net_test.py.

After that you convert it to .onnx like described before and it works wonderful with rembg.

To @suri199507

and How did you get this from model import U2NET

The script to convert the .pth-weights and model to onnx I posted before just sits in the same cloned repository of U²Net, so it imports the models out of model/u2net.py. As far as i know, pytorch needs this information besides the saved model weights (the .pth file) to interprete whats going on and process an .onnx based model.

My response is messier than I thought it'll be. Hope its helpful anyways

this helped me alot!!, thanks!

[khadija23]

onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: input for the following indices
index: 1 Got: 3 Expected: 1
Please fix either the inputs or the model
I'm goting this error with the my exported model to onnx I trained my own U2NET with midv500 dataset

[khadija23]

this is my code to export model
i```
mport io
import numpy as np
import torch.onnx
import pathlib
from utils import models

#torch_model = UNet(3,3)
model_path = "'
batch_size = 1
model = models.UNet(n_channels=1, n_classes=1)
checkpoint = torch.load(pathlib.Path(model_path))
model.load_state_dict(checkpoint)
model.load_state_dict(torch.load(model_path))
model.eval()
dynamic_axes_dict = {
    'actual_input': [0, 2, 3],
'Output': [0]
} 
x = torch.randn(batch_size, 1, 320, 320, requires_grad=True)
torch_out = model(x)

torch.onnx.export(model, x, "model.onnx", export_params=True, opset_version=11, do_constant_folding=True, input_names = ['input'], output_names = ['output'], dynamic_axes = dynamic_axes_dict)

[nazzimsulaiman]

How can I use rembg to load a custom trained model for prediction!

[AeroDEmi]

Is there a way to extract the weights (.pth) from the onnx model?

[RacoonPy-Ai]

this is my code to export model
i```
mport io
import numpy as np
import torch.onnx
import pathlib
from utils import models

#torch_model = UNet(3,3)
model_path = "'
batch_size = 1
model = models.UNet(n_channels=1, n_classes=1)
checkpoint = torch.load(pathlib.Path(model_path))
model.load_state_dict(checkpoint)
model.load_state_dict(torch.load(model_path))
model.eval()
dynamic_axes_dict = {
    'actual_input': [0, 2, 3],
'Output': [0]
} 
x = torch.randn(batch_size, 1, 320, 320, requires_grad=True)
torch_out = model(x)

torch.onnx.export(model, x, "model.onnx", export_params=True, opset_version=11, do_constant_folding=True, input_names = ['input'], output_names = ['output'], dynamic_axes = dynamic_axes_dict)

i have try this script to convert model , model is converted but not working

[Gradylo]

How can I use rembg to load a custom trained model for prediction!

[eliassama]

u2net_train.py or others in model\u2net.py. Anyways, I left them like they were and got nevertheless good results, although my dataset has nothing to do with "salient object detection".

What should I do with my own data set, and I'm wondering is there an easy, quick way to turn a color picture into a black and white binary image that I can customize the body for?

[tdp1996]

How can I use rembg to load a custom trained model for prediction!

hello, I have the same problem. I don't know how to use my custom-trained model with rembg :(

[tdp1996]

Hi guys, how can I use my custom-trained model with rembg?

[danielgatis]

try this:

rembg i -m u2net_custom -x '{"model_path": "~/.u2net/u2net.onnx"}' path/to/input.png path/to/output.png

[Jonathunky]

Dear friends,

I've heavily improved this code, and created a dedicated rembg-trainer repo!

It's much much faster now (uses hardware acceleration if possible, multi-threading where possible), more reliable, easier to start working with, and saves model into onnx format every x iterations, so you can easily compare model behaviour after each x iterations. Should be very intuitive and understandable.

Please kindly take a look. Thanks ever so much!

[jianwei164274]

try this:

rembg i -m u2net_custom -x '{"model_path": "~/.u2net/u2net.onnx"}' path/to/input.png path/to/output.png

Can I use the remove function with a custom model_path?