First of all, thanks a lot for this amazing project and your hard work!

In https://github.com/burn-rs/burn we are trying to load different ONNX models, convert them into burn models, and then use them on different backends. It would be fantastic if we could try your model too. Can you add a way to extract the ONNX model? Using PyTorch is quite feasible.

Thanks a lot in advance!

Hello, thank you for your amazing implementation,
I tried running the inference on custom images, the execution completed but the resulted images are not saved in the directory,
it'll be much appreciated if you would look into this issue
Thank you

Please provide the training code.

Hi ,is it possible to convert DFNet pytorch model to ONNX format?

Training code is necessary to understand what loss functions were used, what was the model architecture etc. It would be a nice idea to add training code too.

Is it possible to set the final inpainted image size same as the input images?

I tried the same and got this error:

Using cpu.
Model model/model_places2.pth loaded.

Inpainting...

Input size: (500, 333)
Traceback (most recent call last):
File "test.py", line 263, in
tester.inpaint(args.output, args.img, args.mask, merge_result=args.merge)
File "test.py", line 221, in inpaint
self.process_batch(batch, output)
File "test.py", line 172, in process_batch
result, alpha, raw = self.model(imgs_miss, masks)
File "/home/sadbhawna/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/sadbhawna/torch/DFNet/model.py", line 260, in forward
out = decode(out, out_en[-i-2])
File "/home/sadbhawna/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/sadbhawna/torch/DFNet/model.py", line 147, in forward
out = torch.cat([out, concat], dim=1)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 2 and 3 in dimension 3 at /opt/conda/conda-bld/pytorch_1574150980135/work/aten/src/TH/generic/THTensor.cpp:612

Hi! Thanks for your amazing work. I'm currently adapting your code to train on my own dataset, with slightly different data format. So I'm thinking about tweaking your data loading module a bit to suit my needs. However, your data loading module seems pretty convoluted, with multithreading and buffers, that I found hard to understand.

I'm wondering, is your code functionally equivalent as torch.data.utils.DataLoader? Can I just implement my own Dataset object and use DataLoader for multi-thread reading? Will it be significantly slower than your implementation? Thanks!

Hi,

I am trying to implement this model, but cannot find lambda parameters setting from your great paper. Like for P=1, Q=1 how to weighted sum them in loss?

thanks

text_r = [targets[i] for i in self.l_text]
text_t = [results[i] for i in self.l_text]
Are these two assignments reversed in loss.py

I have did some tests on Pre-trained model but the missing edges weren't fill. it's seem the edges is not be connected.

Hi Mr Blue,
here I have some questions about training, Could you please give me some suggestions?

1. which layers are the perceptual and style loss used (iin my opinion, the results of resolution with 256, 128, 64 if I trained celeba, is that right? the index in loss.py is [0,1,2])
2. the input of vgg is the results of model's output, is that right?
3. in your loss.py the vgg feature extractor don't sub(mean) and div(std), why?
4. the reconstruction loss don't consider the hole and valid region (like Nvidia partialconv)