Open HongLiuuuuu opened 3 months ago
Here is what I've reverse engineered for the original dataloader
path_to_your_dataset/
img1.jpg
img2.jpg
img3.jpg
img1_mask.png
img2_mask.png
img3_mask.png
Make sure you add you path path_to_your_dataset
in the Trainer
class of the train.py
(take inspiration from config/image_gen_train.yaml
)
Make sure you use you own config system (unless you are in a jupyter notebook, vanilla one has some issue https://github.com/marcoaversa/diffinfinite/issues/3)
then
python -m accelerate.commands.launch train.py
Ps: original dataloader has quite some hardcoded stuff for their specific dataset, you might be better off rewriting yours
Thanks for your help!
I am trying to write the dataloader for my own dataset. However, it is hard for me to understand the returned mask and label in dataset_masks.py without the 'labels.csv' and 'labels.pickle' files. Could you please show some examples of the two files or explain?
here is a hint for your custom dataloder
from torch.utils.data import Dataset,DataLoader
class DatasetCustom(Dataset):
"""
output must be (C x H x W)
img is a torch tensor of 3x512x512, in the range [0.0, 1.0]
mask is a torch tensor of 1x512x512
the idx value in the mask should be :
0: class unknown (mandatory class)
1: your_first_class
2 : your_second_class
with p=0.5, you should feed an empty mask (0 everywhere)(unlabeled data)
"""
# your code here
return img, label
Thanks for this repo and this impressive work! I am trying to train on my own dataset. Could you please provide the dataset structure and training script?