MathieuNlp
commented
2 months ago Hi, happy that you are interested.
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An approach is to split the training dataset into a train set and validation set. You could then create a function to evaluate the validation set like in the ./inference_eval.py (line 35 to 51) with the score you want. Finally print/plot the train loss and validation loss to save the best checkpoint.
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You will need to add a padding (zero padding will work).I used a batch size of 1 so the stacking of tensors wasn’t a problem. However when you go into a larger batch size, the stacking will need the height width dimensions to be equal. The labels in the dataset are not the same size so will need to crop the images (the original ones) or add a transformation in the dataloader process that will crop, center all the images so that you will only need to stack. You can add the transformation in the DatasetSegmentation object in src/dataloader.py class.
Hope it helps.
luobendewugong
Thank you very much for your sharing. I have two questions that I hope to consult with you.
1.If I would like to incorporate a validation set into your code, how should the code be modified?
2.When I change the BATCH_SIZE to 2 or 3, I encounter the following error. How should I modify the code to avoid such errors?
Traceback (most recent call last): File "c:/Users/PC/Documents/Code/Sam_LoRA-main/train.py", line 59, in <module> stk_gt, stk_out = utils.stacking_batch(batch, outputs) File "c:\Users\PC\Documents\Code\Sam_LoRA-main\src\utils.py", line 101, in stacking_batch stk_gt = torch.stack([b["ground_truth_mask"] for b in batch], dim=0) RuntimeError: stack expects each tensor to be equal size, but got [300, 450] at entry 0 and [500, 600] at entry 1Thank you very much！