Closed xmc-andy closed 12 months ago
hi, are you use your own dataset?
you can only stack two same shape tensor into a batch.
So you want to train them together, you may use different dataloader for variable length frame T. In our SD, the input are all in a two images format.
Thanks for your quick reply! Yes, I am using my own dataset, and I am now trying to find a way to perform a collate_fn-like process on input images of different lengths to enable multi-batchsize processing. What is the impact of batchsize=1 and >1 on the training results in my case?
Thank you for your great work! I am doing a single prompt-multiple image input task, I have read all the questions and answers, and found similar questions, according to the question instructions, I try to convert the dataset to SD format, and when batchsize=1, the model can Training, but when batchsize is set to >1, the following error occurs:
batch["net_input"]["patch_images"] = torch.stack([sample["patch_images"] for sample in samples], dim=0) RuntimeError: stack expects each tensor to be equal size, but got [1, 1, 3, 224, 224] at entry 0 and [1, 2, 3, 224, 224] at entry 1
For variable-length image input, is there a way to set the batchsize to be greater than 1?