I have built a Dataset, where I’m doing various checks on the images I’m loading. I’m then passing this DataSet to a DataLoader.
In my DataSet class I’m returning the sample as None if a picture fails my checks and I have a custom collate_fn function which removes all Nones from the retrieved batch and returns the remaining valid samples.
However at this point the returned batch can be of varying size. Is there a way to tell the collate_fn to keep sourcing data until the batch size meets a certain length?
class DataSet():
     def __init__(self, example):
          # initialise dataset
          # load csv file and image directory
          self.example = example
     def __getitem__(self,idx):
          # load one sample
          # if image is too dark return None
          # else 
          # return one image and its equivalent label

dataset = Dataset(csv_file='../', image_dir='../../')

dataloader = DataLoader(dataset , batch_size=4,
                        shuffle=True, num_workers=1, collate_fn = my_collate )

def my_collate(batch): # batch size 4 [{tensor image, tensor label},{},{},{}] could return something like G = [None, {},{},{}]
    batch = list(filter (lambda x:x is not None, batch)) # this gets rid of nones in batch. For example above it would result to G = [{},{},{}]
    # I want len(G) = 4
    # so how to sample another dataset entry?
    return torch.utils.data.dataloader.default_collate(batch)