It’s strange that ConcatDataset does not throw an error, since its __init__ function only takes one argument and it looks like you’ve provided two (both datasets).
Try to create the ConcatDataset like this:
class MyData(Dataset):
    def __init__(self):
        self.data = torch.randn(100)
        
    def __getitem__(self, index):
        return self.data[index]
    
    def __len__(self):
        return len(self.data)
    
data1 = MyData()
data_loader1 = DataLoader(data1, batch_size=1)

for batch_idx, data in enumerate(data_loader1):
    print(batch_idx) # prints to idx 99
    
data2 = MyData()
concat_data = ConcatDataset((data1, data2))
concat_loader = DataLoader(concat_data, batch_size=1)

for batch_idx, data in enumerate(concat_loader):
    print(batch_idx) # prints to idx 199
    
print(len(data_loader1)) # 100
print(len(concat_loader)) # 200

I cannot see line 89. Would you please post the code snippet?
Generally, you could apply both transformations to your dataset.
Since transformations like RandomCrop are applied randomly, one cannot say it’s doubling your data size.
In each iteration your model will see a random transformation of your input.