Order of samples set by datasets.ImageFolder was not maintained while using SequentialSampler for DataLoader across mutiple batches.
If I load my dataset using
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in [‘train’, ‘test’]}
There is an oder of samples defined in image_datasets[‘test’],imgs (gives list of tuples where each tuple has path to the sample and its label)
I am trying to maintain same order of samples across batches while using dataLoader, I assumed if I set the sampler to SequentialSampler then samples loaded across multiple batch will follow the same oder set by ImageFolder.imgs. But that was not the case.
dataloaders['test'] = torch.utils.data.DataLoader(image_datasets['test'],
                                              batch_size=BATCH_SIZE,
                                            shuffle=False, num_workers=1)

Is there any other way to achieve the same?