weight sampler

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.RandomRotation(20),
        transforms.ColorJitter(0.3, 0.3, 0.3),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
#    'val': transforms.Compose([
#        transforms.Resize(256),
#        transforms.CenterCrop(224),
#        transforms.ToTensor(),
#        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
#    ]),

        'test': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}


data_dir = "10folds/10fold_9"

class MonaDataset(datasets.folder.ImageFolder):
    def __init__(self, root, transform=None, target_transform=None,
                 loader=datasets.folder.default_loader):
        super(MonaDataset, self).__init__(root, transform, target_transform, loader)

    def __getitem__(self, index):
        path, target = self.samples[index]
        sample = self.loader(path)
        if self.transform is not None:
            sample = self.transform(sample)
        if self.target_transform is not None:
            target = self.target_transform(target)
        return sample, target, path


image_datasets = {x: MonaDataset(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'test']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'test']}



targets = []
with torch.no_grad():
    for i, (inputs, classes, im_path) in enumerate(dataloaders['test']):
        targets.append(classes.cpu().detach().numpy().tolist())

print(targets)
flat_targets = [item for sublist in targets for item in sublist]
flat_targets = torch.Tensor(flat_targets)
print(flat_targets)
class_sample_count = torch.tensor([(flat_targets == t).sum() for t in torch.unique(flat_targets, sorted=True)])
print(class_sample_count)
weight = 1. / class_sample_count.float()
print(weight)
samples_weight = torch.tensor([weight[t] for t in flat_targets])
sampler = WeightedRandomSampler(samples_weight, len(samples_weight))


dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4, sampler = sampler)
              for x in ['train', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}


class_names = image_datasets['train'].classes


---------------------------------------------------------------------------

[[6, 2, 2, 1], [6, 1, 6, 2], [4, 1, 2, 5], [8, 1, 6, 0], [6, 4, 1, 4], [6, 8, 6, 2], [2, 2, 2, 8], [2, 2, 5, 4], [4, 1, 2, 5], [1, 6, 5, 3], [2, 0, 1, 1], [5, 6, 2, 2], [6, 2, 8, 4], [1, 6, 4, 2], [3, 8, 1, 1], [2, 2, 6, 2], [3, 6, 2, 4], [7, 4, 6, 8], [2, 4, 3, 2], [2, 7, 2, 1], [2, 2, 6]]
tensor([6., 2., 2., 1., 6., 1., 6., 2., 4., 1., 2., 5., 8., 1., 6., 0., 6., 4.,
        1., 4., 6., 8., 6., 2., 2., 2., 2., 8., 2., 2., 5., 4., 4., 1., 2., 5.,
        1., 6., 5., 3., 2., 0., 1., 1., 5., 6., 2., 2., 6., 2., 8., 4., 1., 6.,
        4., 2., 3., 8., 1., 1., 2., 2., 6., 2., 3., 6., 2., 4., 7., 4., 6., 8.,
        2., 4., 3., 2., 2., 7., 2., 1., 2., 2., 6.])
tensor([ 2, 13, 26,  4, 10,  5, 15,  2,  6])
tensor([0.5000, 0.0769, 0.0385, 0.2500, 0.1000, 0.2000, 0.0667, 0.5000, 0.1667])

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-254-06eff51f165c> in <module>()
     92 weight = 1. / class_sample_count.float()
     93 print(weight)
---> 94 samples_weight = torch.tensor([weight[t] for t in flat_targets])
     95 sampler = WeightedRandomSampler(samples_weight, len(samples_weight))
     96

<ipython-input-254-06eff51f165c> in <listcomp>(.0)
     92 weight = 1. / class_sample_count.float()
     93 print(weight)
---> 94 samples_weight = torch.tensor([weight[t] for t in flat_targets])
     95 sampler = WeightedRandomSampler(samples_weight, len(samples_weight))
     96

RuntimeError: tensors used as indices must be long or byte tensors