FedLPA_NeurIPS2024/Subdata.py at main · hetu-project/FedLPA_NeurIPS2024 · GitHub

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# import torch
# from torch.utils.data import Dataset
# import torch.utils.data as data
# from PIL import Image
# import numpy as np
# from torchvision.datasets import MNIST, CIFAR10
# torch.manual_seed(0)
# torch.cuda.manual_seed(0)
# np.random.seed(0)
#
# class MNIST_truncated(data.Dataset):
#
#     def __init__(self, root, dataidxs=None, transform=None, target_transform=None, download=False):
#
#         self.root = root
#         self.dataidxs = dataidxs
#         self.transform = transform
#         self.target_transform = target_transform
#         self.download = download
#
#         self.data, self.target = self.__build_truncated_dataset__()
#
#     def __build_truncated_dataset__(self):
#
#         data = self.root.data
#         target = self.root.targets
#
#         if self.dataidxs is not None:
#             data = data[self.dataidxs]
#             target = target[self.dataidxs]
#
#         return data, target
#
#     def __getitem__(self, index):
#         """
#         Args:
#             index (int): Index
#
#         Returns:
#             tuple: (image, target) where target is index of the target class.
#         """
#         img, target = self.data[index], self.target[index]
#
#         # doing this so that it is consistent with all other datasets
#         # to return a PIL Image
#         img = Image.fromarray(img.numpy(), mode='L')
#
#         if self.transform is not None:
#             img = self.transform(img)
#
#         if self.target_transform is not None:
#             target = self.target_transform(target)
#
#         return (img, target)
#
#     def __len__(self):
#         return len(self.data)
#
# class CIFAR10_truncated(data.Dataset):
#
#     def __init__(self, root, dataidxs=None,transform=None, target_transform=None, download=False):
#
#         self.root = root
#         self.dataidxs = dataidxs
#         self.transform = transform
#         self.target_transform = target_transform
#         self.download = download
#
#         self.data, self.target = self.__build_truncated_dataset__()
#
#     def __build_truncated_dataset__(self):
#
#         data = self.root.data
#         target =np.array( self.root.targets)
# #         target = np.array(cifar_dataobj.targets)
#
#         if self.dataidxs is not None:
#             data = data[self.dataidxs]
#             target = target[self.dataidxs]
#
#         return data, target
#
#     def truncate_channel(self, index):
#         for i in range(index.shape[0]):
#             gs_index = index[i]
#             self.data[gs_index, :, :, 1] = 0.0
#             self.data[gs_index, :, :, 2] = 0.0
#
#     def __getitem__(self, index):
#         """
#         Args:
#             index (int): Index
#
#         Returns:
#             tuple: (image, target) where target is index of the target class.
#         """
#         img, target = self.data[index], self.target[index]
#
#         if self.transform is not None:
#             img = self.transform(img)
#
#         if self.target_transform is not None:
#             target = self.target_transform(target)
#
#         return img, target
#
#     def __len__(self):
#         return len(self.data)
# # class SubDataset(Dataset):
#
# #     def __init__(self, original_dataset, sub_labels,aera=[0,1], target_transform=None):
# #         super().__init__()
# #         self.dataset = original_dataset
# #         self.sub_indeces = []
#
# #         for index in range(len(self.dataset)):
# #             if index<len(self.dataset)*aera[0]  or index>len(self.dataset)*aera[1]:
# #                 continue
# #             if hasattr(original_dataset, "train_labels"):
#
# #                 if self.dataset.target_transform is None:
# #                     label = self.dataset.train_labels[index]
# #                 else:
# #                     label = self.dataset.target_transform(self.dataset.train_labels[index])
# #             elif hasattr(self.dataset, "test_labels"):
# #                 if self.dataset.target_transform is None:
# #                     label = self.dataset.test_labels[index]
# #                 else:
# #                     label = self.dataset.target_transform(self.dataset.test_labels[index])
# #             else:
# #                 label = self.dataset[index][1]
# #             if label in sub_labels:
# #                 self.sub_indeces.append(index)
# #         self.target_transform = target_transform
#
# #     def __len__(self):
# #         return len(self.sub_indeces)
#
# #     def __getitem__(self, index):
# #         sample = self.dataset[self.sub_indeces[index]]
# #         if self.target_transform:
# #             target = self.target_transform(sample[1])
# #             sample = (sample[0], target)
# #         return sample
#