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dataset_unlearn_aug_randGen_forget_solo.py
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import os
import random
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torch
from torchvision.transforms import v2
IMG = '/data/users/lkang/RVL-CDIP/images/'
LABEL_F = '/data/users/lkang/RVL-CDIP/RVL_CDIP_full.npy'
LABEL_S = 'RVL_CDIP_train_class_distil_0.1_dict_mix.npy'
NUM_CLASS = 16
UNCLASS = [0] # unlearn classes
class RVL(Dataset):
'''retain: True return retain set, False return unlearn set'''
def __init__(self, img_dir, label_dir, unlearn_classes, split, retain_o_forget, random_label=False): # split: train, valid, test
self.random_label = random_label
data_all = np.load(label_dir, allow_pickle=True).item()
self.data = self._retain_unlearn(data_all[split], unlearn_classes, retain_o_forget)
self.img_proc = torch.nn.Sequential(
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Resize((224, 224), antialias=True),
v2.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
)
self.img_proc_aug = torch.nn.Sequential(
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Resize((224, 224), antialias=True),
v2.RandomResizedCrop((224, 224), scale=(0.7, 1), ratio=(0.85, 1.15), antialias=True),
v2.RandomHorizontalFlip(p=0.4),
v2.GaussianBlur(kernel_size=5, sigma=(0.1, 5)),
v2.RandomAdjustSharpness(sharpness_factor=2, p=0.4),
v2.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
)
def _retain_unlearn(self, data, unlearn_classes, retain_o_forget): # return: retain_set or forget_set
if retain_o_forget: # True: retain_set
return [item for item in data if int(item[1]) not in unlearn_classes]
else: # False: forget_set
return [item for item in data if int(item[1]) in unlearn_classes]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
record = self.data[idx]
img_url = record[0]
if self.random_label:
ids = list(range(NUM_CLASS))
for cla in UNCLASS:
ids.remove(cla)
gt = random.choice(ids)
else:
clas = record[1]
gt = int(clas)
img = Image.open(f'{IMG}{img_url}').convert('RGB')
img_feat = self.img_proc(img)
sample_info = {'img_url': img_url,
'img': img_feat,
'label': gt,
}
return sample_info
class RVLGEN(Dataset):
'''retain: True return retain set, False return unlearn set'''
def __init__(self, img_dir, label_dir, unlearn_classes, split, retain_o_forget, random_label=False): # split: train, valid, test
self.random_label = random_label
data_all = np.load(label_dir, allow_pickle=True).item()
self.data = self._retain_unlearn(data_all[split], unlearn_classes, retain_o_forget)
self.img_proc = torch.nn.Sequential(
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Resize((224, 224), antialias=True),
v2.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
)
self.img_proc_aug = torch.nn.Sequential(
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Resize((224, 224), antialias=True),
v2.RandomResizedCrop((224, 224), scale=(0.7, 1), ratio=(0.85, 1.15), antialias=True),
v2.RandomHorizontalFlip(p=0.4),
v2.GaussianBlur(kernel_size=5, sigma=(0.1, 5)),
v2.RandomAdjustSharpness(sharpness_factor=2, p=0.4),
v2.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
)
def _retain_unlearn(self, data, unlearn_classes, retain_o_forget): # return: retain_set or forget_set
if retain_o_forget: # True: retain_set
return [item for item in data if int(item[1]) not in unlearn_classes]
else: # False: forget_set
return [item for item in data if int(item[1]) in unlearn_classes]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
record = self.data[idx]
img_url = record[0]
if self.random_label:
ids = list(range(NUM_CLASS))
for cla in UNCLASS:
ids.remove(cla)
gt = random.choice(ids)
else:
clas = record[1]
gt = int(clas)
if gt in UNCLASS:
img_feat = np.random.rand(3, 224, 224)
else:
img = Image.open(f'{IMG}{img_url}').convert('RGB')
img_feat = self.img_proc(img)
sample_info = {'img_url': img_url,
'img': img_feat,
'label': gt,
}
return sample_info
def loadData_full():
data_dir = dict()
for split in ['train', 'valid', 'test']:
data_dir[split] = dict()
data_dir[split]['retain'] = RVL(IMG, LABEL_F, UNCLASS, split, True)
data_dir[split]['forget'] = RVL(IMG, LABEL_F, UNCLASS, split, False)
return data_dir
def loadData_randGen():
split = 'train'
data_dir = dict()
data_dir[split] = dict()
data_dir[split]['retain'] = RVLGEN(IMG, LABEL_S, UNCLASS, split, True)
data_dir[split]['forget'] = RVLGEN(IMG, LABEL_S, UNCLASS, split, False)
data_dir[split]['forget_randomlabel'] = RVLGEN(IMG, LABEL_S, UNCLASS, split, False, True)
return data_dir
if __name__ == '__main__':
res = loadData_subset_contour('RVL_CDIP_train_class_distil_0.1_dict_mix.npy')
next(iter(res['train']['forget']))