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377dccd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | # Copyright 2022-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from backbone.ResNet18_id2 import resnet18_id2
import os
from typing import Optional
import torch.optim
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from backbone.ResNet18 import resnet18
from PIL import Image
from torch.utils.data import Dataset
from datasets.transforms.denormalization import DeNormalize
from datasets.utils.continual_dataset import (ContinualDataset,
store_masked_loaders)
from datasets.utils.validation import get_train_val
from utils.conf import base_path_dataset as base_path
from torchvision.models import mobilenet_v2
import torch
class TinyImagenet(Dataset):
"""
Defines Tiny Imagenet as for the others pytorch datasets.
"""
def __init__(self, root: str, train: bool=True, transform: transforms=None,
target_transform: transforms=None, download: bool=False) -> None:
self.not_aug_transform = transforms.Compose([transforms.ToTensor()])
self.root = root
self.train = train
self.transform = transform
self.target_transform = target_transform
self.download = download
if download:
if os.path.isdir(root) and len(os.listdir(root)) > 0:
print('Download not needed, files already on disk.')
else:
from onedrivedownloader import download
print('Downloading dataset')
ln = "https://unimore365-my.sharepoint.com/:u:/g/personal/263133_unimore_it/EVKugslStrtNpyLGbgrhjaABqRHcE3PB_r2OEaV7Jy94oQ?e=9K29aD"
download(ln, filename=os.path.join(root, 'tiny-imagenet-processed.zip'), unzip=True, unzip_path=root, clean=True)
self.data = []
for num in range(20):
self.data.append(np.load(os.path.join(
root, 'processed/x_%s_%02d.npy' %
('train' if self.train else 'val', num+1))))
self.data = np.concatenate(np.array(self.data))
self.targets = []
for num in range(20):
self.targets.append(np.load(os.path.join(
root, 'processed/y_%s_%02d.npy' %
('train' if self.train else 'val', num+1))))
self.targets = np.concatenate(np.array(self.targets))
def __len__(self):
return len(self.data)
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(np.uint8(255 * img))
original_img = img.copy()
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if hasattr(self, 'logits'):
return img, target, original_img, self.logits[index]
return img, target
class MyTinyImagenet(TinyImagenet):
"""
Defines Tiny Imagenet as for the others pytorch datasets.
"""
def __init__(self, root: str, train: bool=True, transform: transforms=None,
target_transform: transforms=None, download: bool=False) -> None:
super(MyTinyImagenet, self).__init__(
root, train, transform, target_transform, download)
def __getitem__(self, index):
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(np.uint8(255 * img))
original_img = img.copy()
not_aug_img = self.not_aug_transform(original_img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if hasattr(self, 'logits'):
return img, target, not_aug_img, self.logits[index]
return img, target, not_aug_img
class SequentialTinyImagenet(ContinualDataset):
NAME = 'seq-tinyimg'
SETTING = 'class-il'
N_CLASSES_PER_TASK = 20
N_TASKS = 10
N_CLASSES=200
N_CLASSES_PER_TASK = N_CLASSES // N_TASKS
TRANSFORM = transforms.Compose(
[transforms.RandomCrop(64, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4802, 0.4480, 0.3975),
(0.2770, 0.2691, 0.2821))])
def get_data_loaders(self):
transform = self.TRANSFORM
test_transform = transforms.Compose(
[transforms.ToTensor(), self.get_normalization_transform()])
train_dataset = MyTinyImagenet(base_path() + 'TINYIMG',
train=True, download=True, transform=transform)
if self.args.validation:
train_dataset, test_dataset = get_train_val(train_dataset,
test_transform, self.NAME)
else:
test_dataset = TinyImagenet(base_path() + 'TINYIMG',
train=False, download=True, transform=test_transform)
train, test = store_masked_loaders(train_dataset, test_dataset, self)
return train, test
@staticmethod
def get_backbone():
return resnet18(SequentialTinyImagenet.N_CLASSES_PER_TASK
* SequentialTinyImagenet.N_TASKS)
def get_backboneid(self):
return resnet18_id2(SequentialTinyImagenet.N_CLASSES_PER_TASK
* SequentialTinyImagenet.N_TASKS)
@staticmethod
def get_loss():
return F.cross_entropy
def get_transform(self):
transform = transforms.Compose(
[transforms.ToPILImage(), self.TRANSFORM])
return transform
@staticmethod
def get_normalization_transform():
transform = transforms.Normalize((0.4802, 0.4480, 0.3975),
(0.2770, 0.2691, 0.2821))
return transform
@staticmethod
def get_denormalization_transform():
transform = DeNormalize((0.4802, 0.4480, 0.3975),
(0.2770, 0.2691, 0.2821))
return transform
@staticmethod
def get_epochs():
return 100
@staticmethod
def get_batch_size():
return 32
@staticmethod
def get_minibatch_size():
return SequentialTinyImagenet.get_batch_size()
@staticmethod
def get_scheduler(model, args) -> torch.optim.lr_scheduler:
if args.n_epochs==50:
model.opt = torch.optim.SGD(model.net.parameters(), lr=args.lr, weight_decay=args.optim_wd, momentum=args.optim_mom)
scheduler = torch.optim.lr_scheduler.MultiStepLR(model.opt, [35, 45], gamma=0.1, verbose=False)
else:
model.opt = torch.optim.SGD(model.net.parameters(), lr=args.lr, weight_decay=args.optim_wd, momentum=args.optim_mom)
scheduler = torch.optim.lr_scheduler.MultiStepLR(model.opt, [35, 60, 75], gamma=0.1, verbose=False)
return scheduler
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