Train ResNet18 on Unlearnable Dataset
" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Acc 36.99 Loss: 1.73: 100%|██████████| 391/391 [00:20<00:00, 19.17it/s]\n", " 0%| | 0/391 [00:00 pr: continue img = apply_augment(img, name, level) return img class CatDogDataset(datasets.VisionDataset): def __init__(self, root, split='train', transform=None, target_transform=None): self.root = root self.split = split self.transform = transform self.target_transform = target_transform self.img_file_names = os.listdir(os.path.join(root, split)) def __len__(self): return len(self.img_file_names) def __getitem__(self, index): filename = self.img_file_names[index] label = filename[:3] if label == 'cat': label = 0 elif label == 'dog': label = 1 else: print(filename) raise('Unknown label') with open(os.path.join(self.root, self.split, filename), 'rb') as f: img = Image.open(f).convert('RGB') if self.transform is not None: img = self.transform(img) if self.target_transform is not None: label = self.target_transform(label) return img, label class CelebAMini(datasets.CelebA): def __init__(self, root, split="train", target_type="attr", transform=None, target_transform=None, download=False, num_of_classes=1000): super(CelebAMini, self).__init__(root=root, split=split, target_type=target_type, transform=transform, target_transform=target_transform, download=False) split_map = { "train": 0, "valid": 1, "test": 2, "all": None, } split_ = split_map[datasets.utils.verify_str_arg(split.lower(), "split", ("train", "valid", "test", "all"))] fn = partial(os.path.join, self.root, self.base_folder) splits = pandas.read_csv(fn("list_eval_partition.txt"), delim_whitespace=True, header=None, index_col=0) identity = pandas.read_csv(fn("identity_CelebA.txt"), delim_whitespace=True, header=None, index_col=0) mask = slice(None) if split_ is None else (splits[1] == split_) identity = identity[mask] identity = identity[identity[1] < num_of_classes] self.filename = identity.index.values self.identity = identity.values print(self.identity) def __len__(self): return len(self.identity) def __getitem__(self, index): filename = self.filename[index] target = self.identity[index][0] X = Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", filename)) if self.transform is not None: X = self.transform(X) return X, target class Cutout(object): def __init__(self, length): self.length = length def __call__(self, img): h, w = img.size(1), img.size(2) mask = np.ones((h, w), np.float32) y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - self.length // 2, 0, h) y2 = np.clip(y + self.length // 2, 0, h) x1 = np.clip(x - self.length // 2, 0, w) x2 = np.clip(x + self.length // 2, 0, w) mask[y1: y2, x1: x2] = 0. mask = torch.from_numpy(mask) mask = mask.expand_as(img) img *= mask return img class CutMix(Dataset): def __init__(self, dataset, num_class, num_mix=2, beta=1.0, prob=0.5): self.dataset = dataset self.num_class = num_class self.num_mix = num_mix self.beta = beta self.prob = prob def __getitem__(self, index): img, lb = self.dataset[index] lb_onehot = onehot(self.num_class, lb) for _ in range(self.num_mix): r = np.random.rand(1) if self.beta <= 0 or r > self.prob: continue # generate mixed sample lam = np.random.beta(self.beta, self.beta) rand_index = random.choice(range(len(self))) img2, lb2 = self.dataset[rand_index] lb2_onehot = onehot(self.num_class, lb2) bbx1, bby1, bbx2, bby2 = rand_bbox(img.size(), lam) img[:, bbx1:bbx2, bby1:bby2] = img2[:, bbx1:bbx2, bby1:bby2] lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (img.size()[-1] * img.size()[-2])) lb_onehot = lb_onehot * lam + lb2_onehot * (1. - lam) return img, lb_onehot def __len__(self): return len(self.dataset) class MixUp(Dataset): def __init__(self, dataset, num_class, num_mix=2, beta=1.0, prob=0.5): self.dataset = dataset self.num_class = num_class self.num_mix = num_mix self.beta = beta self.prob = prob def __getitem__(self, index): img, lb = self.dataset[index] lb_onehot = onehot(self.num_class, lb) for _ in range(self.num_mix): r = np.random.rand(1) if self.beta <= 0 or r > self.prob: continue # generate mixed sample lam = np.random.beta(self.beta, self.beta) rand_index = random.choice(range(len(self))) img2, lb2 = self.dataset[rand_index] lb2_onehot = onehot(self.num_class, lb2) img = img * lam + img2 * (1-lam) lb_onehot = lb_onehot * lam + lb2_onehot * (1. - lam) return img, lb_onehot def __len__(self): return len(self.dataset) ================================================ FILE: evaluator.py ================================================ import time import models import torch import torch.optim as optim import util from torch.autograd import Variable if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') class Evaluator(): def __init__(self, data_loader, logger, config): self.loss_meters = util.AverageMeter() self.acc_meters = util.AverageMeter() self.acc5_meters = util.AverageMeter() self.criterion = torch.nn.CrossEntropyLoss() self.data_loader = data_loader self.logger = logger self.log_frequency = config.log_frequency if config.log_frequency is not None else 100 self.config = config self.current_acc = 0 self.current_acc_top5 = 0 self.confusion_matrix = torch.zeros(config.num_classes, config.num_classes) return def _reset_stats(self): self.loss_meters = util.AverageMeter() self.acc_meters = util.AverageMeter() self.acc5_meters = util.AverageMeter() self.confusion_matrix = torch.zeros(self.config.num_classes, self.config.num_classes) return def eval(self, epoch, model): model.eval() for i, (images, labels) in enumerate(self.data_loader["test_dataset"]): start = time.time() log_payload = self.eval_batch(images=images, labels=labels, model=model) end = time.time() time_used = end - start display = util.log_display(epoch=epoch, global_step=i, time_elapse=time_used, **log_payload) if self.logger is not None: self.logger.info(display) return def eval_batch(self, images, labels, model): images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True) with torch.no_grad(): pred = model(images) loss = self.criterion(pred, labels) acc, acc5 = util.accuracy(pred, labels, topk=(1, 5)) _, preds = torch.max(pred, 1) for t, p in zip(labels.view(-1), preds.view(-1)): self.confusion_matrix[t.long(), p.long()] += 1 self.loss_meters.update(loss.item(), n=images.size(0)) self.acc_meters.update(acc.item(), n=images.size(0)) self.acc5_meters.update(acc5.item(), n=images.size(0)) payload = {"acc": acc.item(), "acc_avg": self.acc_meters.avg, "acc5": acc5.item(), "acc5_avg": self.acc5_meters.avg, "loss": loss.item(), "loss_avg": self.loss_meters.avg} return payload def _pgd_whitebox(self, model, X, y, random_start=True, epsilon=0.031, num_steps=20, step_size=0.003): model.eval() out = model(X) acc = (out.data.max(1)[1] == y.data).float().sum() X_pgd = Variable(X.data, requires_grad=True) if random_start: random_noise = torch.FloatTensor(*X_pgd.shape).uniform_(-epsilon, epsilon).to(device) X_pgd = Variable(X_pgd.data + random_noise, requires_grad=True) for _ in range(num_steps): opt = optim.SGD([X_pgd], lr=1e-3) opt.zero_grad() with torch.enable_grad(): loss = torch.nn.CrossEntropyLoss()(model(X_pgd), y) loss.backward() eta = step_size * X_pgd.grad.data.sign() X_pgd = Variable(X_pgd.data + eta, requires_grad=True) eta = torch.clamp(X_pgd.data - X.data, -epsilon, epsilon) X_pgd = Variable(X.data + eta, requires_grad=True) X_pgd = Variable(torch.clamp(X_pgd, 0, 1.0), requires_grad=True) acc_pgd = (model(X_pgd).data.max(1)[1] == y.data).float().sum() return acc.item(), acc_pgd.item() ================================================ FILE: fast_autoaugment/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ ================================================ FILE: fast_autoaugment/FastAutoAugment/__init__.py ================================================ ================================================ FILE: fast_autoaugment/FastAutoAugment/archive.py ================================================ # Policy found on CIFAR-10 and CIFAR-100 from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import defaultdict from .augmentations import get_augment, augment_list def arsaug_policy(): exp0_0 = [ [('Solarize', 0.66, 0.34), ('Equalize', 0.56, 0.61)], [('Equalize', 0.43, 0.06), ('AutoContrast', 0.66, 0.08)], [('Color', 0.72, 0.47), ('Contrast', 0.88, 0.86)], [('Brightness', 0.84, 0.71), ('Color', 0.31, 0.74)], [('Rotate', 0.68, 0.26), ('TranslateX', 0.38, 0.88)]] exp0_1 = [ [('TranslateY', 0.88, 0.96), ('TranslateY', 0.53, 0.79)], [('AutoContrast', 0.44, 0.36), ('Solarize', 0.22, 0.48)], [('AutoContrast', 0.93, 0.32), ('Solarize', 0.85, 0.26)], [('Solarize', 0.55, 0.38), ('Equalize', 0.43, 0.48)], [('TranslateY', 0.72, 0.93), ('AutoContrast', 0.83, 0.95)]] exp0_2 = [ [('Solarize', 0.43, 0.58), ('AutoContrast', 0.82, 0.26)], [('TranslateY', 0.71, 0.79), ('AutoContrast', 0.81, 0.94)], [('AutoContrast', 0.92, 0.18), ('TranslateY', 0.77, 0.85)], [('Equalize', 0.71, 0.69), ('Color', 0.23, 0.33)], [('Sharpness', 0.36, 0.98), ('Brightness', 0.72, 0.78)]] exp0_3 = [ [('Equalize', 0.74, 0.49), ('TranslateY', 0.86, 0.91)], [('TranslateY', 0.82, 0.91), ('TranslateY', 0.96, 0.79)], [('AutoContrast', 0.53, 0.37), ('Solarize', 0.39, 0.47)], [('TranslateY', 0.22, 0.78), ('Color', 0.91, 0.65)], [('Brightness', 0.82, 0.46), ('Color', 0.23, 0.91)]] exp0_4 = [ [('Cutout', 0.27, 0.45), ('Equalize', 0.37, 0.21)], [('Color', 0.43, 0.23), ('Brightness', 0.65, 0.71)], [('ShearX', 0.49, 0.31), ('AutoContrast', 0.92, 0.28)], [('Equalize', 0.62, 0.59), ('Equalize', 0.38, 0.91)], [('Solarize', 0.57, 0.31), ('Equalize', 0.61, 0.51)]] exp0_5 = [ [('TranslateY', 0.29, 0.35), ('Sharpness', 0.31, 0.64)], [('Color', 0.73, 0.77), ('TranslateX', 0.65, 0.76)], [('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)], [('Color', 0.92, 0.79), ('Equalize', 0.68, 0.54)], [('Sharpness', 0.87, 0.91), ('Sharpness', 0.93, 0.41)]] exp0_6 = [ [('Solarize', 0.39, 0.35), ('Color', 0.31, 0.44)], [('Color', 0.33, 0.77), ('Color', 0.25, 0.46)], [('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)], [('AutoContrast', 0.32, 0.79), ('Cutout', 0.68, 0.34)], [('AutoContrast', 0.67, 0.91), ('AutoContrast', 0.73, 0.83)]] return exp0_0 + exp0_1 + exp0_2 + exp0_3 + exp0_4 + exp0_5 + exp0_6 def autoaug2arsaug(f): def autoaug(): mapper = defaultdict(lambda: lambda x: x) mapper.update({ 'ShearX': lambda x: float_parameter(x, 0.3), 'ShearY': lambda x: float_parameter(x, 0.3), 'TranslateX': lambda x: int_parameter(x, 10), 'TranslateY': lambda x: int_parameter(x, 10), 'Rotate': lambda x: int_parameter(x, 30), 'Solarize': lambda x: 256 - int_parameter(x, 256), 'Posterize2': lambda x: 4 - int_parameter(x, 4), 'Contrast': lambda x: float_parameter(x, 1.8) + .1, 'Color': lambda x: float_parameter(x, 1.8) + .1, 'Brightness': lambda x: float_parameter(x, 1.8) + .1, 'Sharpness': lambda x: float_parameter(x, 1.8) + .1, 'CutoutAbs': lambda x: int_parameter(x, 20) }) def low_high(name, prev_value): _, low, high = get_augment(name) return float(prev_value - low) / (high - low) policies = f() new_policies = [] for policy in policies: new_policies.append([(name, pr, low_high(name, mapper[name](level))) for name, pr, level in policy]) return new_policies return autoaug @autoaug2arsaug def autoaug_paper_cifar10(): return [ [('Invert', 0.1, 7), ('Contrast', 0.2, 6)], [('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)], [('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)], [('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)], [('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)], [('Color', 0.4, 3), ('Brightness', 0.6, 7)], [('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)], [('Equalize', 0.6, 5), ('Equalize', 0.5, 1)], [('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)], [('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)], [('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)], [('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)], [('Brightness', 0.9, 6), ('Color', 0.2, 6)], [('Solarize', 0.5, 2), ('Invert', 0.0, 3)], [('Equalize', 0.2, 0), ('AutoContrast', 0.6, 0)], [('Equalize', 0.2, 8), ('Equalize', 0.6, 4)], [('Color', 0.9, 9), ('Equalize', 0.6, 6)], [('AutoContrast', 0.8, 4), ('Solarize', 0.2, 8)], [('Brightness', 0.1, 3), ('Color', 0.7, 0)], [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)], [('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)], [('Equalize', 0.8, 8), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)], ] @autoaug2arsaug def autoaug_policy(): """AutoAugment policies found on Cifar.""" exp0_0 = [ [('Invert', 0.1, 7), ('Contrast', 0.2, 6)], [('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)], [('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)], [('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)]] exp0_1 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)], [('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)], [('Equalize', 0.8, 8), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)]] exp0_2 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.0, 2)], [('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 0), ('Solarize', 0.4, 3)], [('Equalize', 0.7, 5), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)]] exp0_3 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 1)], [('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.9, 9)], [('AutoContrast', 0.8, 0), ('TranslateYAbs', 0.7, 9)], [('TranslateYAbs', 0.2, 7), ('Color', 0.9, 6)], [('Equalize', 0.7, 6), ('Color', 0.4, 9)]] exp1_0 = [ [('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)], [('Color', 0.4, 3), ('Brightness', 0.6, 7)], [('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)], [('Equalize', 0.6, 5), ('Equalize', 0.5, 1)], [('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)]] exp1_1 = [ [('Brightness', 0.3, 7), ('AutoContrast', 0.5, 8)], [('AutoContrast', 0.9, 4), ('AutoContrast', 0.5, 6)], [('Solarize', 0.3, 5), ('Equalize', 0.6, 5)], [('TranslateYAbs', 0.2, 4), ('Sharpness', 0.3, 3)], [('Brightness', 0.0, 8), ('Color', 0.8, 8)]] exp1_2 = [ [('Solarize', 0.2, 6), ('Color', 0.8, 6)], [('Solarize', 0.2, 6), ('AutoContrast', 0.8, 1)], [('Solarize', 0.4, 1), ('Equalize', 0.6, 5)], [('Brightness', 0.0, 0), ('Solarize', 0.5, 2)], [('AutoContrast', 0.9, 5), ('Brightness', 0.5, 3)]] exp1_3 = [ [('Contrast', 0.7, 5), ('Brightness', 0.0, 2)], [('Solarize', 0.2, 8), ('Solarize', 0.1, 5)], [('Contrast', 0.5, 1), ('TranslateYAbs', 0.2, 9)], [('AutoContrast', 0.6, 5), ('TranslateYAbs', 0.0, 9)], [('AutoContrast', 0.9, 4), ('Equalize', 0.8, 4)]] exp1_4 = [ [('Brightness', 0.0, 7), ('Equalize', 0.4, 7)], [('Solarize', 0.2, 5), ('Equalize', 0.7, 5)], [('Equalize', 0.6, 8), ('Color', 0.6, 2)], [('Color', 0.3, 7), ('Color', 0.2, 4)], [('AutoContrast', 0.5, 2), ('Solarize', 0.7, 2)]] exp1_5 = [ [('AutoContrast', 0.2, 0), ('Equalize', 0.1, 0)], [('ShearY', 0.6, 5), ('Equalize', 0.6, 5)], [('Brightness', 0.9, 3), ('AutoContrast', 0.4, 1)], [('Equalize', 0.8, 8), ('Equalize', 0.7, 7)], [('Equalize', 0.7, 7), ('Solarize', 0.5, 0)]] exp1_6 = [ [('Equalize', 0.8, 4), ('TranslateYAbs', 0.8, 9)], [('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.6, 9)], [('TranslateYAbs', 0.9, 0), ('TranslateYAbs', 0.5, 9)], [('AutoContrast', 0.5, 3), ('Solarize', 0.3, 4)], [('Solarize', 0.5, 3), ('Equalize', 0.4, 4)]] exp2_0 = [ [('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)], [('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)], [('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)], [('Brightness', 0.9, 6), ('Color', 0.2, 8)], [('Solarize', 0.5, 2), ('Invert', 0.0, 3)]] exp2_1 = [ [('AutoContrast', 0.1, 5), ('Brightness', 0.0, 0)], [('CutoutAbs', 0.2, 4), ('Equalize', 0.1, 1)], [('Equalize', 0.7, 7), ('AutoContrast', 0.6, 4)], [('Color', 0.1, 8), ('ShearY', 0.2, 3)], [('ShearY', 0.4, 2), ('Rotate', 0.7, 0)]] exp2_2 = [ [('ShearY', 0.1, 3), ('AutoContrast', 0.9, 5)], [('TranslateYAbs', 0.3, 6), ('CutoutAbs', 0.3, 3)], [('Equalize', 0.5, 0), ('Solarize', 0.6, 6)], [('AutoContrast', 0.3, 5), ('Rotate', 0.2, 7)], [('Equalize', 0.8, 2), ('Invert', 0.4, 0)]] exp2_3 = [ [('Equalize', 0.9, 5), ('Color', 0.7, 0)], [('Equalize', 0.1, 1), ('ShearY', 0.1, 3)], [('AutoContrast', 0.7, 3), ('Equalize', 0.7, 0)], [('Brightness', 0.5, 1), ('Contrast', 0.1, 7)], [('Contrast', 0.1, 4), ('Solarize', 0.6, 5)]] exp2_4 = [ [('Solarize', 0.2, 3), ('ShearX', 0.0, 0)], [('TranslateXAbs', 0.3, 0), ('TranslateXAbs', 0.6, 0)], [('Equalize', 0.5, 9), ('TranslateYAbs', 0.6, 7)], [('ShearX', 0.1, 0), ('Sharpness', 0.5, 1)], [('Equalize', 0.8, 6), ('Invert', 0.3, 6)]] exp2_5 = [ [('AutoContrast', 0.3, 9), ('CutoutAbs', 0.5, 3)], [('ShearX', 0.4, 4), ('AutoContrast', 0.9, 2)], [('ShearX', 0.0, 3), ('Posterize2', 0.0, 3)], [('Solarize', 0.4, 3), ('Color', 0.2, 4)], [('Equalize', 0.1, 4), ('Equalize', 0.7, 6)]] exp2_6 = [ [('Equalize', 0.3, 8), ('AutoContrast', 0.4, 3)], [('Solarize', 0.6, 4), ('AutoContrast', 0.7, 6)], [('AutoContrast', 0.2, 9), ('Brightness', 0.4, 8)], [('Equalize', 0.1, 0), ('Equalize', 0.0, 6)], [('Equalize', 0.8, 4), ('Equalize', 0.0, 4)]] exp2_7 = [ [('Equalize', 0.5, 5), ('AutoContrast', 0.1, 2)], [('Solarize', 0.5, 5), ('AutoContrast', 0.9, 5)], [('AutoContrast', 0.6, 1), ('AutoContrast', 0.7, 8)], [('Equalize', 0.2, 0), ('AutoContrast', 0.1, 2)], [('Equalize', 0.6, 9), ('Equalize', 0.4, 4)]] exp0s = exp0_0 + exp0_1 + exp0_2 + exp0_3 exp1s = exp1_0 + exp1_1 + exp1_2 + exp1_3 + exp1_4 + exp1_5 + exp1_6 exp2s = exp2_0 + exp2_1 + exp2_2 + exp2_3 + exp2_4 + exp2_5 + exp2_6 + exp2_7 return exp0s + exp1s + exp2s PARAMETER_MAX = 10 def float_parameter(level, maxval): return float(level) * maxval / PARAMETER_MAX def int_parameter(level, maxval): return int(float_parameter(level, maxval)) def no_duplicates(f): def wrap_remove_duplicates(): policies = f() return remove_deplicates(policies) return wrap_remove_duplicates def remove_deplicates(policies): s = set() new_policies = [] for ops in policies: key = [] for op in ops: key.append(op[0]) key = '_'.join(key) if key in s: continue else: s.add(key) new_policies.append(ops) return new_policies def fa_reduced_cifar10(): p = [[["Contrast", 0.8320659688593578, 0.49884310562180767], ["TranslateX", 0.41849883971249136, 0.394023086494538]], [["Color", 0.3500483749890918, 0.43355143929883955], ["Color", 0.5120716140300229, 0.7508299643325016]], [["Rotate", 0.9447932604389472, 0.29723465088990375], ["Sharpness", 0.1564936149799504, 0.47169309978091745]], [["Rotate", 0.5430015349185097, 0.6518626678905443], ["Color", 0.5694844928020679, 0.3494533005430269]], [["AutoContrast", 0.5558922032451064, 0.783136004977799], ["TranslateY", 0.683914191471972, 0.7597025305860181]], [["TranslateX", 0.03489224481658926, 0.021025488042663354], ["Equalize", 0.4788637403857401, 0.3535481281496117]], [["Sharpness", 0.6428916269794158, 0.22791511918580576], ["Contrast", 0.016014045073950323, 0.26811312269487575]], [["Rotate", 0.2972727228410451, 0.7654251516829896], ["AutoContrast", 0.16005809254943348, 0.5380523650108116]], [["Contrast", 0.5823671057717301, 0.7521166301398389], ["TranslateY", 0.9949449214751978, 0.9612671341689751]], [["Equalize", 0.8372126687702321, 0.6944127225621206], ["Rotate", 0.25393282929784755, 0.3261658365286546]], [["Invert", 0.8222011603194572, 0.6597915864008403], ["Posterize", 0.31858707654447327, 0.9541013715579584]], [["Sharpness", 0.41314621282107045, 0.9437344470879956], ["Cutout", 0.6610495837889337, 0.674411664255093]], [["Contrast", 0.780121736705407, 0.40826152397463156], ["Color", 0.344019192125256, 0.1942922781355767]], [["Rotate", 0.17153139555621344, 0.798745732456474], ["Invert", 0.6010555860501262, 0.320742172554767]], [["Invert", 0.26816063450777416, 0.27152062163148327], ["Equalize", 0.6786829200236982, 0.7469412443514213]], [["Contrast", 0.3920564414367518, 0.7493644582838497], ["TranslateY", 0.8941657805606704, 0.6580846856375955]], [["Equalize", 0.875509207399372, 0.9061130537645283], ["Cutout", 0.4940280679087308, 0.7896229623628276]], [["Contrast", 0.3331423298065147, 0.7170041362529597], ["ShearX", 0.7425484291842793, 0.5285117152426109]], [["Equalize", 0.97344237365026, 0.4745759720473106], ["TranslateY", 0.055863458430295276, 0.9625142022954672]], [["TranslateX", 0.6810614083109192, 0.7509937355495521], ["TranslateY", 0.3866463019475701, 0.5185481505576112]], [["Sharpness", 0.4751529944753671, 0.550464012488733], ["Cutout", 0.9472914750534814, 0.5584925992985023]], [["Contrast", 0.054606784909375095, 0.17257080196712182], ["Cutout", 0.6077026782754803, 0.7996504165944938]], [["ShearX", 0.328798428243695, 0.2769563264079157], ["Cutout", 0.9037632437023772, 0.4915809476763595]], [["Cutout", 0.6891202672363478, 0.9951490996172914], ["Posterize", 0.06532762462628705, 0.4005246609075227]], [["TranslateY", 0.6908583592523334, 0.725612120376128], ["Rotate", 0.39907735501746666, 0.36505798032223147]], [["TranslateX", 0.10398364107399072, 0.5913918470536627], ["Rotate", 0.7169811539340365, 0.8283850670648724]], [["ShearY", 0.9526373530768361, 0.4482347365639251], ["Contrast", 0.4203947336351471, 0.41526799558953864]], [["Contrast", 0.24894431199700073, 0.09578870500994707], ["Solarize", 0.2273713345927395, 0.6214942914963707]], [["TranslateX", 0.06331228870032912, 0.8961907489444944], ["Cutout", 0.5110007859958743, 0.23704875994050723]], [["Cutout", 0.3769183548846172, 0.6560944580253987], ["TranslateY", 0.7201924599434143, 0.4132476526938319]], [["Invert", 0.6707431156338866, 0.11622795952464149], ["Posterize", 0.12075972752370845, 0.18024933294172307]], [["Color", 0.5010057264087142, 0.5277767327434318], ["Rotate", 0.9486115946366559, 0.31485546630220784]], [["ShearX", 0.31741302466630406, 0.1991215806270692], ["Invert", 0.3744727015523084, 0.6914113986757578]], [["Brightness", 0.40348479064392617, 0.8924182735724888], ["Brightness", 0.1973098763857779, 0.3939288933689655]], [["Color", 0.01208688664030888, 0.6055693000885217], ["Equalize", 0.433259451147881, 0.420711137966155]], [["Cutout", 0.2620018360076487, 0.11594468278143644], ["Rotate", 0.1310401567856766, 0.7244318146544101]], [["ShearX", 0.15249651845933576, 0.35277277071866986], ["Contrast", 0.28221794032094016, 0.42036586509397444]], [["Brightness", 0.8492912150468908, 0.26386920887886056], ["Solarize", 0.8764208056263386, 0.1258195122766067]], [["ShearX", 0.8537058239675831, 0.8415101816171269], ["AutoContrast", 0.23958568830416294, 0.9889049529564014]], [["Rotate", 0.6463207930684552, 0.8750192129056532], ["Contrast", 0.6865032211768652, 0.8564981333033417]], [["Equalize", 0.8877190311811044, 0.7370995897848609], ["TranslateX", 0.9979660314391368, 0.005683998913244781]], [["Color", 0.6420017551677819, 0.6225337265571229], ["Solarize", 0.8344504978566362, 0.8332856969941151]], [["ShearX", 0.7439332981992567, 0.9747608698582039], ["Equalize", 0.6259189804002959, 0.028017478098245174]], [["TranslateY", 0.39794770293366843, 0.8482966537902709], ["Rotate", 0.9312935630405351, 0.5300586925826072]], [["Cutout", 0.8904075572021911, 0.3522934742068766], ["Equalize", 0.6431186289473937, 0.9930577962126151]], [["Contrast", 0.9183553386089476, 0.44974266209396685], ["TranslateY", 0.8193684583123862, 0.9633741156526566]], [["ShearY", 0.616078299924283, 0.19219314358924766], ["Solarize", 0.1480945914138868, 0.05922109541654652]], [["Solarize", 0.25332455064128157, 0.18853037431947994], ["ShearY", 0.9518390093954243, 0.14603930044061142]], [["Color", 0.8094378664335412, 0.37029830225408433], ["Contrast", 0.29504113617467465, 0.065096365468442]], [["AutoContrast", 0.7075167558685455, 0.7084621693458267], ["Sharpness", 0.03555539453323875, 0.5651948313888351]], [["TranslateY", 0.5969982600930229, 0.9857264201029572], ["Rotate", 0.9898628564873607, 0.1985685534926911]], [["Invert", 0.14915939942810352, 0.6595839632446547], ["Posterize", 0.768535289994361, 0.5997358684618563]], [["Equalize", 0.9162691815967111, 0.3331035307653627], ["Color", 0.8169118187605557, 0.7653910258006366]], [["Rotate", 0.43489185299530897, 0.752215269135173], ["Brightness", 0.1569828560334806, 0.8002808712857853]], [["Invert", 0.931876215328345, 0.029428644395760872], ["Equalize", 0.6330036052674145, 0.7235531014288485]], [["ShearX", 0.5216138393704968, 0.849272958911589], ["AutoContrast", 0.19572688655120263, 0.9786551568639575]], [["ShearX", 0.9899586208275011, 0.22580547500610293], ["Brightness", 0.9831311903178727, 0.5055159610855606]], [["Brightness", 0.29179117009211486, 0.48003584672937294], ["Solarize", 0.7544252317330058, 0.05806581735063043]], [["AutoContrast", 0.8919800329537786, 0.8511261613698553], ["Contrast", 0.49199446084551035, 0.7302297140181429]], [["Cutout", 0.7079723710644835, 0.032565015538375874], ["AutoContrast", 0.8259782090388609, 0.7860708789468442]], [["Posterize", 0.9980262659801914, 0.6725084224935673], ["ShearY", 0.6195568269664682, 0.5444170291816751]], [["Posterize", 0.8687351834713217, 0.9978004914422602], ["Equalize", 0.4532646848325955, 0.6486748015710573]], [["Contrast", 0.2713928776950594, 0.15255249557027806], ["ShearY", 0.9276834387970199, 0.5266542862333478]], [["AutoContrast", 0.5240786618055582, 0.9325642258930253], ["Cutout", 0.38448627892037357, 0.21219415055662394]], [["TranslateX", 0.4299517937295352, 0.20133751201386152], ["TranslateX", 0.6753468310276597, 0.6985621035400441]], [["Rotate", 0.4006472499103597, 0.6704748473357586], ["Equalize", 0.674161668148079, 0.6528530101705237]], [["Equalize", 0.9139902833674455, 0.9015103149680278], ["Sharpness", 0.7289667720691948, 0.7623606352376232]], [["Cutout", 0.5911267429414259, 0.5953141187177585], ["Rotate", 0.5219064817468504, 0.11085141355857986]], [["TranslateX", 0.3620095133946267, 0.26194039409492476], ["Rotate", 0.3929841359545597, 0.4913406720338047]], [["Invert", 0.5175298901458896, 0.001661410821811482], ["Invert", 0.004656581318332242, 0.8157622192213624]], [["AutoContrast", 0.013609693335051465, 0.9318651749409604], ["Invert", 0.8980844358979592, 0.2268511862780368]], [["ShearY", 0.7717126261142194, 0.09975547983707711], ["Equalize", 0.7808494401429572, 0.4141412091009955]], [["TranslateX", 0.5878675721341552, 0.29813268038163376], ["Posterize", 0.21257276051591356, 0.2837285296666412]], [["Brightness", 0.4268335108566488, 0.4723784991635417], ["Cutout", 0.9386262901570471, 0.6597686851494288]], [["ShearX", 0.8259423807590159, 0.6215304795389204], ["Invert", 0.6663365779667443, 0.7729669184580387]], [["ShearY", 0.4801338723951297, 0.5220145420100984], ["Solarize", 0.9165803796596582, 0.04299335502862134]], [["Color", 0.17621114853558817, 0.7092601754635434], ["ShearX", 0.9014406936728542, 0.6028711944367818]], [["Rotate", 0.13073284972300658, 0.9088831512880851], ["ShearX", 0.4228105332316806, 0.7985249783662675]], [["Brightness", 0.9182753692730031, 0.0063635477774044436], ["Color", 0.4279825602663798, 0.28727149118585327]], [["Equalize", 0.578218285372267, 0.9611758542158054], ["Contrast", 0.5471552264150691, 0.8819635504027596]], [["Brightness", 0.3208589067274543, 0.45324733565167497], ["Solarize", 0.5218455808633233, 0.5946097503647126]], [["Equalize", 0.3790381278653, 0.8796082535775276], ["Solarize", 0.4875526773149246, 0.5186585878052613]], [["ShearY", 0.12026461479557571, 0.1336953429068397], ["Posterize", 0.34373988646025766, 0.8557727670803785]], [["Cutout", 0.2396745247507467, 0.8123036135209865], ["Equalize", 0.05022807681008945, 0.6648492261984383]], [["Brightness", 0.35226676470748264, 0.5950011514888855], ["Rotate", 0.27555076067000894, 0.9170063321486026]], [["ShearX", 0.320224630647278, 0.9683584649071976], ["Invert", 0.6905585196648905, 0.5929115667894518]], [["Color", 0.9941395717559652, 0.7474441679798101], ["Sharpness", 0.7559998478658021, 0.6656052889626682]], [["ShearY", 0.4004220568345669, 0.5737646992826074], ["Equalize", 0.9983495213746147, 0.8307907033362303]], [["Color", 0.13726809242038207, 0.9378850119950549], ["Equalize", 0.9853362454752445, 0.42670264496554156]], [["Invert", 0.13514636153298576, 0.13516363849081958], ["Sharpness", 0.2031189356693901, 0.6110226359872745]], [["TranslateX", 0.7360305209630797, 0.41849698571655614], ["Contrast", 0.8972161549144564, 0.7820296625565641]], [["Color", 0.02713118828682548, 0.717110684828096], ["TranslateY", 0.8118759006836348, 0.9120098002024992]], [["Sharpness", 0.2915428949403711, 0.7630303724396518], ["Solarize", 0.22030536162851078, 0.38654526772661757]], [["Equalize", 0.9949114839538582, 0.7193630656062793], ["AutoContrast", 0.00889496657931299, 0.2291400476524672]], [["Rotate", 0.7120948976490488, 0.7804359309791055], ["Cutout", 0.10445418104923654, 0.8022999156052766]], [["Equalize", 0.7941710117902707, 0.8648170634288153], ["Invert", 0.9235642581144047, 0.23810725859722381]], [["Cutout", 0.3669397998623156, 0.42612815083245004], ["Solarize", 0.5896322046441561, 0.40525016166956795]], [["Color", 0.8389858785714184, 0.4805764176488667], ["Rotate", 0.7483931487048825, 0.4731174601400677]], [["Sharpness", 0.19006538611394763, 0.9480745790240234], ["TranslateY", 0.13904429049439282, 0.04117685330615939]], [["TranslateY", 0.9958097661701637, 0.34853788612580905], ["Cutout", 0.2235829624082113, 0.3737887095480745]], [["ShearX", 0.635453761342424, 0.6063917273421382], ["Posterize", 0.8738297843709666, 0.4893042590265556]], [["Brightness", 0.7907245198402727, 0.7082189713070691], ["Color", 0.030313003541849737, 0.6927897798493439]], [["Cutout", 0.6965622481073525, 0.8103522907758203], ["ShearY", 0.6186794303078708, 0.28640671575703547]], [["ShearY", 0.43734910588450226, 0.32549342535621517], ["ShearX", 0.08154980987651872, 0.3286764923112455]], [["AutoContrast", 0.5262462005050853, 0.8175584582465848], ["Contrast", 0.8683217097363655, 0.548776281479276]], [["ShearY", 0.03957878500311707, 0.5102350637943197], ["Rotate", 0.13794708520303778, 0.38035687712954236]], [["Sharpness", 0.634288567312677, 0.6387948309075822], ["AutoContrast", 0.13437288694693272, 0.7150448869023095]], [["Contrast", 0.5198339640088544, 0.9409429390321714], ["Cutout", 0.09489154903321972, 0.6228488803821982]], [["Equalize", 0.8955909061806043, 0.7727336527163008], ["AutoContrast", 0.6459479564441762, 0.7065467781139214]], [["Invert", 0.07214420843537739, 0.15334721382249505], ["ShearX", 0.9242027778363903, 0.5809187849982554]], [["Equalize", 0.9144084379856188, 0.9457539278608998], ["Sharpness", 0.14337499858300173, 0.5978054365425495]], [["Posterize", 0.18894269796951202, 0.14676331276539045], ["Equalize", 0.846204299950047, 0.0720601838168885]], [["Contrast", 0.47354445405741163, 0.1793650330107468], ["Solarize", 0.9086106327264657, 0.7578807802091502]], [["AutoContrast", 0.11805466892967886, 0.6773620948318575], ["TranslateX", 0.584222568299264, 0.9475693349391936]], [["Brightness", 0.5833017701352768, 0.6892593824176294], ["AutoContrast", 0.9073141314561828, 0.5823085733964589]], [["TranslateY", 0.5711231614144834, 0.6436240447620021], ["Contrast", 0.21466964050052473, 0.8042843954486391]], [["Contrast", 0.22967904487976765, 0.2343103109298762], ["Invert", 0.5502897289159286, 0.386181060792375]], [["Invert", 0.7008423439928628, 0.4234003051405053], ["Rotate", 0.77270460187611, 0.6650852696828039]], [["Invert", 0.050618322309703534, 0.24277027926683614], ["TranslateX", 0.789703489736613, 0.5116446685339312]], [["Color", 0.363898083076868, 0.7870323584210503], ["ShearY", 0.009608425513626617, 0.6188625018465327]], [["TranslateY", 0.9447601615216088, 0.8605867115798349], ["Equalize", 0.24139180127003634, 0.9587337957930782]], [["Equalize", 0.3968589440144503, 0.626206375426996], ["Solarize", 0.3215967960673186, 0.826785464835443]], [["TranslateX", 0.06947339047121326, 0.016705969558222122], ["Contrast", 0.6203392406528407, 0.6433525559906872]], [["Solarize", 0.2479835265518212, 0.6335009955617831], ["Sharpness", 0.6260191862978083, 0.18998095149428562]], [["Invert", 0.9818841924943431, 0.03252098144087934], ["TranslateY", 0.9740718042586802, 0.32038951753031475]], [["Solarize", 0.8795784664090814, 0.7014953994354041], ["AutoContrast", 0.8508018319577783, 0.09321935255338443]], [["Color", 0.8067046326105318, 0.13732893832354054], ["Contrast", 0.7358549680271418, 0.7880588355974301]], [["Posterize", 0.5005885536838065, 0.7152229305267599], ["ShearX", 0.6714249591308944, 0.7732232697859908]], [["TranslateY", 0.5657943483353953, 0.04622399873706862], ["AutoContrast", 0.2787442688649845, 0.567024378767143]], 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0.22897510169718616]], [["TranslateX", 0.7631157841429582, 0.6482890521284557], ["Brightness", 0.12681196272427664, 0.3669813784257344]], [["TranslateX", 0.06027722649179801, 0.3101104512201861], ["Sharpness", 0.5652076706249394, 0.05210008400968136]], [["AutoContrast", 0.39213552101583127, 0.5047021194355596], ["ShearY", 0.7164003055682187, 0.8063370761002899]], [["Solarize", 0.9574307011238342, 0.21472064809226854], ["AutoContrast", 0.8102612285047174, 0.716870148067014]], [["Rotate", 0.3592634277567387, 0.6452602893051465], ["AutoContrast", 0.27188430331411506, 0.06003099168464854]], [["Cutout", 0.9529536554825503, 0.5285505311027461], ["Solarize", 0.08478231903311029, 0.15986449762728216]], [["TranslateY", 0.31176130458018936, 0.5642853506158253], ["Equalize", 0.008890883901317648, 0.5146121040955942]], [["Color", 0.40773645085566157, 0.7110398926612682], ["Color", 0.18233100156439364, 0.7830036002758337]], [["Posterize", 0.5793809197821732, 0.043748553135581236], ["Invert", 0.4479962016131668, 0.7349663010359488]], [["TranslateX", 0.1994882312299382, 0.05216859488899439], ["Rotate", 0.48288726352035416, 0.44713829026777585]], [["Posterize", 0.22122838185154603, 0.5034546841241283], ["TranslateX", 0.2538745835410222, 0.6129055170893385]], [["Color", 0.6786559960640814, 0.4529749369803212], ["Equalize", 0.30215879674415336, 0.8733394611096772]], [["Contrast", 0.47316062430673456, 0.46669538897311447], ["Invert", 0.6514906551984854, 0.3053339444067804]], [["Equalize", 0.6443202625334524, 0.8689731394616441], ["Color", 0.7549183794057628, 0.8889001426329578]], [["Solarize", 0.616709740662654, 0.7792180816399313], ["ShearX", 0.9659155537406062, 0.39436937531179495]], [["Equalize", 0.23694011299406226, 0.027711152164392128], ["TranslateY", 0.1677339686527083, 0.3482126536808231]], [["Solarize", 0.15234175951790285, 0.7893840414281341], ["TranslateX", 0.2396395768284183, 0.27727219214979715]], [["Contrast", 0.3792017455380605, 0.32323660409845334], ["Contrast", 0.1356037413846466, 0.9127772969992305]], [["ShearX", 0.02642732222284716, 0.9184662576502115], ["Equalize", 0.11504884472142995, 0.8957638893097964]], [["TranslateY", 0.3193812913345325, 0.8828100030493128], ["ShearY", 0.9374975727563528, 0.09909415611083694]], [["AutoContrast", 0.025840721736048122, 0.7941037581373024], ["TranslateY", 0.498518003323313, 0.5777122846572548]], [["ShearY", 0.6042199307830248, 0.44809668754508836], ["Cutout", 0.3243978207701482, 0.9379740926294765]], [["ShearY", 0.6858549297583574, 0.9993252035788924], ["Sharpness", 0.04682428732773203, 0.21698099707915652]], [["ShearY", 0.7737469436637263, 0.8810127181224531], ["ShearY", 0.8995655445246451, 0.4312416220354539]], [["TranslateY", 0.4953094136709374, 0.8144161580138571], ["Solarize", 0.26301211718928097, 0.518345311180405]], [["Brightness", 0.8820246486031275, 0.571075863786249], ["ShearX", 0.8586669146703955, 0.0060476383595142735]], [["Sharpness", 0.20519233710982254, 0.6144574759149729], ["Posterize", 0.07976625267460813, 0.7480145046726968]], [["ShearY", 0.374075419680195, 0.3386105402023202], ["ShearX", 0.8228083637082115, 0.5885174783155361]], [["Brightness", 0.3528780713814561, 0.6999884884306623], ["Sharpness", 0.3680348120526238, 0.16953358258959617]], [["Brightness", 0.24891223104442084, 0.7973853494920095], ["TranslateX", 0.004256803835524736, 0.0470216343108546]], [["Posterize", 0.1947344282646012, 0.7694802711054367], ["Cutout", 0.9594385534844785, 0.5469744140592429]], [["Invert", 0.19012504762806026, 0.7816140211434693], ["TranslateY", 0.17479746932338402, 0.024249345245078602]], [["Rotate", 0.9669262055946796, 0.510166180775991], ["TranslateX", 0.8990602034610352, 0.6657802719304693]], [["ShearY", 0.5453049050407278, 0.8476872739603525], ["Cutout", 0.14226529093962592, 0.15756960661106634]], [["Equalize", 0.5895291156113004, 0.6797218994447763], ["TranslateY", 0.3541442192192753, 0.05166001155849864]], [["Equalize", 0.39530681662726097, 0.8448335365081087], ["Brightness", 0.6785483272734143, 0.8805568647038574]], [["Cutout", 0.28633258271917905, 0.7750870268336066], ["Equalize", 0.7221097824537182, 0.5865506280531162]], [["Posterize", 0.9044429629421187, 0.4620266401793388], ["Invert", 0.1803008045494473, 0.8073190766288534]], [["Sharpness", 0.7054649148075851, 0.3877207948962055], ["TranslateX", 0.49260224225927285, 0.8987462620731029]], [["Sharpness", 0.11196934729294483, 0.5953704422694938], ["Contrast", 0.13969334315069737, 0.19310569898434204]], [["Posterize", 0.5484346101051778, 0.7914140118600685], ["Brightness", 0.6428044691630473, 0.18811316670808076]], [["Invert", 0.22294834094984717, 0.05173157689962704], ["Cutout", 0.6091129168510456, 0.6280845506243643]], [["AutoContrast", 0.5726444076195267, 0.2799840903601295], ["Cutout", 0.3055752727786235, 0.591639807512993]], [["Brightness", 0.3707116723204462, 0.4049175910826627], ["Rotate", 0.4811601625588309, 0.2710760253723644]], [["ShearY", 0.627791719653608, 0.6877498291550205], ["TranslateX", 0.8751753308366824, 0.011164650018719358]], [["Posterize", 0.33832547954522263, 0.7087039872581657], ["Posterize", 0.6247474435007484, 0.7707784192114796]], [["Contrast", 0.17620186308493468, 0.9946224854942095], ["Solarize", 0.5431896088395964, 0.5867904203742308]], [["ShearX", 0.4667959516719652, 0.8938082224109446], ["TranslateY", 0.7311343008292865, 0.6829842246020277]], [["ShearX", 0.6130281467237769, 0.9924010909612302], ["Brightness", 0.41039241699696916, 0.9753218875311392]], [["TranslateY", 0.0747250386427123, 0.34602725521067534], ["Rotate", 0.5902597465515901, 0.361094672021087]], [["Invert", 0.05234890878959486, 0.36914978664919407], ["Sharpness", 0.42140532878231374, 0.19204058551048275]], [["ShearY", 0.11590485361909497, 0.6518540857972316], ["Invert", 0.6482444740361704, 0.48256237896163945]], [["Rotate", 0.4931329446923608, 0.037076242417301675], ["Contrast", 0.9097939772412852, 0.5619594905306389]], [["Posterize", 0.7311032479626216, 0.4796364593912915], ["Color", 0.13912123993932402, 0.03997286439663705]], [["AutoContrast", 0.6196602944085344, 0.2531430457527588], ["Rotate", 0.5583937060431972, 0.9893379795224023]], [["AutoContrast", 0.8847753125072959, 0.19123028952580057], ["TranslateY", 0.494361716097206, 0.14232297727461696]], [["Invert", 0.6212360716340707, 0.033898871473033165], ["AutoContrast", 0.30839896957008295, 0.23603569542166247]], [["Equalize", 0.8255583546605049, 0.613736933157845], ["AutoContrast", 0.6357166629525485, 0.7894617347709095]], [["Brightness", 0.33840706322846814, 0.07917167871493658], ["ShearY", 0.15693175752528676, 0.6282773652129153]], [["Cutout", 0.7550520024859294, 0.08982367300605598], ["ShearX", 0.5844942417320858, 0.36051195083380105]]] return p def fa_resnet50_rimagenet(): p = [[["ShearY", 0.14143816458479197, 0.513124791615952], ["Sharpness", 0.9290316227291179, 0.9788406212603302]], [["Color", 0.21502874228385338, 0.3698477943880306], ["TranslateY", 0.49865058747734736, 0.4352676987103321]], [["Brightness", 0.6603452126485386, 0.6990174510500261], ["Cutout", 0.7742953773992511, 0.8362550883640804]], [["Posterize", 0.5188375788270497, 0.9863648925446865], ["TranslateY", 0.8365230108655313, 0.6000972236440252]], [["ShearY", 0.9714994964711299, 0.2563663552809896], ["Equalize", 0.8987567223581153, 0.1181761775609772]], [["Sharpness", 0.14346409304565366, 0.5342189791746006], ["Sharpness", 0.1219714162835897, 0.44746801278319975]], [["TranslateX", 0.08089260772173967, 0.028011721602479833], ["TranslateX", 0.34767877352421406, 0.45131294688688794]], [["Brightness", 0.9191164585327378, 0.5143232242627864], ["Color", 0.9235247849934283, 0.30604586249462173]], [["Contrast", 0.4584173187505879, 0.40314219914942756], ["Rotate", 0.550289356406774, 0.38419022293237126]], [["Posterize", 0.37046156420799325, 0.052693291117634544], ["Cutout", 0.7597581409366909, 0.7535799791937421]], [["Color", 0.42583964114658746, 0.6776641859552079], ["ShearY", 0.2864805671096011, 0.07580175477739545]], [["Brightness", 0.5065952125552232, 0.5508640233704984], ["Brightness", 0.4760021616081475, 0.3544313318097987]], [["Posterize", 0.5169630851995185, 0.9466018906715961], ["Posterize", 0.5390336503396841, 0.1171015788193209]], [["Posterize", 0.41153170909576176, 0.7213063942615204], ["Rotate", 0.6232230424824348, 0.7291984098675746]], [["Color", 0.06704687234714028, 0.5278429246040438], ["Sharpness", 0.9146652195810183, 0.4581415618941407]], [["ShearX", 0.22404644446773492, 0.6508620171913467], ["Brightness", 0.06421961538672451, 0.06859528721039095]], [["Rotate", 0.29864103693134797, 0.5244313199644495], ["Sharpness", 0.4006161706584276, 0.5203708477368657]], [["AutoContrast", 0.5748186910788027, 0.8185482599354216], ["Posterize", 0.9571441684265188, 0.1921474117448481]], [["ShearY", 0.5214786760436251, 0.8375629059785009], ["Invert", 0.6872393349333636, 0.9307694335024579]], [["Contrast", 0.47219838080793364, 0.8228524484275648], ["TranslateY", 0.7435518856840543, 0.5888865560614439]], [["Posterize", 0.10773482839638836, 0.6597021018893648], ["Contrast", 0.5218466423129691, 0.562985661685268]], [["Rotate", 0.4401753067886466, 0.055198255925702475], ["Rotate", 0.3702153509335602, 0.5821574425474759]], [["TranslateY", 0.6714729117832363, 0.7145542887432927], ["Equalize", 0.0023263758097700205, 0.25837341854887885]], [["Cutout", 0.3159707561240235, 0.19539664199170742], ["TranslateY", 0.8702824829864558, 0.5832348977243467]], [["AutoContrast", 0.24800812729140026, 0.08017301277245716], ["Brightness", 0.5775505849482201, 0.4905904775616114]], [["Color", 0.4143517886294533, 0.8445937742921498], ["ShearY", 0.28688910858536587, 0.17539366839474402]], [["Brightness", 0.6341134194059947, 0.43683815933640435], ["Brightness", 0.3362277685899835, 0.4612826163288225]], [["Sharpness", 0.4504035748829761, 0.6698294470467474], ["Posterize", 0.9610055612671645, 0.21070714173174876]], [["Posterize", 0.19490421920029832, 0.7235798208354267], ["Rotate", 0.8675551331308305, 0.46335565746433094]], [["Color", 0.35097958351003306, 0.42199181561523186], ["Invert", 0.914112788087429, 0.44775583211984815]], [["Cutout", 0.223575616055454, 0.6328591417299063], ["TranslateY", 0.09269465212259387, 0.5101073959070608]], [["Rotate", 0.3315734525975911, 0.9983593458299167], ["Sharpness", 0.12245416662856974, 0.6258689139914664]], [["ShearY", 0.696116760180471, 0.6317805202283014], ["Color", 0.847501151593963, 0.4440116609830195]], [["Solarize", 0.24945891607225948, 0.7651150206105561], ["Cutout", 0.7229677092930331, 0.12674657348602494]], [["TranslateX", 0.43461945065713675, 0.06476571036747841], ["Color", 0.6139316940180952, 0.7376264330632316]], [["Invert", 0.1933003530637138, 0.4497819016184308], ["Invert", 0.18391634069983653, 0.3199769100951113]], [["Color", 0.20418296626476137, 0.36785101882029814], ["Posterize", 0.624658293920083, 0.8390081535735991]], [["Sharpness", 0.5864963540530814, 0.586672446690273], ["Posterize", 0.1980280647652339, 0.222114611452575]], [["Invert", 0.3543654961628104, 0.5146369635250309], ["Equalize", 0.40751271919434434, 0.4325310837291978]], [["ShearY", 0.22602859359451877, 0.13137880879778158], ["Posterize", 0.7475029061591305, 0.803900538461099]], [["Sharpness", 0.12426276165599924, 0.5965912716602046], ["Invert", 0.22603903038966913, 0.4346802001255868]], [["TranslateY", 0.010307035630661765, 0.16577665156754046], ["Posterize", 0.4114319141395257, 0.829872913683949]], [["TranslateY", 0.9353069865746215, 0.5327821671247214], ["Color", 0.16990443486261103, 0.38794866007484197]], [["Cutout", 0.1028174322829021, 0.3955952903458266], ["ShearY", 0.4311995281335693, 0.48024695395374734]], [["Posterize", 0.1800334334284686, 0.0548749478418862], ["Brightness", 0.7545808536793187, 0.7699080551646432]], [["Color", 0.48695305373084197, 0.6674269768464615], ["ShearY", 0.4306032279086781, 0.06057690550239343]], [["Brightness", 0.4919399683825053, 0.677338905806407], ["Brightness", 0.24112708387760828, 0.42761103121157656]], [["Posterize", 0.4434818644882532, 0.9489450593207714], ["Posterize", 0.40957675116385955, 0.015664946759584186]], [["Posterize", 0.41307949855153797, 0.6843276552020272], ["Rotate", 0.8003545094091291, 0.7002300783416026]], [["Color", 0.7038570031770905, 0.4697612983649519], ["Sharpness", 0.9700016496081002, 0.25185103545948884]], [["AutoContrast", 0.714641656154856, 0.7962423001719023], ["Sharpness", 0.2410097684093468, 0.5919171048019731]], [["TranslateX", 0.8101567644494714, 0.7156447005337443], ["Solarize", 0.5634727831229329, 0.8875158446846]], [["Sharpness", 0.5335258857303261, 0.364743126378182], ["Color", 0.453280875871377, 0.5621962714743068]], [["Cutout", 0.7423678127672542, 0.7726370777867049], ["Invert", 0.2806161382641934, 0.6021111986900146]], [["TranslateY", 0.15190341320343761, 0.3860373175487939], ["Cutout", 0.9980805818665679, 0.05332384819400854]], [["Posterize", 0.36518675678786605, 0.2935819027397963], ["TranslateX", 0.26586180351840005, 0.303641300745208]], [["Brightness", 0.19994509744377761, 0.90813953707639], ["Equalize", 0.8447217761297836, 0.3449396603478335]], [["Sharpness", 0.9294773669936768, 0.999713346583839], ["Brightness", 0.1359744825665662, 0.1658489221872924]], [["TranslateX", 0.11456529257659381, 0.9063795878367734], ["Equalize", 0.017438134319894553, 0.15776887259743755]], [["ShearX", 0.9833726383270114, 0.5688194948373335], ["Equalize", 0.04975615490994345, 0.8078130016227757]], [["Brightness", 0.2654654830488695, 0.8989789725280538], ["TranslateX", 0.3681535065952329, 0.36433345713161036]], [["Rotate", 0.04956524209892327, 0.5371942433238247], ["ShearY", 0.0005527499145153714, 0.56082571605602]], [["Rotate", 0.7918337108932019, 0.5906896260060501], ["Posterize", 0.8223967034091191, 0.450216998388943]], [["Color", 0.43595106766978337, 0.5253013785221605], ["Sharpness", 0.9169421073531799, 0.8439997639348893]], [["TranslateY", 0.20052300197155504, 0.8202662448307549], 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0.7830320527127324, 0.2727062685529881], ["Solarize", 0.527834000867504, 0.20098218845222998]], [["Contrast", 0.366380535288225, 0.39766001659663075], ["Cutout", 0.8708808878088891, 0.20669525734273086]], [["ShearX", 0.6815427281122932, 0.6146858582671569], ["AutoContrast", 0.28330622372053493, 0.931352024154997]], [["AutoContrast", 0.8668174463154519, 0.39961453880632863], ["AutoContrast", 0.5718557712359253, 0.6337062930797239]], [["ShearY", 0.8923152519411871, 0.02480062504737446], ["Cutout", 0.14954159341231515, 0.1422219808492364]], [["Rotate", 0.3733718175355636, 0.3861928572224287], ["Sharpness", 0.5651126520194574, 0.6091103847442831]], [["Posterize", 0.8891714191922857, 0.29600154265251016], ["TranslateY", 0.7865351723963945, 0.5664998548985523]], [["TranslateX", 0.9298214806998273, 0.729856565052017], ["AutoContrast", 0.26349082482341846, 0.9638882609038888]], [["Sharpness", 0.8387378377527128, 0.42146721129032494], ["AutoContrast", 0.9860522000876452, 0.4200699464169384]], [["ShearY", 0.019609159303115145, 0.37197835936879514], ["Cutout", 0.22199340461754258, 0.015932573201085848]], [["Rotate", 0.43871085583928443, 0.3283504258860078], ["Sharpness", 0.6077702068037776, 0.6830305349618742]], [["Contrast", 0.6160211756538094, 0.32029451083389626], ["Cutout", 0.8037631428427006, 0.4025688837399259]], [["TranslateY", 0.051637820936985435, 0.6908417834391846], ["Sharpness", 0.7602756948473368, 0.4927111506643095]], [["Rotate", 0.4973618638052235, 0.45931479729281227], ["TranslateY", 0.04701789716427618, 0.9408779705948676]], [["Rotate", 0.5214194592768602, 0.8371249272013652], ["Solarize", 0.17734812472813338, 0.045020798970228315]], [["ShearX", 0.7457999920079351, 0.19025612553075893], ["Sharpness", 0.5994846101703786, 0.5665094068864229]], [["Contrast", 0.6172655452900769, 0.7811432139704904], ["Cutout", 0.09915620454670282, 0.3963692287596121]], [["TranslateX", 0.2650112299235817, 0.7377261946165307], ["AutoContrast", 0.5019539734059677, 0.26905046992024506]], [["Contrast", 0.6646299821370135, 0.41667784809592945], ["Cutout", 0.9698457154992128, 0.15429001887703997]], [["Sharpness", 0.9467079029475773, 0.44906457469098204], ["Cutout", 0.30036908747917396, 0.4766149689663106]], [["Equalize", 0.6667517691051055, 0.5014839828447363], ["Solarize", 0.4127890336820831, 0.9578274770236529]], [["Cutout", 0.6447384874120834, 0.2868806107728985], ["Cutout", 0.4800990488106021, 0.4757538246206956]], [["Solarize", 0.12560195032363236, 0.5557473475801568], ["Equalize", 0.019957161871490228, 0.5556797187823773]], [["Contrast", 0.12607637375759484, 0.4300633627435161], ["Sharpness", 0.3437273670109087, 0.40493203127714417]], [["AutoContrast", 0.884353334807183, 0.5880138314357569], ["Rotate", 0.9846032404597116, 0.3591877296622974]], [["TranslateX", 0.6862295865975581, 0.5307482119690076], ["Contrast", 0.19439251187251982, 0.3999195825722808]], [["Posterize", 0.4187641835025246, 0.5008988942651585], ["Brightness", 0.6665805605402482, 0.3853288204214253]], [["Posterize", 0.4507470690013903, 0.4232437206624681], ["TranslateX", 0.6054107416317659, 0.38123828040922203]], [["AutoContrast", 0.29562338573283276, 0.35608605102687474], ["TranslateX", 0.909954785390274, 0.20098894888066549]], [["Contrast", 0.6015278411777212, 0.6049140992035096], ["Cutout", 0.47178713636517855, 0.5333747244651914]], [["TranslateX", 0.490851976691112, 0.3829593925141144], ["Sharpness", 0.2716675173824095, 0.5131696240367152]], [["Posterize", 0.4190558294646337, 0.39316689077269873], ["Rotate", 0.5018526072725914, 0.295712490156129]], [["AutoContrast", 0.29624715560691617, 0.10937329832409388], ["Posterize", 0.8770505275992637, 0.43117765012206943]], [["Rotate", 0.6649970092751698, 0.47767131373391974], ["ShearX", 0.6257923540490786, 0.6643337040198358]], [["Sharpness", 0.5553620705849509, 0.8467799429696928], ["Cutout", 0.9006185811918932, 0.3537270716262]], [["ShearY", 0.0007619678283789788, 0.9494591850536303], ["Invert", 0.24267733654007673, 0.7851608409575828]], [["Contrast", 0.9730916198112872, 0.404670123321921], ["Sharpness", 0.5923587793251186, 0.7405792404430281]], [["Cutout", 0.07393909593373034, 0.44569630026328344], ["TranslateX", 0.2460593252211425, 0.4817527814541055]], [["Brightness", 0.31058654119340867, 0.7043749950260936], ["ShearX", 0.7632161538947713, 0.8043681264908555]], [["AutoContrast", 0.4352334371415373, 0.6377550087204297], ["Rotate", 0.2892714673415678, 0.49521052050510556]], [["Equalize", 0.509071051375276, 0.7352913414974414], ["ShearX", 0.5099959429711828, 0.7071566714593619]], [["Posterize", 0.9540506532512889, 0.8498853304461906], ["ShearY", 0.28199061357155397, 0.3161715627214629]], [["Posterize", 0.6740855359097433, 0.684004694936616], ["Posterize", 0.6816720350737863, 0.9654766942980918]], [["Solarize", 0.7149344531717328, 0.42212789795181643], ["Brightness", 0.686601460864528, 0.4263050070610551]], [["Cutout", 0.49577164991501, 0.08394890892056037], ["Rotate", 0.5810369852730606, 0.3320732965776973]], [["TranslateY", 0.1793755480490623, 0.6006520265468684], ["Brightness", 0.3769016576438939, 0.7190746300828186]], [["TranslateX", 0.7226363597757153, 0.3847027238123509], ["Brightness", 0.7641713191794035, 0.36234003077512544]], [["TranslateY", 0.1211227055347106, 0.6693523474608023], ["Brightness", 0.13011180247738063, 0.5126647617294864]], [["Equalize", 0.1501070550869129, 0.0038548909451806557], ["Posterize", 0.8266535939653881, 0.5502199643499207]], [["Sharpness", 0.550624117428359, 0.2023044586648523], ["Brightness", 0.06291556314780017, 0.7832635398703937]], [["Color", 0.3701578205508141, 0.9051537973590863], ["Contrast", 0.5763972727739397, 0.4905511239739898]], [["Rotate", 0.7678527224046323, 0.6723066265307555], ["Solarize", 0.31458533097383207, 0.38329324335154524]], [["Brightness", 0.292050127929522, 0.7047582807953063], ["ShearX", 0.040541891910333805, 0.06639328601282746]], [["TranslateY", 0.4293891393238555, 0.6608516902234284], ["Sharpness", 0.7794685477624004, 0.5168044063408147]], [["Color", 0.3682450402286552, 0.17274523597220048], ["ShearY", 0.3936056470397763, 0.5702597289866161]], [["Equalize", 0.43436990310624657, 0.9207072627823626], ["Contrast", 0.7608688260846083, 0.4759023148841439]], [["Brightness", 0.7926088966143935, 0.8270093925674497], ["ShearY", 0.4924174064969461, 0.47424347505831244]], [["Contrast", 0.043917555279430476, 0.15861903591675125], ["ShearX", 0.30439480405505853, 0.1682659341098064]], [["TranslateY", 0.5598255583454538, 0.721352536005039], ["Posterize", 0.9700921973303752, 0.6882015184440126]], [["AutoContrast", 0.3620887415037668, 0.5958176322317132], ["TranslateX", 0.14213781552733287, 0.6230799786459947]], [["Color", 0.490366889723972, 0.9863152892045195], ["Color", 0.817792262022319, 0.6755656429452775]], [["Brightness", 0.7030707021937771, 0.254633187122679], ["Color", 0.13977318232688843, 0.16378180123959793]], [["AutoContrast", 0.2933247831326118, 0.6283663376211102], ["Sharpness", 0.85430478154147, 0.9753613184208796]], [["Rotate", 0.6674299955457268, 0.48571208708018976], ["Contrast", 0.47491370175907016, 0.6401079552479657]], [["Sharpness", 0.37589579644127863, 0.8475131989077025], ["TranslateY", 0.9985149867598191, 0.057815729375099975]], [["Equalize", 0.0017194373841596389, 0.7888361311461602], ["Contrast", 0.6779293670669408, 0.796851411454113]], [["TranslateY", 0.3296782119072306, 0.39765117357271834], ["Sharpness", 0.5890554357001884, 0.6318339473765834]], [["Posterize", 0.25423810893163856, 0.5400430289894207], ["Sharpness", 0.9273643918988342, 0.6480913470982622]], [["Cutout", 0.850219975768305, 0.4169812455601289], ["Solarize", 0.5418755745870089, 0.5679666650495466]], [["Brightness", 0.008881361977310959, 0.9282562314720516], ["TranslateY", 0.7736066471553994, 0.20041167606029642]], [["Brightness", 0.05382537581401925, 0.6405265501035952], ["Contrast", 0.30484329473639593, 0.5449338155734242]], [["Color", 0.613257119787967, 0.4541503912724138], ["Brightness", 0.9061572524724674, 0.4030159294447347]], [["Brightness", 0.02739111568942537, 0.006028056532326534], ["ShearX", 0.17276751958646486, 0.05967365780621859]], [["TranslateY", 0.4376298213047888, 0.7691816164456199], ["Sharpness", 0.8162292718857824, 0.6054926462265117]], [["Color", 0.37963069679121214, 0.5946919433483344], ["Posterize", 0.08485417284005387, 0.5663580913231766]], [["Equalize", 0.49785780226818316, 0.9999137109183761], ["Sharpness", 0.7685879484682496, 0.6260846154212211]], [["AutoContrast", 0.4190931409670763, 0.2374852525139795], ["Posterize", 0.8797422264608563, 0.3184738541692057]], [["Rotate", 0.7307269024632872, 0.41523609600701106], ["ShearX", 0.6166685870692289, 0.647133807748274]], [["Sharpness", 0.5633713231039904, 0.8276694754755876], ["Cutout", 0.8329340776895764, 0.42656043027424073]], [["ShearY", 0.14934828370884312, 0.8622510773680372], ["Invert", 0.25925989086863277, 0.8813283584888576]], [["Contrast", 0.9457071292265932, 0.43228655518614034], ["Sharpness", 0.8485316947644338, 0.7590298998732413]], [["AutoContrast", 0.8386103589399184, 0.5859583131318076], ["Solarize", 0.466758711343543, 0.9956215363818983]], [["Rotate", 0.9387133710926467, 0.19180564509396503], ["Rotate", 0.5558247609706255, 0.04321698692007105]], [["ShearX", 0.3608716600695567, 0.15206159451532864], ["TranslateX", 0.47295292905710146, 0.5290760596129888]], [["TranslateX", 0.8357685981547495, 0.5991305115727084], ["Posterize", 0.5362929404188211, 0.34398525441943373]], [["ShearY", 0.6751984031632811, 0.6066293622133011], ["Contrast", 0.4122723990263818, 0.4062467515095566]], [["Color", 0.7515349936021702, 0.5122124665429213], ["Contrast", 0.03190514292904123, 0.22903520154660545]], [["Contrast", 0.5448962625054385, 0.38655673938910545], ["AutoContrast", 0.4867400684894492, 0.3433111101096984]], [["Rotate", 0.0008372434310827959, 0.28599951781141714], ["Equalize", 0.37113686925530087, 0.5243929348114981]], [["Color", 0.720054993488857, 0.2010177651701808], ["TranslateX", 0.23036196506059398, 0.11152764304368781]], [["Cutout", 0.859134208332423, 0.6727345740185254], ["ShearY", 0.02159833505865088, 0.46390076266538544]], [["Sharpness", 0.3428232157391428, 0.4067874527486514], ["Brightness", 0.5409415136577347, 0.3698432231874003]], [["Solarize", 0.27303978936454776, 0.9832186173589548], ["ShearY", 0.08831127213044043, 0.4681870331149774]], [["TranslateY", 0.2909309268736869, 0.4059460811623174], ["Sharpness", 0.6425125139803729, 0.20275737203293587]], [["Contrast", 0.32167626214661627, 0.28636162794046977], ["Invert", 0.4712405253509603, 0.7934644799163176]], [["Color", 0.867993060896951, 0.96574321666213], ["Color", 0.02233897320328512, 0.44478933557303063]], [["AutoContrast", 0.1841254751814967, 0.2779992148017741], ["Color", 0.3586283093530607, 0.3696246850445087]], [["Posterize", 0.2052935984046965, 0.16796913860308244], ["ShearX", 0.4807226832843722, 0.11296747254563266]], [["Cutout", 0.2016411266364791, 0.2765295444084803], ["Brightness", 0.3054112810424313, 0.695924264931216]], [["Rotate", 0.8405872184910479, 0.5434142541450815], ["Cutout", 0.4493615138203356, 0.893453735250007]], [["Contrast", 0.8433310507685494, 0.4915423577963278], ["ShearX", 0.22567799557913246, 0.20129892537008834]], [["Contrast", 0.045954277103674224, 0.5043900167190442], ["Cutout", 0.5552992473054611, 0.14436447810888237]], [["AutoContrast", 0.7719296115130478, 0.4440417544621306], ["Sharpness", 0.13992809206158283, 0.7988278670709781]], [["Color", 0.7838574233513952, 0.5971351401625151], ["TranslateY", 0.13562290583925385, 0.2253039635819158]], [["Cutout", 0.24870301109385806, 0.6937886690381568], ["TranslateY", 0.4033400068952813, 0.06253378991880915]], [["TranslateX", 0.0036059390486775644, 0.5234723884081843], ["Solarize", 0.42724862530733526, 0.8697702564187633]], [["Equalize", 0.5446026737834311, 0.9367992979112202], ["ShearY", 0.5943478903735789, 0.42345889214100046]], [["ShearX", 0.18611885697957506, 0.7320849092947314], ["ShearX", 0.3796416430900566, 0.03817761920009881]], [["Posterize", 0.37636778506979124, 0.26807924785236537], ["Brightness", 0.4317372554383255, 0.5473346211870932]], [["Brightness", 0.8100436240916665, 0.3817612088285007], ["Brightness", 0.4193974619003253, 0.9685902764026623]], [["Contrast", 0.701776402197012, 0.6612786008858009], ["Color", 0.19882787177960912, 0.17275597188875483]], [["Color", 0.9538303302832989, 0.48362384535228686], ["ShearY", 0.2179980837345602, 0.37027290936457313]], [["TranslateY", 0.6068028691503798, 0.3919346523454841], ["Cutout", 0.8228303342563138, 0.18372280287814613]], [["Equalize", 0.016416758802906828, 0.642838949194916], ["Cutout", 0.5761717838655257, 0.7600661153497648]], [["Color", 0.9417761826818639, 0.9916074035986558], ["Equalize", 0.2524209308597042, 0.6373703468715077]], [["Brightness", 0.75512589439513, 0.6155072321007569], ["Contrast", 0.32413476940254515, 0.4194739830159837]], [["Sharpness", 0.3339450765586968, 0.9973297539194967], ["AutoContrast", 0.6523930242124429, 0.1053482471037186]], [["ShearX", 0.2961391955838801, 0.9870036064904368], ["ShearY", 0.18705025965909403, 0.4550895821154484]], [["TranslateY", 0.36956447983807883, 0.36371471767143543], ["Sharpness", 0.6860051967688487, 0.2850190720087796]], [["Cutout", 0.13017742151902967, 0.47316674150067195], ["Invert", 0.28923829959551883, 0.9295585654924601]], [["Contrast", 0.7302368472279086, 0.7178974949876642], ["TranslateY", 0.12589674152030433, 0.7485392909494947]], [["Color", 0.6474693117772619, 0.5518269515590674], ["Contrast", 0.24643004970708016, 0.3435581358079418]], [["Contrast", 0.5650327855750835, 0.4843031798040887], ["Brightness", 0.3526684005761239, 0.3005305004600969]], [["Rotate", 0.09822284968122225, 0.13172798244520356], ["Equalize", 0.38135066977857157, 0.5135129123554154]], [["Contrast", 0.5902590645585712, 0.2196062383730596], ["ShearY", 0.14188379126120954, 0.1582612142182743]], [["Cutout", 0.8529913814417812, 0.89734031211874], ["Color", 0.07293767043078672, 0.32577659205278897]], [["Equalize", 0.21401668971453247, 0.040015259500028266], ["ShearY", 0.5126400895338797, 0.4726484828276388]], [["Brightness", 0.8269430025954498, 0.9678362841865166], ["ShearY", 0.17142069814830432, 0.4726727848289514]], [["Brightness", 0.699707089334018, 0.2795501395789335], ["ShearX", 0.5308818178242845, 0.10581814221896294]], [["Equalize", 0.32519644258946145, 0.15763390340309183], ["TranslateX", 0.6149090364414208, 0.7454832565718259]], [["AutoContrast", 0.5404508567155423, 0.7472387762067986], ["Equalize", 0.05649876539221024, 0.5628180219887216]]] return p def fa_reduced_svhn(): p = [[["TranslateX", 0.001576965129744562, 0.43180488809874773], ["Invert", 0.7395307279252639, 0.7538444307982558]], [["Contrast", 0.5762062225409211, 0.7532431872873473], ["TranslateX", 0.45212523461624615, 0.02451684483019846]], [["Contrast", 0.18962433143225088, 0.29481185671147325], ["Contrast", 0.9998112218299271, 0.813015355163255]], [["Posterize", 0.9633391295905683, 0.4136786222304747], ["TranslateY", 0.8011655496664203, 0.44102126789970797]], [["Color", 0.8231185187716968, 0.4171602946893402], ["TranslateX", 0.8684965619113907, 0.36514568324909674]], [["Color", 0.904075230324581, 0.46319140331093767], ["Contrast", 0.4115196534764559, 0.7773329158740563]], [["Sharpness", 0.6600262774093967, 0.8045637700026345], ["TranslateY", 0.5917663766021198, 0.6844241908520602]], [["AutoContrast", 0.16223989311434306, 0.48169653554195924], ["ShearX", 0.5433173232860344, 0.7460278151912152]], [["ShearX", 0.4913604762760715, 0.83391837859561], ["Color", 0.5580367056511908, 0.2961512691312932]], [["Color", 0.18567091721211237, 0.9296983204905286], ["Cutout", 0.6074026199060156, 0.03303273406448193]], [["Invert", 0.8049054771963224, 0.1340792344927909], ["Color", 0.4208839940504979, 0.7096454840962345]], [["ShearX", 0.7997786664546294, 0.6492629575700173], ["AutoContrast", 0.3142777134084793, 0.6526010594925064]], [["TranslateX", 0.2581027144644976, 0.6997433332894101], ["Rotate", 0.45490480973606834, 0.238620570022944]], [["Solarize", 0.837397161027719, 0.9311141273136286], ["Contrast", 0.640364826293148, 0.6299761518677469]], [["Brightness", 0.3782457347141744, 0.7085036717054278], ["Brightness", 0.5346150083208507, 0.5858930737867671]], [["Invert", 0.48780391510474086, 0.610086407879722], ["Color", 0.5601999247616932, 0.5393836220423195]], [["Brightness", 0.00250086643283564, 0.5003355864896979], ["Brightness", 0.003922153283353616, 0.41107110154584925]], [["TranslateX", 0.4073069009685957, 0.9843435292693372], ["Invert", 0.38837085318721926, 0.9298542033875989]], [["ShearY", 0.05479740443795811, 0.9113983424872698], ["AutoContrast", 0.2181108114232728, 0.713996037012164]], [["Brightness", 0.27747508429413903, 0.3217467607288693], ["ShearX", 0.02715239061946995, 0.5430731635396449]], [["Sharpness", 0.08994432959374538, 0.004706443546453831], ["Posterize", 0.10768206853226996, 0.39020299239900236]], [["Cutout", 0.37498679037853905, 0.20784809761469553], ["Color", 0.9825516352194511, 0.7654155662756019]], [["Color", 0.8899349124453552, 0.7797700766409008], ["Rotate", 0.1370222187174981, 0.2622119295138398]], [["Cutout", 0.7088223332663685, 0.7884456023190028], ["Solarize", 0.5362257505160836, 0.6426837537811545]], [["Invert", 0.15686225694987552, 0.5500563899117913], ["Rotate", 0.16315224193260078, 0.4246854030170752]], [["Rotate", 0.005266247922433631, 0.06612026206223394], ["Contrast", 0.06494357829209037, 0.2738420319474947]], [["Cutout", 0.30200619566806275, 0.06558008068236942], ["Rotate", 0.2168576483823022, 0.878645566986328]], [["Color", 0.6358930679444622, 0.613404714161498], ["Rotate", 0.08733206733004326, 0.4348276574435751]], [["Cutout", 0.8834634887239585, 0.0006853845293474659], ["Solarize", 0.38132051231951847, 0.42558752668491195]], [["ShearY", 0.08830136548479937, 0.5522438878371283], ["Brightness", 0.23816560427834074, 0.3033709051157141]], [["Solarize", 0.9015331490756151, 0.9108788708847556], ["Contrast", 0.2057898014670072, 0.03260096030427456]], [["Equalize", 0.9455978685121174, 0.14850077333434056], ["TranslateY", 0.6888705996522545, 0.5300565492007543]], [["Cutout", 0.16942673959343585, 0.7294197201361826], ["TranslateX", 0.41184830642301534, 0.7060207449376135]], [["Color", 0.30133344118702166, 0.24384417956342314], ["Sharpness", 0.4640904544421743, 0.32431840288061864]], [["Sharpness", 0.5195055033472676, 0.9386677467005835], ["Color", 0.9536519432978372, 0.9624043444556467]], [["Rotate", 0.8689597230556101, 0.23955490826730633], ["Contrast", 0.050071600927462656, 0.1309891556004179]], [["Cutout", 0.5349421090878962, 0.08239510727779054], ["Rotate", 0.46064964710717216, 0.9037689320897339]], [["AutoContrast", 0.5625256909986802, 0.5358003783186498], ["Equalize", 0.09204330691163354, 0.4386906784850649]], [["ShearX", 0.0011061172864470226, 0.07150284682189278], ["AutoContrast", 0.6015956946553209, 0.4375362295530898]], [["ShearY", 0.25294276499800983, 0.7937560397859562], ["Brightness", 0.30834103299704474, 0.21960258701547009]], [["Posterize", 0.7423948904688074, 0.4598609935109695], ["Rotate", 0.5510348811675979, 0.26763724868985933]], [["TranslateY", 0.3208729319318745, 0.945513054853888], ["ShearX", 0.4916473963030882, 0.8743840560039451]], [["ShearY", 0.7557718687011286, 0.3125397104722828], ["Cutout", 0.5565359791865849, 0.5151359251135629]], [["AutoContrast", 0.16652786355571275, 0.1101575800958632], ["Rotate", 0.05108851703032641, 0.2612966401802814]], [["Brightness", 0.380296489835016, 0.0428162454174662], ["ShearX", 0.3911934083168285, 0.18933607362790178]], [["Color", 0.002476250465397678, 0.07795275305347571], ["Posterize", 0.08131841266654188, 0.14843363184306413]], [["Cutout", 0.36664558716104434, 0.20904484995063996], ["Cutout", 0.07986452057223141, 0.9287747671053432]], [["Color", 0.9296812469919231, 0.6634239915141935], ["Rotate", 0.07632463573240006, 0.408624029443747]], [["Cutout", 0.7594470171961278, 0.9834672124229463], ["Solarize", 0.4471371303745053, 0.5751101102286562]], [["Posterize", 0.051186719734032285, 0.5110941294710823], ["Sharpness", 0.040432522797391596, 0.42652298706992164]], [["Sharpness", 0.2645335264327221, 0.8844553189835457], ["Brightness", 0.7229600357932696, 0.16660749270785696]], [["Sharpness", 0.6296376086802589, 0.15564989758083458], ["Sharpness", 0.7913410481400365, 0.7022615408082826]], [["Cutout", 0.5517247347343883, 0.43794888517764674], ["ShearX", 0.6951051782530201, 0.6230992857867065]], [["ShearX", 0.9015708556331022, 0.6322135168527783], ["Contrast", 0.4285629283441831, 0.18158321019502988]], [["Brightness", 0.9014292329524769, 0.3660463325457713], ["Invert", 0.6700729097206592, 0.16502732071917703]], [["AutoContrast", 0.6432764477303431, 0.9998909112400834], ["Invert", 0.8124063975545761, 0.8149683327882365]], [["Cutout", 0.6023944009428617, 0.9630976951918225], ["ShearX", 0.2734723568803071, 0.3080911542121765]], [["Sharpness", 0.048949115014412806, 0.44497866256845164], ["Brightness", 0.5611832867244329, 0.12994217480426257]], [["TranslateY", 0.4619112333002525, 0.47317728091588396], ["Solarize", 0.618638784910472, 0.9508297099190338]], [["Sharpness", 0.9656274391147018, 0.3402622993963962], ["Cutout", 0.8452511174508919, 0.3094717093312621]], [["ShearX", 0.04942201651478659, 0.6910568465705691], ["AutoContrast", 0.7155342517619936, 0.8565418847743523]], [["Brightness", 0.5222290590721783, 0.6462675303633422], ["Sharpness", 0.7756317511341633, 0.05010730683866704]], [["Contrast", 0.17098396012942796, 0.9128908626236187], ["TranslateY", 0.1523815376677518, 0.4269909829886339]], [["Cutout", 0.7679024720089866, 0.22229116396644455], ["Sharpness", 0.47714827844878843, 0.8242815864830401]], [["Brightness", 0.9321772357292445, 0.11339758604001371], ["Invert", 0.7021078495093375, 0.27507749184928154]], [["ShearY", 0.7069449324510433, 0.07262757954730437], ["Cutout", 0.6298690227159313, 0.8866813664859028]], [["ShearX", 0.8153137620199989, 0.8478194179953927], ["ShearX", 0.7519451353411938, 0.3914579556959725]], [["Cutout", 0.07152574469472753, 0.2629935229222503], ["TranslateX", 0.43728405510089485, 0.2610201002449789]], [["AutoContrast", 0.5824529633013098, 0.5619551536261955], ["Rotate", 0.45434137552116965, 0.7567169855140041]], [["TranslateY", 0.9338431187142137, 0.14230481341042783], ["Cutout", 0.744797723251028, 0.4346601666787713]], [["ShearX", 0.3197252560289169, 0.8770408070016171], ["Color", 0.7657013088540465, 0.2685586719812284]], [["ShearY", 0.6542181749801549, 0.8148188744344297], ["Sharpness", 0.5108985661436543, 0.9926016115463769]], [["ShearY", 0.39218730620135694, 0.857769946478945], ["Color", 0.39588355914920886, 0.9910530523789284]], [["Invert", 0.4993610396803735, 0.08449723470758526], ["TranslateX", 0.46267456928508305, 0.46691125646493964]], [["Equalize", 0.8640576819821256, 0.3973808869887604], ["ShearY", 0.5491163877063172, 0.422429328786161]], [["Contrast", 0.6146206387722841, 0.8453559854684094], ["TranslateX", 0.7974333014574718, 0.47395476786951773]], [["Contrast", 0.6828704722015236, 0.6952755697785722], ["Brightness", 0.7903069452567497, 0.8350915035109574]], [["Rotate", 0.1211091761531299, 0.9667702562228727], ["Color", 0.47888534537103344, 0.8298620028065332]], [["Equalize", 0.20009722872711086, 0.21851235854853018], ["Invert", 0.4433641154198673, 0.41902203581091935]], [["AutoContrast", 0.6333190204577053, 0.23965630032835372], ["Color", 0.38651217030044804, 0.06447323778198723]], [["Brightness", 0.378274337541471, 0.5482593116308322], ["Cutout", 0.4856574442608347, 0.8889688535495244]], [["Rotate", 0.8201259323479384, 0.7404525573938633], ["Color", 0.28371236449364595, 0.7866003515933161]], [["Brightness", 0.10053196350009105, 0.18814037089411267], ["Sharpness", 0.5572102497672569, 0.04458217557977126]], [["AutoContrast", 0.6445330112376135, 0.48082049184921843], ["TranslateY", 0.378898917914949, 0.9338102625289362]], [["AutoContrast", 0.08482623401924708, 0.25199930695784384], ["Solarize", 0.5981823550521426, 0.19626357596662092]], [["Solarize", 0.4373030803918095, 0.22907881245285625], ["AutoContrast", 0.6383084635487905, 0.29517603235993883]], [["AutoContrast", 0.922112624726991, 0.29398098144910145], ["AutoContrast", 0.8550184811514672, 0.8030331582292343]], [["ShearX", 0.38761582800913896, 0.06304125015084923], ["Contrast", 0.3225758804984975, 0.7089696696094797]], [["TranslateY", 0.27499498563849206, 0.1917583097241206], ["Color", 0.5845853711746438, 0.5353520071667661]], [["ShearY", 0.530881951424285, 0.47961248148116453], ["ShearX", 0.04666387744533289, 0.275772822690165]], [["Solarize", 0.5727309318844802, 0.02889734544563341], ["AutoContrast", 0.638852434854615, 0.9819440776921611]], [["AutoContrast", 0.9766868312173507, 0.9651796447738792], ["AutoContrast", 0.3489760216898085, 0.3082182741354106]], [["Sharpness", 0.13693510871346704, 0.08297205456926067], ["Contrast", 0.3155812019005854, 0.031402991638917896]], [["TranslateY", 0.2664707540547008, 0.4838091910041236], ["ShearX", 0.5935665395229432, 0.7813088248538167]], [["ShearY", 0.7578577752251343, 0.5116014090216161], ["ShearX", 0.8332831240873545, 0.26781876290841017]], [["TranslateY", 0.473254381651761, 0.4203181582821155], ["ShearY", 0.732848696900726, 0.47895514793728433]], [["Solarize", 0.6922689176672292, 0.36403255869823725], ["AutoContrast", 0.910654040826914, 0.888651414068326]], [["ShearX", 0.37326536936166244, 0.47830923320699525], ["Equalize", 0.4724702976076929, 0.8176108279939023]], [["Contrast", 0.3839906424759326, 0.09109695563933692], ["Invert", 0.36305435543972325, 0.5701589223795499]], [["Invert", 0.5175591137387999, 0.38815675919253867], ["TranslateY", 0.1354848160153554, 0.41734106283245065]], [["Color", 0.829616006981199, 0.18631472346156963], ["Color", 0.2465115448326214, 0.9439365672808333]], [["Contrast", 0.18207939197942158, 0.39841173152850873], ["ShearX", 0.16723588254695632, 0.2868649619006758]], [["Posterize", 0.1941909136988733, 0.6322499882557473], ["Contrast", 0.6109060391509794, 0.27329598688783296]], [["AutoContrast", 0.9148775146158022, 0.09129288311923844], ["Sharpness", 0.4222442287436423, 0.847961820057229]], [["Color", 0.21084007475489852, 0.008218056412554131], ["Contrast", 0.43996934555301637, 0.500680146508504]], [["ShearY", 0.6745287915240038, 0.6120305524405164], ["Equalize", 0.467403794543269, 0.2207148995882467]], [["Color", 0.7712823974371379, 0.2839161885566902], ["Color", 0.8725368489709752, 0.3349470222415115]], [["Solarize", 0.5563976601161562, 0.540446614847802], ["Invert", 0.14228071175107454, 0.2242332811481905]], [["Contrast", 0.34596757983998383, 0.9158971503395041], ["Cutout", 0.6823724203724072, 0.5221518922863516]], [["Posterize", 0.3275475232882672, 0.6520033254468702], ["Color", 0.7434224109271398, 0.0824308188060544]], [["Cutout", 0.7295122229650082, 0.277887573018184], ["Brightness", 0.5303655506515258, 0.28628046739964497]], [["Color", 0.8533293996815943, 0.24909788223027743], ["Color", 0.6915962825167857, 0.33592561040195834]], [["TranslateX", 0.0761441550001345, 0.7043906245420134], ["Equalize", 0.670845297717783, 0.30986063097084215]], [["Contrast", 0.30592723366237995, 0.7365013059287382], ["Color", 0.6173835128817455, 0.6417028717640598]], [["Rotate", 0.05558240682703821, 0.7284722849011761], ["Color", 0.7814801133853666, 0.13335113981884217]], [["ShearY", 0.6521743070190724, 0.6272195913574455], ["Rotate", 0.36278432239870423, 0.2335623679787695]], [["Color", 0.6799351102482663, 0.3850250771244986], ["Brightness", 0.613901077818094, 0.2374900558949702]], [["Color", 0.551451255148252, 0.7284757153447965], ["Solarize", 0.4863815212982878, 0.3857941567681324]], [["Contrast", 0.32516343965159267, 0.689921852601276], ["Cutout", 0.5922142001124506, 0.7709605594115009]], [["Brightness", 0.23760063764495856, 0.6392077018854179], ["Brightness", 0.7288124083714078, 0.4487520490201095]], [["Sharpness", 0.5631112298553713, 0.6803534985114782], ["ShearX", 0.6743791169050775, 0.34039227245151127]], [["AutoContrast", 0.8260911840078349, 0.7705607269534767], ["Rotate", 0.8880749478363638, 0.8182460047684648]], [["ShearY", 0.7037620764408412, 0.5219573160970589], ["Posterize", 0.7186150466761102, 0.6187857686944253]], [["TranslateY", 0.2140494926702246, 0.9104233882669488], ["TranslateX", 0.4096039512896902, 0.9692703030784571]], [["Equalize", 0.5404313549028165, 0.04094078980738014], ["AutoContrast", 0.07870278300673744, 0.841020779977939]], [["ShearY", 0.2684638876128488, 0.5599793678740521], ["Cutout", 0.19537995362704022, 0.2400995206366768]], [["AutoContrast", 0.19366394417090382, 0.4130755503251951], ["Sharpness", 0.11735660606190662, 0.39276612830651914]], [["Cutout", 0.8313266945081518, 0.37171822186374703], ["Contrast", 0.5088549187459019, 0.2956405118511817]], [["Cutout", 0.28375485371479847, 0.37020183949342683], ["Posterize", 0.718761436947423, 0.2278804627251678]], [["ShearY", 0.6625840735667625, 0.5045065697748213], ["Rotate", 0.5175257698523389, 0.39496923901188824]], [["Color", 0.6498154010188212, 0.38674158604408604], ["Brightness", 0.8157804892728057, 0.05660118670560971]], [["Color", 0.5512855420254102, 0.7812054820692542], ["Solarize", 0.8851292984174468, 0.2808951606943277]], [["Contrast", 0.35258433539074363, 0.8085377169629859], ["Cutout", 0.5197965849563265, 0.8657111726930974]], [["Cutout", 0.23650925054419358, 0.746860862983295], ["Brightness", 0.8842190203336139, 0.4389347348156118]], [["Rotate", 0.8651460526861932, 0.0031372441327392753], ["Equalize", 0.3909498933963822, 0.6221687914603954]], [["TranslateX", 0.5793690303540427, 0.37939687327382987], ["Invert", 0.846172545690258, 0.36950442052945853]], [["Invert", 0.5151721602607067, 0.5860134277259832], ["Contrast", 0.6868708526377458, 0.2188104093363727]], [["Contrast", 0.28019632529718025, 0.8403553410328943], ["Cutout", 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0.9947658451503181]], [["Posterize", 0.11207465085954937, 0.23296263754645155], ["Cutout", 0.6159972426858633, 0.38289684517298556]], [["TranslateX", 0.7343689718523805, 0.16303049089087485], ["Equalize", 0.3138385390145809, 0.6096356352129273]], [["Solarize", 0.4807269891506887, 0.28116279654856363], ["Posterize", 0.9753467973380021, 0.6327025372916857]], [["Posterize", 0.837244997106023, 0.5586046483574153], ["AutoContrast", 0.9005775602024721, 0.7983389828641411]], [["AutoContrast", 0.8347112949943837, 0.7321850307727004], ["Cutout", 0.3322676575657192, 0.14409873524237032]], [["Equalize", 0.12285967262649124, 0.5368519477089722], ["Posterize", 0.2693593445898034, 0.15098267759162076]], [["Invert", 0.331021587020619, 0.3140868578915853], ["Cutout", 0.48268387543799884, 0.7642598986625201]], [["Equalize", 0.47573794714622175, 0.8628185952549363], ["Solarize", 0.14860046214144496, 0.3739284346347912]], [["AutoContrast", 0.6747373196190459, 0.2912917979635714], ["Posterize", 0.27259573208358623, 0.9643671211873469]], [["Sharpness", 0.15019788105901233, 0.7289238028242861], ["ShearY", 0.7998448015985137, 0.5924798900807636]], [["Brightness", 0.7874052186079156, 0.9446398428550358], ["Equalize", 0.5105557539139616, 0.6719808885741001]], [["ShearX", 0.783252331899515, 0.74960184771181], ["ShearX", 0.4327935527932927, 0.29980994764698565]], [["Rotate", 0.03892023906368644, 0.24868635699639904], ["Cutout", 0.6408903979315637, 0.32135851733523907]], [["Invert", 0.9972802027590713, 0.9374194642823106], ["ShearX", 0.20016463162924894, 0.0052278586143255645]], [["AutoContrast", 0.9328687102578992, 0.44280614999256235], ["Color", 0.05637751621265141, 0.26921974769786455]], [["AutoContrast", 0.2798532308065416, 0.5283914274806746], ["Cutout", 0.12930089032151, 0.25624459046884057]], [["Invert", 0.2397428994839993, 0.31011715409282065], ["Cutout", 0.5875151915473042, 0.7454458580264322]], [["Equalize", 0.374815667651982, 0.9502053862625081], ["Solarize", 0.10100323698574426, 0.5124939317648691]], [["AutoContrast", 0.6009889057852652, 0.3080148907275367], ["Posterize", 0.6543352447742621, 0.17498668744492413]], [["Sharpness", 0.14402909409016001, 0.9239239955843186], ["ShearY", 0.8959818090635513, 0.7258262803413784]], [["Brightness", 0.8672271320432974, 0.8241439816189235], ["Equalize", 0.4954433852960082, 0.6687050430971254]], [["Solarize", 0.47813402689782114, 0.9447222576804901], ["TranslateY", 0.32546974113401694, 0.8367777573080345]], [["Sharpness", 0.48098022972519927, 0.2731904819197933], ["Rotate", 0.14601550238940067, 0.3955290089346866]], [["AutoContrast", 0.3777442613874327, 0.9991495158709968], ["TranslateY", 0.2951496731751222, 0.6276755696126608]], [["Cutout", 0.487150344941835, 0.7976642551725155], ["Solarize", 0.643407733524025, 0.6313641977306543]], [["Rotate", 0.35017053741686033, 0.23960877779589906], ["Sharpness", 0.8741761196478873, 0.12362019972427862]], [["Invert", 0.8849459784626776, 0.48532144354199647], ["Invert", 0.702430443380318, 0.924655906426149]], [["Equalize", 0.6324140359298986, 0.9780539325897597], ["AutoContrast", 0.39105074227907843, 0.3636856607173081]], [["AutoContrast", 0.8049993541952016, 0.3231157206314408], ["ShearY", 0.6675686366141409, 0.7345332792455934]], [["Sharpness", 0.12332351413693327, 0.9345179453120547], ["Solarize", 0.1594280186083361, 0.422049311332906]], [["Rotate", 0.38227253679386375, 0.7664364038099101], ["AutoContrast", 0.5725492572719726, 0.21049701651094446]], [["Brightness", 0.6432891832524184, 0.8243948738979008], ["Equalize", 0.20355899618080098, 0.7983877568044979]], [["ShearY", 0.694393675204811, 0.3686964692262895], ["TranslateX", 0.5593122846101599, 0.3378904046390629]], [["Invert", 0.9139730140623171, 0.7183505086140822], ["Posterize", 0.2675839177893596, 0.21399738931234905]], [["TranslateX", 0.05309461965184896, 0.032983777975422554], ["Sharpness", 0.412621944330688, 0.4752089612268503]], [["Equalize", 0.06901149860261116, 0.27405796188385945], ["AutoContrast", 0.7710451977604326, 0.20474249114426807]], [["ShearX", 0.47416427531072325, 0.2738614239087857], ["Cutout", 0.2820106413231565, 0.6295219975308107]], [["Cutout", 0.19984489885141582, 0.7019895950299546], ["ShearX", 0.4264722378410729, 0.8483962467724536]], [["ShearY", 0.42111446850243256, 0.1837626718066795], ["Brightness", 0.9187856196205942, 0.07478292286531767]], [["Solarize", 0.2832036589192868, 0.8253473638854684], ["Cutout", 0.7279303826662196, 0.615420010694839]], [["ShearX", 0.963251873356884, 0.5625577053738846], ["Color", 0.9637046840298858, 0.9992644813427337]], [["Invert", 0.7976502716811696, 0.43330238739921956], ["ShearY", 0.9113181667853614, 0.9066729024232627]], [["Posterize", 0.5750620807485399, 0.7729691927432935], ["Contrast", 0.4527879467651071, 0.9647739595774402]], [["Posterize", 0.5918751472569104, 0.26467375535556653], ["Posterize", 0.6347402742279589, 0.7476940787143674]], [["Invert", 0.16552404612306285, 0.9829939598708993], ["Solarize", 0.29886553921638087, 0.22487098773064948]], [["Cutout", 0.24209211313246753, 0.5522928952260516], ["AutoContrast", 0.6212831649673523, 0.4191071063984261]], [["ShearX", 0.4726406722647257, 0.26783614257572447], ["TranslateY", 0.251078162624763, 0.26103450676044304]], [["Cutout", 0.8721775527314426, 0.6284108541347894], ["ShearX", 0.7063325779145683, 0.8467168866724094]], [["ShearY", 0.42226987564279606, 0.18012694533480308], ["Brightness", 0.858499853702629, 0.4738929353785444]], [["Solarize", 0.30039851082582764, 0.8151511479162529], ["Cutout", 0.7228873804059033, 0.6174351379837011]], [["ShearX", 0.4921198221896609, 0.5678998037958154], ["Color", 0.7865298825314806, 0.9309020966406338]], [["Invert", 0.8077821007916464, 0.7375015762124386], ["Cutout", 0.032464574567796195, 0.25405044477004846]], [["Color", 0.6061325441870133, 0.2813794250571565], ["TranslateY", 0.5882949270385848, 0.33262043078220227]], [["ShearX", 0.7877331864215293, 0.8001131937448647], ["Cutout", 0.19828215489868783, 0.5949317580743655]], [["Contrast", 0.529508728421701, 0.36477855845285007], ["Color", 0.7145481740509138, 0.2950794787786947]], [["Contrast", 0.9932891064746089, 0.46930062926732646], ["Posterize", 0.9033014136780437, 0.5745902253320527]]] return p def policy_decoder(augment, num_policy, num_op): op_list = augment_list(False) policies = [] for i in range(num_policy): ops = [] for j in range(num_op): op_idx = augment['policy_%d_%d' % (i, j)] op_prob = augment['prob_%d_%d' % (i, j)] op_level = augment['level_%d_%d' % (i, j)] ops.append((op_list[op_idx][0].__name__, op_prob, op_level)) policies.append(ops) return policies ================================================ FILE: fast_autoaugment/FastAutoAugment/aug_mixup.py ================================================ """ Reference : - https://github.com/hysts/pytorch_image_classification/blob/master/augmentations/mixup.py - https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/imagenet_input.py#L120 """ import numpy as np import torch from FastAutoAugment.metrics import CrossEntropyLabelSmooth def mixup(data, targets, alpha): indices = torch.randperm(data.size(0)) shuffled_data = data[indices] shuffled_targets = targets[indices] lam = np.random.beta(alpha, alpha) lam = max(lam, 1. - lam) assert 0.0 <= lam <= 1.0, lam data = data * lam + shuffled_data * (1 - lam) return data, targets, shuffled_targets, lam class CrossEntropyMixUpLabelSmooth(torch.nn.Module): def __init__(self, num_classes, epsilon, reduction='mean'): super(CrossEntropyMixUpLabelSmooth, self).__init__() self.ce = CrossEntropyLabelSmooth(num_classes, epsilon, reduction=reduction) def forward(self, input, target1, target2, lam): # pylint: disable=redefined-builtin return lam * self.ce(input, target1) + (1 - lam) * self.ce(input, target2) ================================================ FILE: fast_autoaugment/FastAutoAugment/augmentations.py ================================================ # code in this file is adpated from rpmcruz/autoaugment # https://github.com/rpmcruz/autoaugment/blob/master/transformations.py import random import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw import numpy as np import torch from torchvision.transforms.transforms import Compose random_mirror = True def ShearX(img, v): # [-0.3, 0.3] assert -0.3 <= v <= 0.3 if random_mirror and random.random() > 0.5: v = -v return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0)) def ShearY(img, v): # [-0.3, 0.3] assert -0.3 <= v <= 0.3 if random_mirror and random.random() > 0.5: v = -v return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0)) def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45] assert -0.45 <= v <= 0.45 if random_mirror and random.random() > 0.5: v = -v v = v * img.size[0] return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0)) def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45] assert -0.45 <= v <= 0.45 if random_mirror and random.random() > 0.5: v = -v v = v * img.size[1] return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v)) def TranslateXAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45] assert 0 <= v <= 10 if random.random() > 0.5: v = -v return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0)) def TranslateYAbs(img, v): # [-150, 150] => percentage: [-0.45, 0.45] assert 0 <= v <= 10 if random.random() > 0.5: v = -v return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v)) def Rotate(img, v): # [-30, 30] assert -30 <= v <= 30 if random_mirror and random.random() > 0.5: v = -v return img.rotate(v) def AutoContrast(img, _): return PIL.ImageOps.autocontrast(img) def Invert(img, _): return PIL.ImageOps.invert(img) def Equalize(img, _): return PIL.ImageOps.equalize(img) def Flip(img, _): # not from the paper return PIL.ImageOps.mirror(img) def Solarize(img, v): # [0, 256] assert 0 <= v <= 256 return PIL.ImageOps.solarize(img, v) def Posterize(img, v): # [4, 8] assert 4 <= v <= 8 v = int(v) return PIL.ImageOps.posterize(img, v) def Posterize2(img, v): # [0, 4] assert 0 <= v <= 4 v = int(v) return PIL.ImageOps.posterize(img, v) def Contrast(img, v): # [0.1,1.9] assert 0.1 <= v <= 1.9 return PIL.ImageEnhance.Contrast(img).enhance(v) def Color(img, v): # [0.1,1.9] assert 0.1 <= v <= 1.9 return PIL.ImageEnhance.Color(img).enhance(v) def Brightness(img, v): # [0.1,1.9] assert 0.1 <= v <= 1.9 return PIL.ImageEnhance.Brightness(img).enhance(v) def Sharpness(img, v): # [0.1,1.9] assert 0.1 <= v <= 1.9 return PIL.ImageEnhance.Sharpness(img).enhance(v) def Cutout(img, v): # [0, 60] => percentage: [0, 0.2] assert 0.0 <= v <= 0.2 if v <= 0.: return img v = v * img.size[0] return CutoutAbs(img, v) def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2] # assert 0 <= v <= 20 if v < 0: return img w, h = img.size x0 = np.random.uniform(w) y0 = np.random.uniform(h) x0 = int(max(0, x0 - v / 2.)) y0 = int(max(0, y0 - v / 2.)) x1 = min(w, x0 + v) y1 = min(h, y0 + v) xy = (x0, y0, x1, y1) color = (125, 123, 114) # color = (0, 0, 0) img = img.copy() PIL.ImageDraw.Draw(img).rectangle(xy, color) return img def SamplePairing(imgs): # [0, 0.4] def f(img1, v): i = np.random.choice(len(imgs)) img2 = PIL.Image.fromarray(imgs[i]) return PIL.Image.blend(img1, img2, v) return f def augment_list(for_autoaug=True): # 16 oeprations and their ranges l = [ (ShearX, -0.3, 0.3), # 0 (ShearY, -0.3, 0.3), # 1 (TranslateX, -0.45, 0.45), # 2 (TranslateY, -0.45, 0.45), # 3 (Rotate, -30, 30), # 4 (AutoContrast, 0, 1), # 5 (Invert, 0, 1), # 6 (Equalize, 0, 1), # 7 (Solarize, 0, 256), # 8 (Posterize, 4, 8), # 9 (Contrast, 0.1, 1.9), # 10 (Color, 0.1, 1.9), # 11 (Brightness, 0.1, 1.9), # 12 (Sharpness, 0.1, 1.9), # 13 (Cutout, 0, 0.2), # 14 # (SamplePairing(imgs), 0, 0.4), # 15 ] if for_autoaug: l += [ (CutoutAbs, 0, 20), # compatible with auto-augment (Posterize2, 0, 4), # 9 (TranslateXAbs, 0, 10), # 9 (TranslateYAbs, 0, 10), # 9 ] return l augment_dict = {fn.__name__: (fn, v1, v2) for fn, v1, v2 in augment_list()} def get_augment(name): return augment_dict[name] def apply_augment(img, name, level): augment_fn, low, high = get_augment(name) return augment_fn(img.copy(), level * (high - low) + low) class Lighting(object): """Lighting noise(AlexNet - style PCA - based noise)""" def __init__(self, alphastd, eigval, eigvec): self.alphastd = alphastd self.eigval = torch.Tensor(eigval) self.eigvec = torch.Tensor(eigvec) def __call__(self, img): if self.alphastd == 0: return img alpha = img.new().resize_(3).normal_(0, self.alphastd) rgb = self.eigvec.type_as(img).clone() \ .mul(alpha.view(1, 3).expand(3, 3)) \ .mul(self.eigval.view(1, 3).expand(3, 3)) \ .sum(1).squeeze() return img.add(rgb.view(3, 1, 1).expand_as(img)) ================================================ FILE: fast_autoaugment/FastAutoAugment/common.py ================================================ import copy import logging import warnings formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s') warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning) warnings.filterwarnings("ignore", "DeprecationWarning: 'saved_variables' is deprecated", UserWarning) def get_logger(name, level=logging.DEBUG): logger = logging.getLogger(name) logger.handlers.clear() logger.setLevel(level) ch = logging.StreamHandler() ch.setLevel(level) ch.setFormatter(formatter) logger.addHandler(ch) return logger def add_filehandler(logger, filepath, level=logging.DEBUG): fh = logging.FileHandler(filepath) fh.setLevel(level) fh.setFormatter(formatter) logger.addHandler(fh) class EMA: def __init__(self, mu): self.mu = mu self.shadow = {} def state_dict(self): return copy.deepcopy(self.shadow) def __len__(self): return len(self.shadow) def __call__(self, module, step=None): if step is None: mu = self.mu else: # see : https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/ExponentialMovingAverage?hl=PL mu = min(self.mu, (1. + step) / (10 + step)) for name, x in module.state_dict().items(): if name in self.shadow: new_average = (1.0 - mu) * x + mu * self.shadow[name] self.shadow[name] = new_average.clone() else: self.shadow[name] = x.clone() ================================================ FILE: fast_autoaugment/FastAutoAugment/data.py ================================================ import logging import numpy as np import os import math import random import torch import torchvision from PIL import Image from torch.utils.data import SubsetRandomSampler, Sampler, Subset, ConcatDataset import torch.distributed as dist from torchvision.transforms import transforms from sklearn.model_selection import StratifiedShuffleSplit from theconf import Config as C from FastAutoAugment.archive import arsaug_policy, autoaug_policy, autoaug_paper_cifar10, fa_reduced_cifar10, fa_reduced_svhn, fa_resnet50_rimagenet from FastAutoAugment.augmentations import * from FastAutoAugment.common import get_logger from FastAutoAugment.imagenet import ImageNet from FastAutoAugment.networks.efficientnet_pytorch.model import EfficientNet logger = get_logger('Fast AutoAugment') logger.setLevel(logging.INFO) _IMAGENET_PCA = { 'eigval': [0.2175, 0.0188, 0.0045], 'eigvec': [ [-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203], ] } _CIFAR_MEAN, _CIFAR_STD = (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010) def get_dataloaders(dataset, batch, dataroot, split=0.15, split_idx=0, multinode=False, target_lb=-1): if 'cifar' in dataset or 'svhn' in dataset: transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD), ]) elif 'imagenet' in dataset: input_size = 224 sized_size = 256 if 'efficientnet' in C.get()['model']['type']: input_size = EfficientNet.get_image_size(C.get()['model']['type']) sized_size = input_size + 32 # TODO # sized_size = int(round(input_size / 224. * 256)) # sized_size = input_size logger.info('size changed to %d/%d.' % (input_size, sized_size)) transform_train = transforms.Compose([ EfficientNetRandomCrop(input_size), transforms.Resize((input_size, input_size), interpolation=Image.BICUBIC), # transforms.RandomResizedCrop(input_size, scale=(0.1, 1.0), interpolation=Image.BICUBIC), transforms.RandomHorizontalFlip(), transforms.ColorJitter( brightness=0.4, contrast=0.4, saturation=0.4, ), transforms.ToTensor(), Lighting(0.1, _IMAGENET_PCA['eigval'], _IMAGENET_PCA['eigvec']), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) transform_test = transforms.Compose([ EfficientNetCenterCrop(input_size), transforms.Resize((input_size, input_size), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) else: raise ValueError('dataset=%s' % dataset) total_aug = augs = None if isinstance(C.get()['aug'], list): logger.debug('augmentation provided.') transform_train.transforms.insert(0, Augmentation(C.get()['aug'])) else: logger.debug('augmentation: %s' % C.get()['aug']) if C.get()['aug'] == 'fa_reduced_cifar10': transform_train.transforms.insert(0, Augmentation(fa_reduced_cifar10())) elif C.get()['aug'] == 'fa_reduced_imagenet': transform_train.transforms.insert(0, Augmentation(fa_resnet50_rimagenet())) elif C.get()['aug'] == 'fa_reduced_svhn': transform_train.transforms.insert(0, Augmentation(fa_reduced_svhn())) elif C.get()['aug'] == 'arsaug': transform_train.transforms.insert(0, Augmentation(arsaug_policy())) elif C.get()['aug'] == 'autoaug_cifar10': transform_train.transforms.insert(0, Augmentation(autoaug_paper_cifar10())) elif C.get()['aug'] == 'autoaug_extend': transform_train.transforms.insert(0, Augmentation(autoaug_policy())) elif C.get()['aug'] in ['default']: pass else: raise ValueError('not found augmentations. %s' % C.get()['aug']) if C.get()['cutout'] > 0: transform_train.transforms.append(CutoutDefault(C.get()['cutout'])) if dataset == 'cifar10': total_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=transform_train) testset = torchvision.datasets.CIFAR10(root=dataroot, train=False, download=True, transform=transform_test) elif dataset == 'reduced_cifar10': total_trainset = torchvision.datasets.CIFAR10(root=dataroot, train=True, download=True, transform=transform_train) sss = StratifiedShuffleSplit(n_splits=1, test_size=46000, random_state=0) # 4000 trainset sss = sss.split(list(range(len(total_trainset))), total_trainset.targets) train_idx, valid_idx = next(sss) targets = [total_trainset.targets[idx] for idx in train_idx] total_trainset = Subset(total_trainset, train_idx) total_trainset.targets = targets testset = torchvision.datasets.CIFAR10(root=dataroot, train=False, download=True, transform=transform_test) elif dataset == 'cifar100': total_trainset = torchvision.datasets.CIFAR100(root=dataroot, train=True, download=True, transform=transform_train) testset = torchvision.datasets.CIFAR100(root=dataroot, train=False, download=True, transform=transform_test) elif dataset == 'svhn': trainset = torchvision.datasets.SVHN(root=dataroot, split='train', download=True, transform=transform_train) extraset = torchvision.datasets.SVHN(root=dataroot, split='extra', download=True, transform=transform_train) total_trainset = ConcatDataset([trainset, extraset]) testset = torchvision.datasets.SVHN(root=dataroot, split='test', download=True, transform=transform_test) elif dataset == 'reduced_svhn': total_trainset = torchvision.datasets.SVHN(root=dataroot, split='train', download=True, transform=transform_train) sss = StratifiedShuffleSplit(n_splits=1, test_size=73257-1000, random_state=0) # 1000 trainset sss = sss.split(list(range(len(total_trainset))), total_trainset.targets) train_idx, valid_idx = next(sss) targets = [total_trainset.targets[idx] for idx in train_idx] total_trainset = Subset(total_trainset, train_idx) total_trainset.targets = targets testset = torchvision.datasets.SVHN(root=dataroot, split='test', download=True, transform=transform_test) elif dataset == 'imagenet': total_trainset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), transform=transform_train) testset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform_test) # compatibility total_trainset.targets = [lb for _, lb in total_trainset.samples] elif dataset == 'reduced_imagenet': # randomly chosen indices # idx120 = sorted(random.sample(list(range(1000)), k=120)) idx120 = [16, 23, 52, 57, 76, 93, 95, 96, 99, 121, 122, 128, 148, 172, 181, 189, 202, 210, 232, 238, 257, 258, 259, 277, 283, 289, 295, 304, 307, 318, 322, 331, 337, 338, 345, 350, 361, 375, 376, 381, 388, 399, 401, 408, 424, 431, 432, 440, 447, 462, 464, 472, 483, 497, 506, 512, 530, 541, 553, 554, 557, 564, 570, 584, 612, 614, 619, 626, 631, 632, 650, 657, 658, 660, 674, 675, 680, 682, 691, 695, 699, 711, 734, 736, 741, 754, 757, 764, 769, 770, 780, 781, 787, 797, 799, 811, 822, 829, 830, 835, 837, 842, 843, 845, 873, 883, 897, 900, 902, 905, 913, 920, 925, 937, 938, 940, 941, 944, 949, 959] total_trainset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), transform=transform_train) testset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform_test) # compatibility total_trainset.targets = [lb for _, lb in total_trainset.samples] sss = StratifiedShuffleSplit(n_splits=1, test_size=len(total_trainset) - 50000, random_state=0) # 4000 trainset sss = sss.split(list(range(len(total_trainset))), total_trainset.targets) train_idx, valid_idx = next(sss) # filter out train_idx = list(filter(lambda x: total_trainset.labels[x] in idx120, train_idx)) valid_idx = list(filter(lambda x: total_trainset.labels[x] in idx120, valid_idx)) test_idx = list(filter(lambda x: testset.samples[x][1] in idx120, range(len(testset)))) targets = [idx120.index(total_trainset.targets[idx]) for idx in train_idx] for idx in range(len(total_trainset.samples)): if total_trainset.samples[idx][1] not in idx120: continue total_trainset.samples[idx] = (total_trainset.samples[idx][0], idx120.index(total_trainset.samples[idx][1])) total_trainset = Subset(total_trainset, train_idx) total_trainset.targets = targets for idx in range(len(testset.samples)): if testset.samples[idx][1] not in idx120: continue testset.samples[idx] = (testset.samples[idx][0], idx120.index(testset.samples[idx][1])) testset = Subset(testset, test_idx) print('reduced_imagenet train=', len(total_trainset)) else: raise ValueError('invalid dataset name=%s' % dataset) if total_aug is not None and augs is not None: total_trainset.set_preaug(augs, total_aug) print('set_preaug-') train_sampler = None if split > 0.0: sss = StratifiedShuffleSplit(n_splits=5, test_size=split, random_state=0) sss = sss.split(list(range(len(total_trainset))), total_trainset.targets) for _ in range(split_idx + 1): train_idx, valid_idx = next(sss) if target_lb >= 0: train_idx = [i for i in train_idx if total_trainset.targets[i] == target_lb] valid_idx = [i for i in valid_idx if total_trainset.targets[i] == target_lb] train_sampler = SubsetRandomSampler(train_idx) valid_sampler = SubsetSampler(valid_idx) if multinode: train_sampler = torch.utils.data.distributed.DistributedSampler(Subset(total_trainset, train_idx), num_replicas=dist.get_world_size(), rank=dist.get_rank()) else: valid_sampler = SubsetSampler([]) if multinode: train_sampler = torch.utils.data.distributed.DistributedSampler(total_trainset, num_replicas=dist.get_world_size(), rank=dist.get_rank()) logger.info(f'----- dataset with DistributedSampler {dist.get_rank()}/{dist.get_world_size()}') trainloader = torch.utils.data.DataLoader( total_trainset, batch_size=batch, shuffle=True if train_sampler is None else False, num_workers=8, pin_memory=True, sampler=train_sampler, drop_last=True) validloader = torch.utils.data.DataLoader( total_trainset, batch_size=batch, shuffle=False, num_workers=4, pin_memory=True, sampler=valid_sampler, drop_last=False) testloader = torch.utils.data.DataLoader( testset, batch_size=batch, shuffle=False, num_workers=8, pin_memory=True, drop_last=False ) return train_sampler, trainloader, validloader, testloader class CutoutDefault(object): """ Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py """ def __init__(self, length): self.length = length def __call__(self, img): h, w = img.size(1), img.size(2) mask = np.ones((h, w), np.float32) y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - self.length // 2, 0, h) y2 = np.clip(y + self.length // 2, 0, h) x1 = np.clip(x - self.length // 2, 0, w) x2 = np.clip(x + self.length // 2, 0, w) mask[y1: y2, x1: x2] = 0. mask = torch.from_numpy(mask) mask = mask.expand_as(img) img *= mask return img class Augmentation(object): def __init__(self, policies): self.policies = policies def __call__(self, img): for _ in range(1): policy = random.choice(self.policies) for name, pr, level in policy: if random.random() > pr: continue img = apply_augment(img, name, level) return img class EfficientNetRandomCrop: def __init__(self, imgsize, min_covered=0.1, aspect_ratio_range=(3./4, 4./3), area_range=(0.08, 1.0), max_attempts=10): assert 0.0 < min_covered assert 0 < aspect_ratio_range[0] <= aspect_ratio_range[1] assert 0 < area_range[0] <= area_range[1] assert 1 <= max_attempts self.min_covered = min_covered self.aspect_ratio_range = aspect_ratio_range self.area_range = area_range self.max_attempts = max_attempts self._fallback = EfficientNetCenterCrop(imgsize) def __call__(self, img): # https://github.com/tensorflow/tensorflow/blob/9274bcebb31322370139467039034f8ff852b004/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc#L111 original_width, original_height = img.size min_area = self.area_range[0] * (original_width * original_height) max_area = self.area_range[1] * (original_width * original_height) for _ in range(self.max_attempts): aspect_ratio = random.uniform(*self.aspect_ratio_range) height = int(round(math.sqrt(min_area / aspect_ratio))) max_height = int(round(math.sqrt(max_area / aspect_ratio))) if max_height * aspect_ratio > original_width: max_height = (original_width + 0.5 - 1e-7) / aspect_ratio max_height = int(max_height) if max_height * aspect_ratio > original_width: max_height -= 1 if max_height > original_height: max_height = original_height if height >= max_height: height = max_height height = int(round(random.uniform(height, max_height))) width = int(round(height * aspect_ratio)) area = width * height if area < min_area or area > max_area: continue if width > original_width or height > original_height: continue if area < self.min_covered * (original_width * original_height): continue if width == original_width and height == original_height: return self._fallback(img) # https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/preprocessing.py#L102 x = random.randint(0, original_width - width) y = random.randint(0, original_height - height) return img.crop((x, y, x + width, y + height)) return self._fallback(img) class EfficientNetCenterCrop: def __init__(self, imgsize): self.imgsize = imgsize def __call__(self, img): """Crop the given PIL Image and resize it to desired size. Args: img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image. output_size (sequence or int): (height, width) of the crop box. If int, it is used for both directions Returns: PIL Image: Cropped image. """ image_width, image_height = img.size image_short = min(image_width, image_height) crop_size = float(self.imgsize) / (self.imgsize + 32) * image_short crop_height, crop_width = crop_size, crop_size crop_top = int(round((image_height - crop_height) / 2.)) crop_left = int(round((image_width - crop_width) / 2.)) return img.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height)) class SubsetSampler(Sampler): r"""Samples elements from a given list of indices, without replacement. Arguments: indices (sequence): a sequence of indices """ def __init__(self, indices): self.indices = indices def __iter__(self): return (i for i in self.indices) def __len__(self): return len(self.indices) ================================================ FILE: fast_autoaugment/FastAutoAugment/imagenet.py ================================================ from __future__ import print_function import os import shutil import torch ARCHIVE_DICT = { 'train': { 'url': 'http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_train.tar', 'md5': '1d675b47d978889d74fa0da5fadfb00e', }, 'val': { 'url': 'http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar', 'md5': '29b22e2961454d5413ddabcf34fc5622', }, 'devkit': { 'url': 'http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_devkit_t12.tar.gz', 'md5': 'fa75699e90414af021442c21a62c3abf', } } import torchvision from torchvision.datasets.utils import check_integrity, download_url # copy ILSVRC/ImageSets/CLS-LOC/train_cls.txt to ./root/ # to skip os walk (it's too slow) using ILSVRC/ImageSets/CLS-LOC/train_cls.txt file class ImageNet(torchvision.datasets.ImageFolder): """`ImageNet
(transfer/direct) | | |-------------------------|------------|------------|-------------|------------------|----| | Wide-ResNet-40-2 | 5.3 | 4.1 | 3.7 | 3.6 / 3.7 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_wresnet40x2_top1_3.52.pth) | | Wide-ResNet-28-10 | 3.9 | 3.1 | 2.6 | 2.7 / 2.7 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_wresnet28x10_top1.pth) | | Shake-Shake(26 2x32d) | 3.6 | 3.0 | 2.5 | 2.7 / 2.5 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_shake26_2x32d_top1_2.68.pth) | | Shake-Shake(26 2x96d) | 2.9 | 2.6 | 2.0 | 2.0 / 2.0 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_shake26_2x96d_top1_1.97.pth) | | Shake-Shake(26 2x112d) | 2.8 | 2.6 | 1.9 | 2.0 / 1.9 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_shake26_2x112d_top1_2.04.pth) | | PyramidNet+ShakeDrop | 2.7 | 2.3 | 1.5 | 1.8 / 1.7 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar10_pyramid272_top1_1.44.pth) | | Model(CIFAR-100) | Baseline | Cutout | AutoAugment | Fast AutoAugment
(transfer/direct) | | |-----------------------|------------|------------|-------------|------------------|----| | Wide-ResNet-40-2 | 26.0 | 25.2 | 20.7 | 20.7 / 20.6 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar100_wresnet40x2_top1_20.43.pth) | | Wide-ResNet-28-10 | 18.8 | 18.4 | 17.1 | 17.3 / 17.3 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar100_wresnet28x10_top1_17.17.pth) | | Shake-Shake(26 2x96d) | 17.1 | 16.0 | 14.3 | 14.9 / 14.6 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar100_shake26_2x96d_top1_15.15.pth) | | PyramidNet+ShakeDrop | 14.0 | 12.2 | 10.7 | 11.9 / 11.7 | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/cifar100_pyramid272_top1_11.74.pth) | ### ImageNet Search : **450 GPU Hours (33x faster than AutoAugment)**, ResNet-50 on Reduced ImageNet | Model | Baseline | AutoAugment | Fast AutoAugment
(Top1/Top5) | | |------------|------------|-------------|------------------|----| | ResNet-50 | 23.7 / 6.9 | 22.4 / 6.2 | **22.4 / 6.3** | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/imagenet_resnet50_top1_22.2.pth) | | ResNet-200 | 21.5 / 5.8 | 20.0 / 5.0 | **19.4 / 4.7** | [Download](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/imagenet_resnet200_top1_19.4.pth) | Notes * We evaluated resnet-50 and resnet-200 with resolution of 224 and 320, respectively. According to the original resnet paper, resnet 200 was tested with the resolution of 320. Also our resnet-200 baseline's performance was similar when we use the resolution. * But with recent our code clean-up and bugfixes, we've found that the baseline performs similar to the baseline even using 224x224. * When we use 224x224, resnet-200 performs **20.0 / 5.2**. Download link for the trained model is [here](https://arena.kakaocdn.net/brainrepo/fast-autoaugment/imagenet_resnet200_res224.pth). We have conducted additional experiments with EfficientNet. | Model | Baseline | AutoAugment | | Our Baseline(Batch) | +Fast AA | |-------|------------|-------------|---|---------------------|----------| | B0 | 23.2 | 22.7 | | 22.96 | 22.68 | ### SVHN Test Search : **1.5 GPU Hours** | | Baseline | AutoAug / Our | Fast AutoAugment | |----------------------------------|---------:|--------------:|--------:| | Wide-Resnet28x10 | 1.5 | 1.1 | 1.1 | ## Run We conducted experiments under - python 3.6.9 - pytorch 1.2.0, torchvision 0.4.0, cuda10 ### Search a augmentation policy Please read ray's document to construct a proper ray cluster : https://github.com/ray-project/ray, and run search.py with the master's redis address. ``` $ python search.py -c confs/wresnet40x2_cifar10_b512.yaml --dataroot ... --redis ... ``` ### Train a model with found policies You can train network architectures on CIFAR-10 / 100 and ImageNet with our searched policies. - fa_reduced_cifar10 : reduced CIFAR-10(4k images), WResNet-40x2 - fa_reduced_imagenet : reduced ImageNet(50k images, 120 classes), ResNet-50 ``` $ export PYTHONPATH=$PYTHONPATH:$PWD $ python FastAutoAugment/train.py -c confs/wresnet40x2_cifar10_b512.yaml --aug fa_reduced_cifar10 --dataset cifar10 $ python FastAutoAugment/train.py -c confs/wresnet40x2_cifar10_b512.yaml --aug fa_reduced_cifar10 --dataset cifar100 $ python FastAutoAugment/train.py -c confs/wresnet28x10_cifar10_b512.yaml --aug fa_reduced_cifar10 --dataset cifar10 $ python FastAutoAugment/train.py -c confs/wresnet28x10_cifar10_b512.yaml --aug fa_reduced_cifar10 --dataset cifar100 ... $ python FastAutoAugment/train.py -c confs/resnet50_b512.yaml --aug fa_reduced_imagenet $ python FastAutoAugment/train.py -c confs/resnet200_b512.yaml --aug fa_reduced_imagenet ``` By adding --only-eval and --save arguments, you can test trained models without training. If you want to train with multi-gpu/node, use `torch.distributed.launch` such as ```bash $ python -m torch.distributed.launch --nproc_per_node={num_gpu_per_node} --nnodes={num_node} --master_addr={master} --master_port={master_port} --node_rank={0,1,2,...,num_node} FastAutoAugment/train.py -c confs/efficientnet_b4.yaml --aug fa_reduced_imagenet ``` ## Citation If you use this code in your research, please cite our [paper](https://arxiv.org/abs/1905.00397). ``` @inproceedings{lim2019fast, title={Fast AutoAugment}, author={Lim, Sungbin and Kim, Ildoo and Kim, Taesup and Kim, Chiheon and Kim, Sungwoong}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2019} } ``` ## Contact for Issues - Ildoo Kim, ildoo.kim@kakaobrain.com ## References & Opensources We increase the batch size and adapt the learning rate accordingly to boost the training. Otherwise, we set other hyperparameters equal to AutoAugment if possible. For the unknown hyperparameters, we follow values from the original references or we tune them to match baseline performances. - **ResNet** : [paper1](https://arxiv.org/abs/1512.03385), [paper2](https://arxiv.org/abs/1603.05027), [code](https://github.com/osmr/imgclsmob/tree/master/pytorch/pytorchcv/models) - **PyramidNet** : [paper](https://arxiv.org/abs/1610.02915), [code](https://github.com/dyhan0920/PyramidNet-PyTorch) - **Wide-ResNet** : [code](https://github.com/meliketoy/wide-resnet.pytorch) - **Shake-Shake** : [code](https://github.com/owruby/shake-shake_pytorch) - **ShakeDrop Regularization** : [paper](https://arxiv.org/abs/1802.02375), [code](https://github.com/owruby/shake-drop_pytorch) - **AutoAugment** : [code](https://github.com/tensorflow/models/tree/master/research/autoaugment) - **Ray** : [code](https://github.com/ray-project/ray) - **HyperOpt** : [code](https://github.com/hyperopt/hyperopt) ================================================ FILE: fast_autoaugment/__init__.py ================================================ ================================================ FILE: fast_autoaugment/archive.py ================================================ # Policy found on CIFAR-10 and CIFAR-100 from __future__ import absolute_import from __future__ import division from __future__ import print_function from collections import defaultdict from FastAutoAugment.augmentations import get_augment, augment_list def arsaug_policy(): exp0_0 = [ [('Solarize', 0.66, 0.34), ('Equalize', 0.56, 0.61)], [('Equalize', 0.43, 0.06), ('AutoContrast', 0.66, 0.08)], [('Color', 0.72, 0.47), ('Contrast', 0.88, 0.86)], [('Brightness', 0.84, 0.71), ('Color', 0.31, 0.74)], [('Rotate', 0.68, 0.26), ('TranslateX', 0.38, 0.88)]] exp0_1 = [ [('TranslateY', 0.88, 0.96), ('TranslateY', 0.53, 0.79)], [('AutoContrast', 0.44, 0.36), ('Solarize', 0.22, 0.48)], [('AutoContrast', 0.93, 0.32), ('Solarize', 0.85, 0.26)], [('Solarize', 0.55, 0.38), ('Equalize', 0.43, 0.48)], [('TranslateY', 0.72, 0.93), ('AutoContrast', 0.83, 0.95)]] exp0_2 = [ [('Solarize', 0.43, 0.58), ('AutoContrast', 0.82, 0.26)], [('TranslateY', 0.71, 0.79), ('AutoContrast', 0.81, 0.94)], [('AutoContrast', 0.92, 0.18), ('TranslateY', 0.77, 0.85)], [('Equalize', 0.71, 0.69), ('Color', 0.23, 0.33)], [('Sharpness', 0.36, 0.98), ('Brightness', 0.72, 0.78)]] exp0_3 = [ [('Equalize', 0.74, 0.49), ('TranslateY', 0.86, 0.91)], [('TranslateY', 0.82, 0.91), ('TranslateY', 0.96, 0.79)], [('AutoContrast', 0.53, 0.37), ('Solarize', 0.39, 0.47)], [('TranslateY', 0.22, 0.78), ('Color', 0.91, 0.65)], [('Brightness', 0.82, 0.46), ('Color', 0.23, 0.91)]] exp0_4 = [ [('Cutout', 0.27, 0.45), ('Equalize', 0.37, 0.21)], [('Color', 0.43, 0.23), ('Brightness', 0.65, 0.71)], [('ShearX', 0.49, 0.31), ('AutoContrast', 0.92, 0.28)], [('Equalize', 0.62, 0.59), ('Equalize', 0.38, 0.91)], [('Solarize', 0.57, 0.31), ('Equalize', 0.61, 0.51)]] exp0_5 = [ [('TranslateY', 0.29, 0.35), ('Sharpness', 0.31, 0.64)], [('Color', 0.73, 0.77), ('TranslateX', 0.65, 0.76)], [('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)], [('Color', 0.92, 0.79), ('Equalize', 0.68, 0.54)], [('Sharpness', 0.87, 0.91), ('Sharpness', 0.93, 0.41)]] exp0_6 = [ [('Solarize', 0.39, 0.35), ('Color', 0.31, 0.44)], [('Color', 0.33, 0.77), ('Color', 0.25, 0.46)], [('ShearY', 0.29, 0.74), ('Posterize', 0.42, 0.58)], [('AutoContrast', 0.32, 0.79), ('Cutout', 0.68, 0.34)], [('AutoContrast', 0.67, 0.91), ('AutoContrast', 0.73, 0.83)]] return exp0_0 + exp0_1 + exp0_2 + exp0_3 + exp0_4 + exp0_5 + exp0_6 def autoaug2arsaug(f): def autoaug(): mapper = defaultdict(lambda: lambda x: x) mapper.update({ 'ShearX': lambda x: float_parameter(x, 0.3), 'ShearY': lambda x: float_parameter(x, 0.3), 'TranslateX': lambda x: int_parameter(x, 10), 'TranslateY': lambda x: int_parameter(x, 10), 'Rotate': lambda x: int_parameter(x, 30), 'Solarize': lambda x: 256 - int_parameter(x, 256), 'Posterize2': lambda x: 4 - int_parameter(x, 4), 'Contrast': lambda x: float_parameter(x, 1.8) + .1, 'Color': lambda x: float_parameter(x, 1.8) + .1, 'Brightness': lambda x: float_parameter(x, 1.8) + .1, 'Sharpness': lambda x: float_parameter(x, 1.8) + .1, 'CutoutAbs': lambda x: int_parameter(x, 20) }) def low_high(name, prev_value): _, low, high = get_augment(name) return float(prev_value - low) / (high - low) policies = f() new_policies = [] for policy in policies: new_policies.append([(name, pr, low_high(name, mapper[name](level))) for name, pr, level in policy]) return new_policies return autoaug @autoaug2arsaug def autoaug_paper_cifar10(): return [ [('Invert', 0.1, 7), ('Contrast', 0.2, 6)], [('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)], [('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)], [('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)], [('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)], [('Color', 0.4, 3), ('Brightness', 0.6, 7)], [('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)], [('Equalize', 0.6, 5), ('Equalize', 0.5, 1)], [('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)], [('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)], [('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)], [('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)], [('Brightness', 0.9, 6), ('Color', 0.2, 8)], [('Solarize', 0.5, 2), ('Invert', 0.0, 3)], [('Equalize', 0.2, 0), ('AutoContrast', 0.6, 0)], [('Equalize', 0.2, 8), ('Equalize', 0.6, 4)], [('Color', 0.9, 9), ('Equalize', 0.6, 6)], [('AutoContrast', 0.8, 4), ('Solarize', 0.2, 8)], [('Brightness', 0.1, 3), ('Color', 0.7, 0)], [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)], [('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)], [('Equalize', 0.8, 8), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)], ] @autoaug2arsaug def autoaug_policy(): """AutoAugment policies found on Cifar.""" exp0_0 = [ [('Invert', 0.1, 7), ('Contrast', 0.2, 6)], [('Rotate', 0.7, 2), ('TranslateXAbs', 0.3, 9)], [('Sharpness', 0.8, 1), ('Sharpness', 0.9, 3)], [('ShearY', 0.5, 8), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.5, 8), ('Equalize', 0.9, 2)]] exp0_1 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 3)], [('TranslateYAbs', 0.9, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 2), ('Solarize', 0.8, 3)], [('Equalize', 0.8, 8), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('AutoContrast', 0.9, 1)]] exp0_2 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.0, 2)], [('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)], [('AutoContrast', 0.9, 0), ('Solarize', 0.4, 3)], [('Equalize', 0.7, 5), ('Invert', 0.1, 3)], [('TranslateYAbs', 0.7, 9), ('TranslateYAbs', 0.7, 9)]] exp0_3 = [ [('Solarize', 0.4, 5), ('AutoContrast', 0.9, 1)], [('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.9, 9)], [('AutoContrast', 0.8, 0), ('TranslateYAbs', 0.7, 9)], [('TranslateYAbs', 0.2, 7), ('Color', 0.9, 6)], [('Equalize', 0.7, 6), ('Color', 0.4, 9)]] exp1_0 = [ [('ShearY', 0.2, 7), ('Posterize2', 0.3, 7)], [('Color', 0.4, 3), ('Brightness', 0.6, 7)], [('Sharpness', 0.3, 9), ('Brightness', 0.7, 9)], [('Equalize', 0.6, 5), ('Equalize', 0.5, 1)], [('Contrast', 0.6, 7), ('Sharpness', 0.6, 5)]] exp1_1 = [ [('Brightness', 0.3, 7), ('AutoContrast', 0.5, 8)], [('AutoContrast', 0.9, 4), ('AutoContrast', 0.5, 6)], [('Solarize', 0.3, 5), ('Equalize', 0.6, 5)], [('TranslateYAbs', 0.2, 4), ('Sharpness', 0.3, 3)], [('Brightness', 0.0, 8), ('Color', 0.8, 8)]] exp1_2 = [ [('Solarize', 0.2, 6), ('Color', 0.8, 6)], [('Solarize', 0.2, 6), ('AutoContrast', 0.8, 1)], [('Solarize', 0.4, 1), ('Equalize', 0.6, 5)], [('Brightness', 0.0, 0), ('Solarize', 0.5, 2)], [('AutoContrast', 0.9, 5), ('Brightness', 0.5, 3)]] exp1_3 = [ [('Contrast', 0.7, 5), ('Brightness', 0.0, 2)], [('Solarize', 0.2, 8), ('Solarize', 0.1, 5)], [('Contrast', 0.5, 1), ('TranslateYAbs', 0.2, 9)], [('AutoContrast', 0.6, 5), ('TranslateYAbs', 0.0, 9)], [('AutoContrast', 0.9, 4), ('Equalize', 0.8, 4)]] exp1_4 = [ [('Brightness', 0.0, 7), ('Equalize', 0.4, 7)], [('Solarize', 0.2, 5), ('Equalize', 0.7, 5)], [('Equalize', 0.6, 8), ('Color', 0.6, 2)], [('Color', 0.3, 7), ('Color', 0.2, 4)], [('AutoContrast', 0.5, 2), ('Solarize', 0.7, 2)]] exp1_5 = [ [('AutoContrast', 0.2, 0), ('Equalize', 0.1, 0)], [('ShearY', 0.6, 5), ('Equalize', 0.6, 5)], [('Brightness', 0.9, 3), ('AutoContrast', 0.4, 1)], [('Equalize', 0.8, 8), ('Equalize', 0.7, 7)], [('Equalize', 0.7, 7), ('Solarize', 0.5, 0)]] exp1_6 = [ [('Equalize', 0.8, 4), ('TranslateYAbs', 0.8, 9)], [('TranslateYAbs', 0.8, 9), ('TranslateYAbs', 0.6, 9)], [('TranslateYAbs', 0.9, 0), ('TranslateYAbs', 0.5, 9)], [('AutoContrast', 0.5, 3), ('Solarize', 0.3, 4)], [('Solarize', 0.5, 3), ('Equalize', 0.4, 4)]] exp2_0 = [ [('Color', 0.7, 7), ('TranslateXAbs', 0.5, 8)], [('Equalize', 0.3, 7), ('AutoContrast', 0.4, 8)], [('TranslateYAbs', 0.4, 3), ('Sharpness', 0.2, 6)], [('Brightness', 0.9, 6), ('Color', 0.2, 8)], [('Solarize', 0.5, 2), ('Invert', 0.0, 3)]] exp2_1 = [ [('AutoContrast', 0.1, 5), ('Brightness', 0.0, 0)], [('CutoutAbs', 0.2, 4), ('Equalize', 0.1, 1)], [('Equalize', 0.7, 7), ('AutoContrast', 0.6, 4)], [('Color', 0.1, 8), ('ShearY', 0.2, 3)], [('ShearY', 0.4, 2), ('Rotate', 0.7, 0)]] exp2_2 = [ [('ShearY', 0.1, 3), ('AutoContrast', 0.9, 5)], [('TranslateYAbs', 0.3, 6), ('CutoutAbs', 0.3, 3)], [('Equalize', 0.5, 0), ('Solarize', 0.6, 6)], [('AutoContrast', 0.3, 5), ('Rotate', 0.2, 7)], [('Equalize', 0.8, 2), ('Invert', 0.4, 0)]] exp2_3 = [ [('Equalize', 0.9, 5), ('Color', 0.7, 0)], [('Equalize', 0.1, 1), ('ShearY', 0.1, 3)], [('AutoContrast', 0.7, 3), ('Equalize', 0.7, 0)], [('Brightness', 0.5, 1), ('Contrast', 0.1, 7)], [('Contrast', 0.1, 4), ('Solarize', 0.6, 5)]] exp2_4 = [ [('Solarize', 0.2, 3), ('ShearX', 0.0, 0)], [('TranslateXAbs', 0.3, 0), ('TranslateXAbs', 0.6, 0)], [('Equalize', 0.5, 9), ('TranslateYAbs', 0.6, 7)], [('ShearX', 0.1, 0), ('Sharpness', 0.5, 1)], [('Equalize', 0.8, 6), ('Invert', 0.3, 6)]] exp2_5 = [ [('AutoContrast', 0.3, 9), ('CutoutAbs', 0.5, 3)], [('ShearX', 0.4, 4), ('AutoContrast', 0.9, 2)], [('ShearX', 0.0, 3), ('Posterize2', 0.0, 3)], [('Solarize', 0.4, 3), ('Color', 0.2, 4)], [('Equalize', 0.1, 4), ('Equalize', 0.7, 6)]] exp2_6 = [ [('Equalize', 0.3, 8), ('AutoContrast', 0.4, 3)], [('Solarize', 0.6, 4), ('AutoContrast', 0.7, 6)], [('AutoContrast', 0.2, 9), ('Brightness', 0.4, 8)], [('Equalize', 0.1, 0), ('Equalize', 0.0, 6)], [('Equalize', 0.8, 4), ('Equalize', 0.0, 4)]] exp2_7 = [ [('Equalize', 0.5, 5), ('AutoContrast', 0.1, 2)], [('Solarize', 0.5, 5), ('AutoContrast', 0.9, 5)], [('AutoContrast', 0.6, 1), ('AutoContrast', 0.7, 8)], [('Equalize', 0.2, 0), ('AutoContrast', 0.1, 2)], [('Equalize', 0.6, 9), ('Equalize', 0.4, 4)]] exp0s = exp0_0 + exp0_1 + exp0_2 + exp0_3 exp1s = exp1_0 + exp1_1 + exp1_2 + exp1_3 + exp1_4 + exp1_5 + exp1_6 exp2s = exp2_0 + exp2_1 + exp2_2 + exp2_3 + exp2_4 + exp2_5 + exp2_6 + exp2_7 return exp0s + exp1s + exp2s PARAMETER_MAX = 10 def float_parameter(level, maxval): return float(level) * maxval / PARAMETER_MAX def int_parameter(level, maxval): return int(float_parameter(level, maxval)) def no_duplicates(f): def wrap_remove_duplicates(): policies = f() return remove_deplicates(policies) return wrap_remove_duplicates def remove_deplicates(policies): s = set() new_policies = [] for ops in policies: key = [] for op in ops: key.append(op[0]) key = '_'.join(key) if key in s: continue else: s.add(key) new_policies.append(ops) return new_policies def fa_reduced_cifar10(): p = [[["Contrast", 0.8320659688593578, 0.49884310562180767], ["TranslateX", 0.41849883971249136, 0.394023086494538]], [["Color", 0.3500483749890918, 0.43355143929883955], ["Color", 0.5120716140300229, 0.7508299643325016]], [["Rotate", 0.9447932604389472, 0.29723465088990375], ["Sharpness", 0.1564936149799504, 0.47169309978091745]], [["Rotate", 0.5430015349185097, 0.6518626678905443], ["Color", 0.5694844928020679, 0.3494533005430269]], [["AutoContrast", 0.5558922032451064, 0.783136004977799], ["TranslateY", 0.683914191471972, 0.7597025305860181]], [["TranslateX", 0.03489224481658926, 0.021025488042663354], ["Equalize", 0.4788637403857401, 0.3535481281496117]], [["Sharpness", 0.6428916269794158, 0.22791511918580576], ["Contrast", 0.016014045073950323, 0.26811312269487575]], [["Rotate", 0.2972727228410451, 0.7654251516829896], ["AutoContrast", 0.16005809254943348, 0.5380523650108116]], [["Contrast", 0.5823671057717301, 0.7521166301398389], ["TranslateY", 0.9949449214751978, 0.9612671341689751]], [["Equalize", 0.8372126687702321, 0.6944127225621206], ["Rotate", 0.25393282929784755, 0.3261658365286546]], [["Invert", 0.8222011603194572, 0.6597915864008403], ["Posterize", 0.31858707654447327, 0.9541013715579584]], [["Sharpness", 0.41314621282107045, 0.9437344470879956], ["Cutout", 0.6610495837889337, 0.674411664255093]], [["Contrast", 0.780121736705407, 0.40826152397463156], ["Color", 0.344019192125256, 0.1942922781355767]], [["Rotate", 0.17153139555621344, 0.798745732456474], ["Invert", 0.6010555860501262, 0.320742172554767]], [["Invert", 0.26816063450777416, 0.27152062163148327], ["Equalize", 0.6786829200236982, 0.7469412443514213]], [["Contrast", 0.3920564414367518, 0.7493644582838497], ["TranslateY", 0.8941657805606704, 0.6580846856375955]], [["Equalize", 0.875509207399372, 0.9061130537645283], ["Cutout", 0.4940280679087308, 0.7896229623628276]], [["Contrast", 0.3331423298065147, 0.7170041362529597], ["ShearX", 0.7425484291842793, 0.5285117152426109]], [["Equalize", 0.97344237365026, 0.4745759720473106], ["TranslateY", 0.055863458430295276, 0.9625142022954672]], [["TranslateX", 0.6810614083109192, 0.7509937355495521], ["TranslateY", 0.3866463019475701, 0.5185481505576112]], [["Sharpness", 0.4751529944753671, 0.550464012488733], ["Cutout", 0.9472914750534814, 0.5584925992985023]], [["Contrast", 0.054606784909375095, 0.17257080196712182], ["Cutout", 0.6077026782754803, 0.7996504165944938]], [["ShearX", 0.328798428243695, 0.2769563264079157], ["Cutout", 0.9037632437023772, 0.4915809476763595]], [["Cutout", 0.6891202672363478, 0.9951490996172914], ["Posterize", 0.06532762462628705, 0.4005246609075227]], [["TranslateY", 0.6908583592523334, 0.725612120376128], ["Rotate", 0.39907735501746666, 0.36505798032223147]], [["TranslateX", 0.10398364107399072, 0.5913918470536627], ["Rotate", 0.7169811539340365, 0.8283850670648724]], [["ShearY", 0.9526373530768361, 0.4482347365639251], ["Contrast", 0.4203947336351471, 0.41526799558953864]], [["Contrast", 0.24894431199700073, 0.09578870500994707], ["Solarize", 0.2273713345927395, 0.6214942914963707]], [["TranslateX", 0.06331228870032912, 0.8961907489444944], ["Cutout", 0.5110007859958743, 0.23704875994050723]], [["Cutout", 0.3769183548846172, 0.6560944580253987], ["TranslateY", 0.7201924599434143, 0.4132476526938319]], [["Invert", 0.6707431156338866, 0.11622795952464149], ["Posterize", 0.12075972752370845, 0.18024933294172307]], [["Color", 0.5010057264087142, 0.5277767327434318], ["Rotate", 0.9486115946366559, 0.31485546630220784]], [["ShearX", 0.31741302466630406, 0.1991215806270692], ["Invert", 0.3744727015523084, 0.6914113986757578]], [["Brightness", 0.40348479064392617, 0.8924182735724888], ["Brightness", 0.1973098763857779, 0.3939288933689655]], [["Color", 0.01208688664030888, 0.6055693000885217], ["Equalize", 0.433259451147881, 0.420711137966155]], [["Cutout", 0.2620018360076487, 0.11594468278143644], ["Rotate", 0.1310401567856766, 0.7244318146544101]], [["ShearX", 0.15249651845933576, 0.35277277071866986], ["Contrast", 0.28221794032094016, 0.42036586509397444]], [["Brightness", 0.8492912150468908, 0.26386920887886056], ["Solarize", 0.8764208056263386, 0.1258195122766067]], [["ShearX", 0.8537058239675831, 0.8415101816171269], ["AutoContrast", 0.23958568830416294, 0.9889049529564014]], [["Rotate", 0.6463207930684552, 0.8750192129056532], ["Contrast", 0.6865032211768652, 0.8564981333033417]], [["Equalize", 0.8877190311811044, 0.7370995897848609], ["TranslateX", 0.9979660314391368, 0.005683998913244781]], [["Color", 0.6420017551677819, 0.6225337265571229], ["Solarize", 0.8344504978566362, 0.8332856969941151]], [["ShearX", 0.7439332981992567, 0.9747608698582039], ["Equalize", 0.6259189804002959, 0.028017478098245174]], [["TranslateY", 0.39794770293366843, 0.8482966537902709], ["Rotate", 0.9312935630405351, 0.5300586925826072]], [["Cutout", 0.8904075572021911, 0.3522934742068766], ["Equalize", 0.6431186289473937, 0.9930577962126151]], [["Contrast", 0.9183553386089476, 0.44974266209396685], ["TranslateY", 0.8193684583123862, 0.9633741156526566]], [["ShearY", 0.616078299924283, 0.19219314358924766], ["Solarize", 0.1480945914138868, 0.05922109541654652]], [["Solarize", 0.25332455064128157, 0.18853037431947994], ["ShearY", 0.9518390093954243, 0.14603930044061142]], [["Color", 0.8094378664335412, 0.37029830225408433], ["Contrast", 0.29504113617467465, 0.065096365468442]], [["AutoContrast", 0.7075167558685455, 0.7084621693458267], ["Sharpness", 0.03555539453323875, 0.5651948313888351]], [["TranslateY", 0.5969982600930229, 0.9857264201029572], ["Rotate", 0.9898628564873607, 0.1985685534926911]], [["Invert", 0.14915939942810352, 0.6595839632446547], ["Posterize", 0.768535289994361, 0.5997358684618563]], [["Equalize", 0.9162691815967111, 0.3331035307653627], ["Color", 0.8169118187605557, 0.7653910258006366]], [["Rotate", 0.43489185299530897, 0.752215269135173], ["Brightness", 0.1569828560334806, 0.8002808712857853]], [["Invert", 0.931876215328345, 0.029428644395760872], ["Equalize", 0.6330036052674145, 0.7235531014288485]], [["ShearX", 0.5216138393704968, 0.849272958911589], ["AutoContrast", 0.19572688655120263, 0.9786551568639575]], [["ShearX", 0.9899586208275011, 0.22580547500610293], ["Brightness", 0.9831311903178727, 0.5055159610855606]], [["Brightness", 0.29179117009211486, 0.48003584672937294], ["Solarize", 0.7544252317330058, 0.05806581735063043]], [["AutoContrast", 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0.7270003309106291, 0.009571215234092656]], [["Sharpness", 0.417607614440786, 0.9532566433338661], ["Posterize", 0.7186586546796782, 0.6936509907073302]], [["ShearX", 0.9555300215926675, 0.1399385550263872], ["Color", 0.9981041061848231, 0.5037462398323248]], [["Equalize", 0.8003487831375474, 0.5413759363796945], ["ShearY", 0.0026607045117773565, 0.019262273030984933]], [["TranslateY", 0.04845391502469176, 0.10063445212118283], ["Cutout", 0.8273170186786745, 0.5045257728554577]], [["TranslateX", 0.9690985344978033, 0.505202991815533], ["TranslateY", 0.7255326592928096, 0.02103609500701631]], [["Solarize", 0.4030771176836736, 0.8424237871457034], ["Cutout", 0.28705805963928965, 0.9601617893682582]], [["Sharpness", 0.16865290353070606, 0.6899673563468826], ["Posterize", 0.3985430034869616, 0.6540651997730774]], [["ShearY", 0.21395578485362032, 0.09519358818949009], ["Solarize", 0.6692821708524135, 0.6462523623552485]], [["AutoContrast", 0.912360598054091, 0.029800239085051583], ["Invert", 0.04319256403746308, 0.7712501517098587]], [["ShearY", 0.9081969961839055, 0.4581560239984739], ["AutoContrast", 0.5313894814729159, 0.5508393335751848]], [["ShearY", 0.860528568424097, 0.8196987216301588], ["Posterize", 0.41134650331494205, 0.3686632018978778]], [["AutoContrast", 0.8753670810078598, 0.3679438326304749], ["Invert", 0.010444228965415858, 0.9581244779208277]], [["Equalize", 0.07071836206680682, 0.7173594756186462], ["Brightness", 0.06111434312497388, 0.16175064669049277]], [["AutoContrast", 0.10522219073562122, 0.9768776621069855], ["TranslateY", 0.2744795945215529, 0.8577967957127298]], [["AutoContrast", 0.7628146493166175, 0.996157376418147], ["Contrast", 0.9255565598518469, 0.6826126662976868]], [["TranslateX", 0.017225816199011312, 0.2470332491402908], ["Solarize", 0.44048494909493807, 0.4492422515972162]], [["ShearY", 0.38885252627795064, 0.10272256704901939], ["Equalize", 0.686154959829183, 0.8973517148655337]], [["Rotate", 0.29628991573592967, 0.16639926575004715], ["ShearX", 0.9013782324726413, 0.0838318162771563]], [["Color", 0.04968391374688563, 0.6138600739645352], ["Invert", 0.11177127838716283, 0.10650198522261578]], [["Invert", 0.49655016367624016, 0.8603374164829688], ["ShearY", 0.40625439617553727, 0.4516437918820778]], [["TranslateX", 0.15015718916062992, 0.13867777502116208], ["Brightness", 0.3374464418810188, 0.7613355669536931]], [["Invert", 0.644644393321966, 0.19005804481199562], ["AutoContrast", 0.2293259789431853, 0.30335723256340186]], [["Solarize", 0.004968793254801596, 0.5370892072646645], ["Contrast", 0.9136902637865596, 0.9510587477779084]], [["Rotate", 0.38991518440867123, 0.24796987467455756], ["Sharpness", 0.9911180315669776, 0.5265657122981591]], [["Solarize", 0.3919646484436238, 0.6814994037194909], ["Sharpness", 0.4920838987787103, 0.023425724294012018]], [["TranslateX", 0.25107587874378867, 0.5414936560189212], ["Cutout", 0.7932919623814599, 0.9891303444820169]], [["Brightness", 0.07863012174272999, 0.045175652208389594], ["Solarize", 0.889609658064552, 0.8228793315963948]], [["Cutout", 0.20477096178169596, 0.6535063675027364], ["ShearX", 0.9216318577173639, 0.2908690977359947]], [["Contrast", 0.7035118947423187, 0.45982709058312454], ["Contrast", 0.7130268070749464, 0.8635123354235471]], [["Sharpness", 0.26319477541228997, 0.7451278726847078], ["Rotate", 0.8170499362173754, 0.13998593411788207]], [["Rotate", 0.8699365715164192, 0.8878057721750832], ["Equalize", 0.06682350555715044, 0.7164702080630689]], [["ShearY", 0.3137466057521987, 0.6747433496011368], ["Rotate", 0.42118828936218133, 0.980121180104441]], [["Solarize", 0.8470375049950615, 0.15287589264139223], ["Cutout", 0.14438435054693055, 0.24296463267973512]], [["TranslateY", 0.08822241792224905, 0.36163911974799356], ["TranslateY", 0.11729726813270003, 0.6230889726445291]], [["ShearX", 0.7720112337718541, 0.2773292905760122], ["Sharpness", 0.756290929398613, 0.27830353710507705]], [["Color", 0.33825031007968287, 0.4657590047522816], ["ShearY", 0.3566628994713067, 0.859750504071925]], [["TranslateY", 0.06830147433378053, 0.9348778582086664], ["TranslateX", 0.15509346516378553, 0.26320778885339435]], [["Posterize", 0.20266751150740858, 0.008351463842578233], ["Sharpness", 0.06506971109417259, 0.7294471760284555]], [["TranslateY", 0.6278911394418829, 0.8702181892620695], ["Invert", 0.9367073860264247, 0.9219230428944211]], [["Sharpness", 0.1553425337673321, 0.17601557714491345], ["Solarize", 0.7040449681338888, 0.08764313147327729]], [["Equalize", 0.6082233904624664, 0.4177428549911376], ["AutoContrast", 0.04987405274618151, 0.34516208204700916]], [["Brightness", 0.9616085936167699, 0.14561237331885468], ["Solarize", 0.8927707736296572, 0.31176907850205704]], [["Brightness", 0.6707778304730988, 0.9046457117525516], ["Brightness", 0.6801448953060988, 0.20015313057149042]], [["Color", 0.8292680845499386, 0.5181603879593888], ["Brightness", 0.08549161770369762, 0.6567870536463203]], [["ShearY", 0.267802208078051, 0.8388133819588173], ["Sharpness", 0.13453409120796123, 0.10028351311149486]], [["Posterize", 0.775796593610272, 0.05359034561289766], ["Cutout", 0.5067360625733027, 0.054451986840317934]], [["TranslateX", 0.5845238647690084, 0.7507147553486293], ["Brightness", 0.2642051786121197, 0.2578358927056452]], [["Cutout", 0.10787517610922692, 0.8147986902794228], ["Contrast", 0.2190149206329539, 0.902210615462459]], [["TranslateX", 0.5663614214181296, 0.05309965916414028], ["ShearX", 0.9682797885154938, 0.41791929533938466]], [["ShearX", 0.2345325577621098, 0.383780128037189], ["TranslateX", 0.7298083748149163, 0.644325797667087]], [["Posterize", 0.5138725709682734, 0.7901809917259563], ["AutoContrast", 0.7966018627776853, 0.14529337543427345]], [["Invert", 0.5973031989249785, 0.417399314592829], ["Solarize", 0.9147539948653116, 0.8221272315548086]], [["Posterize", 0.601596043336383, 0.18969646160963938], ["Color", 0.7527275484079655, 0.431793831326888]], [["Equalize", 0.6731483454430538, 0.7866786558207602], ["TranslateX", 0.97574396899191, 0.5970255778044692]], [["Cutout", 0.15919495850169718, 0.8916094305850562], ["Invert", 0.8351348834751027, 0.4029937360314928]], [["Invert", 0.5894085405226027, 0.7283806854157764], ["Brightness", 0.3973976860470554, 0.949681121498567]], [["AutoContrast", 0.3707914135327408, 0.21192068592079616], ["ShearX", 0.28040127351140676, 0.6754553511344856]], [["Solarize", 0.07955132378694896, 0.15073572961927306], ["ShearY", 0.5735850168851625, 0.27147326850217746]], [["Equalize", 0.678653949549764, 0.8097796067861455], ["Contrast", 0.2283048527510083, 0.15507804874474185]], [["Equalize", 0.286013868374536, 0.186785848694501], ["Posterize", 0.16319021740810458, 0.1201304443285659]], [["Sharpness", 0.9601590830563757, 0.06267915026513238], ["AutoContrast", 0.3813920685124327, 0.294224403296912]], [["Brightness", 0.2703246632402241, 0.9168405377492277], ["ShearX", 0.6156009855831097, 0.4955986055846403]], [["Color", 0.9065504424987322, 0.03393612216080133], ["ShearY", 0.6768595880405884, 0.9981068127818191]], [["Equalize", 0.28812842368483904, 0.300387487349145], ["ShearY", 0.28812248704858345, 0.27105076231533964]], [["Brightness", 0.6864882730513477, 0.8205553299102412], ["Cutout", 0.45995236371265424, 0.5422030370297759]], [["Color", 0.34941404877084326, 0.25857961830158516], ["AutoContrast", 0.3451390878441899, 0.5000938249040454]], [["Invert", 0.8268247541815854, 0.6691380821226468], ["Cutout", 0.46489193601530476, 0.22620873109485895]], [["Rotate", 0.17879730528062376, 0.22670425330593935], ["Sharpness", 0.8692795688221834, 0.36586055020855723]], [["Brightness", 0.31203975139659634, 0.6934046293010939], ["Cutout", 0.31649437872271236, 0.08078625004157935]], [["Cutout", 0.3119482836150119, 0.6397160035509996], ["Contrast", 0.8311248624784223, 0.22897510169718616]], [["TranslateX", 0.7631157841429582, 0.6482890521284557], ["Brightness", 0.12681196272427664, 0.3669813784257344]], [["TranslateX", 0.06027722649179801, 0.3101104512201861], ["Sharpness", 0.5652076706249394, 0.05210008400968136]], [["AutoContrast", 0.39213552101583127, 0.5047021194355596], ["ShearY", 0.7164003055682187, 0.8063370761002899]], [["Solarize", 0.9574307011238342, 0.21472064809226854], ["AutoContrast", 0.8102612285047174, 0.716870148067014]], [["Rotate", 0.3592634277567387, 0.6452602893051465], ["AutoContrast", 0.27188430331411506, 0.06003099168464854]], [["Cutout", 0.9529536554825503, 0.5285505311027461], ["Solarize", 0.08478231903311029, 0.15986449762728216]], [["TranslateY", 0.31176130458018936, 0.5642853506158253], ["Equalize", 0.008890883901317648, 0.5146121040955942]], [["Color", 0.40773645085566157, 0.7110398926612682], ["Color", 0.18233100156439364, 0.7830036002758337]], [["Posterize", 0.5793809197821732, 0.043748553135581236], ["Invert", 0.4479962016131668, 0.7349663010359488]], [["TranslateX", 0.1994882312299382, 0.05216859488899439], ["Rotate", 0.48288726352035416, 0.44713829026777585]], [["Posterize", 0.22122838185154603, 0.5034546841241283], ["TranslateX", 0.2538745835410222, 0.6129055170893385]], [["Color", 0.6786559960640814, 0.4529749369803212], ["Equalize", 0.30215879674415336, 0.8733394611096772]], [["Contrast", 0.47316062430673456, 0.46669538897311447], ["Invert", 0.6514906551984854, 0.3053339444067804]], [["Equalize", 0.6443202625334524, 0.8689731394616441], ["Color", 0.7549183794057628, 0.8889001426329578]], [["Solarize", 0.616709740662654, 0.7792180816399313], ["ShearX", 0.9659155537406062, 0.39436937531179495]], [["Equalize", 0.23694011299406226, 0.027711152164392128], ["TranslateY", 0.1677339686527083, 0.3482126536808231]], [["Solarize", 0.15234175951790285, 0.7893840414281341], ["TranslateX", 0.2396395768284183, 0.27727219214979715]], [["Contrast", 0.3792017455380605, 0.32323660409845334], ["Contrast", 0.1356037413846466, 0.9127772969992305]], [["ShearX", 0.02642732222284716, 0.9184662576502115], ["Equalize", 0.11504884472142995, 0.8957638893097964]], [["TranslateY", 0.3193812913345325, 0.8828100030493128], ["ShearY", 0.9374975727563528, 0.09909415611083694]], [["AutoContrast", 0.025840721736048122, 0.7941037581373024], ["TranslateY", 0.498518003323313, 0.5777122846572548]], [["ShearY", 0.6042199307830248, 0.44809668754508836], ["Cutout", 0.3243978207701482, 0.9379740926294765]], [["ShearY", 0.6858549297583574, 0.9993252035788924], ["Sharpness", 0.04682428732773203, 0.21698099707915652]], [["ShearY", 0.7737469436637263, 0.8810127181224531], ["ShearY", 0.8995655445246451, 0.4312416220354539]], [["TranslateY", 0.4953094136709374, 0.8144161580138571], ["Solarize", 0.26301211718928097, 0.518345311180405]], [["Brightness", 0.8820246486031275, 0.571075863786249], ["ShearX", 0.8586669146703955, 0.0060476383595142735]], [["Sharpness", 0.20519233710982254, 0.6144574759149729], ["Posterize", 0.07976625267460813, 0.7480145046726968]], [["ShearY", 0.374075419680195, 0.3386105402023202], ["ShearX", 0.8228083637082115, 0.5885174783155361]], [["Brightness", 0.3528780713814561, 0.6999884884306623], ["Sharpness", 0.3680348120526238, 0.16953358258959617]], [["Brightness", 0.24891223104442084, 0.7973853494920095], ["TranslateX", 0.004256803835524736, 0.0470216343108546]], [["Posterize", 0.1947344282646012, 0.7694802711054367], ["Cutout", 0.9594385534844785, 0.5469744140592429]], [["Invert", 0.19012504762806026, 0.7816140211434693], ["TranslateY", 0.17479746932338402, 0.024249345245078602]], [["Rotate", 0.9669262055946796, 0.510166180775991], ["TranslateX", 0.8990602034610352, 0.6657802719304693]], [["ShearY", 0.5453049050407278, 0.8476872739603525], ["Cutout", 0.14226529093962592, 0.15756960661106634]], [["Equalize", 0.5895291156113004, 0.6797218994447763], ["TranslateY", 0.3541442192192753, 0.05166001155849864]], [["Equalize", 0.39530681662726097, 0.8448335365081087], ["Brightness", 0.6785483272734143, 0.8805568647038574]], [["Cutout", 0.28633258271917905, 0.7750870268336066], ["Equalize", 0.7221097824537182, 0.5865506280531162]], [["Posterize", 0.9044429629421187, 0.4620266401793388], ["Invert", 0.1803008045494473, 0.8073190766288534]], [["Sharpness", 0.7054649148075851, 0.3877207948962055], ["TranslateX", 0.49260224225927285, 0.8987462620731029]], [["Sharpness", 0.11196934729294483, 0.5953704422694938], ["Contrast", 0.13969334315069737, 0.19310569898434204]], [["Posterize", 0.5484346101051778, 0.7914140118600685], ["Brightness", 0.6428044691630473, 0.18811316670808076]], [["Invert", 0.22294834094984717, 0.05173157689962704], ["Cutout", 0.6091129168510456, 0.6280845506243643]], [["AutoContrast", 0.5726444076195267, 0.2799840903601295], ["Cutout", 0.3055752727786235, 0.591639807512993]], [["Brightness", 0.3707116723204462, 0.4049175910826627], ["Rotate", 0.4811601625588309, 0.2710760253723644]], [["ShearY", 0.627791719653608, 0.6877498291550205], ["TranslateX", 0.8751753308366824, 0.011164650018719358]], [["Posterize", 0.33832547954522263, 0.7087039872581657], ["Posterize", 0.6247474435007484, 0.7707784192114796]], [["Contrast", 0.17620186308493468, 0.9946224854942095], ["Solarize", 0.5431896088395964, 0.5867904203742308]], [["ShearX", 0.4667959516719652, 0.8938082224109446], ["TranslateY", 0.7311343008292865, 0.6829842246020277]], [["ShearX", 0.6130281467237769, 0.9924010909612302], ["Brightness", 0.41039241699696916, 0.9753218875311392]], [["TranslateY", 0.0747250386427123, 0.34602725521067534], ["Rotate", 0.5902597465515901, 0.361094672021087]], [["Invert", 0.05234890878959486, 0.36914978664919407], ["Sharpness", 0.42140532878231374, 0.19204058551048275]], [["ShearY", 0.11590485361909497, 0.6518540857972316], ["Invert", 0.6482444740361704, 0.48256237896163945]], [["Rotate", 0.4931329446923608, 0.037076242417301675], ["Contrast", 0.9097939772412852, 0.5619594905306389]], [["Posterize", 0.7311032479626216, 0.4796364593912915], ["Color", 0.13912123993932402, 0.03997286439663705]], [["AutoContrast", 0.6196602944085344, 0.2531430457527588], ["Rotate", 0.5583937060431972, 0.9893379795224023]], [["AutoContrast", 0.8847753125072959, 0.19123028952580057], ["TranslateY", 0.494361716097206, 0.14232297727461696]], [["Invert", 0.6212360716340707, 0.033898871473033165], ["AutoContrast", 0.30839896957008295, 0.23603569542166247]], [["Equalize", 0.8255583546605049, 0.613736933157845], ["AutoContrast", 0.6357166629525485, 0.7894617347709095]], [["Brightness", 0.33840706322846814, 0.07917167871493658], ["ShearY", 0.15693175752528676, 0.6282773652129153]], [["Cutout", 0.7550520024859294, 0.08982367300605598], ["ShearX", 0.5844942417320858, 0.36051195083380105]]] return p def fa_resnet50_rimagenet(): p = [[["ShearY", 0.14143816458479197, 0.513124791615952], ["Sharpness", 0.9290316227291179, 0.9788406212603302]], [["Color", 0.21502874228385338, 0.3698477943880306], ["TranslateY", 0.49865058747734736, 0.4352676987103321]], [["Brightness", 0.6603452126485386, 0.6990174510500261], ["Cutout", 0.7742953773992511, 0.8362550883640804]], [["Posterize", 0.5188375788270497, 0.9863648925446865], ["TranslateY", 0.8365230108655313, 0.6000972236440252]], [["ShearY", 0.9714994964711299, 0.2563663552809896], ["Equalize", 0.8987567223581153, 0.1181761775609772]], [["Sharpness", 0.14346409304565366, 0.5342189791746006], ["Sharpness", 0.1219714162835897, 0.44746801278319975]], [["TranslateX", 0.08089260772173967, 0.028011721602479833], ["TranslateX", 0.34767877352421406, 0.45131294688688794]], [["Brightness", 0.9191164585327378, 0.5143232242627864], ["Color", 0.9235247849934283, 0.30604586249462173]], [["Contrast", 0.4584173187505879, 0.40314219914942756], ["Rotate", 0.550289356406774, 0.38419022293237126]], [["Posterize", 0.37046156420799325, 0.052693291117634544], ["Cutout", 0.7597581409366909, 0.7535799791937421]], [["Color", 0.42583964114658746, 0.6776641859552079], ["ShearY", 0.2864805671096011, 0.07580175477739545]], [["Brightness", 0.5065952125552232, 0.5508640233704984], ["Brightness", 0.4760021616081475, 0.3544313318097987]], [["Posterize", 0.5169630851995185, 0.9466018906715961], ["Posterize", 0.5390336503396841, 0.1171015788193209]], [["Posterize", 0.41153170909576176, 0.7213063942615204], ["Rotate", 0.6232230424824348, 0.7291984098675746]], [["Color", 0.06704687234714028, 0.5278429246040438], ["Sharpness", 0.9146652195810183, 0.4581415618941407]], [["ShearX", 0.22404644446773492, 0.6508620171913467], ["Brightness", 0.06421961538672451, 0.06859528721039095]], [["Rotate", 0.29864103693134797, 0.5244313199644495], ["Sharpness", 0.4006161706584276, 0.5203708477368657]], [["AutoContrast", 0.5748186910788027, 0.8185482599354216], ["Posterize", 0.9571441684265188, 0.1921474117448481]], [["ShearY", 0.5214786760436251, 0.8375629059785009], ["Invert", 0.6872393349333636, 0.9307694335024579]], [["Contrast", 0.47219838080793364, 0.8228524484275648], ["TranslateY", 0.7435518856840543, 0.5888865560614439]], [["Posterize", 0.10773482839638836, 0.6597021018893648], ["Contrast", 0.5218466423129691, 0.562985661685268]], [["Rotate", 0.4401753067886466, 0.055198255925702475], ["Rotate", 0.3702153509335602, 0.5821574425474759]], [["TranslateY", 0.6714729117832363, 0.7145542887432927], ["Equalize", 0.0023263758097700205, 0.25837341854887885]], [["Cutout", 0.3159707561240235, 0.19539664199170742], ["TranslateY", 0.8702824829864558, 0.5832348977243467]], [["AutoContrast", 0.24800812729140026, 0.08017301277245716], ["Brightness", 0.5775505849482201, 0.4905904775616114]], [["Color", 0.4143517886294533, 0.8445937742921498], ["ShearY", 0.28688910858536587, 0.17539366839474402]], [["Brightness", 0.6341134194059947, 0.43683815933640435], ["Brightness", 0.3362277685899835, 0.4612826163288225]], [["Sharpness", 0.4504035748829761, 0.6698294470467474], ["Posterize", 0.9610055612671645, 0.21070714173174876]], [["Posterize", 0.19490421920029832, 0.7235798208354267], ["Rotate", 0.8675551331308305, 0.46335565746433094]], [["Color", 0.35097958351003306, 0.42199181561523186], ["Invert", 0.914112788087429, 0.44775583211984815]], [["Cutout", 0.223575616055454, 0.6328591417299063], ["TranslateY", 0.09269465212259387, 0.5101073959070608]], [["Rotate", 0.3315734525975911, 0.9983593458299167], ["Sharpness", 0.12245416662856974, 0.6258689139914664]], [["ShearY", 0.696116760180471, 0.6317805202283014], ["Color", 0.847501151593963, 0.4440116609830195]], [["Solarize", 0.24945891607225948, 0.7651150206105561], ["Cutout", 0.7229677092930331, 0.12674657348602494]], [["TranslateX", 0.43461945065713675, 0.06476571036747841], ["Color", 0.6139316940180952, 0.7376264330632316]], [["Invert", 0.1933003530637138, 0.4497819016184308], ["Invert", 0.18391634069983653, 0.3199769100951113]], [["Color", 0.20418296626476137, 0.36785101882029814], ["Posterize", 0.624658293920083, 0.8390081535735991]], [["Sharpness", 0.5864963540530814, 0.586672446690273], ["Posterize", 0.1980280647652339, 0.222114611452575]], [["Invert", 0.3543654961628104, 0.5146369635250309], ["Equalize", 0.40751271919434434, 0.4325310837291978]], [["ShearY", 0.22602859359451877, 0.13137880879778158], ["Posterize", 0.7475029061591305, 0.803900538461099]], [["Sharpness", 0.12426276165599924, 0.5965912716602046], ["Invert", 0.22603903038966913, 0.4346802001255868]], [["TranslateY", 0.010307035630661765, 0.16577665156754046], ["Posterize", 0.4114319141395257, 0.829872913683949]], [["TranslateY", 0.9353069865746215, 0.5327821671247214], ["Color", 0.16990443486261103, 0.38794866007484197]], [["Cutout", 0.1028174322829021, 0.3955952903458266], ["ShearY", 0.4311995281335693, 0.48024695395374734]], [["Posterize", 0.1800334334284686, 0.0548749478418862], ["Brightness", 0.7545808536793187, 0.7699080551646432]], [["Color", 0.48695305373084197, 0.6674269768464615], ["ShearY", 0.4306032279086781, 0.06057690550239343]], [["Brightness", 0.4919399683825053, 0.677338905806407], ["Brightness", 0.24112708387760828, 0.42761103121157656]], [["Posterize", 0.4434818644882532, 0.9489450593207714], ["Posterize", 0.40957675116385955, 0.015664946759584186]], [["Posterize", 0.41307949855153797, 0.6843276552020272], ["Rotate", 0.8003545094091291, 0.7002300783416026]], [["Color", 0.7038570031770905, 0.4697612983649519], ["Sharpness", 0.9700016496081002, 0.25185103545948884]], [["AutoContrast", 0.714641656154856, 0.7962423001719023], ["Sharpness", 0.2410097684093468, 0.5919171048019731]], [["TranslateX", 0.8101567644494714, 0.7156447005337443], ["Solarize", 0.5634727831229329, 0.8875158446846]], [["Sharpness", 0.5335258857303261, 0.364743126378182], ["Color", 0.453280875871377, 0.5621962714743068]], [["Cutout", 0.7423678127672542, 0.7726370777867049], ["Invert", 0.2806161382641934, 0.6021111986900146]], [["TranslateY", 0.15190341320343761, 0.3860373175487939], ["Cutout", 0.9980805818665679, 0.05332384819400854]], [["Posterize", 0.36518675678786605, 0.2935819027397963], ["TranslateX", 0.26586180351840005, 0.303641300745208]], [["Brightness", 0.19994509744377761, 0.90813953707639], ["Equalize", 0.8447217761297836, 0.3449396603478335]], [["Sharpness", 0.9294773669936768, 0.999713346583839], ["Brightness", 0.1359744825665662, 0.1658489221872924]], [["TranslateX", 0.11456529257659381, 0.9063795878367734], ["Equalize", 0.017438134319894553, 0.15776887259743755]], [["ShearX", 0.9833726383270114, 0.5688194948373335], ["Equalize", 0.04975615490994345, 0.8078130016227757]], [["Brightness", 0.2654654830488695, 0.8989789725280538], ["TranslateX", 0.3681535065952329, 0.36433345713161036]], [["Rotate", 0.04956524209892327, 0.5371942433238247], ["ShearY", 0.0005527499145153714, 0.56082571605602]], [["Rotate", 0.7918337108932019, 0.5906896260060501], ["Posterize", 0.8223967034091191, 0.450216998388943]], [["Color", 0.43595106766978337, 0.5253013785221605], ["Sharpness", 0.9169421073531799, 0.8439997639348893]], [["TranslateY", 0.20052300197155504, 0.8202662448307549], ["Sharpness", 0.2875792108435686, 0.6997181624527842]], [["Color", 0.10568089980973616, 0.3349467065132249], ["Brightness", 0.13070947282207768, 0.5757725013960775]], [["AutoContrast", 0.3749999712869779, 0.6665578760607657], ["Brightness", 0.8101178402610292, 0.23271946112218125]], [["Color", 0.6473605933679651, 0.7903409763232029], ["ShearX", 0.588080941572581, 0.27223524148254086]], [["Cutout", 0.46293361616697304, 0.7107761001833921], ["AutoContrast", 0.3063766931658412, 0.8026114219854579]], [["Brightness", 0.7884854981520251, 0.5503669863113797], ["Brightness", 0.5832456158675261, 0.5840349298921661]], [["Solarize", 0.4157539625058916, 0.9161905834309929], ["Sharpness", 0.30628197221802017, 0.5386291658995193]], [["Sharpness", 0.03329610069672856, 0.17066672983670506], ["Invert", 0.9900547302690527, 0.6276238841220477]], [["Solarize", 0.551015648982762, 0.6937104775938737], ["Color", 0.8838491591064375, 0.31596634380795385]], [["AutoContrast", 0.16224182418148447, 0.6068227969351896], ["Sharpness", 0.9599468096118623, 0.4885289719905087]], [["TranslateY", 0.06576432526133724, 0.6899544605400214], ["Posterize", 0.2177096480169678, 0.9949164789616582]], [["Solarize", 0.529820544480292, 0.7576047224165541], ["Sharpness", 0.027047878909321643, 0.45425231553970685]], [["Sharpness", 0.9102526010473146, 0.8311987141993857], ["Invert", 0.5191838751826638, 0.6906136644742229]], [["Solarize", 0.4762773516008588, 0.7703654263842423], ["Color", 0.8048437792602289, 0.4741523094238038]], [["Sharpness", 0.7095055508594206, 0.7047344238075169], ["Sharpness", 0.5059623654132546, 0.6127255499234886]], [["TranslateY", 0.02150725921966186, 0.3515764519224378], ["Posterize", 0.12482170119714735, 0.7829851754051393]], [["Color", 0.7983830079184816, 0.6964694521670339], ["Brightness", 0.3666527856286296, 0.16093151636495978]], [["AutoContrast", 0.6724982375829505, 0.536777706678488], ["Sharpness", 0.43091754837597646, 0.7363240924241439]], [["Brightness", 0.2889770401966227, 0.4556557902380539], ["Sharpness", 0.8805303296690755, 0.6262218017754902]], [["Sharpness", 0.5341939854581068, 0.6697109101429343], ["Rotate", 0.6806606655137529, 0.4896914517968317]], [["Sharpness", 0.5690509737059344, 0.32790632371915096], ["Posterize", 0.7951894258661069, 0.08377850335209162]], [["Color", 0.6124132978216081, 0.5756485920709012], ["Brightness", 0.33053544654445344, 0.23321841707002083]], [["TranslateX", 0.0654795026615917, 0.5227246924310244], ["ShearX", 0.2932320531132063, 0.6732066478183716]], [["Cutout", 0.6226071187083615, 0.01009274433736012], ["ShearX", 0.7176799968189801, 0.3758780240463811]], [["Rotate", 0.18172339508029314, 0.18099184896819184], ["ShearY", 0.7862658331645667, 0.295658135767252]], [["Contrast", 0.4156099177015862, 0.7015784500878446], ["Sharpness", 0.6454135310009, 0.32335858947955287]], [["Color", 0.6215885089922037, 0.6882673235388836], ["Brightness", 0.3539881732605379, 0.39486736455795496]], [["Invert", 0.8164816716866418, 0.7238192000817796], ["Sharpness", 0.3876355847343607, 0.9870077619731956]], [["Brightness", 0.1875628712629315, 0.5068115936257], ["Sharpness", 0.8732419122060423, 0.5028019258530066]], [["Sharpness", 0.6140734993408259, 0.6458239834366959], ["Rotate", 0.5250107862824867, 0.533419456933602]], [["Sharpness", 0.5710893143725344, 0.15551651073007305], ["ShearY", 0.6548487860151722, 0.021365083044319146]], [["Color", 0.7610250354649954, 0.9084452893074055], ["Brightness", 0.6934611792619156, 0.4108071412071374]], [["ShearY", 0.07512550098923898, 0.32923768385754293], ["ShearY", 0.2559588911696498, 0.7082337365398496]], [["Cutout", 0.5401319018926146, 0.004750568603408445], ["ShearX", 0.7473354415031975, 0.34472481968368773]], [["Rotate", 0.02284154583679092, 0.1353450082435801], ["ShearY", 0.8192458031684238, 0.2811653613473772]], [["Contrast", 0.21142896718139154, 0.7230739568811746], ["Sharpness", 0.6902690582665707, 0.13488436112901683]], [["Posterize", 0.21701219600958138, 0.5900695769640687], ["Rotate", 0.7541095031505971, 0.5341162375286219]], [["Posterize", 0.5772853064792737, 0.45808311743269936], ["Brightness", 0.14366050177823675, 0.4644871239446629]], [["Cutout", 0.8951718842805059, 0.4970074074310499], ["Equalize", 0.3863835903119882, 0.9986531042150006]], [["Equalize", 0.039411354473938925, 0.7475477254908457], ["Sharpness", 0.8741966378291861, 0.7304822679596362]], [["Solarize", 0.4908704265218634, 0.5160677350249471], ["Color", 0.24961813832742435, 0.09362352627360726]], [["Rotate", 7.870457075154214e-05, 0.8086950025500952], ["Solarize", 0.10200484521793163, 0.12312889222989265]], [["Contrast", 0.8052564975559727, 0.3403813036543645], ["Solarize", 0.7690158533600184, 0.8234626822018851]], [["AutoContrast", 0.680362728854513, 0.9415320040873628], ["TranslateY", 0.5305871824686941, 0.8030609611614028]], [["Cutout", 0.1748050257378294, 0.06565343731910589], ["TranslateX", 0.1812738872339903, 0.6254461448344308]], [["Brightness", 0.4230502644722749, 0.3346463682905031], ["ShearX", 0.19107198973659312, 0.6715789128604919]], [["ShearX", 0.1706528684548394, 0.7816570201200446], ["TranslateX", 0.494545185948171, 0.4710810058360291]], [["TranslateX", 0.42356251508933324, 0.23865307292867322], ["TranslateX", 0.24407503619326745, 0.6013778508137331]], [["AutoContrast", 0.7719512185744232, 0.3107905373009763], ["ShearY", 0.49448082925617176, 0.5777951230577671]], [["Cutout", 0.13026983827940525, 0.30120438757485657], ["Brightness", 0.8857896834516185, 0.7731541459513939]], [["AutoContrast", 0.6422800349197934, 0.38637401090264556], ["TranslateX", 0.25085431400995084, 0.3170642592664873]], [["Sharpness", 0.22336654455367122, 0.4137774852324138], ["ShearY", 0.22446851054920894, 0.518341735882535]], [["Color", 0.2597579403253848, 0.7289643913060193], ["Sharpness", 0.5227416670468619, 0.9239943674030637]], [["Cutout", 0.6835337711563527, 0.24777620448593812], ["AutoContrast", 0.37260245353051846, 0.4840361183247263]], [["Posterize", 0.32756602788628375, 0.21185124493743707], ["ShearX", 0.25431504951763967, 0.19585996561416225]], [["AutoContrast", 0.07930627591849979, 0.5719381348340309], ["AutoContrast", 0.335512380071304, 0.4208050118308541]], [["Rotate", 0.2924360268257798, 0.5317629242879337], ["Sharpness", 0.4531050021499891, 0.4102650087199528]], [["Equalize", 0.5908862210984079, 0.468742362277498], ["Brightness", 0.08571766548550425, 0.5629320703375056]], [["Cutout", 0.52751122383816, 0.7287774744737556], ["Equalize", 0.28721628275296274, 0.8075179887475786]], [["AutoContrast", 0.24208377391366226, 0.34616549409607644], ["TranslateX", 0.17454707403766834, 0.5278055700078459]], [["Brightness", 0.5511881924749478, 0.999638675514418], ["Equalize", 0.14076197797220913, 0.2573030693317552]], [["ShearX", 0.668731433926434, 0.7564253049646743], ["Color", 0.63235486543845, 0.43954436063340785]], [["ShearX", 0.40511960873276237, 0.5710419512142979], ["Contrast", 0.9256769948746423, 0.7461350716211649]], [["Cutout", 0.9995917204023061, 0.22908419326246265], ["TranslateX", 0.5440902956629469, 0.9965570051216295]], [["Color", 0.22552987172228894, 0.4514558960849747], ["Sharpness", 0.638058150559443, 0.9987829481002615]], [["Contrast", 0.5362775837534763, 0.7052133185951871], ["ShearY", 0.220369845547023, 0.7593922994775721]], [["ShearX", 0.0317785822935219, 0.775536785253455], ["TranslateX", 0.7939510227015061, 0.5355620618496535]], [["Cutout", 0.46027969917602196, 0.31561199122527517], ["Color", 0.06154066467629451, 0.5384660000729091]], [["Sharpness", 0.7205483743301113, 0.552222392539886], ["Posterize", 0.5146496404711752, 0.9224333144307473]], [["ShearX", 0.00014547730356910538, 0.3553954298642108], ["TranslateY", 0.9625736029090676, 0.57403418640424]], [["Posterize", 0.9199917903297341, 0.6690259107633706], ["Posterize", 0.0932558110217602, 0.22279303372106138]], [["Invert", 0.25401453476874863, 0.3354329544078385], ["Posterize", 0.1832673201325652, 0.4304718799821412]], [["TranslateY", 0.02084122674367607, 0.12826181437197323], ["ShearY", 0.655862534043703, 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0.4030159294447347]], [["Brightness", 0.02739111568942537, 0.006028056532326534], ["ShearX", 0.17276751958646486, 0.05967365780621859]], [["TranslateY", 0.4376298213047888, 0.7691816164456199], ["Sharpness", 0.8162292718857824, 0.6054926462265117]], [["Color", 0.37963069679121214, 0.5946919433483344], ["Posterize", 0.08485417284005387, 0.5663580913231766]], [["Equalize", 0.49785780226818316, 0.9999137109183761], ["Sharpness", 0.7685879484682496, 0.6260846154212211]], [["AutoContrast", 0.4190931409670763, 0.2374852525139795], ["Posterize", 0.8797422264608563, 0.3184738541692057]], [["Rotate", 0.7307269024632872, 0.41523609600701106], ["ShearX", 0.6166685870692289, 0.647133807748274]], [["Sharpness", 0.5633713231039904, 0.8276694754755876], ["Cutout", 0.8329340776895764, 0.42656043027424073]], [["ShearY", 0.14934828370884312, 0.8622510773680372], ["Invert", 0.25925989086863277, 0.8813283584888576]], [["Contrast", 0.9457071292265932, 0.43228655518614034], ["Sharpness", 0.8485316947644338, 0.7590298998732413]], [["AutoContrast", 0.8386103589399184, 0.5859583131318076], ["Solarize", 0.466758711343543, 0.9956215363818983]], [["Rotate", 0.9387133710926467, 0.19180564509396503], ["Rotate", 0.5558247609706255, 0.04321698692007105]], [["ShearX", 0.3608716600695567, 0.15206159451532864], ["TranslateX", 0.47295292905710146, 0.5290760596129888]], [["TranslateX", 0.8357685981547495, 0.5991305115727084], ["Posterize", 0.5362929404188211, 0.34398525441943373]], [["ShearY", 0.6751984031632811, 0.6066293622133011], ["Contrast", 0.4122723990263818, 0.4062467515095566]], [["Color", 0.7515349936021702, 0.5122124665429213], ["Contrast", 0.03190514292904123, 0.22903520154660545]], [["Contrast", 0.5448962625054385, 0.38655673938910545], ["AutoContrast", 0.4867400684894492, 0.3433111101096984]], [["Rotate", 0.0008372434310827959, 0.28599951781141714], ["Equalize", 0.37113686925530087, 0.5243929348114981]], [["Color", 0.720054993488857, 0.2010177651701808], ["TranslateX", 0.23036196506059398, 0.11152764304368781]], [["Cutout", 0.859134208332423, 0.6727345740185254], ["ShearY", 0.02159833505865088, 0.46390076266538544]], [["Sharpness", 0.3428232157391428, 0.4067874527486514], ["Brightness", 0.5409415136577347, 0.3698432231874003]], [["Solarize", 0.27303978936454776, 0.9832186173589548], ["ShearY", 0.08831127213044043, 0.4681870331149774]], [["TranslateY", 0.2909309268736869, 0.4059460811623174], ["Sharpness", 0.6425125139803729, 0.20275737203293587]], [["Contrast", 0.32167626214661627, 0.28636162794046977], ["Invert", 0.4712405253509603, 0.7934644799163176]], [["Color", 0.867993060896951, 0.96574321666213], ["Color", 0.02233897320328512, 0.44478933557303063]], [["AutoContrast", 0.1841254751814967, 0.2779992148017741], ["Color", 0.3586283093530607, 0.3696246850445087]], [["Posterize", 0.2052935984046965, 0.16796913860308244], ["ShearX", 0.4807226832843722, 0.11296747254563266]], [["Cutout", 0.2016411266364791, 0.2765295444084803], ["Brightness", 0.3054112810424313, 0.695924264931216]], [["Rotate", 0.8405872184910479, 0.5434142541450815], ["Cutout", 0.4493615138203356, 0.893453735250007]], [["Contrast", 0.8433310507685494, 0.4915423577963278], ["ShearX", 0.22567799557913246, 0.20129892537008834]], [["Contrast", 0.045954277103674224, 0.5043900167190442], ["Cutout", 0.5552992473054611, 0.14436447810888237]], [["AutoContrast", 0.7719296115130478, 0.4440417544621306], ["Sharpness", 0.13992809206158283, 0.7988278670709781]], [["Color", 0.7838574233513952, 0.5971351401625151], ["TranslateY", 0.13562290583925385, 0.2253039635819158]], [["Cutout", 0.24870301109385806, 0.6937886690381568], ["TranslateY", 0.4033400068952813, 0.06253378991880915]], [["TranslateX", 0.0036059390486775644, 0.5234723884081843], ["Solarize", 0.42724862530733526, 0.8697702564187633]], [["Equalize", 0.5446026737834311, 0.9367992979112202], ["ShearY", 0.5943478903735789, 0.42345889214100046]], [["ShearX", 0.18611885697957506, 0.7320849092947314], ["ShearX", 0.3796416430900566, 0.03817761920009881]], [["Posterize", 0.37636778506979124, 0.26807924785236537], ["Brightness", 0.4317372554383255, 0.5473346211870932]], [["Brightness", 0.8100436240916665, 0.3817612088285007], ["Brightness", 0.4193974619003253, 0.9685902764026623]], [["Contrast", 0.701776402197012, 0.6612786008858009], ["Color", 0.19882787177960912, 0.17275597188875483]], [["Color", 0.9538303302832989, 0.48362384535228686], ["ShearY", 0.2179980837345602, 0.37027290936457313]], [["TranslateY", 0.6068028691503798, 0.3919346523454841], ["Cutout", 0.8228303342563138, 0.18372280287814613]], [["Equalize", 0.016416758802906828, 0.642838949194916], ["Cutout", 0.5761717838655257, 0.7600661153497648]], [["Color", 0.9417761826818639, 0.9916074035986558], ["Equalize", 0.2524209308597042, 0.6373703468715077]], [["Brightness", 0.75512589439513, 0.6155072321007569], ["Contrast", 0.32413476940254515, 0.4194739830159837]], [["Sharpness", 0.3339450765586968, 0.9973297539194967], ["AutoContrast", 0.6523930242124429, 0.1053482471037186]], [["ShearX", 0.2961391955838801, 0.9870036064904368], ["ShearY", 0.18705025965909403, 0.4550895821154484]], [["TranslateY", 0.36956447983807883, 0.36371471767143543], ["Sharpness", 0.6860051967688487, 0.2850190720087796]], [["Cutout", 0.13017742151902967, 0.47316674150067195], ["Invert", 0.28923829959551883, 0.9295585654924601]], [["Contrast", 0.7302368472279086, 0.7178974949876642], ["TranslateY", 0.12589674152030433, 0.7485392909494947]], [["Color", 0.6474693117772619, 0.5518269515590674], ["Contrast", 0.24643004970708016, 0.3435581358079418]], [["Contrast", 0.5650327855750835, 0.4843031798040887], ["Brightness", 0.3526684005761239, 0.3005305004600969]], [["Rotate", 0.09822284968122225, 0.13172798244520356], ["Equalize", 0.38135066977857157, 0.5135129123554154]], [["Contrast", 0.5902590645585712, 0.2196062383730596], ["ShearY", 0.14188379126120954, 0.1582612142182743]], [["Cutout", 0.8529913814417812, 0.89734031211874], ["Color", 0.07293767043078672, 0.32577659205278897]], [["Equalize", 0.21401668971453247, 0.040015259500028266], ["ShearY", 0.5126400895338797, 0.4726484828276388]], [["Brightness", 0.8269430025954498, 0.9678362841865166], ["ShearY", 0.17142069814830432, 0.4726727848289514]], [["Brightness", 0.699707089334018, 0.2795501395789335], ["ShearX", 0.5308818178242845, 0.10581814221896294]], [["Equalize", 0.32519644258946145, 0.15763390340309183], ["TranslateX", 0.6149090364414208, 0.7454832565718259]], [["AutoContrast", 0.5404508567155423, 0.7472387762067986], ["Equalize", 0.05649876539221024, 0.5628180219887216]]] return p def fa_reduced_svhn(): p = [[["TranslateX", 0.001576965129744562, 0.43180488809874773], ["Invert", 0.7395307279252639, 0.7538444307982558]], [["Contrast", 0.5762062225409211, 0.7532431872873473], ["TranslateX", 0.45212523461624615, 0.02451684483019846]], [["Contrast", 0.18962433143225088, 0.29481185671147325], ["Contrast", 0.9998112218299271, 0.813015355163255]], [["Posterize", 0.9633391295905683, 0.4136786222304747], ["TranslateY", 0.8011655496664203, 0.44102126789970797]], [["Color", 0.8231185187716968, 0.4171602946893402], ["TranslateX", 0.8684965619113907, 0.36514568324909674]], [["Color", 0.904075230324581, 0.46319140331093767], ["Contrast", 0.4115196534764559, 0.7773329158740563]], [["Sharpness", 0.6600262774093967, 0.8045637700026345], ["TranslateY", 0.5917663766021198, 0.6844241908520602]], [["AutoContrast", 0.16223989311434306, 0.48169653554195924], ["ShearX", 0.5433173232860344, 0.7460278151912152]], [["ShearX", 0.4913604762760715, 0.83391837859561], ["Color", 0.5580367056511908, 0.2961512691312932]], [["Color", 0.18567091721211237, 0.9296983204905286], ["Cutout", 0.6074026199060156, 0.03303273406448193]], [["Invert", 0.8049054771963224, 0.1340792344927909], ["Color", 0.4208839940504979, 0.7096454840962345]], [["ShearX", 0.7997786664546294, 0.6492629575700173], ["AutoContrast", 0.3142777134084793, 0.6526010594925064]], [["TranslateX", 0.2581027144644976, 0.6997433332894101], ["Rotate", 0.45490480973606834, 0.238620570022944]], [["Solarize", 0.837397161027719, 0.9311141273136286], ["Contrast", 0.640364826293148, 0.6299761518677469]], [["Brightness", 0.3782457347141744, 0.7085036717054278], ["Brightness", 0.5346150083208507, 0.5858930737867671]], [["Invert", 0.48780391510474086, 0.610086407879722], ["Color", 0.5601999247616932, 0.5393836220423195]], [["Brightness", 0.00250086643283564, 0.5003355864896979], ["Brightness", 0.003922153283353616, 0.41107110154584925]], [["TranslateX", 0.4073069009685957, 0.9843435292693372], ["Invert", 0.38837085318721926, 0.9298542033875989]], [["ShearY", 0.05479740443795811, 0.9113983424872698], ["AutoContrast", 0.2181108114232728, 0.713996037012164]], [["Brightness", 0.27747508429413903, 0.3217467607288693], ["ShearX", 0.02715239061946995, 0.5430731635396449]], [["Sharpness", 0.08994432959374538, 0.004706443546453831], ["Posterize", 0.10768206853226996, 0.39020299239900236]], [["Cutout", 0.37498679037853905, 0.20784809761469553], ["Color", 0.9825516352194511, 0.7654155662756019]], [["Color", 0.8899349124453552, 0.7797700766409008], ["Rotate", 0.1370222187174981, 0.2622119295138398]], [["Cutout", 0.7088223332663685, 0.7884456023190028], ["Solarize", 0.5362257505160836, 0.6426837537811545]], [["Invert", 0.15686225694987552, 0.5500563899117913], ["Rotate", 0.16315224193260078, 0.4246854030170752]], [["Rotate", 0.005266247922433631, 0.06612026206223394], ["Contrast", 0.06494357829209037, 0.2738420319474947]], [["Cutout", 0.30200619566806275, 0.06558008068236942], ["Rotate", 0.2168576483823022, 0.878645566986328]], [["Color", 0.6358930679444622, 0.613404714161498], ["Rotate", 0.08733206733004326, 0.4348276574435751]], [["Cutout", 0.8834634887239585, 0.0006853845293474659], ["Solarize", 0.38132051231951847, 0.42558752668491195]], [["ShearY", 0.08830136548479937, 0.5522438878371283], ["Brightness", 0.23816560427834074, 0.3033709051157141]], [["Solarize", 0.9015331490756151, 0.9108788708847556], ["Contrast", 0.2057898014670072, 0.03260096030427456]], [["Equalize", 0.9455978685121174, 0.14850077333434056], ["TranslateY", 0.6888705996522545, 0.5300565492007543]], [["Cutout", 0.16942673959343585, 0.7294197201361826], ["TranslateX", 0.41184830642301534, 0.7060207449376135]], [["Color", 0.30133344118702166, 0.24384417956342314], ["Sharpness", 0.4640904544421743, 0.32431840288061864]], [["Sharpness", 0.5195055033472676, 0.9386677467005835], ["Color", 0.9536519432978372, 0.9624043444556467]], [["Rotate", 0.8689597230556101, 0.23955490826730633], ["Contrast", 0.050071600927462656, 0.1309891556004179]], [["Cutout", 0.5349421090878962, 0.08239510727779054], ["Rotate", 0.46064964710717216, 0.9037689320897339]], [["AutoContrast", 0.5625256909986802, 0.5358003783186498], ["Equalize", 0.09204330691163354, 0.4386906784850649]], [["ShearX", 0.0011061172864470226, 0.07150284682189278], ["AutoContrast", 0.6015956946553209, 0.4375362295530898]], [["ShearY", 0.25294276499800983, 0.7937560397859562], ["Brightness", 0.30834103299704474, 0.21960258701547009]], [["Posterize", 0.7423948904688074, 0.4598609935109695], ["Rotate", 0.5510348811675979, 0.26763724868985933]], [["TranslateY", 0.3208729319318745, 0.945513054853888], ["ShearX", 0.4916473963030882, 0.8743840560039451]], [["ShearY", 0.7557718687011286, 0.3125397104722828], ["Cutout", 0.5565359791865849, 0.5151359251135629]], [["AutoContrast", 0.16652786355571275, 0.1101575800958632], ["Rotate", 0.05108851703032641, 0.2612966401802814]], [["Brightness", 0.380296489835016, 0.0428162454174662], ["ShearX", 0.3911934083168285, 0.18933607362790178]], [["Color", 0.002476250465397678, 0.07795275305347571], ["Posterize", 0.08131841266654188, 0.14843363184306413]], [["Cutout", 0.36664558716104434, 0.20904484995063996], ["Cutout", 0.07986452057223141, 0.9287747671053432]], [["Color", 0.9296812469919231, 0.6634239915141935], ["Rotate", 0.07632463573240006, 0.408624029443747]], [["Cutout", 0.7594470171961278, 0.9834672124229463], ["Solarize", 0.4471371303745053, 0.5751101102286562]], [["Posterize", 0.051186719734032285, 0.5110941294710823], ["Sharpness", 0.040432522797391596, 0.42652298706992164]], [["Sharpness", 0.2645335264327221, 0.8844553189835457], ["Brightness", 0.7229600357932696, 0.16660749270785696]], [["Sharpness", 0.6296376086802589, 0.15564989758083458], ["Sharpness", 0.7913410481400365, 0.7022615408082826]], [["Cutout", 0.5517247347343883, 0.43794888517764674], ["ShearX", 0.6951051782530201, 0.6230992857867065]], [["ShearX", 0.9015708556331022, 0.6322135168527783], ["Contrast", 0.4285629283441831, 0.18158321019502988]], [["Brightness", 0.9014292329524769, 0.3660463325457713], ["Invert", 0.6700729097206592, 0.16502732071917703]], [["AutoContrast", 0.6432764477303431, 0.9998909112400834], ["Invert", 0.8124063975545761, 0.8149683327882365]], [["Cutout", 0.6023944009428617, 0.9630976951918225], ["ShearX", 0.2734723568803071, 0.3080911542121765]], [["Sharpness", 0.048949115014412806, 0.44497866256845164], ["Brightness", 0.5611832867244329, 0.12994217480426257]], [["TranslateY", 0.4619112333002525, 0.47317728091588396], ["Solarize", 0.618638784910472, 0.9508297099190338]], [["Sharpness", 0.9656274391147018, 0.3402622993963962], ["Cutout", 0.8452511174508919, 0.3094717093312621]], [["ShearX", 0.04942201651478659, 0.6910568465705691], ["AutoContrast", 0.7155342517619936, 0.8565418847743523]], [["Brightness", 0.5222290590721783, 0.6462675303633422], ["Sharpness", 0.7756317511341633, 0.05010730683866704]], [["Contrast", 0.17098396012942796, 0.9128908626236187], ["TranslateY", 0.1523815376677518, 0.4269909829886339]], [["Cutout", 0.7679024720089866, 0.22229116396644455], ["Sharpness", 0.47714827844878843, 0.8242815864830401]], [["Brightness", 0.9321772357292445, 0.11339758604001371], ["Invert", 0.7021078495093375, 0.27507749184928154]], [["ShearY", 0.7069449324510433, 0.07262757954730437], ["Cutout", 0.6298690227159313, 0.8866813664859028]], 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0.9829939598708993], ["Solarize", 0.29886553921638087, 0.22487098773064948]], [["Cutout", 0.24209211313246753, 0.5522928952260516], ["AutoContrast", 0.6212831649673523, 0.4191071063984261]], [["ShearX", 0.4726406722647257, 0.26783614257572447], ["TranslateY", 0.251078162624763, 0.26103450676044304]], [["Cutout", 0.8721775527314426, 0.6284108541347894], ["ShearX", 0.7063325779145683, 0.8467168866724094]], [["ShearY", 0.42226987564279606, 0.18012694533480308], ["Brightness", 0.858499853702629, 0.4738929353785444]], [["Solarize", 0.30039851082582764, 0.8151511479162529], ["Cutout", 0.7228873804059033, 0.6174351379837011]], [["ShearX", 0.4921198221896609, 0.5678998037958154], ["Color", 0.7865298825314806, 0.9309020966406338]], [["Invert", 0.8077821007916464, 0.7375015762124386], ["Cutout", 0.032464574567796195, 0.25405044477004846]], [["Color", 0.6061325441870133, 0.2813794250571565], ["TranslateY", 0.5882949270385848, 0.33262043078220227]], [["ShearX", 0.7877331864215293, 0.8001131937448647], ["Cutout", 0.19828215489868783, 0.5949317580743655]], [["Contrast", 0.529508728421701, 0.36477855845285007], ["Color", 0.7145481740509138, 0.2950794787786947]], [["Contrast", 0.9932891064746089, 0.46930062926732646], ["Posterize", 0.9033014136780437, 0.5745902253320527]]] return p def policy_decoder(augment, num_policy, num_op): op_list = augment_list(False) policies = [] for i in range(num_policy): ops = [] for j in range(num_op): op_idx = augment['policy_%d_%d' % (i, j)] op_prob = augment['prob_%d_%d' % (i, j)] op_level = augment['level_%d_%d' % (i, j)] ops.append((op_list[op_idx][0].__name__, op_prob, op_level)) policies.append(ops) return policies ================================================ FILE: fast_autoaugment/confs/efficientnet_b0.yaml ================================================ model: type: efficientnet-b0 condconv_num_expert: 1 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 # per gpu epoch: 350 lr: 0.008 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 ================================================ FILE: fast_autoaugment/confs/efficientnet_b0_condconv.yaml ================================================ model: type: efficientnet-b0 condconv_num_expert: 8 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 # per gpu epoch: 350 lr: 0.008 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 mixup: 0.2 ================================================ FILE: fast_autoaugment/confs/efficientnet_b1.yaml ================================================ model: type: efficientnet-b1 condconv_num_expert: 1 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 # per gpu epoch: 350 lr: 0.008 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 ================================================ FILE: fast_autoaugment/confs/efficientnet_b2.yaml ================================================ model: type: efficientnet-b2 condconv_num_expert: 1 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 # per gpu epoch: 350 lr: 0.008 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 ================================================ FILE: fast_autoaugment/confs/efficientnet_b3.yaml ================================================ model: type: efficientnet-b3 condconv_num_expert: 1 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 64 # per gpu epoch: 350 lr: 0.004 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 ================================================ FILE: fast_autoaugment/confs/efficientnet_b4.yaml ================================================ model: type: efficientnet-b4 condconv_num_expert: 1 # if this is greater than 1(eg. 4), it activates condconv. dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 32 # per gpu epoch: 350 lr: 0.002 # 0.256 for 4096 batch lr_schedule: type: 'efficientnet' warmup: multiplier: 1 epoch: 5 optimizer: type: rmsprop decay: 0.00001 clip: 0 ema: 0.9999 ema_interval: -1 lb_smooth: 0.1 ================================================ FILE: fast_autoaugment/confs/pyramid272_cifar.yaml ================================================ model: type: pyramid depth: 272 alpha: 200 bottleneck: True dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 64 epoch: 1800 lr: 0.05 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.00005 ema: 0 ================================================ FILE: fast_autoaugment/confs/resnet200.yaml ================================================ model: type: resnet200 dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 64 epoch: 270 lr: 0.025 lr_schedule: type: 'resnet' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0001 clip: 0 ema: 0 ================================================ FILE: fast_autoaugment/confs/resnet50.yaml ================================================ model: type: resnet50 dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 epoch: 270 lr: 0.05 lr_schedule: type: 'resnet' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0001 clip: 0 ema: 0 ================================================ FILE: fast_autoaugment/confs/resnet50_mixup.yaml ================================================ model: type: resnet50 dataset: imagenet aug: fa_reduced_imagenet cutout: 0 batch: 128 epoch: 270 lr: 0.05 lr_schedule: type: 'resnet' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0001 clip: 0 ema: 0 #lb_smooth: 0.1 mixup: 0.2 ================================================ FILE: fast_autoaugment/confs/shake26_2x112d_cifar.yaml ================================================ model: type: shakeshake26_2x112d dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 128 epoch: 1800 lr: 0.01 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.002 ema: 0 ================================================ FILE: fast_autoaugment/confs/shake26_2x32d_cifar.yaml ================================================ model: type: shakeshake26_2x32d dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 128 epoch: 1800 lr: 0.01 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.001 ema: 0 ================================================ FILE: fast_autoaugment/confs/shake26_2x96d_cifar.yaml ================================================ model: type: shakeshake26_2x96d dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 128 epoch: 1800 lr: 0.01 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.001 ema: 0 ================================================ FILE: fast_autoaugment/confs/wresnet28x10_cifar.yaml ================================================ model: type: wresnet28_10 dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 128 epoch: 200 lr: 0.1 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0005 ema: 0 ================================================ FILE: fast_autoaugment/confs/wresnet28x10_svhn.yaml ================================================ model: type: wresnet28_10 dataset: svhn aug: fa_reduced_svhn cutout: 20 batch: 128 epoch: 200 lr: 0.01 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0005 ema: 0 ================================================ FILE: fast_autoaugment/confs/wresnet40x2_cifar.yaml ================================================ model: type: wresnet40_2 dataset: cifar10 aug: fa_reduced_cifar10 cutout: 16 batch: 128 epoch: 200 lr: 0.1 lr_schedule: type: 'cosine' warmup: multiplier: 1 epoch: 5 optimizer: type: sgd nesterov: True decay: 0.0002 ema: 0 ================================================ FILE: fast_autoaugment/requirements.txt ================================================ git+https://github.com/wbaek/theconf@de32022f8c0651a043dc812d17194cdfd62066e8 git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git@08f7d5e git+https://github.com/ildoonet/pystopwatch2.git git+https://github.com/hyperopt/hyperopt.git pretrainedmodels tqdm tensorboardx sklearn ray matplotlib psutil requests ================================================ FILE: madrys.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import models from torch.autograd import Variable if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') class MadrysLoss(nn.Module): def __init__(self, step_size=0.007, epsilon=0.031, perturb_steps=10, distance='l_inf', cutmix=False): super(MadrysLoss, self).__init__() self.step_size = step_size self.epsilon = epsilon self.perturb_steps = perturb_steps self.distance = distance self.cross_entropy = models.CutMixCrossEntropyLoss() if cutmix else torch.nn.CrossEntropyLoss() def forward(self, model, x_natural, y, optimizer): model.eval() for param in model.parameters(): param.requires_grad = False # generate adversarial example x_adv = x_natural.clone() + self.step_size * torch.randn(x_natural.shape).to(device) if self.distance == 'l_inf': for _ in range(self.perturb_steps): x_adv.requires_grad_() loss_ce = self.cross_entropy(model(x_adv), y) grad = torch.autograd.grad(loss_ce, [x_adv])[0] x_adv = x_adv.detach() + self.step_size * torch.sign(grad.detach()) x_adv = torch.min(torch.max(x_adv, x_natural - self.epsilon), x_natural + self.epsilon) x_adv = torch.clamp(x_adv, 0.0, 1.0) else: x_adv = torch.clamp(x_adv, 0.0, 1.0) for param in model.parameters(): param.requires_grad = True model.train() # x_adv = Variable(x_adv, requires_grad=False) optimizer.zero_grad() logits = model(x_adv) loss = self.cross_entropy(logits, y) return logits, loss ================================================ FILE: main.py ================================================ import argparse import datetime import os import shutil import time import numpy as np import dataset import mlconfig import torch import util import madrys import models from evaluator import Evaluator from trainer import Trainer mlconfig.register(madrys.MadrysLoss) # General Options parser = argparse.ArgumentParser(description='ClasswiseNoise') parser.add_argument('--seed', type=int, default=0, help='seed') parser.add_argument('--version', type=str, default="resnet18") parser.add_argument('--exp_name', type=str, default="test_exp") parser.add_argument('--config_path', type=str, default='configs/cifar10') parser.add_argument('--load_model', action='store_true', default=False) parser.add_argument('--data_parallel', action='store_true', default=False) parser.add_argument('--train', action='store_true', default=False) parser.add_argument('--save_frequency', default=-1, type=int) # Datasets Options parser.add_argument('--train_face', action='store_true', default=False) parser.add_argument('--train_portion', default=1.0, type=float) parser.add_argument('--train_batch_size', default=128, type=int, help='perturb step size') parser.add_argument('--eval_batch_size', default=256, type=int, help='perturb step size') parser.add_argument('--num_of_workers', default=8, type=int, help='workers for loader') parser.add_argument('--train_data_type', type=str, default='CIFAR10') parser.add_argument('--test_data_type', type=str, default='CIFAR10') parser.add_argument('--train_data_path', type=str, default='../datasets') parser.add_argument('--test_data_path', type=str, default='../datasets') parser.add_argument('--perturb_type', default='classwise', type=str, choices=['classwise', 'samplewise'], help='Perturb type') parser.add_argument('--patch_location', default='center', type=str, choices=['center', 'random'], help='Location of the noise') parser.add_argument('--poison_rate', default=1.0, type=float) parser.add_argument('--perturb_tensor_filepath', default=None, type=str) args = parser.parse_args() # Set up Experiments if args.exp_name == '': args.exp_name = 'exp_' + datetime.datetime.now() exp_path = os.path.join(args.exp_name, args.version) log_file_path = os.path.join(exp_path, args.version) checkpoint_path = os.path.join(exp_path, 'checkpoints') checkpoint_path_file = os.path.join(checkpoint_path, args.version) util.build_dirs(exp_path) util.build_dirs(checkpoint_path) logger = util.setup_logger(name=args.version, log_file=log_file_path + ".log") # CUDA Options logger.info("PyTorch Version: %s" % (torch.__version__)) if torch.cuda.is_available(): torch.cuda.manual_seed(args.seed) torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True device = torch.device('cuda') device_list = [torch.cuda.get_device_name(i) for i in range(0, torch.cuda.device_count())] logger.info("GPU List: %s" % (device_list)) else: device = torch.device('cpu') # Load Exp Configs config_file = os.path.join(args.config_path, args.version)+'.yaml' config = mlconfig.load(config_file) config.set_immutable() for key in config: logger.info("%s: %s" % (key, config[key])) shutil.copyfile(config_file, os.path.join(exp_path, args.version+'.yaml')) def train(starting_epoch, model, optimizer, scheduler, criterion, trainer, evaluator, ENV, data_loader): for epoch in range(starting_epoch, config.epochs): logger.info("") logger.info("="*20 + "Training Epoch %d" % (epoch) + "="*20) # Train ENV['global_step'] = trainer.train(epoch, model, criterion, optimizer) ENV['train_history'].append(trainer.acc_meters.avg*100) scheduler.step() # Eval logger.info("="*20 + "Eval Epoch %d" % (epoch) + "="*20) is_best = False if not args.train_face: evaluator.eval(epoch, model) payload = ('Eval Loss:%.4f\tEval acc: %.2f' % (evaluator.loss_meters.avg, evaluator.acc_meters.avg*100)) logger.info(payload) ENV['eval_history'].append(evaluator.acc_meters.avg*100) ENV['curren_acc'] = evaluator.acc_meters.avg*100 ENV['cm_history'].append(evaluator.confusion_matrix.cpu().numpy().tolist()) # Reset Stats trainer._reset_stats() evaluator._reset_stats() else: pass # model.eval() # model.module.classify = True # evaluator.eval(epoch, model) # payload = ('Eval Loss:%.4f\tEval acc: %.2f' % (evaluator.loss_meters.avg, evaluator.acc_meters.avg*100)) # logger.info(payload) # model.classify = False # identity_list = lfw_test.get_lfw_list('lfw_test_pair.txt') # img_paths = [os.path.join('../datasets/lfw-112x112', each) for each in identity_list] # eval_acc = lfw_test.lfw_test(model, img_paths, identity_list, 'lfw_test_pair.txt', args.eval_batch_size, logger=logger) # ENV['curren_acc'] = eval_acc # ENV['best_acc'] = max(ENV['best_acc'], eval_acc) # ENV['eval_history'].append(eval_acc) # # Reset Stats # trainer._reset_stats() # evaluator._reset_stats() # Save Model target_model = model.module if args.data_parallel else model util.save_model(ENV=ENV, epoch=epoch, model=target_model, optimizer=optimizer, scheduler=scheduler, is_best=is_best, filename=checkpoint_path_file) logger.info('Model Saved at %s', checkpoint_path_file) if args.save_frequency > 0 and epoch % args.save_frequency == 0: filename = checkpoint_path_file + '_epoch%d' % (epoch) util.save_model(ENV=ENV, epoch=epoch, model=target_model, optimizer=optimizer, scheduler=scheduler, filename=filename) logger.info('Model Saved at %s', filename) return def main(): model = config.model().to(device) datasets_generator = config.dataset(train_data_type=args.train_data_type, train_data_path=args.train_data_path, test_data_type=args.test_data_type, test_data_path=args.test_data_path, train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, num_of_workers=args.num_of_workers, poison_rate=args.poison_rate, perturb_type=args.perturb_type, patch_location=args.patch_location, perturb_tensor_filepath=args.perturb_tensor_filepath, seed=args.seed) logger.info('Training Dataset: %s' % str(datasets_generator.datasets['train_dataset'])) logger.info('Test Dataset: %s' % str(datasets_generator.datasets['test_dataset'])) if 'Poison' in args.train_data_type: with open(os.path.join(exp_path, 'poison_targets.npy'), 'wb') as f: if not (isinstance(datasets_generator.datasets['train_dataset'], dataset.MixUp) or isinstance(datasets_generator.datasets['train_dataset'], dataset.CutMix)): poison_targets = np.array(datasets_generator.datasets['train_dataset'].poison_samples_idx) np.save(f, poison_targets) logger.info(poison_targets) logger.info('Poisoned: %d/%d' % (len(poison_targets), len(datasets_generator.datasets['train_dataset']))) logger.info('Poisoned samples idx saved at %s' % (os.path.join(exp_path, 'poison_targets'))) logger.info('Poisoned Class %s' % (str(datasets_generator.datasets['train_dataset'].poison_class))) if args.train_portion == 1.0: data_loader = datasets_generator.getDataLoader() train_target = 'train_dataset' else: train_target = 'train_subset' data_loader = datasets_generator._split_validation_set(args.train_portion, train_shuffle=True, train_drop_last=True) logger.info("param size = %fMB", util.count_parameters_in_MB(model)) optimizer = config.optimizer(model.parameters()) scheduler = config.scheduler(optimizer) criterion = config.criterion() trainer = Trainer(criterion, data_loader, logger, config, target=train_target) evaluator = Evaluator(data_loader, logger, config) starting_epoch = 0 ENV = {'global_step': 0, 'best_acc': 0.0, 'curren_acc': 0.0, 'best_pgd_acc': 0.0, 'train_history': [], 'eval_history': [], 'pgd_eval_history': [], 'genotype_list': [], 'cm_history': []} if args.load_model: checkpoint = util.load_model(filename=checkpoint_path_file, model=model, optimizer=optimizer, alpha_optimizer=None, scheduler=scheduler) starting_epoch = checkpoint['epoch'] ENV = checkpoint['ENV'] trainer.global_step = ENV['global_step'] logger.info("File %s loaded!" % (checkpoint_path_file)) if args.data_parallel: model = torch.nn.DataParallel(model) if args.train: train(starting_epoch, model, optimizer, scheduler, criterion, trainer, evaluator, ENV, data_loader) if __name__ == '__main__': for arg in vars(args): logger.info("%s: %s" % (arg, getattr(args, arg))) start = time.time() main() end = time.time() cost = (end - start) / 86400 payload = "Running Cost %.2f Days \n" % cost logger.info(payload) ================================================ FILE: models/DenseNet.py ================================================ ''' https://github.com/kuangliu/pytorch-cifar DenseNet in PyTorch. ''' import math import torch import torch.nn as nn import torch.nn.functional as F class Bottleneck(nn.Module): def __init__(self, in_planes, growth_rate): super(Bottleneck, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.conv1 = nn.Conv2d(in_planes, 4*growth_rate, kernel_size=1, bias=False) self.bn2 = nn.BatchNorm2d(4*growth_rate) self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False) def forward(self, x): out = self.conv1(F.relu(self.bn1(x))) out = self.conv2(F.relu(self.bn2(out))) out = torch.cat([out, x], 1) return out class Transition(nn.Module): def __init__(self, in_planes, out_planes): super(Transition, self).__init__() self.bn = nn.BatchNorm2d(in_planes) self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False) def forward(self, x): out = self.conv(F.relu(self.bn(x))) out = F.avg_pool2d(out, 2) return out class DenseNet(nn.Module): def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10): super(DenseNet, self).__init__() self.growth_rate = growth_rate num_planes = 2*growth_rate self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False) self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0]) num_planes += nblocks[0]*growth_rate out_planes = int(math.floor(num_planes*reduction)) self.trans1 = Transition(num_planes, out_planes) num_planes = out_planes self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1]) num_planes += nblocks[1]*growth_rate out_planes = int(math.floor(num_planes*reduction)) self.trans2 = Transition(num_planes, out_planes) num_planes = out_planes self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2]) num_planes += nblocks[2]*growth_rate out_planes = int(math.floor(num_planes*reduction)) self.trans3 = Transition(num_planes, out_planes) num_planes = out_planes self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3]) num_planes += nblocks[3]*growth_rate self.bn = nn.BatchNorm2d(num_planes) self.linear = nn.Linear(num_planes, num_classes) def _make_dense_layers(self, block, in_planes, nblock): layers = [] for i in range(nblock): layers.append(block(in_planes, self.growth_rate)) in_planes += self.growth_rate return nn.Sequential(*layers) def forward(self, x): out = self.conv1(x) out = self.trans1(self.dense1(out)) out = self.trans2(self.dense2(out)) out = self.trans3(self.dense3(out)) out = self.dense4(out) out = F.avg_pool2d(F.relu(self.bn(out)), 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def DenseNet121(num_classes=10): return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=32, num_classes=num_classes) def DenseNet169(num_classes=10): return DenseNet(Bottleneck, [6, 12, 32, 32], growth_rate=32, num_classes=num_classes) def DenseNet201(num_classes=10): return DenseNet(Bottleneck, [6, 12, 48, 32], growth_rate=32, num_classes=num_classes) def DenseNet161(num_classes=10): return DenseNet(Bottleneck, [6, 12, 36, 24], growth_rate=48, num_classes=num_classes) def densenet_cifar(): return DenseNet(Bottleneck, [6, 12, 24, 16], growth_rate=12) ================================================ FILE: models/ResNet.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import math class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, in_planes, planes, stride=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) out += self.shortcut(x) out = F.relu(out) return out class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=10): super(ResNet, self).__init__() self.in_planes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) self.linear = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def ResNet18(num_classes=10): return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes) def ResNet34(num_classes=10): return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes) def ResNet50(num_classes=10): return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes) def ResNet101(num_classes=10): return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes) def ResNet152(num_classes=10): return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes) def test(): net = ResNet18() y = net(torch.randn(1, 3, 32, 32)) print(y.size()) class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super().__init__() self.conv = nn.Conv2d( in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False ) self.bn = nn.BatchNorm2d( out_planes, eps=0.001, momentum=0.1, affine=True ) self.relu = nn.ReLU(inplace=False) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Block35(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.branch2 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block17(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(896, 128, kernel_size=1, stride=1), BasicConv2d(128, 128, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(128, 128, kernel_size=(7, 1), stride=1, padding=(3, 0)) ) self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block8(nn.Module): def __init__(self, scale=1.0, noReLU=False): super().__init__() self.scale = scale self.noReLU = noReLU self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1792, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=(1, 3), stride=1, padding=(0, 1)), BasicConv2d(192, 192, kernel_size=(3, 1), stride=1, padding=(1, 0)) ) self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1) if not self.noReLU: self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x if not self.noReLU: out = self.relu(out) return out class Mixed_6a(nn.Module): def __init__(self): super().__init__() self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(256, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1), BasicConv2d(192, 256, kernel_size=3, stride=2) ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Mixed_7a(nn.Module): def __init__(self): super().__init__() self.branch0 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2) ) self.branch1 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch2 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch3 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class InceptionResnetV1(nn.Module): def __init__(self, num_classes=10575, face_features=512, dropout_prob=0.6): super().__init__() self.num_classes = num_classes # Define layers self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) self.maxpool_3a = nn.MaxPool2d(3, stride=2) self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2) self.repeat_1 = nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), ) self.mixed_6a = Mixed_6a() self.repeat_2 = nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), ) self.mixed_7a = Mixed_7a() self.repeat_3 = nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), ) self.block8 = Block8(noReLU=True) self.avgpool_1a = nn.AdaptiveAvgPool2d(1) self.dropout = nn.Dropout(dropout_prob) self.last_linear = nn.Linear(1792, face_features, bias=False) self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True) self.fc = nn.Linear(512, self.num_classes) def forward(self, x): x = self.conv2d_1a(x) x = self.conv2d_2a(x) x = self.conv2d_2b(x) x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) x = self.conv2d_4b(x) x = self.repeat_1(x) x = self.mixed_6a(x) x = self.repeat_2(x) x = self.mixed_7a(x) x = self.repeat_3(x) x = self.block8(x) x = self.avgpool_1a(x) x = self.dropout(x) x = self.last_linear(x.view(x.shape[0], -1)) x = self.last_bn(x) if self.training: return self.fc(x) else: return F.normalize(x, p=2, dim=1) ================================================ FILE: models/ToyModel.py ================================================ import torch.nn as nn import torch.nn.functional as F class ConvBrunch(nn.Module): def __init__(self, in_planes, out_planes, kernel_size=3): super(ConvBrunch, self).__init__() padding = (kernel_size - 1) // 2 self.out_conv = nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, padding=padding), nn.BatchNorm2d(out_planes), nn.ReLU()) def forward(self, x): return self.out_conv(x) class ToyModel(nn.Module): def __init__(self, num_classes=10): super(ToyModel, self).__init__() self.block1 = nn.Sequential( ConvBrunch(3, 64, 3), nn.MaxPool2d(kernel_size=2, stride=2), ConvBrunch(64, 128, 3), nn.MaxPool2d(kernel_size=2, stride=2), ConvBrunch(128, 256, 3), nn.MaxPool2d(kernel_size=2, stride=2)) self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(256, num_classes) def forward(self, x): x = self.block1(x) x = self.global_avg_pool(x) x = x.view(-1, 256) x = self.fc(x) return x ================================================ FILE: models/__init__.py ================================================ import mlconfig import torch import torch.nn as nn import torchvision from . import DenseNet, ResNet, ToyModel, inception_resnet_v1 mlconfig.register(torch.optim.SGD) mlconfig.register(torch.optim.Adam) mlconfig.register(torch.optim.lr_scheduler.MultiStepLR) mlconfig.register(torch.optim.lr_scheduler.CosineAnnealingLR) mlconfig.register(torch.optim.lr_scheduler.StepLR) mlconfig.register(torch.optim.lr_scheduler.ExponentialLR) mlconfig.register(torch.nn.CrossEntropyLoss) # Models mlconfig.register(ResNet.ResNet) mlconfig.register(ResNet.ResNet18) mlconfig.register(ResNet.ResNet34) mlconfig.register(ResNet.ResNet50) mlconfig.register(ResNet.ResNet101) mlconfig.register(ResNet.ResNet152) mlconfig.register(ToyModel.ToyModel) mlconfig.register(DenseNet.DenseNet121) mlconfig.register(inception_resnet_v1.InceptionResnetV1) # torchvision models mlconfig.register(torchvision.models.resnet18) mlconfig.register(torchvision.models.resnet50) mlconfig.register(torchvision.models.densenet121) # CUDA Options if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') @mlconfig.register class FocalLoss(nn.Module): def __init__(self, gamma=0, eps=1e-7): super(FocalLoss, self).__init__() self.gamma = gamma self.eps = eps self.ce = torch.nn.CrossEntropyLoss() def forward(self, input, target): logp = self.ce(input, target) p = torch.exp(-logp) loss = (1 - p) ** self.gamma * logp return loss.mean() def cross_entropy(input, target, size_average=True): """ Cross entropy that accepts soft targets Args: pred: predictions for neural network targets: targets, can be soft size_average: if false, sum is returned instead of mean Examples:: input = torch.FloatTensor([[1.1, 2.8, 1.3], [1.1, 2.1, 4.8]]) input = torch.autograd.Variable(out, requires_grad=True) target = torch.FloatTensor([[0.05, 0.9, 0.05], [0.05, 0.05, 0.9]]) target = torch.autograd.Variable(y1) loss = cross_entropy(input, target) loss.backward() """ logsoftmax = torch.nn.LogSoftmax(dim=1) if size_average: return torch.mean(torch.sum(-target * logsoftmax(input), dim=1)) else: return torch.sum(torch.sum(-target * logsoftmax(input), dim=1)) @mlconfig.register class CutMixCrossEntropyLoss(torch.nn.Module): def __init__(self, size_average=True): super().__init__() self.size_average = size_average def forward(self, input, target): if len(target.size()) == 1: target = torch.nn.functional.one_hot(target, num_classes=input.size(-1)) target = target.float().cuda() return cross_entropy(input, target, self.size_average) ================================================ FILE: models/download.py ================================================ import hashlib import os import shutil import sys import tempfile from urllib.request import urlopen, Request try: from tqdm.auto import tqdm # automatically select proper tqdm submodule if available except ImportError: try: from tqdm import tqdm except ImportError: # fake tqdm if it's not installed class tqdm(object): # type: ignore def __init__(self, total=None, disable=False, unit=None, unit_scale=None, unit_divisor=None): self.total = total self.disable = disable self.n = 0 # ignore unit, unit_scale, unit_divisor; they're just for real tqdm def update(self, n): if self.disable: return self.n += n if self.total is None: sys.stderr.write("\r{0:.1f} bytes".format(self.n)) else: sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(self.total))) sys.stderr.flush() def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): if self.disable: return sys.stderr.write('\n') def download_url_to_file(url, dst, hash_prefix=None, progress=True): r"""Download object at the given URL to a local path. Args: url (string): URL of the object to download dst (string): Full path where object will be saved, e.g. `/tmp/temporary_file` hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with `hash_prefix`. Default: None progress (bool, optional): whether or not to display a progress bar to stderr Default: True Example: >>> torch.hub.download_url_to_file('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth', '/tmp/temporary_file') """ file_size = None # We use a different API for python2 since urllib(2) doesn't recognize the CA # certificates in older Python req = Request(url, headers={"User-Agent": "torch.hub"}) u = urlopen(req) meta = u.info() if hasattr(meta, 'getheaders'): content_length = meta.getheaders("Content-Length") else: content_length = meta.get_all("Content-Length") if content_length is not None and len(content_length) > 0: file_size = int(content_length[0]) # We deliberately save it in a temp file and move it after # download is complete. This prevents a local working checkpoint # being overridden by a broken download. dst = os.path.expanduser(dst) dst_dir = os.path.dirname(dst) f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir) try: if hash_prefix is not None: sha256 = hashlib.sha256() with tqdm(total=file_size, disable=not progress, unit='B', unit_scale=True, unit_divisor=1024) as pbar: while True: buffer = u.read(8192) if len(buffer) == 0: break f.write(buffer) if hash_prefix is not None: sha256.update(buffer) pbar.update(len(buffer)) f.close() if hash_prefix is not None: digest = sha256.hexdigest() if digest[:len(hash_prefix)] != hash_prefix: raise RuntimeError('invalid hash value (expected "{}", got "{}")' .format(hash_prefix, digest)) shutil.move(f.name, dst) finally: f.close() if os.path.exists(f.name): os.remove(f.name) ================================================ FILE: models/inception_resnet_v1.py ================================================ import torch from torch import nn from torch.nn import functional as F from .download import download_url_to_file if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super().__init__() self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) self.bn = nn.BatchNorm2d(out_planes, eps=0.001, momentum=0.1, affine=True) self.relu = nn.ReLU(inplace=False) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Block35(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.branch2 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block17(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(896, 128, kernel_size=1, stride=1), BasicConv2d(128, 128, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(128, 128, kernel_size=(7, 1), stride=1, padding=(3, 0)) ) self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block8(nn.Module): def __init__(self, scale=1.0, noReLU=False): super().__init__() self.scale = scale self.noReLU = noReLU self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1792, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=(1, 3), stride=1, padding=(0, 1)), BasicConv2d(192, 192, kernel_size=(3, 1), stride=1, padding=(1, 0)) ) self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1) if not self.noReLU: self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x if not self.noReLU: out = self.relu(out) return out class Mixed_6a(nn.Module): def __init__(self): super().__init__() self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(256, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1), BasicConv2d(192, 256, kernel_size=3, stride=2) ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Mixed_7a(nn.Module): def __init__(self): super().__init__() self.branch0 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2) ) self.branch1 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch2 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch3 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class InceptionResnetV1(nn.Module): """Inception Resnet V1 model with optional loading of pretrained weights. Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface datasets. Pretrained state_dicts are automatically downloaded on model instantiation if requested and cached in the torch cache. Subsequent instantiations use the cache rather than redownloading. Keyword Arguments: pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'. (default: {None}) classify {bool} -- Whether the model should output classification probabilities or feature embeddings. (default: {False}) num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not equal to that used for the pretrained model, the final linear layer will be randomly initialized. (default: {None}) dropout_prob {float} -- Dropout probability. (default: {0.6}) """ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6): super().__init__() # Set simple attributes self.pretrained = pretrained self.classify = classify self.num_classes = num_classes if pretrained == 'vggface2': tmp_classes = 8631 elif pretrained == 'casia-webface': tmp_classes = 10575 elif pretrained is None and self.classify and self.num_classes is None: raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified') # Define layers self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) self.maxpool_3a = nn.MaxPool2d(3, stride=2) self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2) self.repeat_1 = nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), ) self.mixed_6a = Mixed_6a() self.repeat_2 = nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), ) self.mixed_7a = Mixed_7a() self.repeat_3 = nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), ) self.block8 = Block8(noReLU=True) self.avgpool_1a = nn.AdaptiveAvgPool2d(1) self.dropout = nn.Dropout(dropout_prob) self.last_linear = nn.Linear(1792, 512, bias=False) self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True) self.logits = nn.Linear(512, self.num_classes) if pretrained is not None: self.logits = nn.Linear(512, tmp_classes) load_weights(self, pretrained) def forward(self, x): """Calculate embeddings or logits given a batch of input image tensors. Arguments: x {torch.tensor} -- Batch of image tensors representing faces. Returns: torch.tensor -- Batch of embedding vectors or multinomial logits. """ x = self.conv2d_1a(x) x = self.conv2d_2a(x) x = self.conv2d_2b(x) x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) x = self.conv2d_4b(x) x = self.repeat_1(x) x = self.mixed_6a(x) x = self.repeat_2(x) x = self.mixed_7a(x) x = self.repeat_3(x) x = self.block8(x) x = self.avgpool_1a(x) x = self.dropout(x) x = self.last_linear(x.view(x.shape[0], -1)) x = self.last_bn(x) if self.training or self.classify: x = self.logits(x) else: x = F.normalize(x, p=2, dim=1) return x def load_weights(mdl, name): """Download pretrained state_dict and load into model. Arguments: mdl {torch.nn.Module} -- Pytorch model. name {str} -- Name of dataset that was used to generate pretrained state_dict. Raises: ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'. """ if name == 'vggface2': path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt' elif name == 'casia-webface': path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt' else: raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"') model_dir = 'pretrained_checkpoints' os.makedirs(model_dir, exist_ok=True) cached_file = os.path.join(model_dir, os.path.basename(path)) if not os.path.exists(cached_file): download_url_to_file(path, cached_file) state_dict = torch.load(cached_file) mdl.load_state_dict(state_dict) def get_torch_home(): torch_home = os.path.expanduser( os.getenv( 'TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch') ) ) return torch_home ================================================ FILE: perturbation.py ================================================ import argparse import collections import datetime import os import shutil import time import dataset import mlconfig import toolbox import torch import util import madrys import numpy as np from evaluator import Evaluator from tqdm import tqdm from trainer import Trainer mlconfig.register(madrys.MadrysLoss) # General Options parser = argparse.ArgumentParser(description='ClasswiseNoise') parser.add_argument('--seed', type=int, default=0, help='seed') parser.add_argument('--version', type=str, default="resnet18") parser.add_argument('--exp_name', type=str, default="test_exp") parser.add_argument('--config_path', type=str, default='configs/cifar10') parser.add_argument('--load_model', action='store_true', default=False) parser.add_argument('--data_parallel', action='store_true', default=False) # Datasets Options parser.add_argument('--train_batch_size', default=512, type=int, help='perturb step size') parser.add_argument('--eval_batch_size', default=512, type=int, help='perturb step size') parser.add_argument('--num_of_workers', default=8, type=int, help='workers for loader') parser.add_argument('--train_data_type', type=str, default='CIFAR10') parser.add_argument('--train_data_path', type=str, default='../datasets') parser.add_argument('--test_data_type', type=str, default='CIFAR10') parser.add_argument('--test_data_path', type=str, default='../datasets') # Perturbation Options parser.add_argument('--universal_train_portion', default=0.2, type=float) parser.add_argument('--universal_stop_error', default=0.5, type=float) parser.add_argument('--universal_train_target', default='train_subset', type=str) parser.add_argument('--train_step', default=10, type=int) parser.add_argument('--use_subset', action='store_true', default=False) parser.add_argument('--attack_type', default='min-min', type=str, choices=['min-min', 'min-max', 'random'], help='Attack type') parser.add_argument('--perturb_type', default='classwise', type=str, choices=['classwise', 'samplewise'], help='Perturb type') parser.add_argument('--patch_location', default='center', type=str, choices=['center', 'random'], help='Location of the noise') parser.add_argument('--noise_shape', default=[10, 3, 32, 32], nargs='+', type=int, help='noise shape') parser.add_argument('--epsilon', default=8, type=float, help='perturbation') parser.add_argument('--num_steps', default=1, type=int, help='perturb number of steps') parser.add_argument('--step_size', default=0.8, type=float, help='perturb step size') parser.add_argument('--random_start', action='store_true', default=False) args = parser.parse_args() # Convert Eps args.epsilon = args.epsilon / 255 args.step_size = args.step_size / 255 # Set up Experiments if args.exp_name == '': args.exp_name = 'exp_' + datetime.datetime.now() exp_path = os.path.join(args.exp_name, args.version) log_file_path = os.path.join(exp_path, args.version) checkpoint_path = os.path.join(exp_path, 'checkpoints') checkpoint_path_file = os.path.join(checkpoint_path, args.version) util.build_dirs(exp_path) util.build_dirs(checkpoint_path) logger = util.setup_logger(name=args.version, log_file=log_file_path + ".log") # CUDA Options logger.info("PyTorch Version: %s" % (torch.__version__)) if torch.cuda.is_available(): torch.cuda.manual_seed(args.seed) torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True device = torch.device('cuda') device_list = [torch.cuda.get_device_name(i) for i in range(0, torch.cuda.device_count())] logger.info("GPU List: %s" % (device_list)) else: device = torch.device('cpu') # Load Exp Configs config_file = os.path.join(args.config_path, args.version)+'.yaml' config = mlconfig.load(config_file) config.set_immutable() for key in config: logger.info("%s: %s" % (key, config[key])) shutil.copyfile(config_file, os.path.join(exp_path, args.version+'.yaml')) def train(starting_epoch, model, optimizer, scheduler, criterion, trainer, evaluator, ENV, data_loader): for epoch in range(starting_epoch, config.epochs): logger.info("") logger.info("="*20 + "Training Epoch %d" % (epoch) + "="*20) # Train ENV['global_step'] = trainer.train(epoch, model, criterion, optimizer) ENV['train_history'].append(trainer.acc_meters.avg*100) scheduler.step() # Eval logger.info("="*20 + "Eval Epoch %d" % (epoch) + "="*20) evaluator.eval(epoch, model) payload = ('Eval Loss:%.4f\tEval acc: %.2f' % (evaluator.loss_meters.avg, evaluator.acc_meters.avg*100)) logger.info(payload) ENV['eval_history'].append(evaluator.acc_meters.avg*100) ENV['curren_acc'] = evaluator.acc_meters.avg*100 # Reset Stats trainer._reset_stats() evaluator._reset_stats() # Save Model target_model = model.module if args.data_parallel else model util.save_model(ENV=ENV, epoch=epoch, model=target_model, optimizer=optimizer, scheduler=scheduler, filename=checkpoint_path_file) logger.info('Model Saved at %s', checkpoint_path_file) return def universal_perturbation_eval(noise_generator, random_noise, data_loader, model, eval_target=args.universal_train_target): loss_meter = util.AverageMeter() err_meter = util.AverageMeter() random_noise = random_noise.to(device) model = model.to(device) for i, (images, labels) in enumerate(data_loader[eval_target]): images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True) if random_noise is not None: for i in range(len(labels)): class_index = labels[i].item() noise = random_noise[class_index] mask_cord, class_noise = noise_generator._patch_noise_extend_to_img(noise, image_size=images[i].shape, patch_location=args.patch_location) images[i] += class_noise pred = model(images) err = (pred.data.max(1)[1] != labels.data).float().sum() loss = torch.nn.CrossEntropyLoss()(pred, labels) loss_meter.update(loss.item(), len(labels)) err_meter.update(err / len(labels)) return loss_meter.avg, err_meter.avg def universal_perturbation(noise_generator, trainer, evaluator, model, criterion, optimizer, scheduler, random_noise, ENV): # Class-Wise perturbation # Generate Data loader datasets_generator = dataset.DatasetGenerator(train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, train_data_type=args.train_data_type, train_data_path=args.train_data_path, test_data_type=args.test_data_type, test_data_path=args.test_data_path, num_of_workers=args.num_of_workers, seed=args.seed, no_train_augments=True) if args.use_subset: data_loader = datasets_generator._split_validation_set(train_portion=args.universal_train_portion, train_shuffle=True, train_drop_last=True) else: data_loader = datasets_generator.getDataLoader(train_shuffle=True, train_drop_last=True) condition = True data_iter = iter(data_loader['train_dataset']) logger.info('=' * 20 + 'Searching Universal Perturbation' + '=' * 20) if hasattr(model, 'classify'): model.classify = True while condition: if args.attack_type == 'min-min' and not args.load_model: # Train Batch for min-min noise for j in range(0, args.train_step): try: (images, labels) = next(data_iter) except: data_iter = iter(data_loader['train_dataset']) (images, labels) = next(data_iter) images, labels = images.to(device), labels.to(device) # Add Class-wise Noise to each sample train_imgs = [] for i, (image, label) in enumerate(zip(images, labels)): noise = random_noise[label.item()] mask_cord, class_noise = noise_generator._patch_noise_extend_to_img(noise, image_size=image.shape, patch_location=args.patch_location) train_imgs.append(images[i]+class_noise) # Train model.train() for param in model.parameters(): param.requires_grad = True trainer.train_batch(torch.stack(train_imgs).to(device), labels, model, optimizer) for i, (images, labels) in tqdm(enumerate(data_loader[args.universal_train_target]), total=len(data_loader[args.universal_train_target])): images, labels, model = images.to(device), labels.to(device), model.to(device) # Add Class-wise Noise to each sample batch_noise, mask_cord_list = [], [] for i, (image, label) in enumerate(zip(images, labels)): noise = random_noise[label.item()] mask_cord, class_noise = noise_generator._patch_noise_extend_to_img(noise, image_size=image.shape, patch_location=args.patch_location) batch_noise.append(class_noise) mask_cord_list.append(mask_cord) # Update universal perturbation model.eval() for param in model.parameters(): param.requires_grad = False batch_noise = torch.stack(batch_noise).to(device) if args.attack_type == 'min-min': perturb_img, eta = noise_generator.min_min_attack(images, labels, model, optimizer, criterion, random_noise=batch_noise) elif args.attack_type == 'min-max': perturb_img, eta = noise_generator.min_max_attack(images, labels, model, optimizer, criterion, random_noise=batch_noise) else: raise('Invalid attack') class_noise_eta = collections.defaultdict(list) for i in range(len(eta)): x1, x2, y1, y2 = mask_cord_list[i] delta = eta[i][:, x1: x2, y1: y2] class_noise_eta[labels[i].item()].append(delta.detach().cpu()) for key in class_noise_eta: delta = torch.stack(class_noise_eta[key]).mean(dim=0) - random_noise[key] class_noise = random_noise[key] class_noise += delta random_noise[key] = torch.clamp(class_noise, -args.epsilon, args.epsilon) # Eval termination conditions loss_avg, error_rate = universal_perturbation_eval(noise_generator, random_noise, data_loader, model, eval_target=args.universal_train_target) logger.info('Loss: {:.4f} Acc: {:.2f}%'.format(loss_avg, 100 - error_rate*100)) random_noise = random_noise.detach() ENV['random_noise'] = random_noise if args.attack_type == 'min-min': condition = error_rate > args.universal_stop_error elif args.attack_type == 'min-max': condition = error_rate < args.universal_stop_error return random_noise def samplewise_perturbation_eval(random_noise, data_loader, model, eval_target='train_dataset', mask_cord_list=[]): loss_meter = util.AverageMeter() err_meter = util.AverageMeter() # random_noise = random_noise.to(device) model = model.to(device) idx = 0 for i, (images, labels) in enumerate(data_loader[eval_target]): images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True) if random_noise is not None: for i, (image, label) in enumerate(zip(images, labels)): if not torch.is_tensor(random_noise): sample_noise = torch.tensor(random_noise[idx]).to(device) else: sample_noise = random_noise[idx].to(device) c, h, w = image.shape[0], image.shape[1], image.shape[2] mask = np.zeros((c, h, w), np.float32) x1, x2, y1, y2 = mask_cord_list[idx] mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() sample_noise = torch.from_numpy(mask).to(device) images[i] = images[i] + sample_noise idx += 1 pred = model(images) err = (pred.data.max(1)[1] != labels.data).float().sum() loss = torch.nn.CrossEntropyLoss()(pred, labels) loss_meter.update(loss.item(), len(labels)) err_meter.update(err / len(labels)) return loss_meter.avg, err_meter.avg def sample_wise_perturbation(noise_generator, trainer, evaluator, model, criterion, optimizer, scheduler, random_noise, ENV): datasets_generator = dataset.DatasetGenerator(train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, train_data_type=args.train_data_type, train_data_path=args.train_data_path, test_data_type=args.test_data_type, test_data_path=args.test_data_path, num_of_workers=args.num_of_workers, seed=args.seed, no_train_augments=True) if args.train_data_type == 'ImageNetMini' and args.perturb_type == 'samplewise': data_loader = datasets_generator._split_validation_set(0.2, train_shuffle=False, train_drop_last=False) data_loader['train_dataset'] = data_loader['train_subset'] else: data_loader = datasets_generator.getDataLoader(train_shuffle=False, train_drop_last=False) mask_cord_list = [] idx = 0 for images, labels in data_loader['train_dataset']: for i, (image, label) in enumerate(zip(images, labels)): noise = random_noise[idx] mask_cord, _ = noise_generator._patch_noise_extend_to_img(noise, image_size=image.shape, patch_location=args.patch_location) mask_cord_list.append(mask_cord) idx += 1 condition = True train_idx = 0 data_iter = iter(data_loader['train_dataset']) logger.info('=' * 20 + 'Searching Samplewise Perturbation' + '=' * 20) while condition: if args.attack_type == 'min-min' and not args.load_model: # Train Batch for min-min noise for j in tqdm(range(0, args.train_step), total=args.train_step): try: (images, labels) = next(data_iter) except: train_idx = 0 data_iter = iter(data_loader['train_dataset']) (images, labels) = next(data_iter) images, labels = images.to(device), labels.to(device) # Add Sample-wise Noise to each sample for i, (image, label) in enumerate(zip(images, labels)): sample_noise = random_noise[train_idx] c, h, w = image.shape[0], image.shape[1], image.shape[2] mask = np.zeros((c, h, w), np.float32) x1, x2, y1, y2 = mask_cord_list[train_idx] if type(sample_noise) is np.ndarray: mask[:, x1: x2, y1: y2] = sample_noise else: mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() # mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() sample_noise = torch.from_numpy(mask).to(device) images[i] = images[i] + sample_noise train_idx += 1 model.train() for param in model.parameters(): param.requires_grad = True trainer.train_batch(images, labels, model, optimizer) # Search For Noise idx = 0 for i, (images, labels) in tqdm(enumerate(data_loader['train_dataset']), total=len(data_loader['train_dataset'])): images, labels, model = images.to(device), labels.to(device), model.to(device) # Add Sample-wise Noise to each sample batch_noise, batch_start_idx = [], idx for i, (image, label) in enumerate(zip(images, labels)): sample_noise = random_noise[idx] c, h, w = image.shape[0], image.shape[1], image.shape[2] mask = np.zeros((c, h, w), np.float32) x1, x2, y1, y2 = mask_cord_list[idx] if type(sample_noise) is np.ndarray: mask[:, x1: x2, y1: y2] = sample_noise else: mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() # mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() sample_noise = torch.from_numpy(mask).to(device) batch_noise.append(sample_noise) idx += 1 # Update sample-wise perturbation model.eval() for param in model.parameters(): param.requires_grad = False batch_noise = torch.stack(batch_noise).to(device) if args.attack_type == 'min-min': perturb_img, eta = noise_generator.min_min_attack(images, labels, model, optimizer, criterion, random_noise=batch_noise) elif args.attack_type == 'min-max': perturb_img, eta = noise_generator.min_max_attack(images, labels, model, optimizer, criterion, random_noise=batch_noise) else: raise('Invalid attack') for i, delta in enumerate(eta): x1, x2, y1, y2 = mask_cord_list[batch_start_idx+i] delta = delta[:, x1: x2, y1: y2] if torch.is_tensor(random_noise): random_noise[batch_start_idx+i] = delta.detach().cpu().clone() else: random_noise[batch_start_idx+i] = delta.detach().cpu().numpy() # Eval termination conditions loss_avg, error_rate = samplewise_perturbation_eval(random_noise, data_loader, model, eval_target='train_dataset', mask_cord_list=mask_cord_list) logger.info('Loss: {:.4f} Acc: {:.2f}%'.format(loss_avg, 100 - error_rate*100)) if torch.is_tensor(random_noise): random_noise = random_noise.detach() ENV['random_noise'] = random_noise if args.attack_type == 'min-min': condition = error_rate > args.universal_stop_error elif args.attack_type == 'min-max': condition = error_rate < args.universal_stop_error # Update Random Noise to shape if torch.is_tensor(random_noise): new_random_noise = [] for idx in range(len(random_noise)): sample_noise = random_noise[idx] c, h, w = image.shape[0], image.shape[1], image.shape[2] mask = np.zeros((c, h, w), np.float32) x1, x2, y1, y2 = mask_cord_list[idx] mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy() new_random_noise.append(torch.from_numpy(mask)) new_random_noise = torch.stack(new_random_noise) return new_random_noise else: return random_noise def main(): # Setup ENV datasets_generator = dataset.DatasetGenerator(train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, train_data_type=args.train_data_type, train_data_path=args.train_data_path, test_data_type=args.test_data_type, test_data_path=args.test_data_path, num_of_workers=args.num_of_workers, seed=args.seed) data_loader = datasets_generator.getDataLoader() model = config.model().to(device) logger.info("param size = %fMB", util.count_parameters_in_MB(model)) optimizer = config.optimizer(model.parameters()) scheduler = config.scheduler(optimizer) criterion = config.criterion() if args.perturb_type == 'samplewise': train_target = 'train_dataset' else: if args.use_subset: data_loader = datasets_generator._split_validation_set(train_portion=args.universal_train_portion, train_shuffle=True, train_drop_last=True) train_target = 'train_subset' else: data_loader = datasets_generator.getDataLoader(train_shuffle=True, train_drop_last=True) train_target = 'train_dataset' trainer = Trainer(criterion, data_loader, logger, config, target=train_target) evaluator = Evaluator(data_loader, logger, config) ENV = {'global_step': 0, 'best_acc': 0.0, 'curren_acc': 0.0, 'best_pgd_acc': 0.0, 'train_history': [], 'eval_history': [], 'pgd_eval_history': [], 'genotype_list': []} if args.data_parallel: model = torch.nn.DataParallel(model) if args.load_model: checkpoint = util.load_model(filename=checkpoint_path_file, model=model, optimizer=optimizer, alpha_optimizer=None, scheduler=scheduler) ENV = checkpoint['ENV'] trainer.global_step = ENV['global_step'] logger.info("File %s loaded!" % (checkpoint_path_file)) noise_generator = toolbox.PerturbationTool(epsilon=args.epsilon, num_steps=args.num_steps, step_size=args.step_size) if args.attack_type == 'random': noise = noise_generator.random_noise(noise_shape=args.noise_shape) torch.save(noise, os.path.join(args.exp_name, 'perturbation.pt')) logger.info(noise) logger.info(noise.shape) logger.info('Noise saved at %s' % (os.path.join(args.exp_name, 'perturbation.pt'))) elif args.attack_type == 'min-min' or args.attack_type == 'min-max': if args.attack_type == 'min-max': # min-max noise need model to converge first train(0, model, optimizer, scheduler, criterion, trainer, evaluator, ENV, data_loader) if args.random_start: random_noise = noise_generator.random_noise(noise_shape=args.noise_shape) else: random_noise = torch.zeros(*args.noise_shape) if args.perturb_type == 'samplewise': noise = sample_wise_perturbation(noise_generator, trainer, evaluator, model, criterion, optimizer, scheduler, random_noise, ENV) elif args.perturb_type == 'classwise': noise = universal_perturbation(noise_generator, trainer, evaluator, model, criterion, optimizer, scheduler, random_noise, ENV) torch.save(noise, os.path.join(args.exp_name, 'perturbation.pt')) logger.info(noise) logger.info(noise.shape) logger.info('Noise saved at %s' % (os.path.join(args.exp_name, 'perturbation.pt'))) else: raise('Not implemented yet') return if __name__ == '__main__': for arg in vars(args): logger.info("%s: %s" % (arg, getattr(args, arg))) start = time.time() main() end = time.time() cost = (end - start) / 86400 payload = "Running Cost %.2f Days \n" % cost logger.info(payload) ================================================ FILE: requirements.txt ================================================ torch torchvision mlconfig ================================================ FILE: scripts/cifar10/min-max-noise/classwise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-max export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.8 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/min-max-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_path python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10/min-max-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10/min-max-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name rm -rf $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-max-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-max-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-max export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export universal_stop_error=0.9 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/min-max-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_path python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/cifar10/min-max-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # # Submit Adv Training # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10/min-max-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-max-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-min-noise/classwise-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.01 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/min-min-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10/min-min-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" "resnet18_augmentation" "resnet18_add-uniform-noise" "resnet18_classpoison" "resnet18_add-uniform-noise-aug" "resnet18_cutout" "resnet18_cutmix" "resnet18_mixup" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.1 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10/min-min-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-min-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-min-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/min-min-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/cifar10/min-min-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" "resnet18_augmentation" "resnet18_add-uniform-noise" "resnet18_classpoison" "resnet18_add-uniform-noise-aug" "resnet18_cutout" "resnet18_cutmix" "resnet18_mixup" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.1 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done echo resnet18-madrys-1.0-${exp_args} sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path echo resnet18-madrys-0.0-${exp_args} sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10/min-min-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/min-min-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/random-noise/classwise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=random export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.9 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/random-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/cifar10/random-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # echo ${attack_type}-${perturb_type}-resnet18-madrys-1.0 # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 \ # --job-name ${attack_type}-${perturb_type}-resnet18-madrys-1.0 \ # train.slurm resnet18_madrys 1.0 $scripts_path # # echo ${attack_type}-${perturb_type}-resnet18-madrys-0.0 # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 \ # --job-name ${attack_type}-${perturb_type}-resnet18-madrys-0.0 \ # train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10/random-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/random-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/random-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=random export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export universal_stop_error=0.9 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar10/random-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_path python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/cifar10/random-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # # Submit Adv Training # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10/random-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10/random-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-2/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-2noise export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-2 ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-2/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-2/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_add-uniform-noise-aug" # "resnet18_classpoison" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-2/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-2/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=16/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=1 export universal_stop_error=0.01 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-eps=16 ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=16/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=16/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 8:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=16/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=16/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=24/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=24 export step_size=2.4 export num_steps=1 export universal_stop_error=0.01 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-eps=24 ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=24/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=24/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=24/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-eps=24/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch16/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=16 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch16/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch16/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch16/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch16/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch24/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=24 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch24/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch24/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch24/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch24/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch8/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=8 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch8/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 10 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch8/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch8/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-random-patch8/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-transfer-tiny-imagenet/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export exp_args=TinyImageNet-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-transfer-tiny-imagenet ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-transfer-tiny-imagenet/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" "resnet18_augmentation" "resnet18_add-uniform-noise" "resnet18_classpoison" # "resnet18_classpoison_targeted" ) # Poison Rates declare -a poison_rate_arr=( 1.0 0.8 0.6 0.4 0.2 0.0 ) echo $scripts_path # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-transfer-tiny-imagenet/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/classwise-noise-transfer-tiny-imagenet/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training pwd cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=16/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-eps=16 ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=16/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --random_start ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=16/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=16/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=16/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=24/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=24 export step_size=2.4 export num_steps=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-eps=24 ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=24/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --random_start ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=24/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # Submit Adv Training for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path done # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=24/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-eps=24/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch16/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=16 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch16/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch16/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch16/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch16/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch24/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=24 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch24/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch24/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch24/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch24/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch8/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.1 export universal_train_target='train_subset' export patch_location='random' export patch_size=8 export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version}-${patch_location}${patch_size} export exp_path=experiments/cifar10-extension/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar10-extension/${attack_type}-noise/${perturb_type}-noise-${patch_location}-patch${patch_size} ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch8/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --noise_shape 50000 3 $patch_size $patch_size \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --patch_location $patch_location \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch8/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" # "resnet50" # "dense121" "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" # "resnet18_classpoison" # "resnet18_add-uniform-noise-aug" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 # 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch8/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar10-extension/min-min-noise/samplewise-noise-random-patch8/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar100/min-min-noise/classwise-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/cifar100 export dataset_type=CIFAR100 export poison_dataset_type=PoisonCIFAR100 export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.01 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar100/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar100/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar100/min-min-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --noise_shape 100 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/cifar100/min-min-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 8:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/cifar100/min-min-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar100/min-min-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar100/min-min-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar100 export dataset_type=CIFAR100 export poison_dataset_type=PoisonCIFAR100 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export train_step=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar100/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/cifar100/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/cifar100/min-min-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --noise_shape 50000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --train_step $train_step \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/cifar100/min-min-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 8:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/cifar100/min-min-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar100/min-min-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/cifar101/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/cifar10 export dataset_type=CIFAR10 export poison_dataset_type=PoisonCIFAR10 export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/cifar10/${attack_type}_${perturb_type}/${exp_args} ================================================ FILE: scripts/cifar101/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=1.0 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name experiments/cifar101_transfer \ --config_path configs/cifar101 \ --train_data_type PoisonCIFAR101 \ --test_data_type PoisonCIFAR101 \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/face/min-min-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/face export dataset_path=../datasets/casia-112x112-protected-train export test_dataset_path=../datasets/casia-112x112-protected-val export dataset_type=WebFace export poison_dataset_type=WebFace export base_version=InceptionResnet export attack_type=min-min export perturb_type=classwise export epsilon=16 export step_size=1.6 export num_steps=1 export train_step=30 export universal_stop_error=0.1 export universal_train_target='train_dataset' export exp_path=experiments/face export scripts_path=scripts/face ================================================ FILE: scripts/face/min-min-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files exp_name=${exp_path}/search_noise echo $exp_name # Search Universal Perturbation and build datasets cd ../../../ # rm -rf $exp_name pwd python3 perturbation.py --config_path $config_path \ --exp_name $exp_name \ --version $base_version \ --train_data_type WebFace \ --test_data_type WebFace \ --train_data_path /home/lemonbear/DriveN/data/face-search \ --test_data_path /home/lemonbear/DriveN/data/face-search \ --noise_shape 150 3 112 112 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --train_step $train_step \ --train_batch_size 32 \ --eval_batch_size 32 \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/face/min-min-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path} echo $exp_name # Poison Training cd ../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 64 \ --eval_batch_size 64 \ --train --data_parallel --train_face ================================================ FILE: scripts/face/min-min-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="WebFace" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=8 #SBATCH --mem=32G #SBATCH --time 72:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 scripts_path=$2 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path} echo $exp_name # Poison Training cd ../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 64 \ --eval_batch_size 64 \ --train --data_parallel ================================================ FILE: scripts/face/min-min-noise/train_clean.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 exp_name=${exp_path}/clean_train echo $exp_name cd ../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 512 \ --eval_batch_size 512 \ --train --train_face --data_parallel ================================================ FILE: scripts/face/min-min-noise/train_clean.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="WebFace-Clean" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=8 #SBATCH --mem=32G #SBATCH --time 168:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 scripts_path=$2 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 exp_name=${exp_path}/clean_train echo $exp_name cd ../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 512 \ --eval_batch_size 512 \ --train --train_face --data_parallel ================================================ FILE: scripts/face/min-min-noise/train_protected.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 exp_name=${exp_path}/protected_train echo $exp_name cd ../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path ../datasets/casia-112x112-protected \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 512 \ --eval_batch_size 512 \ --train --train_face --data_parallel ================================================ FILE: scripts/face/min-min-noise/train_protected.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="WebFace-Protected" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=8 #SBATCH --mem=32G #SBATCH --time 168:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 scripts_path=$2 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 exp_name=${exp_path}/protected_train echo $exp_name cd ../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --test_data_path $test_dataset_path \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --train_batch_size 512 \ --eval_batch_size 512 \ --train --train_face --data_parallel ================================================ FILE: scripts/imagenet-mini/min-min-noise/classwise-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/imagenet-mini export dataset_path=../datasets/ILSVRC2012 export dataset_type=ImageNetMini export poison_dataset_type=PoisonImageNetMini export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=1 export train_step=100 export universal_stop_error=0.1 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/imagenet-mini/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/imagenet-mini/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/imagenet-mini/min-min-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_path $dataset_path \ --train_data_type $dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --noise_shape 100 3 224 224 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --train_step $train_step \ --train_batch_size 32 \ --eval_batch_size 32 \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/imagenet-mini/min-min-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 48:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done ================================================ FILE: scripts/imagenet-mini/min-min-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --train_data_type $poison_dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/imagenet-mini/min-min-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=24 #SBATCH --mem=32G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise # Load EXP Setting cd $scripts_path source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --train_data_type $poison_dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train_batch_size 64 --eval_batch_size 64 \ --num_of_workers 24 \ --train ================================================ FILE: scripts/imagenet-mini/min-min-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/imagenet-mini export dataset_path=../datasets/ILSVRC2012 export dataset_type=ImageNetMini export poison_dataset_type=PoisonImageNetMini export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=20 export train_step=100 export universal_stop_error=0.1 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/imagenet-mini/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/imagenet-mini/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/imagenet-mini/min-min-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_path $dataset_path \ --train_data_type $dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --noise_shape 25879 3 224 224 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --train_step $train_step \ --train_batch_size 32 \ --eval_batch_size 32 \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/imagenet-mini/min-min-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 48:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done ================================================ FILE: scripts/imagenet-mini/min-min-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --train_data_type $poison_dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train --train_portion 0.2 ================================================ FILE: scripts/imagenet-mini/min-min-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=24 #SBATCH --mem=64G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_path $dataset_path \ --train_data_type $poison_dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train_batch_size 64 --eval_batch_size 64 \ --num_of_workers 24 \ --train --train_portion 0.2 ================================================ FILE: scripts/svhn/min-min-noise/classwise-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/svhn export dataset_type=SVHN export poison_dataset_type=PoisonSVHN export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=1 export universal_stop_error=0.01 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/svhn/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/svhn/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/svhn/min-min-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --noise_shape 10 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: scripts/svhn/min-min-noise/classwise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" # "resnet18_add-uniform-noise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=${attack_type}-${perturb_type}-$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 4:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=${attack_type}-${perturb_type}-$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done ================================================ FILE: scripts/svhn/min-min-noise/classwise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/svhn/min-min-noise/classwise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/svhn/min-min-noise/samplewise-noise/exp_setting.sh ================================================ #!/bin/bash # Exp Setting export config_path=configs/svhn export dataset_type=SVHN export poison_dataset_type=PoisonSVHN export attack_type=min-min export perturb_type=samplewise export base_version=resnet18 export epsilon=8 export step_size=0.8 export num_steps=20 export universal_stop_error=0.01 export universal_train_target='train_dataset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/svhn/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/svhn/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/svhn/min-min-noise/samplewise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_type $dataset_type \ --test_data_type $dataset_type \ --noise_shape 73257 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ ================================================ FILE: scripts/svhn/min-min-noise/samplewise-noise/submit.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Target Models declare -a type_arr=( "resnet18" "resnet50" "dense121" # "resnet18_augmentation" # "resnet18_denoise" ) # Poison Rates declare -a poison_rate_arr=( 1.0 # 0.8 # 0.6 # 0.4 # 0.2 0.0 ) # Submit Jobs for model_name in "${type_arr[@]}" do for poison_rate in "${poison_rate_arr[@]}" do job_name=$exp_args-${model_name}-${poison_rate} echo $job_name sbatch --partition gpgpu --gres=gpu:1 --time 3:00:00 --job-name $job_name train.slurm $model_name $poison_rate $scripts_path done done # # Submit Adv Training # for poison_rate in "${poison_rate_arr[@]}" # do # job_name=$exp_args-resnet18_madrys-${poison_rate} # echo $job_name # sbatch --partition gpgpu --gres=gpu:1 --time 12:00:00 --job-name $job_name train.slurm resnet18_madrys $poison_rate $scripts_path # done # echo resnet18-madrys-1.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-1.0 train.slurm resnet18_madrys 1.0 $scripts_path # echo resnet18-madrys-0.0-${exp_args} # sbatch --partition gpgpu --gres=gpu:1 --time 24:00:00 --job-name ${exp_args}-resnet18-madrys-0.0 train.slurm resnet18_madrys 0.0 $scripts_path ================================================ FILE: scripts/svhn/min-min-noise/samplewise-noise/train.sh ================================================ #!/bin/bash # Load EXP Setting source exp_setting.sh # Training Setting model_name=$1 poison_rate=$2 exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/svhn/min-min-noise/samplewise-noise/train.slurm ================================================ #!/bin/bash #SBATCH --nodes 1 #SBATCH --job-name="c100-universal" #SBATCH --output=slurm-%A-%x.out #SBATCH --account="punim0784" #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=4G #SBATCH --time 4:00:00 #SBATCH --mail-type=ALL #SBATCH --mail-user=pineappleorcas@gmail.com # check that the script is launched with sbatch if [ "x$SLURM_JOB_ID" == "x" ]; then echo "You need to submit your job to the queuing system with sbatch" exit 1 fi # Training Setting model_name=$1 poison_rate=$2 scripts_path=$3 # Run the job from this directory: cd /data/gpfs/projects/punim0784/min-min-noise cd $scripts_path # Load EXP Setting source exp_setting.sh exp_name=${exp_path}/poison_train_${poison_rate} echo $exp_name # Poison Training cd ../../../../ rm -rf ${exp_name}/${model_name} python3 -u main.py --version $model_name \ --exp_name $exp_name \ --config_path $config_path \ --train_data_type $poison_dataset_type \ --test_data_type $dataset_type \ --poison_rate $poison_rate \ --perturb_type $perturb_type \ --perturb_tensor_filepath ${exp_path}/perturbation.pt \ --train ================================================ FILE: scripts/tiny-imagenet/min-min-noise/classwise-noise/exp_setting.sh ================================================ #!/usr/bin/env bash # Exp Setting export config_path=configs/tiny-imagenet export dataset_path=../datasets/ILSVRC2012 export dataset_type=TinyImageNet export poison_dataset_type=PoisonImageNetMini export attack_type=min-min export perturb_type=classwise export base_version=resnet18 export epsilon=16 export step_size=1.6 export num_steps=1 export train_step=250 export universal_stop_error=0.1 export universal_train_target='train_subset' export exp_args=${dataset_type}-eps=${epsilon}-se=${universal_stop_error}-base_version=${base_version} export exp_path=experiments/tiny-imagenet/${attack_type}_${perturb_type}/${exp_args} export scripts_path=scripts/tiny-imagenet/${attack_type}-noise/${perturb_type}-noise ================================================ FILE: scripts/tiny-imagenet/min-min-noise/classwise-noise/search_perturbation_noise.sh ================================================ #!/bin/bash # Load Exp Settings source exp_setting.sh # Remove previous files echo $exp_path # Search Universal Perturbation and build datasets cd ../../../../ pwd rm -rf $exp_name python3 perturbation.py --config_path $config_path \ --exp_name $exp_path \ --version $base_version \ --train_data_path $dataset_path \ --train_data_type $dataset_type \ --test_data_path $dataset_path \ --test_data_type $dataset_type \ --noise_shape 1000 3 32 32 \ --epsilon $epsilon \ --num_steps $num_steps \ --step_size $step_size \ --attack_type $attack_type \ --perturb_type $perturb_type \ --train_step $train_step \ --train_batch_size 32 \ --eval_batch_size 32 \ --universal_train_target $universal_train_target\ --universal_stop_error $universal_stop_error\ --use_subset ================================================ FILE: toolbox.py ================================================ import numpy as np import torch from torch.autograd import Variable if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') class PerturbationTool(): def __init__(self, seed=0, epsilon=0.03137254901, num_steps=20, step_size=0.00784313725): self.epsilon = epsilon self.num_steps = num_steps self.step_size = step_size self.seed = seed np.random.seed(seed) def random_noise(self, noise_shape=[10, 3, 32, 32]): random_noise = torch.FloatTensor(*noise_shape).uniform_(-self.epsilon, self.epsilon).to(device) return random_noise def min_min_attack(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False): if random_noise is None: random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device) perturb_img = Variable(images.data + random_noise, requires_grad=True) perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True) eta = random_noise for _ in range(self.num_steps): opt = torch.optim.SGD([perturb_img], lr=1e-3) opt.zero_grad() model.zero_grad() if isinstance(criterion, torch.nn.CrossEntropyLoss): if hasattr(model, 'classify'): model.classify = True logits = model(perturb_img) loss = criterion(logits, labels) else: logits, loss = criterion(model, perturb_img, labels, optimizer) perturb_img.retain_grad() loss.backward() eta = self.step_size * perturb_img.grad.data.sign() * (-1) perturb_img = Variable(perturb_img.data + eta, requires_grad=True) eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon) perturb_img = Variable(images.data + eta, requires_grad=True) perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True) return perturb_img, eta def min_max_attack(self, images, labels, model, optimizer, criterion, random_noise=None, sample_wise=False): if random_noise is None: random_noise = torch.FloatTensor(*images.shape).uniform_(-self.epsilon, self.epsilon).to(device) perturb_img = Variable(images.data + random_noise, requires_grad=True) perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True) eta = random_noise for _ in range(self.num_steps): opt = torch.optim.SGD([perturb_img], lr=1e-3) opt.zero_grad() model.zero_grad() if isinstance(criterion, torch.nn.CrossEntropyLoss): logits = model(perturb_img) loss = criterion(logits, labels) else: logits, loss = criterion(model, perturb_img, labels, optimizer) loss.backward() eta = self.step_size * perturb_img.grad.data.sign() perturb_img = Variable(perturb_img.data + eta, requires_grad=True) eta = torch.clamp(perturb_img.data - images.data, -self.epsilon, self.epsilon) perturb_img = Variable(images.data + eta, requires_grad=True) perturb_img = Variable(torch.clamp(perturb_img, 0, 1), requires_grad=True) return perturb_img, eta def _patch_noise_extend_to_img(self, noise, image_size=[3, 32, 32], patch_location='center'): c, h, w = image_size[0], image_size[1], image_size[2] mask = np.zeros((c, h, w), np.float32) x_len, y_len = noise.shape[1], noise.shape[1] if patch_location == 'center' or (h == w == x_len == y_len): x = h // 2 y = w // 2 elif patch_location == 'random': x = np.random.randint(x_len // 2, w - x_len // 2) y = np.random.randint(y_len // 2, h - y_len // 2) else: raise('Invalid patch location') x1 = np.clip(x - x_len // 2, 0, h) x2 = np.clip(x + x_len // 2, 0, h) y1 = np.clip(y - y_len // 2, 0, w) y2 = np.clip(y + y_len // 2, 0, w) if type(noise) is np.ndarray: pass else: mask[:, x1: x2, y1: y2] = noise.cpu().numpy() return ((x1, x2, y1, y2), torch.from_numpy(mask).to(device)) ================================================ FILE: trainer.py ================================================ import time import models import torch import util if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') class Trainer(): def __init__(self, criterion, data_loader, logger, config, global_step=0, target='train_dataset'): self.criterion = criterion self.data_loader = data_loader self.logger = logger self.config = config self.log_frequency = config.log_frequency if config.log_frequency is not None else 100 self.loss_meters = util.AverageMeter() self.acc_meters = util.AverageMeter() self.acc5_meters = util.AverageMeter() self.global_step = global_step self.target = target print(self.target) def _reset_stats(self): self.loss_meters = util.AverageMeter() self.acc_meters = util.AverageMeter() self.acc5_meters = util.AverageMeter() def train(self, epoch, model, criterion, optimizer, random_noise=None): model.train() for i, (images, labels) in enumerate(self.data_loader[self.target]): images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True) if random_noise is not None: random_noise = random_noise.detach().to(device) for i in range(len(labels)): class_index = labels[i].item() images[i] += random_noise[class_index].clone() images[i] = torch.clamp(images[i], 0, 1) start = time.time() log_payload = self.train_batch(images, labels, model, optimizer) end = time.time() time_used = end - start if self.global_step % self.log_frequency == 0: display = util.log_display(epoch=epoch, global_step=self.global_step, time_elapse=time_used, **log_payload) self.logger.info(display) self.global_step += 1 return self.global_step def train_batch(self, images, labels, model, optimizer): model.zero_grad() optimizer.zero_grad() if isinstance(self.criterion, torch.nn.CrossEntropyLoss) or isinstance(self.criterion, models.CutMixCrossEntropyLoss): logits = model(images) loss = self.criterion(logits, labels) else: logits, loss = self.criterion(model, images, labels, optimizer) if isinstance(self.criterion, models.CutMixCrossEntropyLoss): _, labels = torch.max(labels.data, 1) loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), self.config.grad_clip) optimizer.step() if logits.shape[1] >= 5: acc, acc5 = util.accuracy(logits, labels, topk=(1, 5)) acc, acc5 = acc.item(), acc5.item() else: acc, = util.accuracy(logits, labels, topk=(1,)) acc, acc5 = acc.item(), 1 self.loss_meters.update(loss.item(), labels.shape[0]) self.acc_meters.update(acc, labels.shape[0]) self.acc5_meters.update(acc5, labels.shape[0]) payload = {"acc": acc, "acc_avg": self.acc_meters.avg, "loss": loss, "loss_avg": self.loss_meters.avg, "lr": optimizer.param_groups[0]['lr'], "|gn|": grad_norm} return payload ================================================ FILE: util.py ================================================ import logging import os import numpy as np import torch if torch.cuda.is_available(): torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True device = torch.device('cuda') else: device = torch.device('cpu') def _patch_noise_extend_to_img(noise, image_size=[3, 32, 32], patch_location='center'): c, h, w = image_size[0], image_size[1], image_size[2] mask = np.zeros((c, h, w), np.float32) x_len, y_len = noise.shape[1], noise.shape[2] if patch_location == 'center' or (h == w == x_len == y_len): x = h // 2 y = w // 2 elif patch_location == 'random': x = np.random.randint(x_len // 2, w - x_len // 2) y = np.random.randint(y_len // 2, h - y_len // 2) else: raise('Invalid patch location') x1 = np.clip(x - x_len // 2, 0, h) x2 = np.clip(x + x_len // 2, 0, h) y1 = np.clip(y - y_len // 2, 0, w) y2 = np.clip(y + y_len // 2, 0, w) mask[:, x1: x2, y1: y2] = noise return mask def setup_logger(name, log_file, level=logging.INFO): """To setup as many loggers as you want""" formatter = logging.Formatter('%(asctime)s %(message)s') console_handler = logging.StreamHandler() console_handler.setFormatter(formatter) file_handler = logging.FileHandler(log_file) file_handler.setFormatter(formatter) logger = logging.getLogger(name) logger.setLevel(level) logger.addHandler(file_handler) logger.addHandler(console_handler) return logger def log_display(epoch, global_step, time_elapse, **kwargs): display = 'epoch=' + str(epoch) + \ '\tglobal_step=' + str(global_step) for key, value in kwargs.items(): if type(value) == str: display = '\t' + key + '=' + value else: display += '\t' + str(key) + '=%.4f' % value display += '\ttime=%.2fit/s' % (1. / time_elapse) return display def accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(1/batch_size)) return res def save_model(filename, epoch, model, optimizer, scheduler, save_best=False, **kwargs): # Torch Save State Dict state = { 'epoch': epoch+1, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'scheduler_state_dict': scheduler.state_dict() if scheduler is not None else None } for key, value in kwargs.items(): state[key] = value torch.save(state, filename + '.pth') filename += '_best.pth' if save_best: torch.save(state, filename) return def load_model(filename, model, optimizer, scheduler, **kwargs): # Load Torch State Dict filename = filename + '.pth' checkpoints = torch.load(filename, map_location=device) model.load_state_dict(checkpoints['model_state_dict']) if optimizer is not None and checkpoints['optimizer_state_dict'] is not None: optimizer.load_state_dict(checkpoints['optimizer_state_dict']) if scheduler is not None and checkpoints['scheduler_state_dict'] is not None: scheduler.load_state_dict(checkpoints['scheduler_state_dict']) return checkpoints def count_parameters_in_MB(model): return sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary_head" not in name)/1e6 def build_dirs(path): if not os.path.exists(path): os.makedirs(path) return class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 self.max = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count self.max = max(self.max, val) def onehot(size, target): vec = torch.zeros(size, dtype=torch.float32) vec[target] = 1. return vec def rand_bbox(size, lam): if len(size) == 4: W = size[2] H = size[3] elif len(size) == 3: W = size[1] H = size[2] else: raise Exception cut_rat = np.sqrt(1. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) # uniform cx = np.random.randint(W) cy = np.random.randint(H) bbx1 = np.clip(cx - cut_w // 2, 0, W) bby1 = np.clip(cy - cut_h // 2, 0, H) bbx2 = np.clip(cx + cut_w // 2, 0, W) bby2 = np.clip(cy + cut_h // 2, 0, H) return bbx1, bby1, bbx2, bby2