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dihcl.py
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# Copyright 2020-present, Tianyi Zhou.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import print_function
import argparse
import os
import sys
import time
import shutil
import random
import copy
import math
import numpy as np
from numpy.testing import assert_array_almost_equal
from PIL import Image, ImageEnhance, ImageOps
from progress.bar import Bar as Bar
from sklearn.metrics import pairwise_distances
# from utils import CIFAR10PolicyAll, RandAugment
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torch.utils.data.sampler as sampler
import torchvision.transforms as transforms
import torchvision.datasets as datasets
# import models.cifar as models
# model_names = sorted(name for name in models.__dict__
# if not name.startswith("__")
# and callable(models.__dict__[name]))
parser = argparse.ArgumentParser(description='Curriculum Learning by Dynamic Instance Hardness (DIHCL)')
parser.add_argument('-d', '--dataset', type=str, default='CIFAR10')
# choices=['CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST', 'SVHN', 'STL10'])
parser.add_argument('-spath', '--save_path', default='result', type=str, metavar='PATH',
help='path to save results')
parser.add_argument('-dpath', '--data_path', default='../data', type=str, metavar='PATH',
help='path to dataset directory')
parser.add_argument('--trialID', default='00/', type=str, metavar='PATH',
help='path to specific trial')
# Optimization parameters
parser.add_argument('--epochs', default=300, type=int, metavar='N',
help='total epochs to run')
parser.add_argument('--schedule', type=int, nargs='+', default=[0, 5, 10, 15, 20, 30, 40, 60, 90, 140, 210, 300],
help='splitting points (epoch number) for multiple episodes of training')
parser.add_argument('--selfsupervise_cut_epoch', default=200, type=int,
help='epoch to stop self-supervision and centrality max')
parser.add_argument('--explore_cut_episode', default=5, type=int,
help='episode to stop update features, centrality, and increasing initial learning rate')
parser.add_argument('--train-batch', default=128, type=int, metavar='N',
help='train batchsize')
parser.add_argument('--test-batch', default=1024, type=int, metavar='N',
help='test batchsize')
parser.add_argument('--lr', '--learning-rate', default=2.0e-1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--lr_min', default=5.0e-4, type=float,
metavar='LR', help='ending learning rate of each episode')
parser.add_argument('--lr_min_decay', default=0.8, type=float,
help='decay factor for ending learning rate after each episode')
parser.add_argument('--lr_max', default=1.0e-1, type=float,
metavar='LR', help='starting learning rate')
parser.add_argument('--lr_max_decay', default=0.9, type=float,
help='decay factor applied to starting learning rate after each episode')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--alpha', default=0.5, type=float,
help='beta distribution parameter for Mix-Up')
parser.add_argument('--alpha_rate', default=1.1, type=float,
help='rate for mixup alpha')
# Neural Nets Architecture
parser.add_argument('--arch', '-a', metavar='ARCH', default='wrn')
parser.add_argument('--depth', type=int, default=28, help='Model depth.')
parser.add_argument('--repeat', type=int, default=3, help='number of blocks (WideResNet)')
parser.add_argument('--widen-factor', type=int, default=10, help='Widen factor. 4 -> 64, 8 -> 128, ...')
parser.add_argument('--drop', '--dropout', default=0.3, type=float, metavar='Dropout', help='Dropout rate')
# GPU and CPU
parser.add_argument('--gpu-id', default='0', type=str, help='id(s) for CUDA_VISIBLE_DEVICES')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--random_state', default=32, type=int, help='random state/seed')
# DIHCL parameters
parser.add_argument('--k', default=1.0, type=float,
help='(initial) ratio for subset selected')
parser.add_argument('--dk', default=0.1, type=float,
help='increase rate of k')
parser.add_argument('--mk', default=0.3, type=float,
help='maximum/minimum of k')
parser.add_argument('--select_ratio', default=0.5, type=float,
help='(initial) ratio of further selection by centrality (when use_centrality=True)')
parser.add_argument('--select_ratio_rate', default=1.1, type=float,
help='multiplication factor applied to select_ratio for each episode')
parser.add_argument('--tmpt', default=20., type=float,
help='1/temperature used in updating EXP3 probability')
parser.add_argument('--tmpt_rate', default=0.8, type=float,
help='rate of temperature')
parser.add_argument('--mod', default=0.05, type=float,
help='(initial) weight of DIH (when use_centrality=True, since the weight for centrality is 1 - mod)')
parser.add_argument('--mod_rate', default=1.5, type=float,
help='multiplcation factor applied to mod every episode')
parser.add_argument('--ema_decay', default=0.9, type=float,
help='decay factor of exponential moving average')
parser.add_argument('--consistency', default=0., type=float,
help='weight of consistency regularization (provided by mean teacher) in the objective')
parser.add_argument('--consistency_rate', default=0.9, type=float,
help='multiplcation factor applied to the consistency weight every episode')
parser.add_argument('--contrastive', default=0., type=float,
help='weight of contrastive regularization (dictionary provided by mean teacher) in the objective')
parser.add_argument('--contrastive_rate', default=0.9, type=float,
help='multiplcation factor applied to the contrastive weight every episode')
# choices of different settings
parser.add_argument('--use_curriculum', action='store_true',
help='if using curriculum learning or not')
parser.add_argument('--use_mean_teacher', action='store_true',
help='maintain a time moving average of the model (ensemble over time) as a teacher for consistency regularization')
parser.add_argument('--bandits_alg', default='EXP3', type=str,
help='which bandits alg to use: EXP3, UCB, TS (Thampson sampling)')
parser.add_argument('--use_loss_as_feedback', action='store_true',
help='set True if use loss as instant feedback, set False if use the change of loss between epochs (only applies to EXP3 and UCB in DIHCL, TS always takes the change in prediction correctness as instant feedback)')
parser.add_argument('--use_random_subsample', action='store_true',
help='random subsample a subset before running centrality max to further select samples')
parser.add_argument('--use_centrality', action='store_true',
help='use centrality score in rating samples for more diversity')
parser.add_argument('--use_kernel_centrality', action='store_true',
help='compute centrality defined on a kernel matrix, if False, compute centrality based on the penultimate-layer features')
parser.add_argument('--use_noisylabel', action='store_true',
help='use training samples with noisy labels')
parser.add_argument('--label_noise_type', default='symmetric', type=str,
help='which type of label noise when --use_noisylabel: symmetric, pairflip')
parser.add_argument('--label_noise_rate', default=0.6, type=float,
help='noise rate on labels when --use_noisylabel')
parser.add_argument('--num_aug', default=0, type=int,
help='number of extra augmentations used for test/inference')
parser.add_argument('--save_dynamics', action='store_true',
help='save training dynamics (require large memory)')
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
device_ids = list(map(int, args.gpu_id.split(',')))
device_ids = range(len(device_ids))
use_cuda = torch.cuda.is_available()
# Random seed
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
if use_cuda:
torch.cuda.manual_seed_all(args.manualSeed)
# name a folder by the trialID to store all the results
folder = args.trialID
def main():
global best_acc
if not os.path.isdir(args.save_path):
mkdir_p(args.save_path)
if not os.path.isdir(os.path.join(args.save_path, folder)):
mkdir_p(os.path.join(args.save_path, folder))
if not os.path.isdir(args.data_path):
mkdir_p(args.data_path)
# prepare datasets to train
print('==> Preparing dataset %s' % args.dataset)
if args.dataset == 'cifar10':
dataloader = datasets.CIFAR10
num_classes = 10
trans_mean = (0.4914, 0.4822, 0.4465)
trans_std = (0.2470, 0.2435, 0.2616)
cutout_size = 16
input_shape = (1, 3, 32, 32)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5),
CIFAR10Policy(),
# CIFAR10PolicyAll(),
# RandAugment(),
cutout(cutout_size, 1.0, False),
transforms.ToTensor(),
transforms.Normalize(trans_mean, trans_std),
])
elif args.dataset == 'cifar100':
dataloader = datasets.CIFAR100
num_classes = 100
trans_mean = (0.5071, 0.4865, 0.4409)
trans_std = (0.2673, 0.2564, 0.2762)
cutout_size = 16
input_shape = (1, 3, 32, 32)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5),
CIFAR10Policy(),
cutout(cutout_size, 1.0, False),
transforms.ToTensor(),
transforms.Normalize(trans_mean, trans_std),
])
elif args.dataset == 'svhn':
dataloader = datasets.SVHN
num_classes = 10
trans_mean = (0.5, 0.5, 0.5)
trans_std = (0.5, 0.5, 0.5)
cutout_size = 20
input_shape = (1, 3, 32, 32)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
cutout(cutout_size, 1.0, False),
transforms.ToTensor(),
transforms.Normalize(trans_mean, trans_std),
])
args.lr_max *= 0.1
args.lr_min *= 0.2
elif args.dataset == 'stl10':
dataloader = datasets.STL10
num_classes = 10
trans_mean = (0.5, 0.5, 0.5)
trans_std = (0.5, 0.5, 0.5)
cutout_size = 32
input_shape = (1, 3, 96, 96)
jitter_params = 0.4
light_params = 0.1
transform_train = transforms.Compose([
transforms.Pad(12),
transforms.RandomCrop(96),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=jitter_params, contrast=jitter_params, saturation=jitter_params, hue=0),
cutout(cutout_size, 1.0, False),
transforms.ToTensor(),
Lighting(light_params, _IMAGENET_PCA['eigval'], _IMAGENET_PCA['eigvec']),
transforms.Normalize(trans_mean, trans_std),
])
args.epochs *= 4
args.selfsupervise_cut_epoch *= 4
args.schedule = [sch*4 for sch in args.schedule]
print(args.epochs, args.schedule)
elif args.dataset == 'fmnist' or args.dataset == 'kmnist' or args.dataset == 'mnist':
if args.dataset == 'mnist':
dataloader = datasets.MNIST
trans_mean = np.array([0.1307])
trans_std = np.array([0.3081])
cutout_size = 8
elif args.dataset == 'fmnist':
dataloader = datasets.FashionMNIST
trans_mean = np.array([0.2860])
trans_std = np.array([0.3530])
cutout_size = 8
elif args.dataset == 'kmnist':
dataloader = datasets.KMNIST
trans_mean = np.array([0.1904])
trans_std = np.array([0.3475])
cutout_size = 14
num_classes = 10
input_shape = (1, 1, 28, 28)
args.lr_max *= 0.2
args.lr_min *= 0.2
transform_train = transforms.Compose([
# transforms.Pad(4),
# transforms.RandomCrop(28),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
# transforms.RandomRotation(10),
cutout(cutout_size, 1.0, False),
transforms.ToTensor(),
transforms.Normalize(trans_mean, trans_std),
])
transform_train2 = TransformTwice(transform_train, args.use_mean_teacher)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(trans_mean, trans_std),
])
if args.dataset == 'stl10' or args.dataset == 'svhn':
trainset = dataloader(root=args.data_path, split='train', download=True, transform=transform_train2)
testset = dataloader(root=args.data_path, split='test', download=False, transform=transform_test)
trainset0 = dataloader(root=args.data_path, split='train', download=True, transform=transform_test)
if args.num_aug > 0:
testset_aug = dataloader(root=args.data_path, split='test', download=False, transform=transform_train)
trainset_aug = dataloader(root=args.data_path, split='train', download=False, transform=transform_train)
n_train = len(trainset.labels)
else:
trainset = dataloader(root=args.data_path+'/'+args.dataset, train=True, download=True, transform=transform_train2)
testset = dataloader(root=args.data_path+'/'+args.dataset, train=False, download=False, transform=transform_test)
trainset0 = dataloader(root=args.data_path+'/'+args.dataset, train=True, download=True, transform=transform_test)
if args.num_aug > 0:
testset_aug = dataloader(root=args.data_path+'/'+args.dataset, train=False, download=False, transform=transform_train)
trainset_aug = dataloader(root=args.data_path+'/'+args.dataset, train=True, download=False, transform=transform_train)
n_train = len(trainset.targets)
target_copy = trainset.targets
if args.use_noisylabel:
# noisy label
true_target_copy = target_copy.copy()
target_copy, actual_noise_rate = noisify(train_labels=np.asarray(target_copy), noise_type=args.label_noise_type, noise_rate=args.label_noise_rate, random_state=args.random_state, nb_classes=num_classes)
target_copy = target_copy.tolist()
trainset.targets = target_copy
noise_or_not_label = np.transpose(target_copy)==np.transpose(true_target_copy)
if use_cuda:
target_copy = torch.cuda.LongTensor(target_copy)
else:
target_copy = torch.LongTensor(target_copy)
trainloader_val = data.DataLoader(trainset0, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
testloader = data.DataLoader(testset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
if args.num_aug > 0:
testloader_aug = data.DataLoader(testset_aug, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
trainloader_aug = data.DataLoader(trainset_aug, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
else:
testloader_aug = None
trainloader_aug = None
# Initialize Neural Nets Model
print("==> creating model '{}'".format(args.arch))
if args.dataset == 'fmnist' or args.dataset == 'kmnist' or args.dataset == 'mnist':
model = torch.nn.DataParallel(models.PreActResNet34_MNIST().cuda(), device_ids=device_ids)
if args.use_mean_teacher:
ema_model = torch.nn.DataParallel(models.PreActResNet34_MNIST().cuda(), device_ids=device_ids)
for param in ema_model.parameters():
param.detach_()
init_as_ema(ema_model, model)
else:
ema_model = None
else:
model = torch.nn.DataParallel(wrn(input_shape = input_shape, num_classes=num_classes, depth=args.depth,
widen_factor=args.widen_factor, repeat = args.repeat, dropRate=args.drop, bias=True).cuda(), device_ids=device_ids)
if args.use_mean_teacher:
ema_model = torch.nn.DataParallel(wrn(input_shape = input_shape, num_classes=num_classes, depth=args.depth,
widen_factor=args.widen_factor, repeat = args.repeat, dropRate=args.drop, bias=True).cuda(), device_ids=device_ids)
for param in ema_model.parameters():
param.detach_()
init_as_ema(ema_model, model)
else:
ema_model = None
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
# initialize log files
title = args.dataset + args.arch
logger = Logger(os.path.join(args.save_path, folder + 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
log_backup = open(os.path.join(args.save_path, folder + 'log_backup.txt'), 'w')
log_result = ()
# initialization of variables storing training results
best_acc = 0.0
schedule_idx = 0
train_sample_num = 0
batches_counter = 0
rand_train_set_old = None
batches_this_episode = np.ceil(float(n_train) / float(args.train_batch)) * (args.schedule[schedule_idx+1] - args.schedule[schedule_idx])
epoch_time = np.zeros(args.epochs)
# initialize DIH
acc_flips = np.zeros(n_train)
acc_diff_loss = np.zeros(n_train)
acc_diff_loss_raw = np.zeros(n_train)
acc_loss_raw = np.zeros(n_train)
acc_loss = np.zeros(n_train)
all_pace = np.zeros(n_train)
selects = np.zeros(n_train)
TS_A = 2.0*np.ones(n_train)
TS_B = 2.0*np.ones(n_train)
sigma_UCB = np.sqrt(2.0 * np.log(float(args.epochs)))
if args.save_dynamics:
select_dy_train = ()
correct_dy_train = ()
loss_dy_train = ()
pred_dy_train = ()
correct_dy_test = ()
loss_dy_test = ()
pred_dy_test = ()
# training epochs
for epoch in range(args.epochs):
epoch_start_time = time.time()
# At the begining of each episode, before training begins, we need to update some training parameters
if epoch == args.schedule[min(schedule_idx + 1, len(args.schedule) - 1)]:
batches_counter = 0
schedule_idx += 1
if schedule_idx < args.explore_cut_episode:
args.lr_max /= args.lr_max_decay
else:
args.lr_max *= args.lr_max_decay
args.lr_min *= args.lr_min_decay
args.consistency *= args.consistency_rate
if args.consistency < 1.e-2 or epoch >= args.selfsupervise_cut_epoch:
args.consistency = 0.
args.contrastive *= args.contrastive_rate
if args.contrastive < 1.e-3 or epoch >= args.selfsupervise_cut_epoch:
args.contrastive = 0.
if args.use_curriculum:
# update k0 (random subsampling budget)
args.k = max([args.k * (1.0-args.dk), args.mk])
args.select_ratio = min(args.select_ratio_rate * args.select_ratio, 1.0)
k0 = int(np.floor(args.k * n_train))
if args.use_centrality:
subset_size = int(args.select_ratio * k0)
args.mod *= args.mod_rate
batches_per_epoch = np.ceil(float(subset_size) / float(args.train_batch))
else:
batches_per_epoch = np.ceil(float(k0) / float(args.train_batch))
args.mod *= args.mod_rate
args.tmpt = max(1., args.tmpt_rate * args.tmpt)
args.alpha = min(1., args.alpha_rate * args.alpha)
# moving average for DIHCL
acc_flips *= args.ema_decay
acc_diff_loss *= args.ema_decay
all_pace *= args.ema_decay
acc_loss *= args.ema_decay
acc_diff_loss_raw *= args.ema_decay
acc_loss_raw *= args.ema_decay
TS_A *= args.ema_decay
TS_B *= args.ema_decay
# update the size of selected subset and the number of training batches in the current episode
batches_this_episode = batches_per_epoch * (args.schedule[schedule_idx+1] - args.schedule[schedule_idx])
# one-pass inference of all training samples
train_loss, train_acc, all_loss, all_correct, _, train_fea, train_pred, _ = test(trainloader_val, trainloader_aug, model if ema_model is None else ema_model, criterion, epoch, args.num_aug, use_cuda)
# compute instant feedback
all_loss_epoch = all_loss.cpu().numpy().astype(float)
diff_loss = np.minimum(np.abs(all_loss_epoch - all_loss_old), 10.0)
all_correct_epoch = all_correct.cpu().numpy().astype(float)
diff_flips = np.abs(all_correct_epoch - all_correct_old)
all_pred_epoch = train_pred.cpu().numpy()
# save dynamics
if args.save_dynamics:
loss_dy_train = loss_dy_train + (all_loss_epoch, )
correct_dy_train = correct_dy_train + (all_correct_epoch, )
pred_dy_train = pred_dy_train + (all_pred_epoch, )
# store the current feedback for next iteration comparison
all_loss_old = all_loss_epoch.copy()
all_correct_old = all_correct_epoch.copy()
all_pred_old = all_pred_epoch.copy()
# update DIH
acc_diff_loss_raw += diff_loss
acc_loss_raw += all_loss_epoch
if args.bandits_alg == 'EXP3':
acc_diff_loss += diff_loss / all_pace
acc_loss += all_loss_epoch / args.lr
acc_flips += diff_flips / all_pace
elif args.bandits_alg == 'UCB':
acc_diff_loss = acc_diff_loss_raw / selects
acc_diff_loss += 1.0e-3 * np.mean(acc_diff_loss) * sigma_UCB * np.sqrt(1.0 / selects)
acc_loss = acc_loss_raw / selects
acc_loss += 1.0e-3 * np.mean(acc_loss) * sigma_UCB * np.sqrt(1.0 / selects)
elif args.bandits_alg == 'TS':
TS_A += diff_flips
TS_B += 1.0 - diff_flips
all_pace = np.zeros(n_train)
# update the centrality of every training sample using pairwise similarity in the penultimate-layer feature space
# it measures how representative of each sample for the data distribution and encourages diversity in data selection
# it can be replaced by facility location function or other submodular functions if submodulax maximization is available
if args.use_centrality and epoch <= args.schedule[args.explore_cut_episode]:
modular_estimate = 1.0
centrality_estimate = 1.0
if args.use_kernel_centrality:
if epoch > args.schedule[1]:
del train_sims, centrality
train_sims, centrality = compute_sims(np.clip(train_fea, 1.0e-10, 1.0e+10), sigma = 20., metric = 'cos')
else:
train_fea, centrality = compute_feamat(np.clip(train_fea, 1.0e-10, 1.0e+10))
else:
batches_per_epoch = np.ceil(float(n_train) / float(args.train_batch))
batches_this_episode = batches_per_epoch * (args.schedule[schedule_idx+1] - args.schedule[schedule_idx])
# initialize mean teacher model by the current model before training begins
if args.use_mean_teacher:
if epoch == args.schedule[1]:
print('Initialize mean teacher!')
init_as_ema(ema_model, model)
else:
print('Re-initialize model by Ema!')
init_as_ema(model, ema_model, 1.e-2)
# warm starting episode (epochs from args.schedule[0] to args.schedule[1])
if epoch < args.schedule[1] or (not args.use_curriculum):
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
# one-pass training on the whole training set
old_lr = args.lr
indices = np.random.permutation(n_train)
train_sample_num += n_train
trainloader = data.DataLoader(Subset(trainset, indices), batch_size=args.train_batch, shuffle=False, num_workers=args.workers)
if epoch < args.schedule[1]:
train_loss, train_acc, all_loss_per_sample, all_correct_per_sample, all_pred_per_sample, _, _, batches_counter, pace_delta = train(trainloader, model, ema_model, criterion, optimizer, batches_counter, batches_this_episode, use_cuda, args.ema_decay, 0., 0., args.alpha, lr_schedule = 'linear')
else:
train_loss, train_acc, all_loss_per_sample, all_correct_per_sample, all_pred_per_sample, _, _, batches_counter, pace_delta = train(trainloader, model, ema_model, criterion, optimizer, batches_counter, batches_this_episode, use_cuda, args.ema_decay, 0., 0., args.alpha, lr_schedule = 'cosine')
# compute instant feedback
all_loss_epoch = np.zeros(n_train)
all_loss_epoch[indices] = all_loss_per_sample.cpu().numpy().astype(float)
if epoch > 0:
diff_loss = np.minimum(np.abs(all_loss_epoch - all_loss_old), 10.0)
all_correct_epoch = np.zeros(n_train)
all_correct_epoch[indices] = all_correct_per_sample.cpu().numpy().astype(float)
if epoch > 0:
diff_flips = np.abs(all_correct_epoch - all_correct_old)
all_pred_epoch = np.zeros(n_train)
all_pred_epoch[indices] = all_pred_per_sample.cpu().numpy()
# save dynamics
if args.save_dynamics:
loss_dy_train = loss_dy_train + (all_loss_epoch, )
correct_dy_train = correct_dy_train + (all_correct_epoch, )
pred_dy_train = pred_dy_train + (all_pred_epoch, )
# store the current feedback for next iteration comparison
all_loss_old = all_loss_epoch.copy()
all_correct_old = all_correct_epoch.copy()
all_pred_old = all_pred_epoch.copy()
# update DIH
selects += 1.0
if epoch > 0:
acc_diff_loss_raw += diff_loss
acc_loss_raw += all_loss_epoch
if args.bandits_alg == 'EXP3':
acc_diff_loss += diff_loss / all_pace
acc_loss += all_loss_epoch / old_lr
acc_flips += diff_flips / all_pace
elif args.bandits_alg == 'UCB':
acc_diff_loss = acc_diff_loss_raw / selects
acc_diff_loss += 1.0e-3 * np.mean(acc_diff_loss) * sigma_UCB * np.sqrt(1.0 / selects)
acc_loss = acc_loss_raw / selects
acc_loss += 1.0e-3 * np.mean(acc_loss) * sigma_UCB * np.sqrt(1.0 / selects)
elif args.bandits_alg == 'TS':
TS_A += diff_flips
TS_B += 1.0 - diff_flips
all_pace[indices] = 0.0
all_pace += pace_delta
# every episode except the warm starting episode (after epoch args.schedule[1]) selects a subset of data for training
if args.use_curriculum and epoch >= args.schedule[1]:
print('\nEpoch: [%d | %d] LR: %f, subset size k: %d' % (epoch + 1, args.epochs, state['lr'], k0))
# compute sampling probability from current DIH
acc_loss_mean = np.mean(acc_loss)
acc_diff_loss_mean = np.mean(acc_diff_loss)
if args.bandits_alg == 'TS':# TS in DIHCL
utility = np.random.beta(TS_A, TS_B)
else: # applies for EXP3 or UCB in DIHCL
if args.use_loss_as_feedback:
utility = acc_loss / acc_loss_mean
else:
utility = acc_diff_loss / acc_diff_loss_mean
rand_prob = args.tmpt * utility
rand_prob = np.exp(rand_prob - np.max(rand_prob))
rand_prob /= np.sum(rand_prob)
print('rand_prob min, max: ', rand_prob.min(), rand_prob.max())
# sampling a subset according to rand_prob
if args.use_centrality and epoch < args.selfsupervise_cut_epoch:
mod = args.mod * (centrality_estimate / modular_estimate)
if args.use_random_subsample:
rand_train_set = np.random.choice(n_train, k0, p = rand_prob, replace=False)
train_subset = np.argpartition(mod * rand_prob[rand_train_set] + centrality[rand_train_set], -subset_size)[-subset_size:]
rand_train_set = rand_train_set[train_subset]
else:
p = mod * rand_prob + centrality
rand_train_set = np.random.choice(n_train, subset_size, p = p / p.sum(), replace=False)
centrality_estimate, modular_estimate = centrality[rand_train_set].sum(), rand_prob[rand_train_set].sum()
else:
rand_train_set = np.random.choice(n_train, k0, p = rand_prob, replace=False)
# train model on the selected subset rand_train_set
train_sample_num += len(rand_train_set)
trainselectloader = data.DataLoader(trainset, batch_size=args.train_batch, sampler=SubsetSampler(rand_train_set), num_workers=args.workers)
if rand_train_set_old is not None:
print('new samples selected comparing to previous epoch:', len(np.setdiff1d(rand_train_set, rand_train_set_old, assume_unique=True)))
rand_train_set_old = rand_train_set.copy()
old_lr = args.lr
train_loss1, train_acc, loss_rest, correct_rest, pred_rest, _, logit_rest, batches_counter, pace_delta2 = train(trainselectloader, model, ema_model, criterion, optimizer, batches_counter, batches_this_episode, use_cuda, args.ema_decay, args.consistency, args.contrastive, args.alpha, lr_schedule = 'cosine')
if epoch > args.schedule[min(schedule_idx, len(args.schedule)-1)]:
train_loss = train_loss1
# compute instant feedback
all_loss_epoch = all_loss_old.copy()
loss_rest = loss_rest.cpu().numpy().astype(float)
all_loss_epoch[rand_train_set] = loss_rest
diff_loss = np.minimum(np.abs(all_loss_epoch[rand_train_set] - all_loss_old[rand_train_set]), 10.0)
all_correct_epoch = all_correct_old.copy()
all_correct_epoch[rand_train_set] = correct_rest.cpu().numpy().astype(float)
diff_flips = np.abs(all_correct_epoch[rand_train_set] - all_correct_old[rand_train_set])
all_pred_epoch = all_pred_old.copy()
all_pred_epoch[rand_train_set] = pred_rest.cpu().numpy()
# save dynamics
if args.save_dynamics:
loss_dy_train = loss_dy_train + (all_loss_epoch, )
correct_dy_train = correct_dy_train + (all_correct_epoch, )
pred_dy_train = pred_dy_train + (all_pred_epoch, )
# store the current feedback for next iteration comparison
all_loss_old = all_loss_epoch.copy()
all_correct_old = all_correct_epoch.copy()
all_pred_old = all_pred_epoch.copy()
# update DIH
selects[rand_train_set] += 1.0
acc_diff_loss_raw[rand_train_set] += diff_loss
acc_loss_raw[rand_train_set] += loss_rest
if epoch > args.schedule[schedule_idx]:
if args.bandits_alg == 'EXP3':
rc1 = rand_prob[rand_train_set] + 1.0e-8
rc = 1.e+4 * np.power(all_pace[rand_train_set], 1.e-2) * rc1
acc_diff_loss[rand_train_set] += diff_loss / rc
acc_loss[rand_train_set] += loss_rest / rc1
acc_flips[rand_train_set] += diff_flips / rc
elif args.bandits_alg == 'UCB':
acc_diff_loss[rand_train_set] = acc_diff_loss_raw[rand_train_set] / selects[rand_train_set]
acc_diff_loss[rand_train_set] += 1.0e-3 * acc_diff_loss_mean * sigma_UCB * np.sqrt(1.0 / selects[rand_train_set])
acc_loss[rand_train_set] = acc_loss_raw[rand_train_set] / selects[rand_train_set]
acc_loss[rand_train_set] += 1.0e-3 * acc_loss_mean * sigma_UCB * np.sqrt(1.0 / selects[rand_train_set])
elif args.bandits_alg == 'TS':
TS_A[rand_train_set] += diff_flips
TS_B[rand_train_set] += 1.0 - diff_flips
all_pace[rand_train_set] = 0.0
all_pace += pace_delta2
# inference on test set
epoch_time[epoch] = time.time() - epoch_start_time
test_loss, test_acc, all_loss_test, all_correct_test, _, test_fea, all_pred_test, _ = test(testloader, None, model, criterion, epoch, 0, use_cuda)
if args.use_mean_teacher:
ema_test_loss, ema_test_acc, _, _, _, _, _, _ = test(testloader, None, ema_model, criterion, epoch, 0, use_cuda)
if args.save_dynamics:
loss_dy_test = loss_dy_test + (all_loss_test.cpu().numpy().astype(float), )
correct_dy_test = correct_dy_test + (all_correct_test.cpu().numpy().astype(float), )
pred_dy_test = pred_dy_test + (all_pred_test.cpu().numpy(), )
select_dy_train = select_dy_train + (selects.copy(), )
torch.cuda.empty_cache()
# save results to log files
logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc])
if args.use_mean_teacher:
result_iter = np.asarray([epoch, train_loss, test_loss, train_acc, test_acc, ema_test_loss, ema_test_acc, epoch_time[epoch], train_sample_num])
else:
result_iter = np.asarray([epoch, train_loss, test_loss, train_acc, test_acc, epoch_time[epoch], train_sample_num])
log_result = log_result + (result_iter, )
log_backup.write("\t".join(map(str, result_iter))+'\n')
log_backup.flush()
best_acc = np.max([best_acc, test_acc, ema_test_acc])
# save result and log files
log_backup.close()
logger.close()
np.savetxt(os.path.join(args.save_path, folder + 'log_result.txt'), np.vstack(log_result))
if args.save_dynamics:
np.savez(os.path.join(args.save_path, folder + args.dataset + '_dy.npz'),
loss_train = np.stack(loss_dy_train), loss_test = np.stack(loss_dy_test),
correct_train = np.stack(correct_dy_train), correct_test = np.stack(correct_dy_test),
pred_train = np.stack(pred_dy_train), pred_test = np.stack(pred_dy_test),
select_train = np.stack(select_dy_train), time_train = epoch_time)
print('Best acc:', best_acc)
#--------------------------------DATA AUTOAUGMENTATION-------------------------------
class CIFAR10Policy(object):
""" Randomly choose one of the best 25 Sub-policies on CIFAR10.
Example:
>>> policy = CIFAR10Policy()
>>> transformed = policy(image)
Example as a PyTorch Transform:
>>> transform=transforms.Compose([
>>> transforms.Resize(256),
>>> CIFAR10Policy(),
>>> transforms.ToTensor()])
"""
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor),
SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def __repr__(self):
return "AutoAugment CIFAR10 Policy"
class SVHNPolicy(object):
""" Randomly choose one of the best 25 Sub-policies on SVHN.
Example:
>>> policy = SVHNPolicy()
>>> transformed = policy(image)
Example as a PyTorch Transform:
>>> transform=transforms.Compose([
>>> transforms.Resize(256),
>>> SVHNPolicy(),
>>> transforms.ToTensor()])
"""
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def __repr__(self):
return "AutoAugment SVHN Policy"
class SubPolicy(object):
def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
Subranges = {
"shearX": np.linspace(0, 0.3, 10),
"shearY": np.linspace(0, 0.3, 10),
"translateX": np.linspace(0, 150 / 331, 10),
"translateY": np.linspace(0, 150 / 331, 10),
"rotate": np.linspace(0, 30, 10),
"color": np.linspace(0.0, 0.9, 10),
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
"solarize": np.linspace(256, 0, 10),
"contrast": np.linspace(0.0, 0.9, 10),
"sharpness": np.linspace(0.0, 0.9, 10),
"brightness": np.linspace(0.0, 0.9, 10),
"autocontrast": [0] * 10,
"equalize": [0] * 10,
"invert": [0] * 10
}
# from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
def rotate_with_fill(img, magnitude):
rot = img.convert("RGBA").rotate(magnitude)
return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
Subfunc = {
"shearX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"shearY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"translateX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
fillcolor=fillcolor),
"translateY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
fillcolor=fillcolor),
"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
# "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])),
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
"equalize": lambda img, magnitude: ImageOps.equalize(img),
"invert": lambda img, magnitude: ImageOps.invert(img)
}
# self.name = "{}_{:.2f}_and_{}_{:.2f}".format(
# operation1, ranges[operation1][magnitude_idx1],
# operation2, ranges[operation2][magnitude_idx2])
self.p1 = p1
self.operation1 = Subfunc[operation1]
self.magnitude1 = Subranges[operation1][magnitude_idx1]
self.p2 = p2
self.operation2 = Subfunc[operation2]
self.magnitude2 = Subranges[operation2][magnitude_idx2]
def __call__(self, img):
if random.random() < self.p1: img = self.operation1(img, self.magnitude1)
if random.random() < self.p2: img = self.operation2(img, self.magnitude2)
return img
def mixup_data(x, y, alpha=1.0, use_cuda=True):
'''Returns mixed inputs, pairs of targets, and lambda'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
if use_cuda:
index = torch.randperm(batch_size).cuda()
else:
index = torch.randperm(batch_size)
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam, index
# -----------------------------DATASET and DATALOADER-----------------------------------
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,
supporting integer indexing in range from 0 to len(self) exclusive.
"""
def __getitem__(self, index):
raise NotImplementedError
def __len__(self):
raise NotImplementedError
def __add__(self, other):
return ConcatDataset([self, other])
class SubsetSampler(sampler.Sampler):
"""Samples elements randomly from a given list of indices, without replacement.
Arguments:
indices (list): a list of indices
"""
def __init__(self, indices):
self.indices = indices
def __iter__(self):
return (self.indices[i] for i in range(len(self.indices)))
def __len__(self):
return len(self.indices)
class Subset(Dataset):
def __init__(self, dataset, indices):
self.dataset = dataset
self.indices = indices
def __getitem__(self, idx):
return self.dataset[self.indices[idx]]
def __len__(self):
return len(self.indices)
#------------------------------Neural Network Structures (WideResNet)--------------------------------
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)#, momentum = 0.001
self.relu1 = nn.LeakyReLU(inplace=False)#, negative_slope = 0.02)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)#, momentum = 0.001
self.relu2 = nn.LeakyReLU(inplace=False)#, negative_slope = 0.02)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, bias=False) or None
def forward(self, x):
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
super(NetworkBlock, self).__init__()
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
layers = []
for i in range(nb_layers):
layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class Flatten(nn.Module):
def __init__(self, d):