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train_cifar10_ce.py
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train_cifar10_ce.py
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import os
import time
import math
import argparse
import numpy as np
import pandas as pd
import torch
import torch.optim as optim
from torchvision import datasets, transforms
from utils import train, test, get_pred
from dataset import DATASET_CUSTOM
from networks.wideresnet import Wide_ResNet
from augmentation.autoaugment import CIFAR10Policy
from augmentation.cutout import Cutout
def log(path, str):
print(str)
with open(path, 'a') as file:
file.write(str)
def main():
# Settings
parser = argparse.ArgumentParser(description='PyTorch CIFAR-10')
parser.add_argument('--batch_size', type=int, default=128, help='input batch size for training')
parser.add_argument('--epochs', type=int, default=200, help='number of epochs to train')
parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
parser.add_argument('--dp', type=float, default=0.2, help='dropout rate')
parser.add_argument('--aug', type=str, default='strong', help='Type of data augmentation {none, standard, strong}')
parser.add_argument('--noise_pattern', type=str, default='uniform', help='Noise pattern')
parser.add_argument('--noise_rate', type=float, default=0.2, help='Noise rate')
parser.add_argument('--val_size', type=int, default=5000, help='size of (noisy) validation set')
parser.add_argument('--save_model', action='store_true', default=False, help='For Saving the current Model')
parser.add_argument('--teacher_path', type=str, default=None, help='Path of the teacher model')
parser.add_argument('--gpu_id', type=int, default=0, help='index of gpu to use')
parser.add_argument('--test_batch_size', type=int, default=200, help='input batch size for testing')
parser.add_argument('--seed', type=int, default=0, help='random seed (default: 0)')
args = parser.parse_args()
if args.teacher_path is None:
exp_name = 'cifar10_{}{:.1f}_dp{:.1f}_aug{}_seed{}'.format(args.noise_pattern, args.noise_rate, args.dp, args.aug, args.seed)
else:
exp_name = 'cifar10_{}{:.1f}_dp{:.1f}_aug{}_student_seed{}'.format(args.noise_pattern, args.noise_rate, args.dp, args.aug, args.seed)
logpath = '{}.txt'.format(exp_name)
log(logpath, 'Settings: {}\n'.format(args))
torch.manual_seed(args.seed)
device = torch.device('cuda:'+str(args.gpu_id) if torch.cuda.is_available() else 'cpu')
# Datasets
root = './data/CIFAR10'
kwargs = {'num_workers': 4, 'pin_memory': True} if torch.cuda.is_available() else {}
if args.aug=='standard':
train_transform = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
elif args.aug=='strong':
train_transform = transforms.Compose([transforms.RandomCrop(32, padding=4, fill=128), transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
Cutout(n_holes=1, length=16), # (https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py)
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
else:
train_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
dataset = datasets.CIFAR10(root, train=True, download=True)
data, label = dataset.data, dataset.targets
label_noisy= list(pd.read_csv(os.path.join('./data/CIFAR10/label_noisy', args.noise_pattern+str(args.noise_rate)+'.csv'))['label_noisy'].values.astype(int))
train_dataset = DATASET_CUSTOM(root, data[:-args.val_size], label_noisy[:-args.val_size], transform=train_transform)
val_dataset = DATASET_CUSTOM(root, data[-args.val_size:], label_noisy[-args.val_size:], transform=test_transform)
test_dataset = datasets.CIFAR10(root, train=False, transform=test_transform)
if args.teacher_path is not None:
teacher_model = Wide_ResNet(args.dp).to(device)
teacher_model.load_state_dict(torch.load(args.teacher_path))
distill_dataset = DATASET_CUSTOM(root, data[:-args.val_size], label_noisy[:-args.val_size], transform=test_transform)
pred = get_pred(teacher_model, device, distill_dataset, args.test_batch_size)
log(logpath, 'distilled noise rate: {:.2f}\n'.format(1-(np.array(label[:-args.val_size])==pred).sum()/len(pred)))
train_dataset.targets = pred
del teacher_model
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False, **kwargs)
# Building model
def learning_rate(lr_init, epoch):
optim_factor = 0
if(epoch > 160):
optim_factor = 3
elif(epoch > 120):
optim_factor = 2
elif(epoch > 60):
optim_factor = 1
return lr_init*math.pow(0.2, optim_factor)
model = Wide_ResNet(args.dp).to(device)
# Training
val_best, epoch_best, test_at_best = 0, 0, 0
for epoch in range(1, args.epochs + 1):
t0 = time.time()
optimizer = optim.SGD(model.parameters(), lr=learning_rate(args.lr, epoch), momentum=0.9, weight_decay=5e-4)
_, train_acc = train(args, model, device, train_loader, optimizer, epoch)
_, val_acc = test(args, model, device, val_loader)
_, test_acc = test(args, model, device, test_loader)
if val_acc>val_best:
val_best, test_at_best, epoch_best = val_acc, test_acc, epoch
if args.save_model:
torch.save(model.state_dict(), '{}_best.pth'.format(exp_name))
log(logpath, 'Epoch: {}/{}, Time: {:.1f}s. '.format(epoch, args.epochs, time.time()-t0))
log(logpath, 'Train: {:.2f}%, Val: {:.2f}%, Test: {:.2f}%; Val_best: {:.2f}%, Test_at_best: {:.2f}%, Epoch_best: {}\n'.format(
100*train_acc, 100*val_acc, 100*test_acc, 100*val_best, 100*test_at_best, epoch_best))
# Saving
if args.save_model:
torch.save(model.state_dict(), '{}_last.pth'.format(exp_name))
if __name__ == '__main__':
main()