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train.py
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import argparse
import os
import json
import torch
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as transforms
from time import time, gmtime, strftime
from model import ResNeXt
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Trains ResNeXt on CIFAR dataset')
# Dataset arguments
parser.add_argument('--data_path', '-dp', type=str, default='cifar10', help='Root for the CIFAR dataset')
parser.add_argument('--dataset', '-ds', type=str, default='cifar10', choices=['cifar2', 'cifar5', 'cifar10', 'cifar100'], help='Choose between Cifar2/5/10/100')
# Optimization options
parser.add_argument('--epochs', '-e', type=int, default=300, help='Number of epochs to train')
parser.add_argument('--learning_rate', '-lr', type=float, default=0.1, help='Learning rate')
parser.add_argument('--decay', '-d', type=float, default=0.0005, help='Weight decay for L2 loss')
parser.add_argument('--momentum', '-m', type=float, default=0.9, help='Momentum')
parser.add_argument('--schedule', type=int, nargs='+', default=[150, 225], help='Schedule to decrease learning rate at given epochs')
parser.add_argument('--gamma', type=float, default=0.1, help='Learning rate is multiplied by gamma on schedule')
parser.add_argument('--batch_size', '-b', type=int, default=128, help='Training batch size')
parser.add_argument('--test_bs', type=float, default=10, help='Test batch size')
# Architecture options
parser.add_argument('--depth', type=int, default=29, help='Depth of the model')
parser.add_argument('--cardinality', type=int, default=8, help='Model cardinality or group')
parser.add_argument('--base_width', type=int, default=64, help='Number of channels in each group')
parser.add_argument('--widen_factor', type=int, default=4, help='Widen factor. 4 -> 64, 8 -> 128, ...')
# Checkpoint options
parser.add_argument('--save', '-s', type=str, default='./snapshot', help='Folder to save model checkpoints')
parser.add_argument('--load', '-l', type=str, help='Checkpoint path to resume model training')
parser.add_argument('--log', type=str, default='./log', help='Log folder')
# GPU
parser.add_argument('--gpus', type=int, default=1, help='0 = CPU')
parser.add_argument('--prefetch', type=int, default=2, help='Pre-fetching threads')
args = parser.parse_args()
timestamp = strftime('%d-%m-%Y_%H-%M-%S', gmtime())
log_loc = 'log-' + timestamp + '.txt'
model_loc = 'model-' + timestamp + '.pytorch'
train_data_dir = args.data_path + '/train'
validation_data_dir = args.data_path + '/validation'
if not os.path.isdir(args.log):
os.makedirs(args.log)
log = open(os.path.join(args.log, log_loc), 'w')
state = {k : v for k, v in args._get_kwargs()}
log.write(json.dumps(state) + '\n')
# Calculate number of epochs wrt batch size
args.epochs = args.epochs * 128 // args.batch_size
args.schedule = [x * 128 // args.batch_size for x in args.schedule]
if not os.path.isdir(args.data_path):
os.makedirs(args.data_path)
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
train_transforms = transforms.Compose([transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)])
test_transform = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)])
if args.dataset == 'cifar2':
train_data = dset.ImageFolder(train_data_dir, transform=train_transform)
test_data = dset.ImageFolder(validation_data_dir, transform=test_transform)
nlabels = 2
elif args.dataset == 'cifar5':
train_data = dset.ImageFolder(train_data_dir, transform=train_transform)
test_data = dset.ImageFolder(validation_data_dir, transform=test_transform)
nlabels = 5
elif args.dataset == 'cifar10':
train_data = dset.ImageFolder(train_data_dir, transform=train_transform)
test_data = dset.ImageFolder(validation_data_dir, transform=test_transform)
nlabels = 10
else:
train_data = dset.CIFAR100(args.data_path, train=True, transform=train_transform, download=True)
test_data = dset.CIFAR100(args.data_path, train=False, transform=test_transform, download=True)
nlabels = 100
train_loader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=args.batch_size)
test_loader = torch.utils.data.DataLoader(test_data, shuffle=False, batch_size=args.test_bs)
if not os.path.isdir(args.save):
os.makedirs(args.save)
net = ResNeXt(args.cardinality, args.depth, args.base_width, nlabels, args.widen_factor)
print(net)
if args.ngpu > 1:
net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
if args.ngpu > 0:
net.cuda()
optimizer = torch.optim.SGD(net.parameters(), state['learning_rate'], momentum=state['momentum'], weight_decay=state['decay'], nesterov=True)
# Training
def train():
net.train()
loss_avg = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = torch.autograd.Variable(data), torch.autograd.Variable(target)
output = net(data)
optimizer.zero_grad()
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
loss_avg = loss_avg * 0.2 + loss.data[0] * 0.8
state['train_loss'] = loss_avg
# Testing
def test():
net.eval()
loss_avg = 0.0
correct = 0
for batch_idx, (data, target) in enumerate(test_loader):
data, target = torch.autograd.Variable(data.cuda()), torch.autograd.Variable(target.cuda())
output = net(data)
loss = F.cross_entropy(output, target)
pred = output.data.max(1)[1]
correct += pred.eq(target.data).sum()
loss_avg += loss.data[0]
state['test_loss'] = loss_avg / len(test_loader)
state['test_accuracy'] = correct / len(test_loader.dataset)
start_time = time.time()
prev_time = start_time
args.save = os.path.join(args.save, model_loc)
start_epoch = 0
# Resume
if args.load:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(args.load), 'Error: no checkpoint directory found!'
args.save = args.load
checkpoint = torch.load(args.load)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['cur_epoch']
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
state['best_accuracy'] = 0.0
for epoch in range(args.epoch):
if epoch in args.schedule:
state['learning_rate'] *= args.gamma
for param_group in optimizer.param_groups:
param_group['lr'] = state['learning_rate']
state['epoch'] = epoch
train()
test()
if state['test_accuracy'] > state['best_accuracy']:
state['best_accuracy'] = state['test_accuracy']
torch.save({
'cur_epoch': epoch + 1,
'state_dict': net.state_dict(),
'acc': state['test_accuracy'],
'best_acc': state['best_accuracy'],
'optimizer' : optimizer.state_dict(),
}, args.save)
log.write('%s\n' % json.dumps(state))
log.flush()
time_taken = time.time() - prev_time
total_time_taken = time.time() - start_time
prev_time = time.time()
print(state)
print("Time taken: ", time_taken)
print("Total time taken: ", total_time_taken)
print('Best Accuracy: %f' % state['best_accuracy'])
log.close()