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train.py
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train.py
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# https://github.com/andreasveit/densenet-pytorch/blob/master/train.py
import argparse
import os
import shutil
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
from models import *
from utils import *
# used for logging to TensorBoard
from tensorboard_logger import configure, log_value
parser = argparse.ArgumentParser(description='PyTorch DenseNet Training')
parser.add_argument('--dataset', default="cifar10", type=str,
help='choose dataset name (cifar10, cifar100, svhn)')
parser.add_argument('--epochs', default=200, type=int,
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int,
help='manual epoch number (useful on restarts)')
parser.add_argument('--num_class', default=10, type=int,
help='number of class (default: 10)')
parser.add_argument('-b', '--batch-size', default=64, type=int,
help='mini-batch size (default: 64)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
help='weight decay (default: 1e-4)')
parser.add_argument('--decay_epoch_num1', '-d1', default=150, type=int,
help='set first epoch number of decay for learning rate (default: 150)')
parser.add_argument('--decay_epoch_num2', '-d2', default=225, type=int,
help='set second epoch number of decay for learning rate (default: 225)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
help='print frequency (default: 10)')
parser.add_argument('--model', default="dense", type=str,
help='choose model name (dense, resnet, lenet)')
parser.add_argument('--layers', default=100, type=int,
help='total number of layers (default: 100)')
parser.add_argument('--depth', default=28, type=int,
help='depth of wideresnet (default: 28)')
parser.add_argument('--widen_factor', default=10, type=int,
help='widen factor of wideresnet (default: 10)')
parser.add_argument('--z_dim', default=84, type=int,
help='dimension of penultimate layer (default: 84)')
parser.add_argument('--num_valid', default=5000, type=int,
help='number of validation data splitted from train data (default: 5000)')
parser.add_argument('--growth', default=12, type=int,
help='number of new channels per layer (default: 12)')
parser.add_argument('--num_exp', default=1, type=int,
help='number of experiment')
parser.add_argument('--droprate', default=0, type=float,
help='dropout probability (default: 0.0)')
parser.add_argument('--no-augment', dest='augment', action='store_false',
help='whether to use standard augmentation (default: True)')
parser.add_argument('--reduce', default=0.5, type=float,
help='compression rate in transition stage (default: 0.5)')
parser.add_argument('--no-bottleneck', dest='bottleneck', action='store_false',
help='To not use bottleneck block')
parser.add_argument('--resume', default='', type=str,
help='path to latest checkpoint (default: none)')
parser.add_argument('--tensorboard',
help='Log progress to TensorBoard', action='store_true')
parser.add_argument('--epoch_weight',
help='save weight each epoch', action='store_true')
tp = lambda x: list(map(int, x.split(',')))
parser.add_argument('--rep', default="1,1,1", type=tp,
help='add reparameterization (default: 1,1,1)')
parser.set_defaults(bottleneck=True)
parser.set_defaults(augment=True)
best_prec1 = 0
def main():
global args, best_prec1, weights_dir
args = parser.parse_args()
# Data loading code
if args.dataset == 'svhn':
normalize = transforms.Normalize(mean=[x / 255.0 for x in[109.9, 109.7, 113.8]],
std=[x / 255.0 for x in [50.1, 50.6, 50.8]])
else:
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
if args.augment:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
else:
transform_train = transforms.Compose([
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.ToTensor(),
normalize
])
kwargs = {'num_workers': 1, 'pin_memory': True}
if args.dataset == "cifar10":
trainset = datasets.CIFAR10('./data', train=True, download=True, transform=transform_train)
elif args.dataset == "cifar100":
trainset = datasets.CIFAR100('./data', train=True, download=True, transform=transform_train)
elif args.dataset == "svhn":
trainset = datasets.SVHN('./data', split="train", download=True, transform=transform_train)
validationset = Subset(trainset, np.arange(args.num_valid))
validationset.dataset.transform = transform_test
trainset = Subset(trainset, np.arange(args.num_valid, len(trainset)))
train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = DataLoader(validationset, batch_size=args.batch_size, shuffle=True, **kwargs)
rep_ind = "".join(np.array(args.rep).astype("str"))
if args.model == "dense":
model_name = "DenseNet_BC_{}_{}_{}".format(rep_ind, args.layers, args.growth)
model = DenseNet3(args.layers, args.num_class, args.growth, reduction=args.reduce,
bottleneck=args.bottleneck, dropRate=args.droprate, rep=args.rep)
elif args.model == "resnet":
model_name = "WideResnet_{}_{}_{}".format(rep_ind, args.depth, args.widen_factor)
model = WideResNet(args.depth, args.num_class, args.widen_factor, args.droprate, rep=args.rep)
elif args.model == "lenet":
model_name = "Lenet_{}_{}".format(rep_ind, args.z_dim)
model = LeNet5(args.z_dim, args.num_class, rep=args.rep)
weights_dir = "{}_{}_{}_{}".format(
model_name, args.num_class, args.num_exp, args.dataset)
if args.tensorboard: configure("weights/%s" % (weights_dir))
print("model: {}".format(weights_dir))
# get the number of model parameters
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
model = model.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss().cuda()
if args.model == "lenet":
optimizer = torch.optim.Adam(model.parameters(), args.lr)
else:
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
nesterov=True,
weight_decay=args.weight_decay)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion, epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
if args.epoch_weight:
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best, 'checkpoint_epoch{}.pth.tar'.format(epoch + 1))
else:
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
print('Best accuracy: ', best_prec1)
def train(train_loader, model, criterion, optimizer, epoch):
"""Train for one epoch on the training set"""
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
ID_list = range(args.num_class)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
input, target = mask(input, target, ID_list)
target = target.cuda()
input = input.cuda()
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1 = accuracy(output.data, target, topk=(1,))[0]
losses.update(loss.data, input.size(0))
top1.update(prec1, input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch+1, i, len(train_loader), batch_time=batch_time,
loss=losses, top1=top1))
# log to TensorBoard
if args.tensorboard:
log_value('train_loss', losses.avg, epoch)
log_value('train_acc', top1.avg, epoch)
def validate(val_loader, model, criterion, epoch):
"""Perform validation on the validation set"""
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
ID_list = range(args.num_class)
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
with torch.no_grad():
input, target = mask(input, target, ID_list)
target = target.cuda()
input = input.cuda()
# compute output
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1 = accuracy(output.data, target, topk=(1,))[0]
losses.update(loss.data, input.size(0))
top1.update(prec1, input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1))
print(' * Prec@1 {top1.avg:.3f}'.format(top1=top1))
# log to TensorBoard
if args.tensorboard:
log_value('val_loss', losses.avg, epoch)
log_value('val_acc', top1.avg, epoch)
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
"""Saves checkpoint to disk"""
directory = "weights/%s/" % (weights_dir)
if not os.path.exists(directory):
os.makedirs(directory)
filename = directory + filename
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'weights/%s/' % (weights_dir) + 'model_best.pth.tar')
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
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 after 150 and 225 epochs"""
lr = args.lr * (0.1 ** (epoch // args.decay_epoch_num1)) * (
0.1 ** (epoch // args.decay_epoch_num2))
# log to TensorBoard
if args.tensorboard:
log_value('learning_rate', lr, epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
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_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()