'''
Training script for ImageNet
Copyright (c) Wei YANG, 2017
'''
from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import models.imagenet as customized_models
from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
from utils.radam import RAdam, AdamW
# from tensorboardX import SummaryWriter
# writer = SummaryWriter(logdir='/cps/gadam/log_imagenet/')
# Models
default_model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
customized_models_names = sorted(name for name in customized_models.__dict__
if name.islower() and not name.startswith("__")
and callable(customized_models.__dict__[name]))
for name in customized_models.__dict__:
if name.islower() and not name.startswith("__") and callable(customized_models.__dict__[name]):
models.__dict__[name] = customized_models.__dict__[name]
model_names = default_model_names + customized_models_names
# Parse arguments
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# Datasets
parser.add_argument('-d', '--data', default='path to dataset', type=str)
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
# Optimization options
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--train-batch', default=256, type=int, metavar='N',
help='train batchsize (default: 256)')
parser.add_argument('--test-batch', default=200, type=int, metavar='N',
help='test batchsize (default: 200)')
parser.add_argument('--optimizer', default='sgd', type=str, help='optimizer sgd|adam|radam')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--beta1', default=0.9, type=float,
help='beta1 for adam')
parser.add_argument('--beta2', default=0.999, type=float,
help='beta2 for adam')
parser.add_argument('--drop', '--dropout', default=0, type=float,
metavar='Dropout', help='Dropout ratio')
parser.add_argument('--schedule', type=int, nargs='+', default=[150, 225],
help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
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)')
# Checkpoints
parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
help='path to save checkpoint (default: checkpoint)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
# Architecture
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('--depth', type=int, default=29, help='Model depth.')
parser.add_argument('--cardinality', type=int, default=32, help='ResNet cardinality (group).')
parser.add_argument('--base-width', type=int, default=4, help='ResNet base width.')
parser.add_argument('--widen-factor', type=int, default=4, help='Widen factor. 4 -> 64, 8 -> 128, ...')
# Miscs
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
#Device options
parser.add_argument('--gpu-id', default='0', type=str,
help='id(s) for CUDA_VISIBLE_DEVICES')
parser.add_argument('--model_name', default='sgd')
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
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)
best_acc = 0 # best test accuracy
def main():
global best_acc
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.train_batch, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
elif args.arch.startswith('resnext'):
model = models.__dict__[args.arch](
baseWidth=args.base_width,
cardinality=args.cardinality,
)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
print(' Total params: %.2fM' % (sum(p.numel() for p in model.parameters())/1000000.0))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer.lower() == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'adam':
optimizer = AdamW(model.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), weight_decay=args.weight_decay)
elif args.optimizer.lower() == 'radam':
optimizer = RAdam(model.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), weight_decay=args.weight_decay)
# Resume
title = 'ImageNet-' + args.arch
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
args.checkpoint = os.path.dirname(args.resume)
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.'])
if args.evaluate:
print('\nEvaluation only')
test_loss, test_acc = test(val_loader, model, criterion, start_epoch, use_cuda)
print(' Test Loss: %.8f, Test Acc: %.2f' % (test_loss, test_acc))
return
# Train and val
for epoch in range(start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, state['lr']))
train_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, use_cuda)
test_loss, test_acc = test(val_loader, model, criterion, epoch, use_cuda)
# append logger file
logger.append([state['lr'], train_loss, test_loss, train_acc, test_acc])
# writer.add_scalars('loss_tracking/train_loss', {args.model_name: train_loss}, epoch)
# writer.add_scalars('loss_tracking/test_loss', {args.model_name: test_loss}, epoch)
# writer.add_scalars('loss_tracking/train_acc', {args.model_name: train_acc}, epoch)
# writer.add_scalars('loss_tracking/test_acc', {args.model_name: test_acc}, epoch)
# save model
is_best = test_acc > best_acc
best_acc = max(test_acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'acc': test_acc,
'best_acc': best_acc,
'optimizer' : optimizer.state_dict(),
}, is_best, checkpoint=args.checkpoint)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
print('Best acc:')
print(best_acc)
def train(train_loader, model, criterion, optimizer, epoch, use_cuda):
# switch to train mode
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
bar = Bar('Processing', max=len(train_loader))
for batch_idx, (inputs, targets) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(non_blocking=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)
# compute output
outputs = model(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data[0], inputs.size(0))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.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()
# plot progress
bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
batch=batch_idx + 1,
size=len(train_loader),
data=data_time.val,
bt=batch_time.val,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
top1=top1.avg,
top5=top5.avg,
)
bar.next()
bar.finish()
return (losses.avg, top1.avg)
def test(val_loader, model, criterion, epoch, use_cuda):
global best_acc
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
bar = Bar('Processing', max=len(val_loader))
for batch_idx, (inputs, targets) in enumerate(val_loader):
# measure data loading time
data_time.update(time.time() - end)
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
# compute output
outputs = model(inputs)
loss = criterion(outputs, targets)
# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data[0], inputs.size(0))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f} | top5: {top5: .4f}'.format(
batch=batch_idx + 1,
size=len(val_loader),
data=data_time.avg,
bt=batch_time.avg,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
top1=top1.avg,
top5=top5.avg,
)
bar.next()
bar.finish()
return (losses.avg, top1.avg)
def save_checkpoint(state, is_best, checkpoint='checkpoint', filename='checkpoint.pth.tar'):
filepath = os.path.join(checkpoint, filename)
torch.save(state, filepath)
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'model_best.pth.tar'))
def adjust_learning_rate(optimizer, epoch):
global state
if epoch in args.schedule:
state['lr'] *= args.gamma
for param_group in optimizer.param_groups:
param_group['lr'] = state['lr']
if __name__ == '__main__':
main()
# writer.close()
