return top1.avg, top3.avg, top5.avg, losses.avg
if __name__ == '__main__':
# load data
train_set = get_data_sets(train_cfg['data_set_dir'], 'train')
val_set = get_data_sets(train_cfg['data_set_dir'], 'val')
# load model
if model_cfg['if_pre_train']:
model = load_model(model_cfg['pre_trained_path'],
data_classes=model_cfg['data_classes'])
else:
model = PeleeNet(num_classes=model_cfg['model_classes'])
if train_cfg['cuda']:
model = model.cuda()
#print(model.state_dict())
# init the summery
writer = init_summery('runs', 'train_val')
#global optimizer_conv
optimizer_conv = optim.Adam(model.parameters(),
lr=train_cfg['init_lr'],
weight_decay=0.0001)
#schedule = optim.lr_scheduler.ReduceLROnPlateau(optimizer_conv, factor=0.1, patience=2, verbose=False, threshold=0.1, cooldown=0, min_lr=train_cfg['min_lr'])
schedule = optim.lr_scheduler.ReduceLROnPlateau(optimizer_conv,
factor=0.9,
patience=2,
threshold=0.01,
min_lr=train_cfg['min_lr'])
def main():
global args, best_acc1
args = parser.parse_args()
print( 'args:',args)
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)
# Val data loading
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(args.input_dim+32),
transforms.CenterCrop(args.input_dim),
transforms.ToTensor(),
normalize,
]))
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
num_classes = len(val_dataset.classes)
print('Total classes: ',num_classes)
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch == 'peleenet':
model = PeleeNet(num_classes=num_classes)
else:
print("=> unsupported model '{}'. creating PeleeNet by default.".format(args.arch))
model = PeleeNet(num_classes=num_classes)
if args.distributed:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# 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_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
elif args.pretrained:
if os.path.isfile(args.pretrained):
checkpoint = torch.load(args.pretrained)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {}, acc@1 {})"
.format(args.pretrained, checkpoint['epoch'], checkpoint['best_acc1']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
if args.evaluate:
validate(val_loader, model, criterion)
return
# Training data loading
traindir = os.path.join(args.data, 'train')
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(args.input_dim),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion)
# remember best Acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best)