def create_model(num_classes, engine='torch'):
if engine == 'torch':
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)
# print(model)
model = torch.nn.DataParallel(model).cuda()
if args.weights:
if os.path.isfile(args.weights):
print("=> loading checkpoint '{}'".format(args.weights))
checkpoint = torch.load(args.weights)
model.load_state_dict(checkpoint['state_dict'])
else:
print("=> no checkpoint found at '{}'".format(args.weights))
cudnn.benchmark = True
else:
# create caffe model
import caffe
caffe.set_mode_gpu()
caffe.set_device(0)
model_def = args.deploy
model_weights = args.weights
model = caffe.Net(
model_def, # defines the structure of the model
model_weights, # contains the trained weights
caffe.TEST) # use test mode (e.g., don't perform dropout)
return model
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 = torch.nn.parallel.DistributedDataParallel(model)
else:
# DataParallel will divide
model = torch.nn.DataParallel(model)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss()
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.weights):
checkpoint = torch.load(args.weights,
map_location=torch.device('cpu'))
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))
if args.evaluate:
validate(val_loader, model, criterion)
return
if args.tune:
model.eval()
model.module.fuse_model()
import ilit
tuner = ilit.Tuner("./conf.yaml")
q_model = tuner.tune(model)
exit(0)
# 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)