def get_trained_model_optimizer(args, device, train_loader, val_loader, criterion):
if args.model == 'LeNet':
model = LeNet().to(device)
if args.load_pretrained_model:
model.load_state_dict(torch.load(args.pretrained_model_dir))
optimizer = torch.optim.Adadelta(model.parameters(), lr=1e-4)
else:
optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
elif args.model == 'vgg16':
model = VGG(depth=16).to(device)
if args.load_pretrained_model:
model.load_state_dict(torch.load(args.pretrained_model_dir))
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
elif args.model == 'resnet18':
model = ResNet18().to(device)
if args.load_pretrained_model:
model.load_state_dict(torch.load(args.pretrained_model_dir))
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
elif args.model == 'resnet50':
model = ResNet50().to(device)
if args.load_pretrained_model:
model.load_state_dict(torch.load(args.pretrained_model_dir))
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
scheduler = MultiStepLR(
optimizer, milestones=[int(args.pretrain_epochs*0.5), int(args.pretrain_epochs*0.75)], gamma=0.1)
else:
raise ValueError("model not recognized")
if not args.load_pretrained_model:
best_acc = 0
best_epoch = 0
for epoch in range(args.pretrain_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(model, device, criterion, val_loader)
if acc > best_acc:
best_acc = acc
best_epoch = epoch
state_dict = model.state_dict()
model.load_state_dict(state_dict)
print('Best acc:', best_acc)
print('Best epoch:', best_epoch)
if args.save_model:
torch.save(state_dict, os.path.join(args.experiment_data_dir, 'model_trained.pth'))
print('Model trained saved to %s' % args.experiment_data_dir)
return model, optimizer
def main(args):
# prepare dataset
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader, val_loader, criterion = get_data(args)
model, optimizer = get_trained_model_optimizer(args, device, train_loader,
val_loader, criterion)
def short_term_fine_tuner(model, epochs=1):
for epoch in range(epochs):
train(args, model, device, train_loader, criterion, optimizer,
epoch)
def trainer(model, optimizer, criterion, epoch, callback):
return train(args,
model,
device,
train_loader,
criterion,
optimizer,
epoch=epoch,
callback=callback)
def evaluator(model):
return test(model, device, criterion, val_loader)
# used to save the performance of the original & pruned & finetuned models
result = {'flops': {}, 'params': {}, 'performance': {}}
flops, params = count_flops_params(model, get_input_size(args.dataset))
result['flops']['original'] = flops
result['params']['original'] = params
evaluation_result = evaluator(model)
print('Evaluation result (original model): %s' % evaluation_result)
result['performance']['original'] = evaluation_result
# module types to prune, only "Conv2d" supported for channel pruning
if args.base_algo in ['l1', 'l2']:
op_types = ['Conv2d']
elif args.base_algo == 'level':
op_types = ['default']
config_list = [{'sparsity': args.sparsity, 'op_types': op_types}]
dummy_input = get_dummy_input(args, device)
if args.pruner == 'L1FilterPruner':
pruner = L1FilterPruner(model, config_list)
elif args.pruner == 'L2FilterPruner':
pruner = L2FilterPruner(model, config_list)
elif args.pruner == 'ActivationMeanRankFilterPruner':
pruner = ActivationMeanRankFilterPruner(model, config_list)
elif args.pruner == 'ActivationAPoZRankFilterPruner':
pruner = ActivationAPoZRankFilterPruner(model, config_list)
elif args.pruner == 'NetAdaptPruner':
pruner = NetAdaptPruner(model,
config_list,
short_term_fine_tuner=short_term_fine_tuner,
evaluator=evaluator,
base_algo=args.base_algo,
experiment_data_dir=args.experiment_data_dir)
elif args.pruner == 'ADMMPruner':
# users are free to change the config here
if args.model == 'LeNet':
if args.base_algo in ['l1', 'l2']:
config_list = [{
'sparsity': 0.8,
'op_types': ['Conv2d'],
'op_names': ['conv1']
}, {
'sparsity': 0.92,
'op_types': ['Conv2d'],
'op_names': ['conv2']
}]
elif args.base_algo == 'level':
config_list = [{
'sparsity': 0.8,
'op_names': ['conv1']
}, {
'sparsity': 0.92,
'op_names': ['conv2']
}, {
'sparsity': 0.991,
'op_names': ['fc1']
}, {
'sparsity': 0.93,
'op_names': ['fc2']
}]
else:
raise ValueError('Example only implemented for LeNet.')
pruner = ADMMPruner(model,
config_list,
trainer=trainer,
num_iterations=2,
training_epochs=2)
elif args.pruner == 'SimulatedAnnealingPruner':
pruner = SimulatedAnnealingPruner(
model,
config_list,
evaluator=evaluator,
base_algo=args.base_algo,
cool_down_rate=args.cool_down_rate,
experiment_data_dir=args.experiment_data_dir)
elif args.pruner == 'AutoCompressPruner':
pruner = AutoCompressPruner(
model,
config_list,
trainer=trainer,
evaluator=evaluator,
dummy_input=dummy_input,
num_iterations=3,
optimize_mode='maximize',
base_algo=args.base_algo,
cool_down_rate=args.cool_down_rate,
admm_num_iterations=30,
admm_training_epochs=5,
experiment_data_dir=args.experiment_data_dir)
else:
raise ValueError("Pruner not supported.")
# Pruner.compress() returns the masked model
# but for AutoCompressPruner, Pruner.compress() returns directly the pruned model
model = pruner.compress()
evaluation_result = evaluator(model)
print('Evaluation result (masked model): %s' % evaluation_result)
result['performance']['pruned'] = evaluation_result
if args.save_model:
pruner.export_model(
os.path.join(args.experiment_data_dir, 'model_masked.pth'),
os.path.join(args.experiment_data_dir, 'mask.pth'))
print('Masked model saved to %s', args.experiment_data_dir)
# model speed up
if args.speed_up:
if args.pruner != 'AutoCompressPruner':
if args.model == 'LeNet':
model = LeNet().to(device)
elif args.model == 'vgg16':
model = VGG(depth=16).to(device)
elif args.model == 'resnet18':
model = ResNet18().to(device)
elif args.model == 'resnet50':
model = ResNet50().to(device)
elif args.model == 'mobilenet_v2':
model = models.mobilenet_v2(pretrained=False).to(device)
model.load_state_dict(
torch.load(
os.path.join(args.experiment_data_dir,
'model_masked.pth')))
masks_file = os.path.join(args.experiment_data_dir, 'mask.pth')
m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
m_speedup.speedup_model()
evaluation_result = evaluator(model)
print('Evaluation result (speed up model): %s' % evaluation_result)
result['performance']['speedup'] = evaluation_result
torch.save(
model.state_dict(),
os.path.join(args.experiment_data_dir, 'model_speed_up.pth'))
print('Speed up model saved to %s', args.experiment_data_dir)
flops, params = count_flops_params(model, get_input_size(args.dataset))
result['flops']['speedup'] = flops
result['params']['speedup'] = params
if args.fine_tune:
if args.dataset == 'mnist':
optimizer = torch.optim.Adadelta(model.parameters(), lr=1)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
elif args.dataset == 'cifar10' and args.model == 'vgg16':
optimizer = torch.optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
scheduler = MultiStepLR(optimizer,
milestones=[
int(args.fine_tune_epochs * 0.5),
int(args.fine_tune_epochs * 0.75)
],
gamma=0.1)
elif args.dataset == 'cifar10' and args.model == 'resnet18':
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=5e-4)
scheduler = MultiStepLR(optimizer,
milestones=[
int(args.fine_tune_epochs * 0.5),
int(args.fine_tune_epochs * 0.75)
],
gamma=0.1)
elif args.dataset == 'cifar10' and args.model == 'resnet50':
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=5e-4)
scheduler = MultiStepLR(optimizer,
milestones=[
int(args.fine_tune_epochs * 0.5),
int(args.fine_tune_epochs * 0.75)
],
gamma=0.1)
best_acc = 0
for epoch in range(args.fine_tune_epochs):
train(args, model, device, train_loader, criterion, optimizer,
epoch)
scheduler.step()
acc = evaluator(model)
if acc > best_acc:
best_acc = acc
torch.save(
model.state_dict(),
os.path.join(args.experiment_data_dir,
'model_fine_tuned.pth'))
print('Evaluation result (fine tuned): %s' % best_acc)
print('Fined tuned model saved to %s', args.experiment_data_dir)
result['performance']['finetuned'] = best_acc
with open(os.path.join(args.experiment_data_dir, 'result.json'),
'w+') as f:
json.dump(result, f)
parser.add_argument('--attacker', default='stepll', type=str,
help='option2: attacker for generating adv input')
parser.add_argument('--loss_schema', default='averaged', type=str,
help='option3: loss schema')
# reproducible
torch.manual_seed(66)
np.random.seed(66)
######################################### modify accordingly ##################################################
# adv models: the static model used to generate adv input images
# fixed to memory for all the trainings to speed up.
adv_resnet18 = ResNet18()
adv_resnet50 = ResNet50()
adv_mobilenet_125 = MobileNetV2(width_mult=1.25)
adv_googlenet = GoogLeNet()
adv_models = [adv_resnet18, adv_resnet50, adv_mobilenet_125, adv_googlenet]
adv_model_names = ['resnet18', 'resnet50', 'mobilenet_125', 'googlenet']
# models: models for be adv training
# loaded only on its training to save memory.
model_classes = [ ResNet34, ResNet101, MobileNetV2, MobileNetV2]
model_names = [ 'resnet34', 'resnet101', 'mobilenet_1', 'mobilenet_075']
params = {
'mobilenet_1': 1.0,
'mobilenet_075': 0.75,