def flops_counter(args):
# model speed up
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader, val_loader, criterion = get_data(args)
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 = models.resnet18(pretrained=False,
num_classes=10).to(device)
elif args.model == 'mobilenet_v2':
model = models.mobilenet_v2(pretrained=False).to(device)
def evaluator(model):
return test(model, device, criterion, val_loader)
model.load_state_dict(
torch.load(
os.path.join(args.experiment_data_dir,
'model_fine_tuned.pth')))
masks_file = os.path.join(args.experiment_data_dir, 'mask.pth')
dummy_input = get_dummy_input(args, device)
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)
with open(os.path.join(args.experiment_data_dir,
'performance.json')) as f:
result = json.load(f)
result['speedup'] = evaluation_result
with open(os.path.join(args.experiment_data_dir, 'performance.json'),
'w+') as f:
json.dump(result, f)
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)
else:
model = torch.load(
os.path.join(args.experiment_data_dir, 'model_fine_tuned.pth'))
model.eval()
flops, params = count_flops_params(model, (1, 3, 32, 32))
with open(os.path.join(args.experiment_data_dir, 'flops.json'),
'w+') as f:
json.dump({'FLOPS': int(flops), 'params': int(params)}, f)
def model_inference(config):
model_trained = './experiment_data/resnet_bn/model_fine_tuned_first.pth'
rn50 = resnet50()
m_paras = torch.load(model_trained)
##delete mask in pth
m_new = collections.OrderedDict()
mask = dict()
for key in m_paras:
if 'weight_mask_b' in key: continue
if 'weight_mask' in key:
if 'module_added' not in key:
mask[key.replace('.weight_mask', '')] = dict()
mask[key.replace('.weight_mask', '')]['weight'] = m_paras[key]
mask[key.replace('.weight_mask', '')]['bias'] = m_paras[key]
else:
mask[key.replace('.relu1.module_added.weight_mask',
'.bn3')] = {}
mask[key.replace('.relu1.module_added.weight_mask',
'.bn3')]['weight'] = m_paras[key]
mask[key.replace('.relu1.module_added.weight_mask',
'.bn3')]['bias'] = m_paras[key]
if '0.relu1' in key:
mask[key.replace('relu1.module_added.weight_mask',
'downsample.1')] = {}
mask[key.replace('relu1.module_added.weight_mask',
'downsample.1')]['weight'] = m_paras[key]
mask[key.replace('relu1.module_added.weight_mask',
'downsample.1')]['bias'] = m_paras[key]
continue
if 'module_added' in key:
continue
elif 'module' in key:
m_new[key.replace('module.', '')] = m_paras[key]
else:
m_new[key] = m_paras[key]
for key in mask:
#modify the weight and bias of model with pruning
m_new[key + '.weight'] = m_new[key + '.weight'].data.mul(
mask[key]['weight'])
m_new[key + '.bias'] = m_new[key + '.bias'].data.mul(mask[key]['bias'])
rn50.load_state_dict(m_new)
rn50.cuda()
rn50.eval()
torch.save(mask, 'taylor_mask.pth')
mask_file = './taylor_mask.pth'
dummy_input = torch.randn(64, 3, 224, 224).cuda()
use_mask_out = use_speedup_out = None
rn = rn50
rn_mask_out = rn(dummy_input)
model = rn50
if use_mask:
torch.onnx.export(model,
dummy_input,
'resnet_masked_taylor_1700.onnx',
export_params=True,
opset_version=12,
do_constant_folding=True,
input_names=['inputs'],
output_names=['proba'],
dynamic_axes={
'inputs': [0],
'mask': [0]
},
keep_initializers_as_inputs=True)
start = time.time()
for _ in range(32):
use_mask_out = model(dummy_input)
elapsed_t = time.time() - start
print('elapsed time when use mask: ', elapsed_t)
_logger.info(
'for batch size 64 and with 32 runs, the elapsed time is {}'.
format(elapsed_t))
print('before speed up===================')
flops, paras = count_flops_params(model, (1, 3, 224, 224))
_logger.info(
'flops and parameters before speedup is {} FLOPS and {} params'.format(
flops, paras))
if use_speedup:
dummy_input.cuda()
m_speedup = ModelSpeedup(model, dummy_input, mask_file, 'cuda')
m_speedup.speedup_model()
print('==' * 20)
print('Start inference')
torch.onnx.export(model,
dummy_input,
'resnet_taylor_1700.onnx',
export_params=True,
opset_version=12,
do_constant_folding=True,
input_names=['inputs'],
output_names=['proba'],
dynamic_axes={
'inputs': [0],
'mask': [0]
},
keep_initializers_as_inputs=True)
start = time.time()
for _ in range(32):
use_speedup_out = model(dummy_input)
elasped_t1 = time.time() - start
print('elapsed time when use speedup: ', elasped_t1)
_logger.info(
'elasped time with batch_size 64 and in 32 runs is {}'.format(
elasped_t1))
#print('After speedup model is ',model)
_logger.info('model structure after speedup is ====')
_logger.info(model)
print('=================')
print('After speedup')
flops, paras = count_flops_params(model, (1, 3, 224, 224))
_logger.info(
'After speedup flops are {} and number of parameters are {}'.format(
flops, paras))
if compare_results:
print(rn_mask_out)
print('another is', use_speedup_out)
if torch.allclose(rn_mask_out, use_speedup_out, atol=1e-6): #-07):
print('the outputs from use_mask and use_speedup are the same')
else:
raise RuntimeError(
'the outputs from use_mask and use_speedup are different')
# start the accuracy check
criterion = nn.CrossEntropyLoss()
with torch.no_grad():
start = time.time()
evaluate(model,
criterion,
data_loader_test,
device="cuda",
print_freq=20)
print('elapsed time is ', time.time() - start)
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)
def model_inference(config):
masks_file = './speedup_test/mask_new.pth'
shape_mask = './speedup_test/mask_new.pth'
org_mask = './speedup_test/mask.pth'
rn50 = models.resnet50()
m_paras = torch.load('./speedup_test/model_fine_tuned.pth')
##delete mask in pth
m_new = collections.OrderedDict()
for key in m_paras:
if 'mask' in key: continue
if 'module' in key:
m_new[key.replace('module.', '')] = m_paras[key]
else:
m_new[key] = m_paras[key]
rn50.load_state_dict(m_new)
rn50.cuda()
rn50.eval()
dummy_input = torch.randn(64, 3, 224, 224).cuda()
use_mask_out = use_speedup_out = None
rn = rn50
apply_compression_results(rn, org_mask, 'cuda')
rn_mask_out = rn(dummy_input)
model = rn50
# must run use_mask before use_speedup because use_speedup modify the model
if use_mask:
apply_compression_results(model, masks_file, 'cuda')
torch.onnx.export(model,
dummy_input,
'resnet_masked.onnx',
export_params=True,
opset_version=12,
do_constant_folding=True,
input_names=['inputs'],
output_names=['proba'],
dynamic_axes={
'inputs': [0],
'mask': [0]
},
keep_initializers_as_inputs=True)
start = time.time()
for _ in range(32):
use_mask_out = model(dummy_input)
print('elapsed time when use mask: ', time.time() - start)
print('Model is ', model)
print('before speed up===================')
# print(para)
# print(model.state_dict()[para])
# print(model.state_dict()[para].shape)
flops, paras = count_flops_params(model, (1, 3, 224, 224))
print(
'flops and parameters before speedup is {} FLOPS and {} params'.format(
flops, paras))
if use_speedup:
dummy_input.cuda()
m_speedup = ModelSpeedup(model, dummy_input, shape_mask, 'cuda')
m_speedup.speedup_model()
print('==' * 20)
print('Start inference')
torch.onnx.export(model,
dummy_input,
'resnet_fpgm.onnx',
export_params=True,
opset_version=12,
do_constant_folding=True,
input_names=['inputs'],
output_names=['proba'],
dynamic_axes={
'inputs': [0],
'mask': [0]
},
keep_initializers_as_inputs=True)
start = time.time()
for _ in range(32):
use_speedup_out = model(dummy_input)
print('elapsed time when use speedup: ', time.time() - start)
print('After speedup model is ', model)
print('=================')
print('After speedup')
flops, paras = count_flops_params(model, (1, 3, 224, 224))
print(
'flops and parameters before speedup is {} FLOPS and {} params'.format(
flops, paras))
#for para in model.state_dict():
# print(para)
# print(model.state_dict()[para])
# print(model.state_dict()[para].shape)
if compare_results:
print(rn_mask_out)
print('another is', use_speedup_out)
if torch.allclose(rn_mask_out, use_speedup_out, atol=1e-6): #-07):
print('the outputs from use_mask and use_speedup are the same')
else:
raise RuntimeError(
'the outputs from use_mask and use_speedup are different')
# start the accuracy check
criterion = nn.CrossEntropyLoss()
with torch.no_grad():
start = time.time()
evaluate(model,
criterion,
data_loader_test,
device="cuda",
print_freq=20)
print('elapsed time is ', time.time() - start)
def main(args):
# prepare dataset
torch.manual_seed(0)
model = resnet50()
model.load_state_dict(torch.load(args.pretrained_model_dir))
inited = init_distributed(True) #use nccl fro communication
print('all cudas numbers are ', get_world_size())
distributed = (get_world_size() > 1) and inited
paral = get_world_size()
#device = torch.device('cuda',args.local_rank) if distributed else torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#args.rank = get_rank()
device = set_device(args.cuda, args.local_rank)
#write to tensorboard
logger = Logger("logs/" + str(args.local_rank))
print(distributed)
print('local rank is {}'.format(args.local_rank))
train_loader, val_loader, criterion = get_data(args.dataset, args.data_dir,
args.batch_size,
args.test_batch_size)
print('to distribute ', distributed)
model = model.to(args.local_rank)
if distributed:
model = DDP(model, device_ids=[args.local_rank
]) #, output_device=args.local_rank)
#elif args.mgpu:
# model=torch.nn.DataParallel(model).cuda()
else:
model = model.cuda()
#model = torch.nn.DataParallel(model).cuda()
criterion = criterion.to(args.local_rank)
#for module in model.named_modules():
# print('%'*20)
# print(module)
# module types to prune, only "BatchNorm2d" supported for channel pruning with Taylor
config_step = {
'bn_statics': args.bn_statistics,
'fre': args.freq,
'tot_pru': args.tot_pru,
'save_path': args.experiment_data_dir
}
config_list = [{
'op_types': ['BatchNorm2d', 'ReLU'],
'must_names': 'layer',
'include_names': ['bn1', 'bn2', 'relu1']
}]
dummy_input = get_dummy_input(args, args.local_rank)
if args.pruner == 'TaylorPruner':
pruner = TaylorPruner(device,
model,
config_list,
config_step,
dependency_aware=False,
optimizer=None)
else:
raise ValueError("Pruner not supported.")
# Pruner.compress() returns the masked model
model = pruner.compress()
if args.pruner == 'TaylorPruner':
params_update = [
param[1] for param in model.named_parameters()
if 'mask' not in param[0]
]
not_updates = [
param[1] for param in model.named_parameters()
if 'mask' in param[0]
]
#print(params_update)
params_all = [{
'params': params_update
}, {
'params': not_updates,
'lr': 0.0
}]
if args.fine_tune:
if args.dataset in ['imagenet'] and args.model == 'resnet50':
optimizer = torch.optim.SGD(params_all,
lr=0.01,
momentum=0.5,
weight_decay=1e-8)
scheduler = MultiStepLR(optimizer,
milestones=[
int(args.fine_tune_epochs * 0.3),
int(args.fine_tune_epochs * 0.6),
int(args.fine_tune_epochs * 0.8)
],
gamma=0.1)
pruner.optimizer = optimizer
pruner.patch_optimizer(pruner.masker.calc_contributions)
pruner.keep_org_step()
else:
raise ValueError
def short_term_fine_tuner(model, epochs=1):
for epoch in range(epochs):
train(pruner, args, model, device, train_loader, criterion,
optimizer, epoch, logger)
def trainer(pruner, model, optimizer, criterion, epoch, callback):
return train(pruner,
args,
model,
device,
train_loader,
criterion,
optimizer,
epoch=epoch,
logger=logger,
callback=callback)
def evaluator(model, step):
return test(model, device, criterion, val_loader, step, logger)
# used to save the performance of the original & pruned & finetuned models
result = {'flops': {}, 'params': {}, 'performance': {}}
#print(model)
for module in model.named_modules():
print('DEBUG===name of module is ', module[0])
print('buffers are: ', [buff for buff in module[1].buffers()])
print('para are: ', [para for para in module[1].named_parameters()])
flops, params = count_flops_params(model, get_input_size(args.dataset))
result['flops']['original'] = flops
result['params']['original'] = params
evaluation_result = evaluator(model, 0)
print('Evaluation result (original model): %s' % evaluation_result)
result['performance']['original'] = evaluation_result
if args.local_rank == 0 and 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)
def wrapped(module):
return isinstance(module, BNTaylorPrunerMasker)
wrap_mask = [
module for module in model.named_modules() if wrapped(module[1])
]
for idx, mm in enumerate(wrap_mask):
print('====****' * 10, idx)
print(mm[0])
print(mm[1].state_dict().keys())
print('weight mask is ', mm[1].state_dict()['weight_mask'])
if 'bias_mask' in mm[1].state_dict():
print('bias mask is ', mm[1].state_dict()['bias_mask'])
if args.mgpu: model = torch.nn.DataParallel(model).cuda()
print('local rank is', args.local_rank)
if args.fine_tune:
best_acc = 0
for epoch in range(args.fine_tune_epochs):
print('start fine tune for epoch {}/{}'.format(
epoch, args.fine_tune_epochs))
stime = time.time()
train(pruner, args, model, args.local_rank, train_loader,
criterion, optimizer, epoch, logger)
scheduler.step()
acc = evaluator(model, epoch)
print('end fine tune for epoch {}/{} for {} seconds'.format(
epoch, args.fine_tune_epochs,
time.time() - stime))
if acc > best_acc and args.local_rank == 0:
best_acc = acc
torch.save(
model,
os.path.join(args.experiment_data_dir, args.model,
'finetune_model.pt'))
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
if args.local_rank == 0:
with open(os.path.join(args.experiment_data_dir, 'result.json'),
'w+') as f:
json.dump(result, f)
def main(args):
# prepare dataset
torch.manual_seed(0)
#device = torch.device('cuda',args.local_rank) if distributed else torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = set_device(args.cuda, args.local_rank)
inited = init_distributed(True) #use nccl fro communication
print('all cudas numbers are ', get_world_size())
distributed = (get_world_size() > 1) and inited
paral = get_world_size()
args.rank = get_rank()
#write to tensorboard
logger = Logger("logs/" + str(args.rank))
print(distributed)
#device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('device is', device)
print('rank is {} local rank is {}'.format(args.rank, args.local_rank))
train_loader, val_loader, criterion = get_data(args.dataset, args.data_dir,
args.batch_size,
args.test_batch_size)
model = torchvision.models.resnet50(pretrained=True)
model = model.cuda()
print('to distribute ', distributed)
if distributed:
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
#model = torch.nn.DataParallel(model).cuda()
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)
criterion = criterion.cuda()
#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, logger)
def trainer(model, optimizer, criterion, epoch, callback):
return train(args,
model,
device,
train_loader,
criterion,
optimizer,
epoch=epoch,
logger=logger,
callback=callback)
def evaluator(model, step):
return test(model, device, criterion, val_loader, step, logger)
# 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, 0)
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,
'exclude_names': 'downsample'
}]
dummy_input = get_dummy_input(args, device)
if args.pruner == 'FPGMPruner':
pruner = MyPruner(model, config_list)
else:
raise ValueError("Pruner not supported.")
# Pruner.compress() returns the masked model
model = pruner.compress()
evaluation_result = evaluator(model, 0)
print('Evaluation result (masked model): %s' % evaluation_result)
result['performance']['pruned'] = evaluation_result
if args.rank == 0 and 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)
def wrapped(module):
return isinstance(module, BNWrapper) or isinstance(
module, PrunerModuleWrapper)
wrap_mask = [
module for module in model.named_modules() if wrapped(module[1])
]
for mm in wrap_mask:
print('====****' * 10)
print(mm[0])
print(mm[1].state_dict().keys())
print('weight mask is ', mm[1].state_dict()['weight_mask'])
if 'bias_mask' in mm[1].state_dict():
print('bias mask is ', mm[1].state_dict()['bias_mask'])
if args.fine_tune:
if args.dataset in ['imagenet'] and args.model == 'resnet50':
optimizer = torch.optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=1e-4)
scheduler = MultiStepLR(optimizer,
milestones=[
int(args.fine_tune_epochs * 0.3),
int(args.fine_tune_epochs * 0.6),
int(args.fine_tune_epochs * 0.8)
],
gamma=0.1)
else:
raise ValueError("Pruner not supported.")
best_acc = 0
for epoch in range(args.fine_tune_epochs):
print('start fine tune for epoch {}/{}'.format(
epoch, args.fine_tune_epochs))
stime = time.time()
train(args, model, device, train_loader, criterion, optimizer,
epoch, logger)
scheduler.step()
acc = evaluator(model, epoch)
print('end fine tune for epoch {}/{} for {} seconds'.format(
epoch, args.fine_tune_epochs,
time.time() - stime))
if acc > best_acc and args.rank == 0:
best_acc = acc
torch.save(
model,
os.path.join(args.experiment_data_dir, args.model,
'finetune_model.pt'))
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
if args.rank == 0:
with open(os.path.join(args.experiment_data_dir, 'result.json'),
'w+') as f:
json.dump(result, f)
