def test_speedup_integration(self):
# skip this test on windows(7GB mem available) due to memory limit
# Note: hack trick, may be updated in the future
if 'win' in sys.platform or 'Win' in sys.platform:
print(
'Skip test_speedup_integration on windows due to memory limit!'
)
return
Gen_cfg_funcs = [generate_random_sparsity, generate_random_sparsity_v2]
for model_name in [
'resnet18',
'mobilenet_v2',
'squeezenet1_1',
'densenet121',
'densenet169',
# 'inception_v3' inception is too large and may fail the pipeline
'resnet50'
]:
for gen_cfg_func in Gen_cfg_funcs:
kwargs = {'pretrained': True}
if model_name == 'resnet50':
# testing multiple groups
kwargs = {'pretrained': False, 'groups': 4}
Model = getattr(models, model_name)
net = Model(**kwargs).to(device)
speedup_model = Model(**kwargs).to(device)
net.eval() # this line is necessary
speedup_model.eval()
# random generate the prune config for the pruner
cfgs = gen_cfg_func(net)
print("Testing {} with compression config \n {}".format(
model_name, cfgs))
pruner = L1FilterPruner(net, cfgs)
pruner.compress()
pruner.export_model(MODEL_FILE, MASK_FILE)
pruner._unwrap_model()
state_dict = torch.load(MODEL_FILE)
speedup_model.load_state_dict(state_dict)
zero_bn_bias(net)
zero_bn_bias(speedup_model)
data = torch.ones(BATCH_SIZE, 3, 128, 128).to(device)
ms = ModelSpeedup(speedup_model, data, MASK_FILE)
ms.speedup_model()
speedup_model.eval()
ori_out = net(data)
speeded_out = speedup_model(data)
ori_sum = torch.sum(ori_out).item()
speeded_sum = torch.sum(speeded_out).item()
print('Sum of the output of %s (before speedup):' % model_name,
ori_sum)
print('Sum of the output of %s (after speedup):' % model_name,
speeded_sum)
assert (abs(ori_sum - speeded_sum) / abs(ori_sum) < RELATIVE_THRESHOLD) or \
(abs(ori_sum - speeded_sum) < ABSOLUTE_THRESHOLD)
def prune_model_l1(model):
config_list = [{
'sparsity': SPARSITY,
'op_types': ['Conv2d']
}]
pruner = L1FilterPruner(model, config_list)
pruner.compress()
pruner.export_model(model_path=MODEL_FILE, mask_path=MASK_FILE)
def test_speedup_tupleunpack(self):
"""This test is reported in issue3645"""
model = TupleUnpack_Model()
cfg_list = [{'op_types': ['Conv2d'], 'sparsity': 0.5}]
dummy_input = torch.rand(2, 3, 224, 224)
pruner = L1FilterPruner(model, cfg_list)
pruner.compress()
model(dummy_input)
pruner.export_model(MODEL_FILE, MASK_FILE)
ms = ModelSpeedup(model, dummy_input, MASK_FILE, confidence=8)
ms.speedup_model()
def test_mask_conflict(self):
outdir = os.path.join(prefix, 'masks')
os.makedirs(outdir, exist_ok=True)
for name in model_names:
print('Test mask conflict for %s' % name)
model = getattr(models, name)
net = model().to(device)
dummy_input = torch.ones(1, 3, 224, 224).to(device)
# random generate the prune sparsity for each layer
cfglist = []
for layername, layer in net.named_modules():
if isinstance(layer, nn.Conv2d):
# pruner cannot allow the sparsity to be 0 or 1
sparsity = np.random.uniform(0.01, 0.99)
cfg = {
'op_types': ['Conv2d'],
'op_names': [layername],
'sparsity': sparsity
}
cfglist.append(cfg)
pruner = L1FilterPruner(net, cfglist)
pruner.compress()
ck_file = os.path.join(outdir, '%s.pth' % name)
mask_file = os.path.join(outdir, '%s_mask' % name)
pruner.export_model(ck_file, mask_file)
pruner._unwrap_model()
# Fix the mask conflict
fixed_mask = fix_mask_conflict(mask_file, net, dummy_input)
# use the channel dependency groud truth to check if
# fix the mask conflict successfully
for dset in channel_dependency_ground_truth[name]:
lset = list(dset)
for i, _ in enumerate(lset):
assert fixed_mask[lset[0]]['weight'].size(0) == fixed_mask[
lset[i]]['weight'].size(0)
w_index1 = self.get_pruned_index(
fixed_mask[lset[0]]['weight'])
w_index2 = self.get_pruned_index(
fixed_mask[lset[i]]['weight'])
assert w_index1 == w_index2
if hasattr(fixed_mask[lset[0]], 'bias'):
b_index1 = self.get_pruned_index(
fixed_mask[lset[0]]['bias'])
b_index2 = self.get_pruned_index(
fixed_mask[lset[i]]['bias'])
assert b_index1 == b_index2
def test_speedup_integration(self):
for model_name in [
'resnet18',
'squeezenet1_1',
'mobilenet_v2',
'densenet121',
# 'inception_v3' inception is too large and may fail the pipeline
'densenet169',
'resnet50'
]:
kwargs = {'pretrained': True}
if model_name == 'resnet50':
# testing multiple groups
kwargs = {'pretrained': False, 'groups': 4}
Model = getattr(models, model_name)
net = Model(**kwargs).to(device)
speedup_model = Model(**kwargs).to(device)
net.eval() # this line is necessary
speedup_model.eval()
# random generate the prune config for the pruner
cfgs = generate_random_sparsity(net)
pruner = L1FilterPruner(net, cfgs)
pruner.compress()
pruner.export_model(MODEL_FILE, MASK_FILE)
pruner._unwrap_model()
state_dict = torch.load(MODEL_FILE)
speedup_model.load_state_dict(state_dict)
zero_bn_bias(net)
zero_bn_bias(speedup_model)
data = torch.ones(BATCH_SIZE, 3, 128, 128).to(device)
ms = ModelSpeedup(speedup_model, data, MASK_FILE)
ms.speedup_model()
speedup_model.eval()
ori_out = net(data)
speeded_out = speedup_model(data)
ori_sum = torch.sum(ori_out).item()
speeded_sum = torch.sum(speeded_out).item()
print('Sum of the output of %s (before speedup):' % model_name,
ori_sum)
print('Sum of the output of %s (after speedup):' % model_name,
speeded_sum)
assert (abs(ori_sum - speeded_sum) / abs(ori_sum) < RELATIVE_THRESHOLD) or \
(abs(ori_sum - speeded_sum) < ABSOLUTE_THRESHOLD)
def test_multiplication_speedup(self):
"""
Model from issue 4540.
"""
class Net(torch.nn.Module):
def __init__(self, ):
super(Net, self).__init__()
self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
self.input = torch.nn.Conv2d(3, 8, 3)
self.bn = torch.nn.BatchNorm2d(8)
self.fc1 = torch.nn.Conv2d(8, 16, 1)
self.fc2 = torch.nn.Conv2d(16, 8, 1)
self.activation = torch.nn.ReLU()
self.scale_activation = torch.nn.Hardsigmoid()
self.out = torch.nn.Conv2d(8, 12, 1)
def forward(self, input):
input = self.activation(self.bn(self.input(input)))
scale = self.avgpool(input)
out1 = self.activation(self.fc1(scale))
out1 = self.scale_activation(self.fc2(out1))
return self.out(out1 * input)
model = Net().to(device)
model.eval()
im = torch.ones(1, 3, 512, 512).to(device)
model(im)
cfg_list = []
for name, module in model.named_modules():
if isinstance(module, torch.nn.Conv2d):
cfg_list.append({
'op_types': ['Conv2d'],
'sparsity': 0.3,
'op_names': [name]
})
pruner = L1FilterPruner(model, cfg_list)
pruner.compress()
pruner.export_model(MODEL_FILE, MASK_FILE)
pruner._unwrap_model()
ms = ModelSpeedup(model, im, MASK_FILE)
ms.speedup_model()
def test_convtranspose_model(self):
ori_model = TransposeModel()
dummy_input = torch.rand(1, 3, 8, 8)
config_list = [{'sparsity': 0.5, 'op_types': ['Conv2d']}]
pruner = L1FilterPruner(ori_model, config_list)
pruner.compress()
ori_model(dummy_input)
pruner.export_model(MODEL_FILE, MASK_FILE)
pruner._unwrap_model()
new_model = TransposeModel()
state_dict = torch.load(MODEL_FILE)
new_model.load_state_dict(state_dict)
ms = ModelSpeedup(new_model, dummy_input, MASK_FILE)
ms.speedup_model()
zero_bn_bias(ori_model)
zero_bn_bias(new_model)
ori_out = ori_model(dummy_input)
new_out = new_model(dummy_input)
ori_sum = torch.sum(ori_out)
speeded_sum = torch.sum(new_out)
print('Tanspose Speedup Test: ori_sum={} speedup_sum={}'.format(ori_sum, speeded_sum))
assert (abs(ori_sum - speeded_sum) / abs(ori_sum) < RELATIVE_THRESHOLD) or \
(abs(ori_sum - speeded_sum) < ABSOLUTE_THRESHOLD)
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.experiment_data_dir, exist_ok=True)
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=64,
)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'data', train=False, transform=transform),
batch_size=1000)
# Step1. Model Pretraining
model = NaiveModel().to(device)
criterion = torch.nn.NLLLoss()
optimizer = optim.Adadelta(model.parameters(), lr=args.pretrain_lr)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
flops, params, _ = count_flops_params(model, (1, 1, 28, 28), verbose=False)
if args.pretrained_model_dir is None:
args.pretrained_model_dir = os.path.join(args.experiment_data_dir,
f'pretrained.pth')
best_acc = 0
for epoch in range(args.pretrain_epochs):
train(args, model, device, train_loader, criterion, optimizer,
epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
model.load_state_dict(state_dict)
torch.save(state_dict, args.pretrained_model_dir)
print(f'Model saved to {args.pretrained_model_dir}')
else:
state_dict = torch.load(args.pretrained_model_dir)
model.load_state_dict(state_dict)
best_acc = test(args, model, device, criterion, test_loader)
dummy_input = torch.randn([1000, 1, 28, 28]).to(device)
time_cost = get_model_time_cost(model, dummy_input)
# 125.49 M, 0.85M, 93.29, 1.1012
print(
f'Pretrained model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_acc: .2f}, Time Cost: {time_cost}'
)
# Step2. Model Pruning
config_list = [{'sparsity': args.sparsity, 'op_types': ['Conv2d']}]
kw_args = {}
if args.dependency_aware:
dummy_input = torch.randn([1000, 1, 28, 28]).to(device)
print('Enable the dependency_aware mode')
# note that, not all pruners support the dependency_aware mode
kw_args['dependency_aware'] = True
kw_args['dummy_input'] = dummy_input
pruner = L1FilterPruner(model, config_list, **kw_args)
model = pruner.compress()
pruner.get_pruned_weights()
mask_path = os.path.join(args.experiment_data_dir, 'mask.pth')
model_path = os.path.join(args.experiment_data_dir, 'pruned.pth')
pruner.export_model(model_path=model_path, mask_path=mask_path)
pruner._unwrap_model() # unwrap all modules to normal state
# Step3. Model Speedup
m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
m_speedup.speedup_model()
print('model after speedup', model)
flops, params, _ = count_flops_params(model, dummy_input, verbose=False)
acc = test(args, model, device, criterion, test_loader)
time_cost = get_model_time_cost(model, dummy_input)
print(
f'Pruned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {acc: .2f}, Time Cost: {time_cost}'
)
# Step4. Model Finetuning
optimizer = optim.Adadelta(model.parameters(), lr=args.pretrain_lr)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
best_acc = 0
for epoch in range(args.finetune_epochs):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
model.load_state_dict(state_dict)
save_path = os.path.join(args.experiment_data_dir, f'finetuned.pth')
torch.save(state_dict, save_path)
flops, params, _ = count_flops_params(model, dummy_input, verbose=True)
time_cost = get_model_time_cost(model, dummy_input)
# FLOPs 28.48 M, #Params: 0.18M, Accuracy: 89.03, Time Cost: 1.03
print(
f'Finetuned model FLOPs {flops/1e6:.2f} M, #Params: {params/1e6:.2f}M, Accuracy: {best_acc: .2f}, Time Cost: {time_cost}'
)
print(f'Model saved to {save_path}')
# Step5. Model Quantization via QAT
config_list = [{
'quant_types': ['weight', 'output'],
'quant_bits': {
'weight': 8,
'output': 8
},
'op_names': ['conv1']
}, {
'quant_types': ['output'],
'quant_bits': {
'output': 8
},
'op_names': ['relu1']
}, {
'quant_types': ['weight', 'output'],
'quant_bits': {
'weight': 8,
'output': 8
},
'op_names': ['conv2']
}, {
'quant_types': ['output'],
'quant_bits': {
'output': 8
},
'op_names': ['relu2']
}]
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
quantizer = QAT_Quantizer(model, config_list, optimizer)
quantizer.compress()
# Step6. Quantization Aware Training
best_acc = 0
for epoch in range(1):
train(args, model, device, train_loader, criterion, optimizer, epoch)
scheduler.step()
acc = test(args, model, device, criterion, test_loader)
if acc > best_acc:
best_acc = acc
state_dict = model.state_dict()
calibration_path = os.path.join(args.experiment_data_dir,
'calibration.pth')
calibration_config = quantizer.export_model(model_path, calibration_path)
print("calibration_config: ", calibration_config)
# Step7. Model Speedup
batch_size = 32
input_shape = (batch_size, 1, 28, 28)
engine = ModelSpeedupTensorRT(model,
input_shape,
config=calibration_config,
batchsize=32)
engine.compress()
test_trt(engine, test_loader)
# The Yolo can be downloaded at https://github.com/eriklindernoren/PyTorch-YOLOv3.git
prefix = '/home/user/PyTorch-YOLOv3' # replace this path with yours
# Load the YOLO model
model = models.load_model(
"%s/config/yolov3.cfg" % prefix,
"%s/yolov3.weights" % prefix).cpu()
model.eval()
dummy_input = torch.rand(8, 3, 320, 320)
model(dummy_input)
# Generate the config list for pruner
# Filter the layers that may not be able to prune
not_safe = not_safe_to_prune(model, dummy_input)
cfg_list = []
for name, module in model.named_modules():
if name in not_safe:
continue
if isinstance(module, torch.nn.Conv2d):
cfg_list.append({'op_types':['Conv2d'], 'sparsity':0.6, 'op_names':[name]})
# Prune the model
pruner = L1FilterPruner(model, cfg_list)
pruner.compress()
pruner.export_model('./model', './mask')
pruner._unwrap_model()
# Speedup the model
ms = ModelSpeedup(model, dummy_input, './mask')
ms.speedup_model()
model(dummy_input)
def main():
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
'./data.cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=64,
shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
'./data.cifar10',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=200,
shuffle=False)
model = VGG(depth=16)
model.to(device)
# Train the base VGG-16 model
print('=' * 10 + 'Train the unpruned base model' + '=' * 10)
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, 160, 0)
for epoch in range(160):
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
lr_scheduler.step(epoch)
torch.save(model.state_dict(), 'vgg16_cifar10.pth')
# Test base model accuracy
print('=' * 10 + 'Test on the original model' + '=' * 10)
model.load_state_dict(torch.load('vgg16_cifar10.pth'))
test(model, device, test_loader)
# top1 = 93.51%
# Pruning Configuration, all convolution layers are pruned out 80% filters according to the L1 norm
configure_list = [{
'sparsity': 0.8,
'op_types': ['Conv2d'],
}]
# Prune model and test accuracy without fine tuning.
print('=' * 10 + 'Test on the pruned model before fine tune' + '=' * 10)
pruner = L1FilterPruner(model, configure_list)
model = pruner.compress()
test(model, device, test_loader)
# top1 = 10.00%
# Fine tune the pruned model for 40 epochs and test accuracy
print('=' * 10 + 'Fine tuning' + '=' * 10)
optimizer_finetune = torch.optim.SGD(model.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=1e-4)
best_top1 = 0
kd_teacher_model = VGG(depth=16)
kd_teacher_model.to(device)
kd_teacher_model.load_state_dict(torch.load('vgg16_cifar10.pth'))
kd = KnowledgeDistill(kd_teacher_model, kd_T=5)
for epoch in range(40):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer_finetune, kd)
top1 = test(model, device, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
# mask_path stores mask_dict of the pruned model
pruner.export_model(model_path='pruned_vgg16_cifar10.pth',
mask_path='mask_vgg16_cifar10.pth')
# Test the exported model
print('=' * 10 + 'Test on the pruned model after fine tune' + '=' * 10)
new_model = VGG(depth=16)
new_model.to(device)
new_model.load_state_dict(torch.load('pruned_vgg16_cifar10.pth'))
test(new_model, device, test_loader)
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.dataset, args.data_dir, args.batch_size, args.test_batch_size)
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', 'fpgm']:
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 == 'FPGMPruner':
pruner = FPGMPruner(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', 'fpgm']:
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)
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 main():
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
'./data.cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=64,
shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
'./data.cifar10',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])),
batch_size=200,
shuffle=False)
model = VGG(depth=16)
model.to(device)
# Train the base VGG-16 model
print('=' * 10 + 'Train the unpruned base model' + '=' * 10)
optimizer = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, 160, 0)
for epoch in range(160):
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
lr_scheduler.step(epoch)
torch.save(model.state_dict(), 'vgg16_cifar10.pth')
# Test base model accuracy
print('=' * 10 + 'Test on the original model' + '=' * 10)
model.load_state_dict(torch.load('vgg16_cifar10.pth'))
test(model, device, test_loader)
# top1 = 93.51%
# Pruning Configuration, in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
# Conv_1, Conv_8, Conv_9, Conv_10, Conv_11, Conv_12 are pruned with 50% sparsity, as 'VGG-16-pruned-A'
configure_list = [{
'sparsity':
0.5,
'op_types': ['default'],
'op_names': [
'feature.0', 'feature.24', 'feature.27', 'feature.30',
'feature.34', 'feature.37'
]
}]
# Prune model and test accuracy without fine tuning.
print('=' * 10 + 'Test on the pruned model before fine tune' + '=' * 10)
optimizer_finetune = torch.optim.SGD(model.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=1e-4)
pruner = L1FilterPruner(model, configure_list, optimizer_finetune)
model = pruner.compress()
test(model, device, test_loader)
# top1 = 88.19%
# Fine tune the pruned model for 40 epochs and test accuracy
print('=' * 10 + 'Fine tuning' + '=' * 10)
best_top1 = 0
for epoch in range(40):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer_finetune)
top1 = test(model, device, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
# mask_path stores mask_dict of the pruned model
pruner.export_model(model_path='pruned_vgg16_cifar10.pth',
mask_path='mask_vgg16_cifar10.pth')
# Test the exported model
print('=' * 10 + 'Test on the pruned model after fine tune' + '=' * 10)
new_model = VGG(depth=16)
new_model.to(device)
new_model.load_state_dict(torch.load('pruned_vgg16_cifar10.pth'))
test(new_model, device, test_loader)
