def main():
os.chdir(os.path.dirname(__file__))
args = get_arguments()
constr_weight = get_constraint(args.weight_bits, 'weight')
constr_activation = get_constraint(args.activation_bits, 'activation')
if args.dataset == 'cifar10':
network = resnet20
dataloader = dataloader_cifar
else:
if args.network == 'resnet18':
network = resnet18
elif args.network == 'resnet50':
network = resnet50
else:
print('Not Support Network Type: %s' % args.network)
return
dataloader = dataloader_imagenet
train_loader = dataloader(args.data_root,
split='train',
batch_size=args.batch_size)
test_loader = dataloader(args.data_root,
split='test',
batch_size=args.batch_size)
net = network(quan_first_last=args.quan_first_last,
constr_activation=constr_activation,
preactivation=args.preactivation)
model_path = os.path.join(args.model_root, args.model_name + '.pth')
name_weights_old = torch.load(model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
net.load_state_dict(name_weights_new)
add_lsqmodule(net, constr_weight)
print(net)
net = net.cuda()
net = nn.DataParallel(net, device_ids=range(cuda.device_count()))
quan_activation = isinstance(constr_activation, np.ndarray)
postfix = '_w' if not quan_activation else '_a'
new_model_name = args.prefix + args.model_name + '_lsq' + postfix
cache_root = os.path.join('.', 'cache')
train_loger = LogHelper(new_model_name, cache_root, quan_activation,
args.resume)
optimizer, lr_scheduler = get_optimizer(net=net,
optimizer=args.optimizer,
lr_base=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler=args.lr_scheduler,
total_epoch=args.total_epoch,
quan_activation=quan_activation)
trainer = Trainer(net=net,
train_loader=train_loader,
test_loader=test_loader,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
model_name=new_model_name,
train_loger=train_loger)
trainer(total_epoch=args.total_epoch,
save_check_point=True,
resume=args.resume)
def main():
args = get_arguments()
constr_activation = get_constraint(args.activation_bits, 'activation')
net = mixnet_s(quan_first=True,
quan_last=True,
constr_activation=constr_activation,
preactivation=False,
bw_act=args.activation_bits)
test_loader = dataloader_imagenet(args.data_root, split='test', batch_size=args.batch_size)
add_lsqmodule(net, bit_width=args.weight_bits)
name_weights_old = torch.load(args.model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new)
criterion = torch.nn.CrossEntropyLoss()
score = get_micronet_score(net, args.weight_bits, args.activation_bits)
# Calculate accuracy
net = net.cuda()
quan_perf_epoch = eval_performance(net, test_loader, criterion)
accuracy = quan_perf_epoch[1]
print("Accuracy:", accuracy)
print("Score:", score)
def main():
os.chdir(os.path.dirname(__file__))
args = get_arguments()
constr_weight = get_constraint(args.weight_bits, 'weight')
constr_activation = get_constraint(args.activation_bits, 'activation')
if args.dataset == 'cifar10':
network = resnet20
dataloader = dataloader_cifar10
elif args.dataset == 'cifar100':
t_net = WRN40_6()
state = torch.load(
"/prj/neo_lv/user/ybhalgat/LSQ-KD-0911/cifar100_pretrained/wrn40_6.pth"
)
t_net.load_state_dict(state)
network = WRN40_4
dataloader = dataloader_cifar100
else:
if args.network == 'resnet18':
network = resnet18
elif args.network == 'resnet50':
network = resnet50
elif args.network == 'efficientnet-b0':
t_net = EfficientNet.from_pretrained("efficientnet-b1")
network = efficientnet_b0
elif args.network == "mixnet_s":
t_net = MixNet(net_type=args.teacher)
t_net.load_state_dict(
torch.load("../imagenet_pretrained/" + args.teacher + ".pth"))
network = mixnet_s
else:
print('Not Support Network Type: %s' % args.network)
return
dataloader = dataloader_imagenet
train_loader = dataloader(args.data_root,
split='train',
batch_size=args.batch_size)
test_loader = dataloader(args.data_root,
split='test',
batch_size=args.batch_size)
net = network(quan_first=args.quan_first,
quan_last=args.quan_last,
constr_activation=constr_activation,
preactivation=args.preactivation,
bw_act=args.activation_bits)
# net.load_state_dict(name_weights_new, strict=False)
if args.cem:
##### CEM vector for 1.5x_W7A7_CEM prefinetuning 72%
# cem_input = [7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7,
# 7, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5,
# 7, 7, 7, 6, 7, 7, 7, 7, 7, 5, 7, 7, 7, 6, 4, 7, 7, 6,
# 6, 6, 7, 7, 7, 7, 5, 7, 7, 7, 6, 4, 7, 7, 5, 5, 4, 7,
# 7, 6, 5, 5, 7, 5, 7, 5, 5, 3]
##### CEM vector for 1.5x_W7A7_CEM prefinetuning 70%
cem_input = [
7, 7, 7, 7, 7, 5, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 7, 7, 6, 7, 6, 7,
7, 7, 7, 6, 7, 7, 7, 7, 6, 7, 7, 7, 7, 4, 7, 6, 7, 5, 7, 7, 7, 7,
7, 5, 7, 7, 7, 5, 5, 7, 7, 7, 5, 6, 7, 7, 7, 6, 4, 7, 7, 6, 5, 4,
7, 6, 5, 5, 4, 7, 7, 6, 5, 4, 7, 7, 6, 5, 5, 3
]
strategy_path = "/prj/neo_lv/user/ybhalgat/LSQ-implementation/lsq_quantizer/cem_strategy_relaxed.txt"
with open(strategy_path) as fp:
strategy = fp.readlines()
strategy = [x.strip().split(",") for x in strategy]
##### CEM vector for W6A6_CEM
# cem_input = [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
# 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
# 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,
# 1, 0, 1, 1, 0, 1, 1, 1, 1, 1]
strat = {}
act_strat = {}
for idx, width in enumerate(cem_input):
weight_layer_name = strategy[idx][1]
act_layer_name = strategy[idx][0]
for name, module in net.named_modules():
if name.startswith('module'):
name = name[7:] # remove `module.`
if name == weight_layer_name:
strat[name] = int(cem_input[idx])
if name == act_layer_name:
act_strat[name] = int(cem_input[idx])
add_lsqmodule(net, bit_width=args.weight_bits, strategy=strat)
for name, module in net.named_modules():
if name in act_strat:
if "efficientnet" in args.network:
if "_in_act_quant" in name or "first_act" in name or "_head_act_quant0" in name or "_head_act_quant1" in name:
temp_constr_act = get_constraint(
act_strat[name], 'weight') #symmetric
else:
temp_constr_act = get_constraint(
act_strat[name], 'activation') #asymmetric
elif "mixnet" in args.network:
if "last_act" in name or "out_act_quant" in name or "first_act" in name:
temp_constr_act = get_constraint(
act_strat[name], 'weight') #symmetric
else:
temp_constr_act = get_constraint(
act_strat[name], 'activation') #asymmetric
module.constraint = temp_constr_act
elif args.manual:
if args.network == "wrn40_4":
strategy = {
"block3.layer.0.conv2": 3,
"block3.layer.2.conv1": 3,
"block3.layer.3.conv1": 3,
"block3.layer.4.conv1": 3,
"block3.layer.2.conv2": 3,
"block3.layer.1.conv2": 3,
"block3.layer.3.conv2": 3,
"block3.layer.1.conv1": 3,
"block3.layer.5.conv1": 2,
"block1.layer.1.conv2": 1
}
act_strategy = {
"block3.layer.0.relu2": 3,
"block3.layer.2.relu1": 3,
"block3.layer.3.relu1": 3,
"block3.layer.4.relu1": 3,
"block3.layer.2.relu2": 3,
"block3.layer.1.relu2": 3,
"block3.layer.3.relu2": 3,
"block3.layer.1.relu1": 3,
"block3.layer.5.relu1": 2,
"block1.layer.1.relu2": 1
}
elif args.network == 'efficientnet-b0':
strategy = {
"_fc": 3,
"_conv_head": 5,
"_blocks.15._project_conv": 5,
"_blocks.15._expand_conv": 4,
"_blocks.14._expand_conv": 4,
"_blocks.13._expand_conv": 4,
"_blocks.12._expand_conv": 4,
"_blocks.13._project_conv": 4,
"_blocks.14._project_conv": 4,
"_blocks.12._project_conv": 5,
"_blocks.9._expand_conv": 4,
"_blocks.10._expand_conv": 4
}
act_strategy = {
"_head_act_quant1": 3,
"_head_act_quant0": 5,
"_blocks.15._pre_proj_activation": 5,
"_blocks.15._in_act_quant": 4,
"_blocks.14._in_act_quant": 4,
"_blocks.13._in_act_quant": 4,
"_blocks.12._in_act_quant": 4,
"_blocks.13._pre_proj_activation": 4,
"_blocks.14._pre_proj_activation": 4,
"_blocks.12._pre_proj_activation": 5,
"_blocks.9._in_act_quant": 4,
"_blocks.10._in_act_quant": 4
}
#strategy = {"_fc": 3,
# "_conv_head": 4,
# "_blocks.15._project_conv": 4,
# "_blocks.14._project_conv": 4,
# "_blocks.13._project_conv": 3,
# "_blocks.13._expand_conv": 4,
# "_blocks.12._project_conv": 4,
# "_blocks.12._expand_conv": 5,
# "_blocks.14._expand_conv": 4,
# "_blocks.15._expand_conv": 4,
# "_blocks.9._project_conv": 4}
# #"_blocks.10._project_conv": 4,
# #"_blocks.9._expand_conv": 4,
# #"_blocks.10._expand_conv": 4,
# #"_blocks.7._expand_conv": 4,
# #"_blocks.11._expand_conv": 4}
#act_strategy = {"_head_act_quant1": 3,
# "_head_act_quant0": 4,
# "_blocks.15._pre_proj_activation": 4,
# "_blocks.14._pre_proj_activation": 4,
# "_blocks.13._pre_proj_activation": 3,
# "_blocks.13._in_act_quant": 4,
# "_blocks.12._pre_proj_activation": 4,
# "_blocks.12._in_act_quant": 5,
# "_blocks.14._in_act_quant": 4,
# "_blocks.15._in_act_quant": 4,
# "_blocks.9._pre_proj_activation": 4}
# #"_blocks.10._pre_proj_activation": 4,
# #"_blocks.9._in_act_quant": 4,
# #"_blocks.10._in_act_quant": 4,
# #"_blocks.7._in_act_quant": 4,
# #"_blocks.11._in_act_quant": 4}
add_lsqmodule(net, bit_width=args.weight_bits, strategy=strategy)
for name, module in net.named_modules():
if name in act_strategy:
if "_in_act_quant" in name or "first_act" in name or "_head_act_quant0" in name or "_head_act_quant1" in name:
temp_constr_act = get_constraint(act_strategy[name],
'weight') #symmetric
else:
temp_constr_act = get_constraint(act_strategy[name],
'activation') #asymmetric
module.constraint = temp_constr_act
elif args.haq:
if args.network == 'resnet50':
strategy = [
6, 6, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 3, 5, 4, 3, 5,
4, 3, 4, 4, 4, 2, 5, 4, 3, 3, 5, 3, 2, 5, 3, 2, 4, 3, 2, 5, 3,
2, 5, 3, 4, 2, 5, 2, 3, 4, 2, 3, 4
]
elif args.network == 'efficientnet-b0':
strategy = [
7, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 7, 6, 7, 6, 7, 6, 5, 6, 5, 6, 4,
5, 6, 5, 6, 4, 4, 5, 4, 5, 2, 3, 4, 3, 4, 2, 3, 4, 4, 7, 5, 2,
4, 2, 5, 5, 2, 4, 2, 5, 5, 2, 4, 2, 5, 5, 2, 4, 3, 3, 2
]
add_lsqmodule(net, strategy=strategy)
else:
add_lsqmodule(net, bit_width=args.weight_bits)
model_path = os.path.join(args.model_root, args.model_name + '.pth.tar')
if not os.path.exists(model_path):
model_path = model_path[:-4]
name_weights_old = torch.load(model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new, strict=False)
print(net)
net = net.cuda()
net = nn.DataParallel(net)
t_net = t_net.cuda()
t_net = nn.DataParallel(t_net)
if args.pruned:
start_LSQ(net)
quan_activation = isinstance(constr_activation, np.ndarray)
postfix = '_w' if not quan_activation else '_a'
new_model_name = args.prefix + args.model_name + '_lsq' + postfix
cache_root = os.path.join('.', 'cache')
train_loger = LogHelper(new_model_name, cache_root, quan_activation,
args.resume)
optimizer, lr_scheduler, optimizer_t, lr_scheduler_t = get_optimizer(
s_net=net,
t_net=t_net,
optimizer=args.optimizer,
lr_base=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler=args.lr_scheduler,
total_epoch=args.total_epoch,
quan_activation=quan_activation,
act_lr_factor=args.act_lr_factor,
weight_lr_factor=args.weight_lr_factor)
trainer = Trainer(net=net,
t_net=t_net,
train_loader=train_loader,
test_loader=test_loader,
optimizer=optimizer,
optimizer_t=optimizer_t,
lr_scheduler=lr_scheduler,
lr_scheduler_t=lr_scheduler_t,
model_name=new_model_name,
train_loger=train_loger,
pruned=args.pruned)
trainer(total_epoch=args.total_epoch,
save_check_point=True,
resume=args.resume)
def cem_eval(
cem_input,
data_root="/nvme/users/tijmen/imagenet/",
batch_size=350,
strategy_path="/prj/neo_lv/user/ybhalgat/LSQ-implementation/lsq_quantizer/cem_strategy_relaxed.txt",
model_path="/prj/neo_lv/scratch/jinwonl/finetune/lsq_quantizer/models/1.5x_W8A8_CEM_re1/efficientnet-b0.pth",
activation_bits=7,
weight_bits=7):
constr_activation = get_constraint(activation_bits, 'activation')
network = efficientnet_b0
dataloader = dataloader_imagenet
test_loader = dataloader(data_root, split='test', batch_size=batch_size)
with open(strategy_path) as fp:
strategy = fp.readlines()
strategy = [x.strip().split(",") for x in strategy]
net = network(quan_first=True,
quan_last=True,
constr_activation=constr_activation,
preactivation=False,
bw_act=activation_bits)
strat = {}
act_strat = {}
for idx, flag in enumerate(cem_input):
weight_layer_name = strategy[idx][1]
act_layer_name = strategy[idx][0]
for name, module in net.named_modules():
if name.startswith('module'):
name = name[7:] # remove `module.`
if name == weight_layer_name:
strat[name] = int(cem_input[idx])
if name == act_layer_name:
act_strat[name] = int(cem_input[idx])
add_lsqmodule(net, bit_width=weight_bits, strategy=strat)
for name, module in net.named_modules():
if name in act_strat:
if "_in_act_quant" in name or "first_act" in name or "_head_act_quant0" in name or "_head_act_quant1" in name:
temp_constr_act = get_constraint(act_strat[name],
'weight') #symmetric
else:
temp_constr_act = get_constraint(act_strat[name],
'activation') #asymmetric
module.constraint = temp_constr_act
net.load_state_dict(torch.load(model_path))
criterion = nn.CrossEntropyLoss()
# Calculate score
flops_model = add_flops_counting_methods(net)
flops_model.eval().start_flops_count()
input_res = (3, 224, 224)
batch = torch.ones(()).new_empty(
(1, *input_res),
dtype=next(flops_model.parameters()).dtype,
device=next(flops_model.parameters()).device)
_ = flops_model(batch)
flops_count = flops_model.compute_average_flops_cost(
bw_weight=activation_bits, bw_act=weight_bits, strategy=(strat, strat))
params_count = get_model_parameters_number(flops_model,
bw_weight=weight_bits,
w_strategy=strat)
flops_model.stop_flops_count()
score = params_count / 6900000.0 + flops_count / 1170000000.0
# Calculate accuracy
net = net.cuda()
net = nn.DataParallel(net, device_ids=range(cuda.device_count()))
quan_perf_epoch = eval_performance(net, test_loader, criterion)
accuracy = quan_perf_epoch[1]
return accuracy, score
def main():
os.chdir(os.path.dirname(__file__))
args = get_arguments()
constr_weight = get_constraint(args.weight_bits, 'weight')
constr_activation = get_constraint(args.activation_bits, 'activation')
if args.dataset == 'cifar10':
network = resnet20
dataloader = dataloader_cifar10
elif args.dataset == 'cifar100':
t_net = ResNet(depth=56, num_classes=100)
state = torch.load("/prj/neo_lv/user/ybhalgat/LSQ-KD/cifar100_pretrained/resnet56.pth.tar")
t_net.load_state_dict(state)
network = resnet20
dataloader = dataloader_cifar100
else:
if args.network == 'resnet18':
network = resnet18
elif args.network == 'resnet50':
network = resnet50
elif args.network == 'efficientnet-b0':
t_net = EfficientNet.from_pretrained("efficientnet-b3")
network = efficientnet_b0
else:
print('Not Support Network Type: %s' % args.network)
return
dataloader = dataloader_imagenet
train_loader = dataloader(args.data_root, split='train', batch_size=args.batch_size)
test_loader = dataloader(args.data_root, split='test', batch_size=args.batch_size)
net = network(quan_first=args.quan_first,
quan_last=args.quan_last,
constr_activation=constr_activation,
preactivation=args.preactivation,
bw_act=args.activation_bits)
model_path = os.path.join(args.model_root, args.model_name + '.pth.tar')
if not os.path.exists(model_path):
model_path = model_path[:-4]
name_weights_old = torch.load(model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new)
# net.load_state_dict(name_weights_new, strict=False)
if not args.haq:
add_lsqmodule(net, bit_width=args.weight_bits)
else:
if args.network == 'resnet50':
strategy = [6, 6, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 3, 5, 4, 3, 5, 4, 3, 4, 4, 4, 2, 5,
4, 3, 3, 5, 3, 2, 5, 3, 2, 4, 3, 2, 5, 3, 2, 5, 3, 4, 2, 5, 2, 3, 4, 2, 3, 4]
elif args.network == 'efficientnet-b0':
strategy = [7, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 6, 6, 6,
6, 6, 6, 6, 6, 7, 6, 7, 6, 7, 6, 5, 6, 5, 6, 4, 5, 6, 5, 6, 4, 4, 5, 4, 5, 2,
3, 4, 3, 4, 2, 3, 4, 4, 7, 5, 2, 4, 2, 5, 5, 2, 4, 2, 5, 5, 2, 4, 2, 5, 5, 2,
4, 3, 3, 2]
add_lsqmodule(net, strategy=strategy)
print(net)
net = net.cuda()
net = nn.DataParallel(net, device_ids=range(cuda.device_count()))
t_net = t_net.cuda()
t_net = nn.DataParallel(t_net, device_ids=range(cuda.device_count()))
quan_activation = isinstance(constr_activation, np.ndarray)
postfix = '_w' if not quan_activation else '_a'
new_model_name = args.prefix + args.model_name + '_lsq' + postfix
cache_root = os.path.join('.', 'cache')
train_loger = LogHelper(new_model_name, cache_root, quan_activation, args.resume)
optimizer, lr_scheduler, optimizer_t = get_optimizer(s_net=net,
t_net=t_net,
optimizer=args.optimizer,
lr_base=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler=args.lr_scheduler,
total_epoch=args.total_epoch,
quan_activation=quan_activation,
act_lr_factor=args.act_lr_factor,
weight_lr_factor=args.weight_lr_factor)
trainer = Trainer(net=net,
t_net=t_net,
train_loader=train_loader,
test_loader=test_loader,
optimizer=optimizer,
optimizer_t=optimizer_t,
lr_scheduler=lr_scheduler,
model_name=new_model_name,
train_loger=train_loger)
trainer(total_epoch=args.total_epoch,
save_check_point=True,
resume=args.resume)
def main():
args = get_arguments()
constr_activation = get_constraint(args.activation_bits, 'activation')
net = WRN40_4(quan_first=False,
quan_last=False,
constr_activation=constr_activation,
preactivation=False,
bw_act=args.activation_bits)
test_loader = dataloader_cifar100(args.data_root,
split='test',
batch_size=args.batch_size)
add_lsqmodule(net, bit_width=args.weight_bits)
if args.cem:
strategy = {
"block3.layer.0.conv2": 3,
"block3.layer.2.conv1": 3,
"block3.layer.3.conv1": 3,
"block3.layer.4.conv1": 3,
"block3.layer.2.conv2": 3,
"block3.layer.1.conv2": 3,
"block3.layer.3.conv2": 3,
"block3.layer.1.conv1": 3,
"block3.layer.5.conv1": 2,
"block1.layer.1.conv2": 1
}
act_strategy = {
"block3.layer.0.relu2": 3,
"block3.layer.2.relu1": 3,
"block3.layer.3.relu1": 3,
"block3.layer.4.relu1": 3,
"block3.layer.2.relu2": 3,
"block3.layer.1.relu2": 3,
"block3.layer.3.relu2": 3,
"block3.layer.1.relu1": 3,
"block3.layer.5.relu1": 2,
"block1.layer.1.relu2": 1
}
add_lsqmodule(net, bit_width=args.weight_bits, strategy=strategy)
for name, module in net.named_modules():
if name in act_strategy:
if "_in_act_quant" in name or "first_act" in name or "_head_act_quant0" in name or "_head_act_quant1" in name:
temp_constr_act = get_constraint(act_strategy[name],
'weight') #symmetric
else:
temp_constr_act = get_constraint(act_strategy[name],
'activation') #asymmetric
module.constraint = temp_constr_act
name_weights_old = torch.load(args.model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new)
criterion = torch.nn.CrossEntropyLoss()
score = get_micronet_score(net,
args.weight_bits,
args.activation_bits,
weight_strategy=strategy,
activation_strategy=act_strategy,
input_res=(3, 32, 32),
baseline_params=36500000,
baseline_MAC=10490000000)
# Calculate accuracy
net = net.cuda()
quan_perf_epoch = eval_performance(net, test_loader, criterion)
accuracy = quan_perf_epoch[1]
print("Accuracy:", accuracy)
print("Score:", score)
def main():
args = get_arguments()
constr_activation = get_constraint(args.activation_bits, 'activation')
net = efficientnet_b0(quan_first=True,
quan_last=True,
constr_activation=constr_activation,
preactivation=False,
bw_act=args.activation_bits)
test_loader = dataloader_imagenet(args.data_root,
split='test',
batch_size=args.batch_size)
add_lsqmodule(net, bit_width=args.weight_bits)
if args.cem:
##### CEM vector for 1.5x_W7A7_CEM
cem_input = [
7, 7, 7, 7, 7, 5, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 7, 7, 6, 7, 6, 7,
7, 7, 7, 6, 7, 7, 7, 7, 6, 7, 7, 7, 7, 4, 7, 6, 7, 5, 7, 7, 7, 7,
7, 5, 7, 7, 7, 5, 5, 7, 7, 7, 5, 6, 7, 7, 7, 6, 4, 7, 7, 6, 5, 4,
7, 6, 5, 5, 4, 7, 7, 6, 5, 4, 7, 7, 6, 5, 5, 3
]
strategy_path = "lsq_quantizer/cem_strategy_relaxed.txt"
with open(strategy_path) as fp:
strategy = fp.readlines()
strategy = [x.strip().split(",") for x in strategy]
strat = {}
act_strat = {}
for idx, width in enumerate(cem_input):
weight_layer_name = strategy[idx][1]
act_layer_name = strategy[idx][0]
for name, module in net.named_modules():
if name.startswith('module'):
name = name[7:] # remove `module.`
if name == weight_layer_name:
strat[name] = int(cem_input[idx])
if name == act_layer_name:
act_strat[name] = int(cem_input[idx])
add_lsqmodule(net, bit_width=args.weight_bits, strategy=strat)
for name, module in net.named_modules():
if name in act_strat:
if "_in_act_quant" in name or "first_act" in name or "_head_act_quant0" in name or "_head_act_quant1" in name:
temp_constr_act = get_constraint(act_strat[name],
'weight') #symmetric
else:
temp_constr_act = get_constraint(act_strat[name],
'activation') #asymmetric
module.constraint = temp_constr_act
name_weights_old = torch.load(args.model_path)
name_weights_new = net.state_dict()
name_weights_new.update(name_weights_old)
load_checkpoint(net, name_weights_new)
score = get_micronet_score(net,
args.weight_bits,
args.activation_bits,
weight_strategy=strat,
activation_strategy=act_strat)
criterion = torch.nn.CrossEntropyLoss()
# Calculate accuracy
net = net.cuda()
quan_perf_epoch = eval_performance(net, test_loader, criterion)
accuracy = quan_perf_epoch[1]
print("Accuracy:", accuracy)
print("Score:", score)