print('******Prune Quant model******')
#checkpoint = torch.load('../prune/models_save/nin_refine.pth')
checkpoint = torch.load(args.prune_quant)
cfg = checkpoint['cfg']
if args.model_type == 0:
model = nin.Net(cfg=checkpoint['cfg'])
else:
model = nin_gc.Net(cfg=checkpoint['cfg'])
model.load_state_dict(checkpoint['state_dict'])
best_acc = 0
print('***ori_model***\n', model)
quantize.prepare(model, inplace=True, a_bits=args.a_bits,
w_bits=args.w_bits, q_type=args.q_type,
q_level=args.q_level, device=device,
weight_observer=args.weight_observer,
bn_fuse=args.bn_fuse,
bn_fuse_cali=args.bn_fuse_cali,
pretrained_model=args.pretrained_model,
qaft=args.qaft,
ptq=args.ptq,
percentile=args.percentile)
print('\n***quant_model***\n', model)
elif args.prune_qaft:
print('******Prune QAFT model******')
#checkpoint = torch.load('models_save/nin_bn_fused.pth')
checkpoint = torch.load(args.prune_qaft)
cfg = checkpoint['cfg']
if args.model_type == 0:
model = nin.Net(cfg=checkpoint['cfg'])
else:
model = nin_gc.Net(cfg=checkpoint['cfg'])
print('***ori_model***\n', model)
print('******Prune Quant model******')
if args.model_type == 0:
checkpoint = torch.load('../models_save/nin.pth')
quant_model_train = nin.Net(cfg=checkpoint['cfg'])
else:
checkpoint = torch.load('../models_save/nin_gc.pth')
quant_model_train = nin_gc.Net(cfg=checkpoint['cfg'])
else:
if args.model_type == 0:
checkpoint = torch.load('../models_save/nin.pth')
quant_model_train = nin.Net()
else:
checkpoint = torch.load('../models_save/nin_gc.pth')
quant_model_train = nin_gc.Net()
quant_bn_fused_model_inference = copy.deepcopy(quant_model_train)
quantize.prepare(quant_model_train, inplace=True, A=args.A, W=args.W)
quantize.prepare(quant_bn_fused_model_inference,
inplace=True,
A=args.A,
W=args.W,
quant_inference=True)
quant_model_train.load_state_dict(checkpoint['state_dict'])
quant_bn_fused_model_inference.load_state_dict(checkpoint['state_dict'])
# ********************** quant_model_train ************************
torch.save(quant_model_train, 'models_save/quant_model_train.pth')
torch.save(quant_model_train.state_dict(),
'models_save/quant_model_train_para.pth')
model_array = np.array(quant_model_train)
model_para_array = np.array(quant_model_train.state_dict())
np.savetxt('models_save/quant_model_train.txt', [model_array],
model = nin.Net()
else:
model = nin_gc.Net()
best_acc = 0
for m in model.modules():
if isinstance(m, nn.Conv2d):
init.xavier_uniform_(m.weight)
if m.bias is not None:
init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
init.zeros_(m.bias)
print('***ori_model***\n', model)
quantize.prepare(model,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits)
print('\n***quant_model***\n', model)
if not args.cpu:
model.cuda()
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
base_lr = float(args.lr)
param_dict = dict(model.named_parameters())
params = []
for key, value in param_dict.items():
params += [{'params': [value], 'lr': base_lr, 'weight_decay': args.wd}]
if args.model_type == 0:
checkpoint = torch.load('../models_save/nin.pth')
quant_model_train = nin.Net(cfg=checkpoint['cfg'])
else:
checkpoint = torch.load('../models_save/nin_gc.pth')
quant_model_train = nin_gc.Net(cfg=checkpoint['cfg'])
else:
if args.model_type == 0:
checkpoint = torch.load('../models_save/nin.pth')
quant_model_train = nin.Net()
else:
checkpoint = torch.load('../models_save/nin_gc.pth')
quant_model_train = nin_gc.Net()
quant_model_inference = copy.deepcopy(quant_model_train)
quantize.prepare(quant_model_train,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits)
quantize.prepare(quant_model_inference,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits,
quant_inference=True)
quant_model_train.load_state_dict(checkpoint['state_dict'])
quant_model_inference.load_state_dict(checkpoint['state_dict'])
# ********************** quant_model_train ************************
torch.save(quant_model_train, 'models_save/quant_model_train.pth')
torch.save(quant_model_train.state_dict(),
'models_save/quant_model_train_para.pth')
model_array = np.array(quant_model_train)
model_para_array = np.array(quant_model_train.state_dict())
print('******Prune Refine model******')
#checkpoint = torch.load('../prune/models_save/nin_refine.pth')
checkpoint = torch.load(args.prune_refine)
cfg = checkpoint['cfg']
if args.model_type == 0:
model = nin.Net(cfg=checkpoint['cfg'])
else:
model = nin_gc.Net(cfg=checkpoint['cfg'])
model.load_state_dict(checkpoint['state_dict'])
best_acc = 0
print('***ori_model***\n', model)
quantize.prepare(model,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits,
q_type=args.q_type,
q_level=args.q_level,
device=device,
weight_observer=args.weight_observer,
bn_fuse=args.bn_fuse)
print('\n***quant_model***\n', model)
elif args.refine:
print('******Float Refine model******')
#checkpoint = torch.load('models_save/nin.pth')
state_dict = torch.load(args.refine)
if args.model_type == 0:
model = nin.Net()
else:
model = nin_gc.Net()
model.load_state_dict(state_dict)
best_acc = 0
quant_bn_fused_model_train = nin.Net(cfg=checkpoint['cfg'])
else:
checkpoint = torch.load('../models_save/nin_gc_bn_fused.pth')
quant_bn_fused_model_train = nin_gc.Net(cfg=checkpoint['cfg'])
else:
if args.model_type == 0:
checkpoint = torch.load('../models_save/nin_bn_fused.pth')
quant_bn_fused_model_train = nin.Net()
else:
checkpoint = torch.load('../models_save/nin_gc_bn_fused.pth')
quant_bn_fused_model_train = nin_gc.Net()
quant_bn_fused_model_inference = copy.deepcopy(quant_bn_fused_model_train)
quantize.prepare(quant_bn_fused_model_train,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits,
q_type=args.q_type,
q_level=args.q_level,
device=device,
bn_fuse=1)
quantize.prepare(quant_bn_fused_model_inference,
inplace=True,
a_bits=args.a_bits,
w_bits=args.w_bits,
q_type=args.q_type,
q_level=args.q_level,
device=device,
bn_fuse=1,
quant_inference=True)
quant_bn_fused_model_train.load_state_dict(checkpoint['state_dict'])
quant_bn_fused_model_inference.load_state_dict(checkpoint['state_dict'])
# cifar10类别
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# model
if args.prune_refine:
print('******Prune Refine model******')
#checkpoint = torch.load('../prune/models_save/nin_refine.pth')
checkpoint = torch.load(args.prune_refine)
if args.model_type == 0:
model = nin.Net(cfg=checkpoint['cfg'])
else:
model = nin_gc.Net(cfg=checkpoint['cfg'])
model.load_state_dict(checkpoint['state_dict'])
best_acc = 0
print('***ori_model***\n', model)
quantize.prepare(model, inplace=True, A=args.A, W=args.W)
print('\n***quant_model***\n', model)
elif args.refine:
print('******Float Refine model******')
#checkpoint = torch.load('models_save/nin.pth')
state_dict = torch.load(args.refine)
if args.model_type == 0:
model = nin.Net()
else:
model = nin_gc.Net()
model.load_state_dict(state_dict)
best_acc = 0
print('***ori_model***\n', model)
quantize.prepare(model, inplace=True, A=args.A, W=args.W)
print('\n***quant_model***\n', model)
elif args.resume:
