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model.py
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model.py
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import torch
from torch import nn
from models import resnet, pre_resnet, wide_resnet, densenet, simpleCNN
from models import my_model
def generate_model(opt):
assert opt.mode in ['score', 'feature']
if opt.mode == 'score':
last_fc = True
elif opt.mode == 'feature':
last_fc = False
assert opt.model_name in ['resnet', 'preresnet', 'wideresnet', 'resnext', 'densenet', 'simpleCNN']
if opt.model_name == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
if opt.model_depth == 10:
# model = resnet.resnet10(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
# sample_size1=opt.sample_size1_fmri, sample_size2=opt.sample_size2_fmri,
# sample_duration=opt.sample_duration_fmri, opt=opt, last_fc=last_fc)
model = my_model.main(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, opt=opt, last_fc=last_fc, dim=256)
elif opt.model_depth == 18:
model = resnet.resnet18(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1_fmri, sample_size2=opt.sample_size2_fmri,
sample_duration=opt.sample_duration_fmri, opt=opt, last_fc=last_fc)
elif opt.model_depth == 34:
model = resnet.resnet34(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1_fmri, sample_size2=opt.sample_size2_fmri,
sample_duration=opt.sample_duration_fmri, opt=opt, last_fc=last_fc)
elif opt.model_depth == 50:
model = resnet.resnet50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1_fmri, sample_size2=opt.sample_size2_fmri,
sample_duration=opt.sample_duration_fmri, opt=opt, last_fc=last_fc)
elif opt.model_depth == 101:
model = resnet.resnet101(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1_fmri, sample_size2=opt.sample_size2_fmri,
sample_duration=opt.sample_duration_fmri, opt=opt, last_fc=last_fc)
elif opt.model_depth == 152:
model = resnet.resnet152(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_depth == 200:
model = resnet.resnet200(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_name == 'wideresnet':
assert opt.model_depth in [50]
if opt.model_depth == 50:
model = wide_resnet.resnet50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
'''
elif opt.model_name == 'resnext':
assert opt.model_depth in [50, 101, 152]
if opt.model_depth == 50:
model = resnext.resnet50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_depth == 101:
model = resnext.resnet101(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_depth == 152:
model = resnext.resnet152(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size, sample_duration=opt.sample_duration,
last_fc=last_fc)
'''
elif opt.model_name == 'preresnet':
assert opt.model_depth in [18, 34, 50, 101, 152, 200]
if opt.model_depth == 18:
model = pre_resnet.resnet18(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_depth == 34:
model = pre_resnet.resnet34(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc, opt=opt)
elif opt.model_depth == 50:
model = pre_resnet.resnet50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_depth == 101:
model = pre_resnet.resnet101(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_depth == 152:
model = pre_resnet.resnet152(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_depth == 200:
model = pre_resnet.resnet200(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut,
sample_size1=opt.sample_size1, sample_size2=opt.sample_size2,
sample_duration=opt.sample_duration, last_fc=last_fc)
elif opt.model_name == 'densenet':
assert opt.model_depth in [121, 169, 201, 264]
if opt.model_depth == 121:
model = densenet.densenet121(num_classes=opt.n_classes, sample_size1=opt.sample_size1,
sample_size2=opt.sample_size2, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_depth == 169:
model = densenet.densenet169(num_classes=opt.n_classes,sample_size1=opt.sample_size1,
sample_size2=opt.sample_size2, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_depth == 201:
model = densenet.densenet201(num_classes=opt.n_classes,sample_size1=opt.sample_size1,
sample_size2=opt.sample_size2, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_depth == 264:
model = densenet.densenet264(num_classes=opt.n_classes,sample_size1=opt.sample_size1,
sample_size2=opt.sample_size2, sample_duration=opt.sample_duration,
last_fc=last_fc)
elif opt.model_name == 'simpleCNN':
assert opt.model_depth in [8]
if opt.model_depth == 8:
model = simpleCNN._3DCNN()
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
net_dict = model.state_dict()
else:
net_dict = model.state_dict()
# load pretrain
if opt.pretrain_path != '':
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
pretrain_dict = {k: v for k, v in pretrain['state_dict'].items() if k in net_dict.keys()}
net_dict.update(pretrain_dict)
model.load_state_dict(net_dict)
new_parameters = []
for pname, p in model.named_parameters():
for layer_name in opt.new_layer_names:
if pname.find(layer_name) >= 0:
new_parameters.append(p)
break
new_parameters_id = list(map(id, new_parameters))
if not opt.train_pretrain:
for p in model.parameters():
# freeze the pretrained parameters that not in new_parameters
if id(p) not in new_parameters_id:
p.requires_grad = False
base_parameters = list(filter(lambda p: id(p) not in new_parameters_id, model.parameters()))
parameters = {'base_parameters': base_parameters,
'new_parameters': new_parameters}
return model, parameters
return model, model.parameters()