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model.py
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import torch
from torch import nn
from models import resnet, resnet2p1d, pre_act_resnet, wide_resnet, resnext, densenet, resnet_strg
def get_module_name(name):
name = name.split('.')
if name[0] == 'module':
i = 1
else:
i = 0
if name[i] == 'features':
i += 1
return name[i]
def get_fine_tuning_parameters(model, ft_begin_module):
if not ft_begin_module:
return model.parameters()
parameters = []
add_flag = False
for k, v in model.named_parameters():
if ft_begin_module == get_module_name(k):
add_flag = True
if add_flag:
parameters.append({'params': v})
return parameters
def generate_model(opt):
assert opt.model in [
'resnet', 'resnet2p1d', 'preresnet', 'wideresnet', 'resnext', 'densenet',
'resnet_strg'
]
if opt.model == 'resnet':
model = resnet.generate_model(model_depth=opt.model_depth,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool,
widen_factor=opt.resnet_widen_factor)
elif opt.model == 'resnet_strg':
model = resnet_strg.generate_model(model_depth=opt.model_depth,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool,
widen_factor=opt.resnet_widen_factor)
elif opt.model == 'resnet2p1d':
model = resnet2p1d.generate_model(model_depth=opt.model_depth,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool,
widen_factor=opt.resnet_widen_factor)
elif opt.model == 'wideresnet':
model = wide_resnet.generate_model(
model_depth=opt.model_depth,
k=opt.wide_resnet_k,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool)
elif opt.model == 'resnext':
model = resnext.generate_model(model_depth=opt.model_depth,
cardinality=opt.resnext_cardinality,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool)
elif opt.model == 'preresnet':
model = pre_act_resnet.generate_model(
model_depth=opt.model_depth,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
shortcut_type=opt.resnet_shortcut,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool)
elif opt.model == 'densenet':
model = densenet.generate_model(model_depth=opt.model_depth,
n_classes=opt.n_classes,
n_input_channels=opt.n_input_channels,
conv1_t_size=opt.conv1_t_size,
conv1_t_stride=opt.conv1_t_stride,
no_max_pool=opt.no_max_pool)
return model
def load_pretrained_model(model, pretrain_path, model_name, n_finetune_classes,
is_strg=False):
if pretrain_path:
print('loading pretrained model {}'.format(pretrain_path))
pretrain = torch.load(pretrain_path, map_location='cpu')
model.load_state_dict(pretrain['state_dict'])
if is_strg:
return model
tmp_model = model
if model_name == 'densenet':
tmp_model.classifier = nn.Linear(tmp_model.classifier.in_features,
n_finetune_classes)
else:
tmp_model.fc = nn.Linear(tmp_model.fc.in_features,
n_finetune_classes)
return model
def make_data_parallel(model, is_distributed, device):
if is_distributed:
if device.type == 'cuda' and device.index is not None:
torch.cuda.set_device(device)
model.to(device)
model = nn.parallel.DistributedDataParallel(model,
device_ids=[device])
else:
model.to(device)
model = nn.parallel.DistributedDataParallel(model)
elif device.type == 'cuda':
model = nn.DataParallel(model, device_ids=None).cuda()
return model