def generate_model(opt):
assert opt.model in [
'c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'resnetl',
'shufflenet', 'mobilenetv2', 'shufflenetv2'
]
if opt.model == 'c3d':
from models.c3d import get_fine_tuning_parameters
model = c3d.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'squeezenet':
from models.squeezenet import get_fine_tuning_parameters
model = squeezenet.get_model(version=opt.version,
num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'shufflenet':
from models.shufflenet import get_fine_tuning_parameters
model = shufflenet.get_model(groups=opt.groups,
width_mult=opt.width_mult,
num_classes=opt.n_classes)
elif opt.model == 'shufflenetv2':
from models.shufflenetv2 import get_fine_tuning_parameters
model = shufflenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'resnext':
assert opt.model_depth in [50, 101, 152]
from models.resnext import get_fine_tuning_parameters
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)
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)
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)
elif opt.model == 'resnetl':
assert opt.model_depth in [10]
from models.resnetl import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnetl.resnetl10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
from models.resnet import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnet.resnet10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 18:
model = resnet.resnet18(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = resnet.resnet34(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnet.resnet101(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnet.resnet152(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = resnet.resnet200(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path,
map_location=torch.device('cpu'))
# print(opt.arch)
# print(pretrain['arch'])
# assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
# model = _modify_first_conv_layer(model)
# model = model.cuda()
parameters = get_fine_tuning_parameters(model, opt.ft_portion)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
return model, model.parameters()
def generate_model(args):
assert args.model_type in [
'resnet', 'shufflenet', 'shufflenetv2', 'mobilenet', 'mobilenetv2'
]
if args.pre_train_model == False or args.mode == 'test':
print('Without Pre-trained model')
if args.model_type == 'resnet':
assert args.model_depth in [18, 50, 101]
if args.model_depth == 18:
model = resnet.resnet18(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type=args.shortcut_type,
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_depth == 50:
model = resnet.resnet50(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type=args.shortcut_type,
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_depth == 101:
model = resnet.resnet101(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type=args.shortcut_type,
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_type == 'shufflenet':
model = shufflenet.get_model(groups=args.groups,
width_mult=args.width_mult,
output_dim=args.feature_dim,
pre_train=args.pre_train_model)
elif args.model_type == 'shufflenetv2':
model = shufflenetv2.get_model(output_dim=args.feature_dim,
sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
elif args.model_type == 'mobilenet':
model = mobilenet.get_model(sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
elif args.model_type == 'mobilenetv2':
model = mobilenetv2.get_model(sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
model = nn.DataParallel(model, device_ids=None)
else:
if args.model_type == 'resnet':
pre_model_path = './premodels/kinetics_resnet_' + str(
args.model_depth) + '_RGB_16_best.pth'
###default pre-trained model is trained on kinetics dataset which has 600 classes
if args.model_depth == 18:
model = resnet.resnet18(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type='A',
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_depth == 50:
model = resnet.resnet50(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type='B',
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_depth == 101:
model = resnet.resnet101(output_dim=args.feature_dim,
sample_size=args.sample_size,
sample_duration=args.sample_duration,
shortcut_type='B',
tracking=args.tracking,
pre_train=args.pre_train_model)
elif args.model_type == 'shufflenet':
pre_model_path = './premodels/kinetics_shufflenet_' + str(
args.width_mult) + 'x_G3_RGB_16_best.pth'
model = shufflenet.get_model(groups=args.groups,
width_mult=args.width_mult,
output_dim=args.feature_dim,
pre_train=args.pre_train_model)
elif args.model_type == 'shufflenetv2':
pre_model_path = './premodels/kinetics_shufflenetv2_' + str(
args.width_mult) + 'x_RGB_16_best.pth'
model = shufflenetv2.get_model(output_dim=args.feature_dim,
sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
elif args.model_type == 'mobilenet':
pre_model_path = './premodels/kinetics_mobilenet_' + str(
args.width_mult) + 'x_RGB_16_best.pth'
model = mobilenet.get_model(sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
elif args.model_type == 'mobilenetv2':
pre_model_path = './premodels/kinetics_mobilenetv2_' + str(
args.width_mult) + 'x_RGB_16_best.pth'
model = mobilenetv2.get_model(sample_size=args.sample_size,
width_mult=args.width_mult,
pre_train=args.pre_train_model)
model = nn.DataParallel(
model, device_ids=None) # in order to load pre-trained model
model_dict = model.state_dict()
pretrained_dict = torch.load(pre_model_path)['state_dict']
#print(len(pretrained_dict.keys()))
#print({k for k, v in pretrained_dict.items() if k not in model_dict})
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
#print(len(pretrained_dict.keys()))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model = _construct_depth_model(model)
if args.use_cuda:
model = model.cuda()
return model
def generate_model(opt):
assert opt.model in [
'c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'shufflenet',
'mobilenetv2', 'shufflenetv2', 'slow_mobilenetv2', 'fast_mobilenetv2',
'slow_fast_mobilenetv2'
]
if opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'fast_mobilenetv2':
from models.fast_mobilenetv2 import get_fine_tuning_parameters
model = fast_mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'slow_mobilenetv2':
from models.slow_mobilenetv2 import get_fine_tuning_parameters
model = slow_mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'slow_fast_mobilenetv2':
from models.slow_fast_mobilenetv2 import get_fine_tuning_parameters
model = slow_fast_mobilenetv2.get_model(
num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult_slow=opt.width_mult_slow,
beta=opt.beta,
fusion_kernel_size=opt.fusion_kernel_size,
fusion_conv_channel_ratio=opt.fusion_conv_channel_ratio,
slow_frames=opt.slow_frames,
fast_frames=opt.fast_frames,
lateral_connection_section_indices=opt.
lateral_connection_section_indices)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
assert torch.cuda.is_available()
print("Using GPU {}".format(torch.cuda.get_device_name(0)))
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path,
map_location=torch.device('cpu'))
model.load_state_dict(pretrain['state_dict'])
if opt.inference:
return model, []
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2',
'slow_fast_mobilenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
parameters = get_fine_tuning_parameters(model, opt.ft_portion)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2',
''
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
if opt.resume_path:
if not opt.no_cuda:
model = model.cuda()
model_state = torch.load(opt.resume_path,
map_location=torch.device('cpu'))
model.load_state_dict(model_state['state_dict'])
return model, model.parameters()
def generate_model(opt):
assert opt.model in [
'resnet', 'preresnet', 'wideresnet', 'resnext', 'densenet',
'mobilenet', 'mobilenetv2'
]
if opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
from models.resnet import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnet.resnet10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 18:
model = resnet.resnet18(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = resnet.resnet34(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnet.resnet101(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnet.resnet152(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = resnet.resnet200(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'wideresnet':
assert opt.model_depth in [50]
from models.wide_resnet import get_fine_tuning_parameters
if opt.model_depth == 50:
model = wide_resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
k=opt.wide_resnet_k,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnext':
assert opt.model_depth in [50, 101, 152]
from models.resnext import get_fine_tuning_parameters
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)
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)
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)
elif opt.model == 'preresnet':
assert opt.model_depth in [18, 34, 50, 101, 152, 200]
from models.pre_act_resnet import get_fine_tuning_parameters
if opt.model_depth == 18:
model = pre_act_resnet.resnet18(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = pre_act_resnet.resnet34(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = pre_act_resnet.resnet50(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = pre_act_resnet.resnet101(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = pre_act_resnet.resnet152(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = pre_act_resnet.resnet200(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'densenet':
assert opt.model_depth in [121, 169, 201, 264]
from models.densenet import get_fine_tuning_parameters
if opt.model_depth == 121:
model = densenet.densenet121(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 169:
model = densenet.densenet169(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 201:
model = densenet.densenet201(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 264:
model = densenet.densenet264(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
if not opt.no_cuda:
if not opt.no_cuda_predict:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
print("Pretrain arch", pretrain['arch'])
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
ft_begin_index = opt.ft_begin_index
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
ft_begin_index = 'complete' if ft_begin_index == 0 else 'last_layer'
elif opt.model == 'densenet':
model.module.classifier = nn.Linear(
model.module.classifier.in_features,
opt.n_finetune_classes)
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
print("Finetuning at:", ft_begin_index)
parameters = get_fine_tuning_parameters(model, ft_begin_index)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
ft_begin_index = opt.ft_begin_index
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
ft_begin_index = 'complete' if ft_begin_index == 0 else 'last_layer'
elif opt.model == 'densenet':
model.classifier = nn.Linear(model.classifier.in_features,
opt.n_finetune_classes)
else:
model.fc = nn.Linear(model.fc.in_features,
opt.n_finetune_classes)
print("Finetuning at:", ft_begin_index)
parameters = get_fine_tuning_parameters(model, ft_begin_index)
return model, parameters
return model, model.parameters()
def generate_model(opt):
assert opt.model in [
'c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'resnetl',
'shufflenet', 'mobilenetv2', 'shufflenetv2'
]
if opt.model == 'c3d':
from models.c3d import get_fine_tuning_parameters
model = c3d.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'squeezenet':
from models.squeezenet import get_fine_tuning_parameters
model = squeezenet.get_model(version=opt.version,
num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'shufflenet':
from models.shufflenet import get_fine_tuning_parameters
model = shufflenet.get_model(groups=opt.groups,
width_mult=opt.width_mult,
num_classes=opt.n_classes)
elif opt.model == 'shufflenetv2':
from models.shufflenetv2 import get_fine_tuning_parameters
model = shufflenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'resnext':
assert opt.model_depth in [50, 101, 152]
from models.resnext import get_fine_tuning_parameters
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)
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)
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)
elif opt.model == 'resnetl':
assert opt.model_depth in [10]
from models.resnetl import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnetl.resnetl10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
from models.resnet import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnet.resnet10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 18:
model = resnet.resnet18(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = resnet.resnet34(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnet.resnet101(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnet.resnet152(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = resnet.resnet200(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path,
map_location=torch.device('cpu'))
# print(opt.arch)
# print(pretrain['arch'])
# assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
model.module.classifier = model.module.classifier.cuda()
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
if opt.modality == 'RGB' and opt.model != 'c3d':
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(
model, 3, 3) ##### Check models trained (3,7,7) or (7,7,7)
elif opt.modality == 'Depth':
print(
"[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Done. Flow model ready.")
elif opt.modality == 'RGB-D':
print(
"[INFO]: Converting the pretrained model to RGB+D init model")
model = _construct_rgbdepth_model(model)
print("[INFO]: Done. RGB-D model ready.")
modules = list(model.modules())
first_conv_idx = list(
filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
conv_layer = modules[first_conv_idx]
if conv_layer.kernel_size[0] > opt.sample_duration:
model = _modify_first_conv_layer(model,
int(opt.sample_duration / 2), 1)
parameters = get_fine_tuning_parameters(model, opt.ft_portion)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
if opt.modality == 'RGB' and opt.model != 'c3d':
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model, 3, 3)
elif opt.modality == 'Depth':
print(
"[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Deoth model ready.")
elif opt.modality == 'RGB-D':
print(
"[INFO]: Converting the pretrained model to RGB-D init model")
model = _construct_rgbdepth_model(model)
print("[INFO]: Done. RGB-D model ready.")
modules = list(model.modules())
first_conv_idx = list(
filter(lambda x: isinstance(modules[x], nn.Conv3d),
list(range(len(modules)))))[0]
conv_layer = modules[first_conv_idx]
if conv_layer.kernel_size[0] > opt.sample_duration:
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model,
int(opt.sample_duration / 2), 1)
if opt.model == 'c3d': # CHECK HERE
model.fc = nn.Linear(model.fc[0].in_features,
model.fc[0].out_features)
else:
model.fc = nn.Linear(model.fc.in_features, opt.n_finetune_classes)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
return model, model.parameters()
def generate_model(opt):
assert opt.model in [
'c3d', 'squeezenet', 'mobilenet', 'shufflenet', 'mobilenetv2',
'shufflenetv2'
]
if opt.model == 'c3d':
from models.c3d import get_fine_tuning_parameters
model = c3d.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'squeezenet':
from models.squeezenet import get_fine_tuning_parameters
model = squeezenet.get_model(version=opt.version,
num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'shufflenet':
from models.shufflenet import get_fine_tuning_parameters
model = shufflenet.get_model(groups=opt.groups,
width_mult=opt.width_mult,
num_classes=opt.n_classes)
elif opt.model == 'shufflenetv2':
from models.shufflenetv2 import get_fine_tuning_parameters
model = shufflenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenet':
from models.mobilenet import get_fine_tuning_parameters
model = mobilenet.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.get_model(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
pytorch_total_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total number of trainable parameters: ", pytorch_total_params)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path,
map_location=torch.device('cpu'))
model_dict = model.state_dict()
pretrain_dict = {
k: v
for k, v in pretrain['state_dict'].items() if k in model_dict
}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
# if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']:
# model.module.classifier = nn.Sequential(
# nn.Dropout(0.8),
# nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes))
# model.module.classifier = model.module.classifier.cuda()
# elif opt.model == 'squeezenet':
# model.module.classifier = nn.Sequential(
# nn.Dropout(p=0.5),
# nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1),
# nn.ReLU(inplace=True),
# nn.AvgPool3d((1,4,4), stride=1))
# model.module.classifier = model.module.classifier.cuda()
# else:
# model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes)
# model.module.fc = model.module.fc.cuda()
model = _modify_first_conv_layer(model)
model = model.cuda()
parameters = get_fine_tuning_parameters(model, opt.ft_portion)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model in [
'mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2'
]:
model.module.classifier = nn.Sequential(
nn.Dropout(0.9),
nn.Linear(model.module.classifier[1].in_features,
opt.n_finetune_classes))
elif opt.model == 'squeezenet':
model.module.classifier = nn.Sequential(
nn.Dropout(p=0.5),
nn.Conv3d(model.module.classifier[1].in_channels,
opt.n_finetune_classes,
kernel_size=1), nn.ReLU(inplace=True),
nn.AvgPool3d((1, 4, 4), stride=1))
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
return model, model.parameters()
