def generate_model(opt):
assert opt.model in ['resnet', 'resnetl', 'resnext', 'c3d']
if opt.model == 'resnet':
assert opt.model_depth in [10]
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 == '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 == 'resnext':
assert opt.model_depth in [101]
from models.resnext import get_fine_tuning_parameters
if 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 == 'c3d':
assert opt.model_depth in [10]
from models.c3d import get_fine_tuning_parameters
if opt.model_depth == 10:
model = c3d.c3d_v1(sample_size=opt.sample_size,
sample_duration=opt.sample_duration,
num_classes=opt.n_classes)
if not opt.no_cuda:
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)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model == 'c3d': # CHECK HERE
model.module.fc = nn.Linear(model.module.fc[0].in_features,
opt.n_finetune_classes)
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_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'])
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
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(opt):
assert opt.model in [
'resnet', 'resnetl', 'resnext', 'c3d', 'mobilenetv2', 'shufflenetv2',
"mstcn"
]
if opt.model == 'resnet':
assert opt.model_depth in [10, 50]
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 == 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 == 'resnetl':
assert opt.model_depth in [10, 18]
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_depth == 18:
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 == 'resnext':
assert opt.model_depth in [101]
from models.resnext import get_fine_tuning_parameters
if 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 == 'c3d':
assert opt.model_depth in [10]
from models.c3d import get_fine_tuning_parameters
if opt.model_depth == 10:
model = c3d.c3d_v1(sample_size=opt.sample_size,
sample_duration=opt.sample_duration,
num_classes=opt.n_classes)
elif opt.model == 'mobilenetv2':
from models.mobilenetv2 import get_fine_tuning_parameters
model = mobilenetv2.mob_v2(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == 'shufflenetv2':
from models.shufflenetv2 import get_fine_tuning_parameters
model = shufflenetv2.shf_v2(num_classes=opt.n_classes,
sample_size=opt.sample_size,
width_mult=opt.width_mult)
elif opt.model == "mstcn":
model = MultiStageTemporalConvNet(
embed_size=opt.embedding_dim,
encoder=opt.tcn_encoder,
n_classes=opt.n_classes,
input_size=(opt.sample_size, opt.sample_size),
input_channels=4 if opt.modality != "RGB" else 3,
num_stages=opt.tcn_stages,
causal_config=opt.tcn_causality,
CTHW_layout=True,
use_preprocessing=opt.use_preprocessing)
if not opt.no_cuda:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
if opt.pretrain_dataset == 'jester':
if opt.sample_duration < 32 and opt.model != 'c3d':
model = _modify_first_conv_layer(model, 3, 3)
if opt.model in ['mobilenetv2', 'shufflenetv2']:
del pretrain['state_dict']['module.classifier.1.weight']
del pretrain['state_dict']['module.classifier.1.bias']
else:
del pretrain['state_dict']['module.fc.weight']
del pretrain['state_dict']['module.fc.bias']
model.load_state_dict(pretrain['state_dict'], strict=False)
if opt.modality in ['RGB', 'flo'] and opt.model != 'c3d':
print("[INFO]: RGB model is used for init model")
if opt.dataset != 'jester' and not opt.no_first_lay:
model = _modify_first_conv_layer(
model, 3, 3) ##### Check models trained (3,7,7) or (7,7,7)
elif opt.modality in ['Depth', 'seg']:
print(
"[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Done. Flow model ready.")
elif opt.modality in ['RGB-D', 'RGB-flo', 'RGB-seg']:
if opt.model != "mstcn":
print(
"[INFO]: Converting the pretrained model to RGB+D init model"
)
model = _construct_rgbdepth_model(model)
if opt.no_first_lay:
model = _modify_first_conv_layer(
model, 3,
4) ##### Check models trained (3,7,7) or (7,7,7)
print("[INFO]: Done. RGB-D model ready.")
if opt.pretrain_dataset == opt.dataset:
model.load_state_dict(pretrain['state_dict'])
elif opt.pretrain_dataset in ['egogesture', 'nv', 'denso']:
del pretrain['state_dict']['module.fc.weight']
del pretrain['state_dict']['module.fc.bias']
model.load_state_dict(pretrain['state_dict'], strict=False)
# Check first kernel size
if opt.model != "mstcn":
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.module.fc = nn.Linear(model.module.fc[0].in_features,
model.module.fc[0].out_features)
model.module.fc = model.module.fc.cuda()
elif opt.model in ['mobilenetv2', '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()
elif opt.model != "mstcn":
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
if opt.model != "mstcn":
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
else:
parameters = model.trainable_parameters()
model = nn.DataParallel(model, device_ids=None)
model = model.cuda()
return model, parameters
else:
print('ERROR no cuda')
return model, model.parameters()
def generate_model(opt, modality):
assert opt.model in ['resnet', 'resnetl', 'resnext', 'c3d']
if opt.model == 'resnet':
# assert opt.model_depth in [10]
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.n_frames_per_clip)
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.n_frames_per_clip)
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.n_frames_per_clip)
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.n_frames_per_clip)
elif opt.model == 'resnext':
assert opt.model_depth in [101]
from models.resnext import get_fine_tuning_parameters
if 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.n_frames_per_clip)
elif opt.model == 'c3d':
assert opt.model_depth in [10]
from models.c3d import get_fine_tuning_parameters
if opt.model_depth == 10:
model = c3d.c3d_v1(sample_size=opt.sample_size,
sample_duration=opt.n_frames_per_clip,
num_classes=opt.n_classes)
if not opt.no_cuda:
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, map_location='cpu')
# assert opt.arch == pretrain['arch']
feature_state_dict = {
key: value
for key, value in pretrain['state_dict'].items()
if key not in ['module.fc.weight', 'module.fc.bias']
}
state_dict = deepcopy(model.state_dict())
state_dict.update(feature_state_dict)
model.load_state_dict(state_dict)
# model.load_state_dict(pretrain['state_dict'])
if 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 modality == 'Depth':
print(
"[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Done. Flow model ready.")
elif 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.")
# Check first kernel size
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.n_frames_per_clip:
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model,
int(opt.n_frames_per_clip / 2), 1)
if opt.model == 'c3d': # CHECK HERE
model.module.fc = nn.Linear(model.module.fc[0].in_features,
model.module.fc[0].out_features)
model.module.fc = model.module.fc.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_begin_index)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path, map_location='cpu')
# assert opt.arch == pretrain['arch']
feature_state_dict = {
key.replace('module.', ''): value
for key, value in pretrain['state_dict'].items()
if key not in ['module.fc.weight', 'module.fc.bias']
}
state_dict = deepcopy(model.state_dict())
state_dict.update(feature_state_dict)
model.load_state_dict(state_dict)
# model.load_state_dict(pretrain['state_dict'])
if modality == 'RGB' and opt.model != 'c3d':
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model, 3, 3)
elif modality == 'Depth':
print(
"[INFO]: Converting the pretrained model to Depth init model")
model = _construct_depth_model(model)
print("[INFO]: Deoth model ready.")
elif 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.")
# Check first kernel size
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.n_frames_per_clip:
print("[INFO]: RGB model is used for init model")
model = _modify_first_conv_layer(model,
int(opt.n_frames_per_clip / 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', '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', 'resnext', 'resnet', 'resnetl',
'shufflenet', 'mobilenetv2', 'shufflenetv2']
if opt.model == 'resnetl':
assert opt.model_depth in [10] # 깊이는 10만 된다!
from models.resnetl import get_fine_tuning_parameters # 전이학습을 위함.
if opt.model_depth == 10:
model = resnetl.resnetl10(
num_classes=opt.n_classes, # 클래스 개수.
shortcut_type=opt.resnet_shortcut, # 디폴트 값 : 'B'
sample_size=opt.sample_size, # 디폴트 값 : 112
sample_duration=opt.sample_duration) # 디폴트 값 : 16 , 입력 프레임
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.resnext50(
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.resnext101(
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.resnext152(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
if not opt.no_cuda:
if opt.gpus == '0':
model = model.cuda()
else:
opt.gpus = opt.local_rank
torch.cuda.set_device(opt.gpus)
model = model.cuda()
#model = nn.DataParallel(model, device_ids=None) # 병렬처리를 위함인데, 안쓸 것 같음.
pytorch_total_params = sum(p.numel() for p in model.parameters() if
p.requires_grad) # grad를 하는 파라미터들을 모두 더한다.
print("Total number of trainable parameters: ", pytorch_total_params) # 파라미터 값 출력
if opt.pretrain_path: # 전이학습.
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 = modify_kernels(opt, model, opt.pretrain_modality)
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_kernels(opt, model, opt.modality)
else: # 전이학습이 아닐때
pass
model = modify_kernels(opt, model, opt.modality)
parameters = get_fine_tuning_parameters(model, opt.ft_portion) # 전이학습할때만 적용 지금은 그냥 파라미터 그대로 반환됨.
return model, parameters