def generate_model(opt):
assert opt.model_depth in [50, 101, 152]
if opt.model_depth == 50:
model = torchvision.models.resnet50(pretrained=True)
model = I3ResNet(model, opt.sample_duration)
elif opt.model_depth == 101:
model = torchvision.models.resnet101(pretrained=True)
model = I3ResNet(model, opt.sample_duration)
elif opt.model_depth == 152:
model = torchvision.models.resnet152(pretrained=True)
model = I3ResNet(model, opt.sample_duration)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch'], 'Unmatched model from pretrained path.'
state_dict = pretrain['state_dict']
local_state = model.state_dict()
for name, param in local_state.items():
if 'fc' not in name:
key = 'module.' + name
if key in state_dict:
input_param = state_dict[key].data
param.copy_(input_param)
base_model = model
model = GCN(opt.n_box_per_frame, opt.step_per_layer, opt.n_classes, opt.basenet_fixed_layers)
model.create_architecture(base_model)
del base_model
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
parameters = []
arch_parameters = []
for key, value in dict(model.named_parameters()).items():
if value.requires_grad:
if 'arch_weights' not in key:
parameters += [{'params':[value]}]
else:
arch_parameters += [{'params':[value]}]
return model, parameters, arch_parameters
else:
parameters = []
arch_parameters = []
for key, value in dict(model.named_parameters()).items():
if value.requires_grad:
if 'arch_weights' not in key:
parameters += [{'params':[value]}]
else:
arch_parameters += [{'params':[value]}]
return model, parameters, arch_parameters
#%%
sens[sens > 0] = 1
if sens_attr:
sens[sens > 0] = 1
# Model and optimizer
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
nclass=1,
dropout=args.dropout)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
if args.cuda:
model.cuda()
features = features.cuda()
# adj = adj.cuda()
sens = sens.cuda()
# idx_sens_train = idx_sens_train.cuda()
# idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
sens = sens.cuda()
idx_sens_train = idx_sens_train.cuda()
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
# Train model
t_total = time.time()
best_acc = 0.0
best_test = 0.0