model_rgb.load_state_dict(
torch.load(os.path.join(args.snapshot_root, '.pth')))
model_depth.load_state_dict(
torch.load(os.path.join(args.snapshot_root, '.pth')))
model_fusion.load_state_dict(
torch.load(os.path.join(args.snapshot_root, '.pth')))
else:
vgg19_bn = torchvision.models.vgg19_bn(pretrained=True)
model_rgb.copy_params_from_vgg19_bn(vgg19_bn)
model_depth.copy_params_from_vgg19_bn(vgg19_bn)
if cuda:
model_rgb = model_rgb.cuda()
model_depth = model_depth.cuda()
model_fusion = model_fusion.cuda()
if args.phase == 'train':
# Trainer: class, defined in trainer.py
optimizer_rgb = optim.SGD(model_rgb.parameters(),
lr=cfg['lr'],
momentum=cfg['momentum'],
weight_decay=cfg['weight_decay'])
optimizer_depth = optim.SGD(model_depth.parameters(),
lr=cfg['lr'],
momentum=cfg['momentum'],
weight_decay=cfg['weight_decay'])
optimizer_fusion = optim.SGD(model_fusion.parameters(),
lr=cfg['lr'],
momentum=cfg['momentum'],
