Python download_PF_pascalの例

コード例 #1

ファイル: train_weak.py プロジェクト: codealphago/weakalign

if use_cuda:
    torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)

# CNN model and loss
print('Creating CNN model...')

model = TwoStageCNNGeometric(use_cuda=use_cuda,
                             return_correlation=True,
                             **arg_groups['model'])

# Download validation dataset if needed
if args.eval_dataset_path=='' and args.eval_dataset=='pf-pascal':
    args.eval_dataset_path='datasets/proposal-flow-pascal/'
if args.eval_dataset=='pf-pascal' and not exists(args.eval_dataset_path):
    download_PF_pascal(args.eval_dataset_path)

# load pre-trained model
if args.model!='':
    checkpoint = torch.load(args.model, map_location=lambda storage, loc: storage)
    checkpoint['state_dict'] = OrderedDict([(k.replace('vgg', 'model'), v) for k, v in checkpoint['state_dict'].items()])
        
    for name, param in model.FeatureExtraction.state_dict().items():
        model.FeatureExtraction.state_dict()[name].copy_(checkpoint['state_dict']['FeatureExtraction.' + name])    
    for name, param in model.FeatureRegression.state_dict().items():
        model.FeatureRegression.state_dict()[name].copy_(checkpoint['state_dict']['FeatureRegression.' + name])
    for name, param in model.FeatureRegression2.state_dict().items():
        model.FeatureRegression2.state_dict()[name].copy_(checkpoint['state_dict']['FeatureRegression2.' + name])
        
if args.model_aff!='':
    checkpoint_aff = torch.load(args.model_aff, map_location=lambda storage, loc: storage)

コード例 #2

ファイル: train_weak.py プロジェクト: nyummvc/Arbicon-Net

if use_cuda:
    torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)

# CNN model and loss
print('Creating CNN model...')

model = TwoStageCNNGeometric(use_cuda=use_cuda,
                             return_correlation=True,
                             **arg_groups['model'])

# Download validation dataset if needed
if args.eval_dataset_path == '' and args.eval_dataset == 'pf-pascal':
    args.eval_dataset_path = 'datasets/proposal-flow-pascal/'
if args.eval_dataset == 'pf-pascal' and not exists(args.eval_dataset_path):
    download_PF_pascal(args.eval_dataset_path)

# load pre-trained model
if args.model != '':
    checkpoint = torch.load(args.model,
                            map_location=lambda storage, loc: storage)
    checkpoint['state_dict'] = OrderedDict([
        (k.replace('vgg', 'model'), v)
        for k, v in checkpoint['state_dict'].items()
    ])

    for name, param in model.FeatureExtraction.state_dict().items():
        model.FeatureExtraction.state_dict()[name].copy_(
            checkpoint['state_dict']['FeatureExtraction.' + name])
    for name, param in model.FeatureRegression.state_dict().items():
        model.FeatureRegression.state_dict()[name].copy_(