def __init__(self, encoder, checkpoint_path, cuda, num_classes=400):
model_type = "{}_vtn".format(encoder)
args, _ = generate_args(model=model_type, n_classes=num_classes, layer_norm=False, cuda=cuda)
#print(checkpoint_path)
args.pretrain_path = checkpoint_path
self.model, _ = create_model(args, model_type)
if cuda:
self.model = self.model.module
self.model.eval()
self.model.cuda()
# we train on GPU thus it automatically loads to GPU
checkpoint = torch.load(str(checkpoint_path))
else:
self.model.eval()
checkpoint = torch.load(str(checkpoint_path), map_location=lambda storage, loc: storage)
self.device = torch.device('cuda' if cuda else 'cpu')
self.model.load_checkpoint(checkpoint['state_dict'])
#print(self.model)
#load_state(self.model, checkpoint['state_dict'])
self.preprocessing = make_preprocessing(args)
#elf.tp_preprocessing = Compose([args.temporal_stride, LoopPadding(args.sample_duration / args.temporal_stride)])
self.embeds = deque(maxlen=(args.sample_duration * args.temporal_stride))
def __init__(self, encoder, checkpoint_path, num_classes=400):
model_type = "{}_vtn".format(encoder)
args, _ = generate_args(model=model_type,
n_classes=num_classes,
layer_norm=False)
self.model, _ = create_model(args, model_type)
self.model = self.model.module
self.model.eval()
self.model.cuda()
checkpoint = torch.load(str(checkpoint_path))
load_state(self.model, checkpoint['state_dict'])
self.preprocessing = make_preprocessing(args)
self.embeds = deque(maxlen=(args.sample_duration *
args.temporal_stride))
def test_bool_flags(self):
args, _ = generate_args('--no-val')
assert args.val is False
def test_kwargs_is_setting_args(self):
args, _ = generate_args(encoder='test')
assert args.encoder == 'test'
def test_returns_namespace(self):
args, _ = generate_args()
assert isinstance(args, Namespace)