def __init__(self, model=None, dataset=None):
self.model = model
self.data_loader = data.WavLoader(dataset)
self.device = None
self.torch_rng_state = t.get_rng_state()
if t.cuda.is_available():
self.torch_cuda_rng_states = t.cuda.get_rng_state_all()
else:
self.torch_cuda_rng_states = None
def __init__(self, step=0, model=None, dataset=None, optim=None):
self.model = model
self.data_loader = data.WavLoader(dataset)
self.optim = optim
self.step = step
self.device = None
self.torch_rng_state = torch.get_rng_state()
if torch.cuda.is_available():
self.torch_cuda_rng_states = torch.cuda.get_rng_state_all()
else:
self.torch_cuda_rng_states = None
def load(self, ckpt_file, dat_file):
sinfo = torch.load(ckpt_file)
# This is the required order for model and data init
self.model = pickle.loads(sinfo['model'])
# ignore the pickled dataset characteristics
dataset = data.MfccInference(pickle.loads(sinfo['dataset']))
dataset.load_data(dat_file)
self.model.post_init(dataset)
self.model.load_state_dict(sinfo['model_state_dict'])
dataset.post_init(self.model)
self.data_loader = data.WavLoader(dataset)
def load(self, ckpt_file, dat_file):
sinfo = torch.load(ckpt_file)
# This is the required order for model and data init
self.model = pickle.loads(sinfo['model'])
dataset = pickle.loads(sinfo['dataset'])
dataset.load_data(dat_file)
self.model.post_init(dataset)
self.model.load_state_dict(sinfo['model_state_dict'])
dataset.post_init(self.model)
self.data_loader = data.WavLoader(dataset)
self.optim = torch.optim.Adam(self.model.parameters())
self.optim.load_state_dict(sinfo['optim'])
self.step = sinfo['step']
self.torch_rng_state = sinfo['rand_state']
self.torch_cuda_rng_states = sinfo['cuda_rand_states']
def load(self, ckpt_file, dat_file):
ckpt = t.load(ckpt_file)
# This is the required order for model and data init
self.model = pickle.loads(ckpt['model'])
# win batch of 1 is inference mode
self.model.override(n_win_batch=1)
# ignore the pickled dataset characteristics
dataset = data.MfccInference(pickle.loads(ckpt['dataset']), dat_file)
# dataset.load_data(dat_file)
self.model.post_init(dataset)
sub_state = {
k: v
for k, v in ckpt['model_state_dict'].items()
if '_lead' not in k and 'left_wing_size' not in k
}
self.model.load_state_dict(sub_state, strict=False)
dataset.post_init(self.model)
self.data_loader = data.WavLoader(dataset)
def __init__(self, model=None, dataset=None):
self.model = model
self.data_loader = data.WavLoader(dataset)
self.device = None
