def __init__(self, encoder, decoder, encoder_preproc, decoder_preproc):
super().__init__()
self.enc_preproc = registry.lookup(
'encoder', encoder['name']).Preproc(**encoder_preproc)
self.dec_preproc = registry.lookup(
'decoder', decoder['name']).Preproc(**decoder_preproc)
def __init__(self, logger, config):
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self.logger = logger
self.train_config = registry.instantiate(TrainConfig, config['train'])
self.data_random = random_state.RandomContext(
self.train_config.data_seed)
self.model_random = random_state.RandomContext(
self.train_config.model_seed)
self.init_random = random_state.RandomContext(
self.train_config.init_seed)
with self.init_random:
# 0. Construct preprocessors
self.model_preproc = registry.instantiate(registry.lookup(
'model', config['model']).Preproc,
config['model'],
unused_keys=('name', ))
self.model_preproc.load()
# 1. Construct model
self.model = registry.construct('model',
config['model'],
unused_keys=('encoder_preproc',
'decoder_preproc'),
preproc=self.model_preproc,
device=self.device)
self.model.to(self.device)
def __init__(self, config):
self.config = config
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
torch.set_num_threads(1)
# 0. Construct preprocessors
self.model_preproc = registry.instantiate(
registry.lookup('model', config['model']).Preproc, config['model'])
self.model_preproc.load()
def __init__(self, logger, config, gpu):
if torch.cuda.is_available():
self.device = torch.device('cuda:{}'.format(gpu))
else:
self.device = torch.device('cpu')
random.seed(1)
numpy.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
self.logger = logger
self.train_config = registry.instantiate(TrainConfig, config['train'])
self.train_config.eval_every_n = 500
self.train_config.save_every_n = 500
self.data_random = random_state.RandomContext(
self.train_config.data_seed)
self.model_random = random_state.RandomContext(
self.train_config.model_seed)
self.init_random = random_state.RandomContext(
self.train_config.init_seed)
with self.init_random:
# 0. Construct preprocessors
self.model_preproc = registry.instantiate(registry.lookup(
'model', config['model']).Preproc,
config['model'],
unused_keys=('name', ))
self.model_preproc.load()
# 1. Construct model
self.model = registry.construct('model',
config['model'],
unused_keys=('encoder_preproc',
'decoder_preproc'),
preproc=self.model_preproc,
device=self.device)
self.model.to(self.device)
def __init__(self, config):
self.config = config
self.model_preproc = registry.instantiate(
registry.lookup('model', config['model']).Preproc, config['model'])
