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'])