def __init__(
self,
grammar_mdl: NASGrammarModel,
pretrained_vae: bool,
freeze_vae: bool,
vae_hparams: dict = stgs.VAE_HPARAMS,
pred_hparams: dict = stgs.PRED_HPARAMS,
):
super().__init__()
# make separate Namespaces for convenience
self.vae_hparams, self.pred_hparams = vae_hparams, pred_hparams
# make Namespace of combined hyperparameters for compatibility with PL:
self.hparams = {}
for k, v in vae_hparams.items():
self.hparams['_'.join(['vae', k])] = v
for k, v in pred_hparams.items():
self.hparams['_'.join(['pred', k])] = v
self.hparams = Namespace(**self.hparams)
self.vae = NA_VAE(self.vae_hparams)
if pretrained_vae:
self.vae.load_state_dict(torch.load(self.hparams.vae_weights_path))
if freeze_vae:
self.vae.freeze()
print('VAE encoder frozen.')
self.predictor = PerfPredictor(grammar_mdl, self.pred_hparams)
def __init__(self, grammar, device, hparams=stgs.VAE_HPARAMS):
"""
Load trained encoder/decoder and grammar model
:param grammar: A nas_grammar.Grammar object
:param hparams: dict, hyperparameters for the VAE and the grammar model
"""
self._grammar = grammar
self.device = device
self.hp = hparams
self.max_len = self.hp['max_len']
self._productions = self._grammar.GCFG.productions()
self._prod_map = make_prod_map(grammar.GCFG)
self._parser = nltk.ChartParser(grammar.GCFG)
self._tokenize = make_tokenizer(grammar.GCFG)
self._n_chars = len(self._productions)
self._lhs_map = grammar.lhs_map
self.vae = NA_VAE(self.hp)
self.vae.eval()
t_var = globals()[t]
if isinstance(t_var, (torch.Tensor, np.ndarray)):
print(t, ':', t_var.shape)
elif isinstance(t_var, (list, dict, tuple, str)):
print(t, ':', len(t_var))
if value:
print(t_var)
else:
pass
min_depth = 3
max_depth = stgs.VAE_HPARAMS['max_depth'] # maximum network depth
print(f'Using maximum sequence length of {stgs.VAE_HPARAMS["max_len"]}.')
torch.cuda.empty_cache()
vae = NA_VAE(stgs.VAE_HPARAMS)
vae = vae.float()
vae = vae.cuda()
# vae.load_state_dict(torch.load(f'{checkpoint_path}/weights.pt'))
torch.cuda.empty_cache()
version = datetime.strftime(datetime.fromtimestamp(seed),
'%Y-%m-%d..%H.%M.%S')
logger = TensorBoardLogger(checkpoint_path, version=version)
checkpoint = ModelCheckpoint(filepath=checkpoint_path,
save_top_k=1,
verbose=True,
monitor='loss',
mode='min')
early_stop = EarlyStopping(
monitor='loss',
