def main():
"""Main function"""
args = parse_args()
smiles_list = uc.read_smi_file(args.input_smiles_path)
LOG.info("Building vocabulary")
tokenizer = mv.SMILESTokenizer()
vocabulary = mv.create_vocabulary(smiles_list, tokenizer=tokenizer)
tokens = vocabulary.tokens()
LOG.info("Vocabulary contains %d tokens: %s", len(tokens), tokens)
network_params = {
'num_layers': args.num_layers,
'layer_size': args.layer_size,
'cell_type': args.cell_type,
'embedding_layer_size': args.embedding_layer_size,
'dropout': args.dropout
}
model = mm.Model(no_cuda=True,
vocabulary=vocabulary,
tokenizer=tokenizer,
network_params=network_params,
max_sequence_length=args.max_sequence_length)
LOG.info("Saving model at %s", args.output_model_path)
model.save(args.output_model_path)
def _initialize_dataloader(self, training_set):
dataset = md.Dataset(smiles_list=training_set,
vocabulary=self.model.vocabulary,
tokenizer=mv.SMILESTokenizer())
return tud.DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
collate_fn=md.Dataset.collate_fn)
def _initialize_dataloader(self, path):
training_set = uc.read_smi_file(path)
dataset = md.Dataset(smiles_list=training_set,
vocabulary=self._model.vocabulary,
tokenizer=mv.SMILESTokenizer())
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=self._batch_size,
shuffle=self._shuffle_each_epoch,
collate_fn=md.Dataset.collate_fn)
return dataloader
def run(self):
"""
Performs the creation of the model.
"""
if self._already_run:
return
LOG.info("Building vocabulary")
tokenizer = mv.SMILESTokenizer()
vocabulary = mv.create_vocabulary(self._smiles_list,
tokenizer=tokenizer)
tokens = vocabulary.tokens()
LOG.info("Vocabulary contains %d tokens: %s", len(tokens), tokens)
LOG.info("Saving model at %s", self._output_model_path)
network_params = {
'num_layers': self._num_layers,
'layer_size': self._layer_size,
'embedding_layer_size': self._embedding_layer_size,
'dropout': self._dropout,
'memory_cells': self._memory_cells,
'cell_size': self._cell_size,
'read_heads': self._read_heads,
'num_controller_layers': self._num_controller_layers,
'controller_type': self._controller_type,
'model_type': self._model_type
}
model = mm.Model(vocabulary=vocabulary,
tokenizer=tokenizer,
network_params=network_params,
model_type=self._model_type,
max_sequence_length=self._max_sequence_length)
model_folder = model.model_name.split('.')[0]
storage_folder_path = os.path.join(self._output_model_path,
model_folder)
i = 0
while os.path.exists(storage_folder_path):
if i == 0:
storage_folder_path += '(%s)' % i
else:
cut_path = storage_folder_path[:-3]
storage_folder_path = cut_path + '(%s)' % i
i += 1
os.makedirs(storage_folder_path)
self._output_model_path = os.path.join(storage_folder_path,
model.model_name)
model.model_dir = storage_folder_path
model.save(self._output_model_path)
LOG.info('Model saved!')
LOG.info(model.__dict__)
def _initialize_dataloader(self, path):
training_set = chem_smiles.read_smiles_file(
path,
standardize=self._config.standardize,
randomize=self._config.randomize)
dataset = reinvent_dataset.Dataset(
smiles_list=training_set,
vocabulary=self._model.vocabulary,
tokenizer=reinvent_vocabulary.SMILESTokenizer())
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=self._config.batch_size,
shuffle=self._config.shuffle_each_epoch,
collate_fn=reinvent_dataset.Dataset.collate_fn)
return dataloader
def run(self):
"""
Carries out the creation of the model.
"""
tokenizer = voc.SMILESTokenizer()
vocabulary = voc.create_vocabulary(self._smiles_list, tokenizer=tokenizer)
network_params = {
'num_layers': self._num_layers,
'layer_size': self._layer_size,
'cell_type': self._cell_type,
'embedding_layer_size': self._embedding_layer_size,
'dropout': self._dropout,
'layer_normalization': self._layer_normalization
}
model = reinvent.Model(no_cuda=True, vocabulary=vocabulary, tokenizer=tokenizer, network_params=network_params, max_sequence_length=self._max_sequence_length)
model.save(self._output_model_path)
return model
def load_from_file(cls, file_path, mode="train"):
"""
Loads a model from a single file
:param file_path: Path of the file where the model data was previously stored.
:param mode: Mode to load the model as (training or eval).
:return: A new instance of the Model or an exception if it was not possible to load it.
"""
if torch.cuda.is_available():
save_dict = torch.load(file_path)
else:
save_dict = torch.load(file_path,
map_location=lambda storage, loc: storage)
network_params = save_dict.get("network_params", {})
model = Model(vocabulary=save_dict['vocabulary'],
tokenizer=save_dict.get('tokenizer',
mv.SMILESTokenizer()),
network_params=network_params,
max_sequence_length=save_dict['max_sequence_length'],
mode=mode)
model.network.load_state_dict(save_dict["network"])
return model
def load_from_file(cls, file_path: str, sampling_mode=False):
"""
Loads a model from a single file
:param file_path: input file path
:return: new instance of the RNN or an exception if it was not possible to load it.
"""
if torch.cuda.is_available():
save_dict = torch.load(file_path)
else:
save_dict = torch.load(file_path, map_location=lambda storage, loc: storage)
network_params = save_dict.get("network_params", {})
model = Model(
vocabulary=save_dict['vocabulary'],
tokenizer=save_dict.get('tokenizer', mv.SMILESTokenizer()),
network_params=network_params,
max_sequence_length=save_dict['max_sequence_length']
)
model.network.load_state_dict(save_dict["network"])
if sampling_mode:
model.network.eval()
return model
def load_from_file(cls, file_path, sampling_mode=False):
"""
Loads a model from a single file
:param file: filpath as string
:return: new instance of the RNN or None if it was not possible to load
"""
if torch.cuda.is_available():
save_dict = torch.load(file_path)
else:
save_dict = torch.load(file_path, map_location=lambda storage, loc: storage)
network_params = save_dict.get("network_params", {})
path_parts = file_path.split('/')
# name of the model is the name of the dir
model_name = path_parts[len(path_parts) - 2] + Model.MODEL_EXTENSION
# popping the model file name, leaving only dir path
path_parts.pop()
model = Model(
vocabulary=save_dict['vocabulary'],
tokenizer=save_dict.get('tokenizer', mv.SMILESTokenizer()),
network_params=network_params,
max_sequence_length=save_dict['max_sequence_length'],
model_type=save_dict['model_type'],
model_name=model_name,
model_dir='/'.join(path_parts),
)
model.network.load_state_dict(save_dict["network"])
if sampling_mode:
torch.no_grad()
model.network.eval()
return model
def setUp(self):
self.tokenizer = mv.SMILESTokenizer()
def test_create(self):
voc = mv.create_vocabulary(smiles_list=tfv.SMILES_LIST, tokenizer=mv.SMILESTokenizer())
self.assertEqual(voc, tfv.simple())
