def __init__(self, schema, lexicon, model_path, fact_check, decoding, timed_session=False, consecutive_entity=True, realizer=None):
super(NeuralSystem, self).__init__()
self.schema = schema
self.lexicon = lexicon
self.timed_session = timed_session
self.consecutive_entity = consecutive_entity
# Load arguments
args_path = os.path.join(model_path, 'config.json')
config = read_json(args_path)
config['batch_size'] = 1
config['gpu'] = 0 # Don't need GPU for batch_size=1
config['decoding'] = decoding
args = argparse.Namespace(**config)
mappings_path = os.path.join(model_path, 'vocab.pkl')
mappings = read_pickle(mappings_path)
vocab = mappings['vocab']
# TODO: different models have the same key now
args.dropout = 0
logstats.add_args('model_args', args)
model = build_model(schema, mappings, args)
# Tensorflow config
if args.gpu == 0:
print 'GPU is disabled'
config = tf.ConfigProto(device_count = {'GPU': 0})
else:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.5, allow_growth=True)
config = tf.ConfigProto(device_count = {'GPU': 1}, gpu_options=gpu_options)
# NOTE: need to close the session when done
tf_session = tf.Session(config=config)
tf.initialize_all_variables().run(session=tf_session)
# Load TF model parameters
ckpt = tf.train.get_checkpoint_state(model_path+'-best')
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
saver = tf.train.Saver()
saver.restore(tf_session, ckpt.model_checkpoint_path)
self.model_name = args.model
if self.model_name == 'attn-copy-encdec':
args.entity_target_form = 'graph'
copy = True
else:
copy = False
preprocessor = Preprocessor(schema, lexicon, args.entity_encoding_form, args.entity_decoding_form, args.entity_target_form, args.prepend)
textint_map = TextIntMap(vocab, mappings['entity'], preprocessor)
Env = namedtuple('Env', ['model', 'tf_session', 'preprocessor', 'vocab', 'copy', 'textint_map', 'stop_symbol', 'remove_symbols', 'max_len', 'evaluator', 'prepend', 'consecutive_entity', 'realizer'])
self.env = Env(model, tf_session, preprocessor, mappings['vocab'], copy, textint_map, stop_symbol=vocab.to_ind(markers.EOS), remove_symbols=map(vocab.to_ind, (markers.EOS, markers.PAD)), max_len=20, evaluator=FactEvaluator() if fact_check else None, prepend=args.prepend, consecutive_entity=self.consecutive_entity, realizer=realizer)
def __init__(self, schema, price_tracker, retriever, model_path, mappings, timed_session=False):
super(NeuralRankerSystem, self).__init__()
self.schema = schema
self.price_tracker = price_tracker
self.timed_session = timed_session
# Load arguments
args_path = os.path.join(model_path, 'config.json')
config = read_json(args_path)
# TODO: handle this properly
config['batch_size'] = 1
config['pretrained_wordvec'] = None
args = argparse.Namespace(**config)
mappings_path = os.path.join(mappings, 'vocab.pkl')
mappings = read_pickle(mappings_path)
vocab = mappings['vocab']
logstats.add_args('model_args', args)
model = build_model(schema, mappings, None, args)
# Tensorflow config
if args.gpu == 0:
print 'GPU is disabled'
config = tf.ConfigProto(device_count = {'GPU': 0})
else:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.5, allow_growth=True)
config = tf.ConfigProto(device_count = {'GPU': 1}, gpu_options=gpu_options)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# NOTE: need to close the session when done
tf_session = tf.Session(config=config)
tf_session.run(tf.global_variables_initializer())
# Load TF model parameters
ckpt = tf.train.get_checkpoint_state(model_path+'-best')
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
saver = tf.train.Saver()
saver.restore(tf_session, ckpt.model_checkpoint_path)
preprocessor = Preprocessor(schema, price_tracker, 'canonical', 'canonical', 'canonical')
textint_map = TextIntMap(vocab, preprocessor)
int_markers = SpecialSymbols(*[mappings['vocab'].to_ind(m) for m in markers])
model_config = {'retrieve': True}
batcher = DialogueBatcherFactory.get_dialogue_batcher(model_config, int_markers=int_markers, slot_filling=False, kb_pad=mappings['kb_vocab'].to_ind(markers.PAD))
StreamingDialogue.textint_map = textint_map
StreamingDialogue.num_context = args.num_context
StreamingDialogue.mappings = mappings
Env = namedtuple('Env', ['ranker', 'retriever', 'tf_session', 'preprocessor', 'mappings', 'textint_map', 'batcher'])
self.env = Env(model, retriever, tf_session, preprocessor, mappings, textint_map, batcher)
default=False,
action='store_true',
help='Test using the best model on dev set')
parser.add_argument('--verbose',
default=False,
action='store_true',
help='More prints')
options.add_data_generator_arguments(parser)
options.add_model_arguments(parser)
cocoa.options.add_trainer_arguments(parser)
args = parser.parse_args()
random.seed(args.random_seed)
create_path(args.stats_file)
logstats.init(args.stats_file, args.verbose)
logstats.add_args('config', args)
model_args = args
if torch.cuda.is_available() and not args.gpuid:
print("WARNING: You have a CUDA device, should run with -gpuid 0")
if args.gpuid:
cuda.set_device(args.gpuid[0])
if args.random_seed > 0:
torch.cuda.manual_seed(args.random_seed)
loading_timer = tm.time()
schema = Schema(model_args.schema_path, None)
data_generator = get_data_generator(args, model_args, schema)
mappings = data_generator.mappings
def __init__(self,
schema,
price_tracker,
model_path,
mappings_path,
decoding,
index=None,
num_candidates=20,
retriever_context_len=2,
timed_session=False):
super(NeuralSystem, self).__init__()
self.schema = schema
self.price_tracker = price_tracker
self.timed_session = timed_session
# Load arguments
args_path = os.path.join(model_path, 'config.json')
config = read_json(args_path)
config['batch_size'] = 1
config['gpu'] = 0 # Don't need GPU for batch_size=1
config['decoding'] = decoding
config['pretrained_wordvec'] = None
args = argparse.Namespace(**config)
vocab_path = os.path.join(mappings_path, 'vocab.pkl')
mappings = read_pickle(vocab_path)
vocab = mappings['vocab']
# TODO: different models have the same key now
args.dropout = 0
logstats.add_args('model_args', args)
model = build_model(schema, mappings, None, args)
# Tensorflow config
if args.gpu == 0:
print 'GPU is disabled'
config = tf.ConfigProto(device_count={'GPU': 0})
else:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5,
allow_growth=True)
config = tf.ConfigProto(device_count={'GPU': 1},
gpu_options=gpu_options)
# NOTE: need to close the session when done
tf_session = tf.Session(config=config)
tf.initialize_all_variables().run(session=tf_session)
# Load TF model parameters
ckpt = tf.train.get_checkpoint_state(model_path + '-best')
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
saver = tf.train.Saver()
saver.restore(tf_session, ckpt.model_checkpoint_path)
# Model config tells data generator which batcher to use
model_config = {}
if args.retrieve or args.model in ('ir', 'selector'):
model_config['retrieve'] = True
if args.predict_price:
model_config['price'] = True
self.model_name = args.model
preprocessor = Preprocessor(schema, price_tracker,
args.entity_encoding_form,
args.entity_decoding_form,
args.entity_target_form)
textint_map = TextIntMap(vocab, preprocessor)
int_markers = SpecialSymbols(
*[mappings['vocab'].to_ind(m) for m in markers])
dialogue_batcher = DialogueBatcherFactory.get_dialogue_batcher(
model_config,
int_markers=int_markers,
slot_filling=False,
kb_pad=mappings['kb_vocab'].to_ind(markers.PAD))
# Retriever
if args.model == 'selector':
retriever = Retriever(index,
context_size=retriever_context_len,
num_candidates=num_candidates)
else:
retriever = None
#TODO: class variable is not a good way to do this
Dialogue.mappings = mappings
Dialogue.textint_map = textint_map
Dialogue.preprocessor = preprocessor
Dialogue.num_context = args.num_context
Env = namedtuple('Env', [
'model', 'tf_session', 'preprocessor', 'vocab', 'textint_map',
'stop_symbol', 'remove_symbols', 'max_len', 'dialogue_batcher',
'retriever'
])
self.env = Env(model,
tf_session,
preprocessor,
mappings['vocab'],
textint_map,
stop_symbol=vocab.to_ind(markers.EOS),
remove_symbols=map(vocab.to_ind,
(markers.EOS, markers.PAD)),
max_len=20,
dialogue_batcher=dialogue_batcher,
retriever=retriever)
def __init__(self, args, schema, lexicon, model_path, timed):
super(PytorchNeuralSystem, self).__init__()
self.schema = schema
self.lexicon = lexicon
self.timed_session = timed
# TODO: do we need the dummy parser?
dummy_parser = argparse.ArgumentParser(description='duh')
options.add_model_arguments(dummy_parser)
options.add_data_generator_arguments(dummy_parser)
dummy_args = dummy_parser.parse_known_args([])[0]
# Load the model.
mappings, model, model_args = model_builder.load_test_model(
model_path, args, dummy_args.__dict__)
logstats.add_args('model_args', model_args)
self.model_name = model_args.model
utterance_vocab = mappings['utterance_vocab']
kb_vocab = mappings['kb_vocab']
self.mappings = mappings
text_generator = get_generator(model, utterance_vocab,
Scorer(args.alpha), args, model_args)
builder = UtteranceBuilder(utterance_vocab, args.n_best, has_tgt=True)
preprocessor = Preprocessor(schema, lexicon,
model_args.entity_encoding_form,
model_args.entity_decoding_form,
model_args.entity_target_form)
textint_map = TextIntMap(utterance_vocab, preprocessor)
remove_symbols = map(utterance_vocab.to_ind,
(markers.EOS, markers.PAD))
use_cuda = use_gpu(args)
dialogue_batcher = DialogueBatcherFactory.get_dialogue_batcher(
model=self.model_name,
kb_pad=None,
mappings=mappings,
num_context=model_args.num_context)
#TODO: class variable is not a good way to do this
Dialogue.preprocessor = preprocessor
Dialogue.textint_map = textint_map
Dialogue.mappings = mappings
Dialogue.num_context = model_args.num_context
Env = namedtuple('Env', [
'model', 'utterance_vocab', 'kb_vocab', 'preprocessor',
'textint_map', 'stop_symbol', 'remove_symbols', 'gt_prefix',
'max_len', 'dialogue_batcher', 'cuda', 'dialogue_generator',
'utterance_builder', 'model_args'
])
self.env = Env(model,
utterance_vocab,
kb_vocab,
preprocessor,
textint_map,
stop_symbol=utterance_vocab.to_ind(markers.EOS),
remove_symbols=remove_symbols,
gt_prefix=1,
max_len=20,
dialogue_batcher=dialogue_batcher,
cuda=use_cuda,
dialogue_generator=text_generator,
utterance_builder=builder,
model_args=model_args)