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)
vocab_path = os.path.join(args.init_from, 'vocab.pkl')
saved_config = read_json(config_path)
saved_config['decoding'] = args.decoding
saved_config['batch_size'] = args.batch_size
model_args = argparse.Namespace(**saved_config)
# Checkpoint
if args.test and args.best:
ckpt = tf.train.get_checkpoint_state(args.init_from + '-best')
else:
ckpt = tf.train.get_checkpoint_state(args.init_from)
assert ckpt, 'No checkpoint found'
assert ckpt.model_checkpoint_path, 'No model path found in checkpoint'
# Load vocab
mappings = read_pickle(vocab_path)
print 'Done [%fs]' % (time.time() - start)
else:
# Save config
if not os.path.isdir(args.checkpoint):
os.makedirs(args.checkpoint)
config_path = os.path.join(args.checkpoint, 'config.json')
write_json(vars(args), config_path)
model_args = args
mappings = None
ckpt = None
schema = Schema(model_args.schema_path, model_args.domain)
scenario_db = ScenarioDB.from_dict(schema, read_json(args.scenarios_path))
dataset = read_dataset(scenario_db, args)
print 'Building lexicon...'
parser.add_argument('--stats', nargs='+', help='Path to files containing the stats of a transcript')
parser.add_argument('--names', nargs='+', help='Names of systems corresponding to stats files')
parser.add_argument('--output', default='.', help='Path to output figure')
parser.add_argument('--attr', default=False, action='store_true', help='Plot mentioned attributes')
parser.add_argument('--completion', default=False, action='store_true', help='Plot completion')
parser.add_argument('--ngram-freqs', default=False, action='store_true', help='Plot ngram frequencies')
parser.add_argument('--utterance-freqs', default=False, action='store_true', help='Plot utterance frequencies')
parser.add_argument('--act-freqs', default=False, action='store_true', help='Plot speech act frequencies')
args = parser.parse_args()
if args.ngram_freqs:
stats = {}
#stats_files = ['%s_ngram_counts.pkl' % x for x in args.stats]
stats_files = args.stats
for name, stats_file in izip(args.names, stats_files):
stats[name] = read_pickle(stats_file)
k = 10
interval = 0.2
bar_width = 0.2
names = ['Human'] + [x for x in args.names if x != 'Human']
colors = ['b', 'g', 'r', 'y', 'c', 'm'][:len(names)]
for n in xrange(1, 4):
plt.cla()
ngrams = set()
for name in names:
sorted_words = sorted(stats[name][n].iteritems(), key=lambda x: x[1], reverse=True)
ngrams.update([x[0] for x in sorted_words[:k]])
ngrams = sorted(list(ngrams), key=lambda x: stats['Human'][n][x], reverse=True)[:15]
label = [' '.join(x) if isinstance(x, tuple) else x for x in ngrams]
pos = np.arange(len(ngrams))[::-1]
for i, (name, color) in enumerate(izip(names, colors)):