class MDRGWordPolicy(SysPolicy):
def __init__(self,
archive_file=DEFAULT_ARCHIVE_FILE,
cuda_device=DEFAULT_CUDA_DEVICE,
model_file=None):
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for MDRG is specified!")
archive_file = cached_path(model_file)
temp_path = tempfile.mkdtemp()
zip_ref = zipfile.ZipFile(archive_file, 'r')
zip_ref.extractall(temp_path)
zip_ref.close()
self.dic = pickle.load(
open(os.path.join(temp_path, 'mdrg/svdic.pkl'), 'rb'))
# Load dictionaries
with open(os.path.join(temp_path,
'mdrg/input_lang.index2word.json')) as f:
input_lang_index2word = json.load(f)
with open(os.path.join(temp_path,
'mdrg/input_lang.word2index.json')) as f:
input_lang_word2index = json.load(f)
with open(os.path.join(temp_path,
'mdrg/output_lang.index2word.json')) as f:
output_lang_index2word = json.load(f)
with open(os.path.join(temp_path,
'mdrg/output_lang.word2index.json')) as f:
output_lang_word2index = json.load(f)
self.response_model = Model(args, input_lang_index2word,
output_lang_index2word,
input_lang_word2index,
output_lang_word2index)
self.response_model.loadModel(os.path.join(temp_path, 'mdrg/mdrg'))
shutil.rmtree(temp_path)
self.prev_state = init_state()
self.prev_active_domain = None
def predict(self, state):
try:
response, active_domain = predict(self.response_model,
self.prev_state,
self.prev_active_domain, state,
self.dic)
except Exception as e:
print('Response generation error', e)
response = 'What did you say?'
active_domain = None
self.prev_state = deepcopy(state)
self.prev_active_domain = active_domain
return response
def loadModel(num):
# Load dictionaries
with open(os.path.join(DATA_PATH, 'input_lang.index2word.json')) as f:
input_lang_index2word = json.load(f)
with open(os.path.join(DATA_PATH, 'input_lang.word2index.json')) as f:
input_lang_word2index = json.load(f)
with open(os.path.join(DATA_PATH, 'output_lang.index2word.json')) as f:
output_lang_index2word = json.load(f)
with open(os.path.join(DATA_PATH, 'output_lang.word2index.json')) as f:
output_lang_word2index = json.load(f)
# Reload existing checkpoint
model = Model(args, input_lang_index2word, output_lang_index2word,
input_lang_word2index, output_lang_word2index)
model.loadModel(iter=num)
return model
def __init__(self,
archive_file=DEFAULT_ARCHIVE_FILE,
cuda_device=DEFAULT_CUDA_DEVICE,
model_file=None):
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for MDRG is specified!")
archive_file = cached_path(model_file)
temp_path = tempfile.mkdtemp()
zip_ref = zipfile.ZipFile(archive_file, 'r')
zip_ref.extractall(temp_path)
zip_ref.close()
self.dic = pickle.load(
open(os.path.join(temp_path, 'mdrg/svdic.pkl'), 'rb'))
# Load dictionaries
with open(os.path.join(temp_path,
'mdrg/input_lang.index2word.json')) as f:
input_lang_index2word = json.load(f)
with open(os.path.join(temp_path,
'mdrg/input_lang.word2index.json')) as f:
input_lang_word2index = json.load(f)
with open(os.path.join(temp_path,
'mdrg/output_lang.index2word.json')) as f:
output_lang_index2word = json.load(f)
with open(os.path.join(temp_path,
'mdrg/output_lang.word2index.json')) as f:
output_lang_word2index = json.load(f)
self.response_model = Model(args, input_lang_index2word,
output_lang_index2word,
input_lang_word2index,
output_lang_word2index)
self.response_model.loadModel(os.path.join(temp_path, 'mdrg/mdrg'))
shutil.rmtree(temp_path)
self.prev_state = init_state()
self.prev_active_domain = None
def loadModelAndData(num):
# Load dictionaries
with open('data/input_lang.index2word.json') as f:
input_lang_index2word = json.load(f)
with open('data/input_lang.word2index.json') as f:
input_lang_word2index = json.load(f)
with open('data/output_lang.index2word.json') as f:
output_lang_index2word = json.load(f)
with open('data/output_lang.word2index.json') as f:
output_lang_word2index = json.load(f)
# Reload existing checkpoint
model = Model(args, input_lang_index2word, output_lang_index2word,
input_lang_word2index, output_lang_word2index)
if args.load_param:
model.loadModel(iter=num)
# Load data
if os.path.exists(args.decode_output):
shutil.rmtree(args.decode_output)
os.makedirs(args.decode_output)
else:
os.makedirs(args.decode_output)
if os.path.exists(args.valid_output):
shutil.rmtree(args.valid_output)
os.makedirs(args.valid_output)
else:
os.makedirs(args.valid_output)
# Load validation file list:
# with open('data/val_dials.json') as outfile:
with open('data/x_dials.json') as outfile:
val_dials = json.load(outfile)
# Load test file list:
# with open('data/test_dials.json') as outfile:
with open('data/x_dials.json') as outfile:
test_dials = json.load(outfile)
return model, val_dials, test_dials
# load data and dictionaries
with open('data/input_lang.index2word.json') as f:
input_lang_index2word = json.load(f)
with open('data/input_lang.word2index.json') as f:
input_lang_word2index = json.load(f)
with open('data/output_lang.index2word.json') as f:
output_lang_index2word = json.load(f)
with open('data/output_lang.word2index.json') as f:
output_lang_word2index = json.load(f)
return input_lang_index2word, output_lang_index2word, input_lang_word2index, output_lang_word2index
if __name__ == '__main__':
input_lang_index2word, output_lang_index2word, input_lang_word2index, output_lang_word2index = loadDictionaries(
)
# Load training file list:
with open('data/train_dials.json') as outfile:
train_dials = json.load(outfile)
# Load validation file list:
with open('data/val_dials.json') as outfile:
val_dials = json.load(outfile)
model = Model(args, input_lang_index2word, output_lang_index2word,
input_lang_word2index, output_lang_word2index)
if args.load_param:
model.loadModel(args.epoch_load)
trainIters(model, n_epochs=args.max_epochs, args=args)