def __init__(self, batch, config, is_train=True):
self.batch = batch
self.config = config
self.data_cfg = config.data_cfg
self.data_dir = config.data_dir
self.is_train = is_train
self.losses = {}
self.report = {}
self.mid_result = {}
self.vis_image = {}
vocab_path = os.path.join(self.data_dir, 'vocab.pkl')
self.vocab = cPickle.load(open(vocab_path, 'rb'))
answer_dict_path = os.path.join(self.data_dir, 'answer_dict.pkl')
self.answer_dict = cPickle.load(open(answer_dict_path, 'rb'))
self.num_answer = len(self.answer_dict['vocab'])
ws_dict_path = os.path.join(self.data_dir, 'wordset_dict5.pkl')
self.ws_dict = cPickle.load(open(ws_dict_path, 'rb'))
self.num_ws = len(self.ws_dict['vocab'])
self.wordset_map = modules.learn_embedding_map(self.ws_dict,
scope='wordset_map')
self.v_word_map = modules.LearnGloVe(self.vocab, scope='V_GloVe')
self.l_word_map = modules.LearnGloVe(self.vocab, scope='L_GloVe')
self.l_answer_word_map = modules.LearnAnswerGloVe(self.answer_dict)
self.build()
def __init__(self, batches, config, is_train=True):
self.batches = batches
self.config = config
self.report = {}
self.output = {}
self.no_object = config.no_object
self.no_region = config.no_region
self.use_blank_fill = config.use_blank_fill
self.object_batch_size = config.object_batch_size
self.region_batch_size = config.region_batch_size
self.object_num_k = config.object_num_k
self.object_max_name_len = config.object_max_name_len
self.region_max_len = config.region_max_len
# model parameters
self.finetune_enc_I = config.finetune_enc_I
self.no_V_grad_enc_L = config.no_V_grad_enc_L
self.no_V_grad_dec_L = config.no_V_grad_dec_L
self.no_L_grad_dec_L = config.no_L_grad_dec_L
self.use_embed_transform = config.use_embed_transform
self.use_dense_predictor = config.use_dense_predictor
self.no_glove = config.no_glove
self.vocab = json.load(open(config.vocab_path, 'r'))
self.wordset = modules.used_wordset(config.used_wordset_path)
self.wordset_vocab = {}
with h5py.File(config.used_wordset_path, 'r') as f:
wordset = list(f['used_wordset'].value)
self.wordset_vocab['vocab'] = [self.vocab['vocab'][w]
for w in wordset]
self.wordset_vocab['dict'] = {w: i for i, w in
enumerate(self.wordset_vocab['vocab'])}
if self.no_glove:
self.glove_all = modules.learn_embedding_map(self.vocab)
else: self.glove_all = modules.glove_embedding_map(self.vocab)
self.glove_wordset = tf.nn.embedding_lookup(self.glove_all,
self.wordset)
predictor_embed = self.glove_wordset
if self.use_embed_transform:
predictor_embed = modules.embedding_transform(
predictor_embed, W_DIM, L_DIM, is_train=is_train)
if self.use_dense_predictor:
self.word_predictor = tf.layers.Dense(
len(self.wordset_vocab['vocab']), use_bias=True, name='WordPredictor')
else:
self.word_predictor = modules.WordPredictor(predictor_embed,
trainable=is_train,
name='WordPredictor')
self.build(is_train=is_train)
def __init__(self, batch, config, is_train=True):
self.batch = batch
self.config = config
self.data_cfg = config.data_cfg
self.data_dir = config.data_dir
self.is_train = is_train
self.losses = {}
self.report = {}
self.mid_result = {}
self.vis_image = {}
vocab_path = os.path.join(self.data_dir, 'vocab.pkl')
self.vocab = cPickle.load(open(vocab_path, 'rb'))
answer_dict_path = os.path.join(self.data_dir, 'answer_dict.pkl')
self.answer_dict = cPickle.load(open(answer_dict_path, 'rb'))
self.num_answer = len(self.answer_dict['vocab'])
ws_dict_path = os.path.join(self.data_dir, 'wordset_dict5.pkl')
self.ws_dict = cPickle.load(open(ws_dict_path, 'rb'))
self.num_ws = len(self.ws_dict['vocab'])
enwiki_dict_path = os.path.join(
self.data_dir, 'enwiki_context_dict_w3_p{}_n5.pkl'.format(config.enwiki_preprocessing))
self.enwiki_dict = cPickle.load(open(enwiki_dict_path, 'rb'))
self.num_context_vocab = len(self.enwiki_dict['context_word_vocab'])
self.max_context_len = self.enwiki_dict['max_context_len']
self.enwiki_vocab_dict = {
'vocab': self.enwiki_dict['context_word_vocab'],
'dict': self.enwiki_dict['context_word_dict'],
}
self.wordset_map = modules.learn_embedding_map(
self.ws_dict, scope='wordset_map')
self.enwiki_map = modules.learn_embedding_map(
self.enwiki_vocab_dict, scope='enwiki_map')
self.v_word_map = modules.LearnGloVe(self.vocab, scope='V_GloVe')
self.l_word_map = modules.LearnGloVe(self.vocab, scope='L_GloVe')
self.l_answer_word_map = modules.LearnAnswerGloVe(self.answer_dict)
self.build()
raise ValueError('Do not overwrite: {}'.format(config.save_dir))
vocab_path = os.path.join(config.data_dir, 'vocab.pkl')
vocab = cPickle.load(open(vocab_path, 'rb'))
answer_dict_path = os.path.join(config.data_dir, 'answer_dict.pkl')
answer_dict = cPickle.load(open(answer_dict_path, 'rb'))
num_answer = len(answer_dict['vocab'])
ws_dict_path = os.path.join(
config.data_dir,
'wordset_dict5_depth{}.pkl'.format(int(config.expand_depth)))
ws_dict = cPickle.load(open(ws_dict_path, 'rb'))
num_ws = len(ws_dict['vocab'])
wordset_map = modules.learn_embedding_map(ws_dict, scope='wordset_map')
L_DIM = 1024
wordset_embed = tf.tanh(wordset_map)
wordset_ft = modules.fc_layer(wordset_embed,
L_DIM,
use_bias=True,
use_bn=False,
use_ln=True,
activation_fn=tf.tanh,
is_training=False,
scope='wordset_ft')
session_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),