def fetch_hidden_vector(hparam, vocab_size, data, model_path): task = transformer_nli_hidden(hparam, vocab_size, 0, False) sess = init_session() sess.run(tf.global_variables_initializer()) load_model_w_scope(sess, model_path, ["bert"]) batches = get_batches_ex(data, hparam.batch_size, 4) def batch2feed_dict(batch): x0, x1, x2, y = batch feed_dict = { task.x_list[0]: x0, task.x_list[1]: x1, task.x_list[2]: x2, task.y: y, } return feed_dict def pred_fn(): outputs = [] for batch in batches: x0, x1, x2, y = batch all_layers, emb_outputs = sess.run( [task.all_layers, task.embedding_output], feed_dict=batch2feed_dict(batch)) outputs.append((all_layers, emb_outputs, x0)) return outputs return pred_fn()
def __init__(self, hparam, voca_size, start_model_path): print("AgreePredictor") tf.reset_default_graph() self.task = transformer_weight(hparam, voca_size, False) self.sess = init_session() self.sess.run(tf.global_variables_initializer()) load_model_w_scope(self.sess, start_model_path, ['bert', 'cls_dense'])
def fetch_hidden_vector(hparam, vocab_size, run_name, data_loader, model_path): print("fetch_hidden_vector:", run_name) task = transformer_nli_hidden(hparam, vocab_size, 0, False) sess = init_session() sess.run(tf.global_variables_initializer()) load_model_w_scope(sess, model_path, ["bert"]) dev_batches = get_batches_ex(data_loader.get_dev_data(), hparam.batch_size, 4) def batch2feed_dict(batch): x0, x1, x2, y = batch feed_dict = { task.x_list[0]: x0, task.x_list[1]: x1, task.x_list[2]: x2, task.y: y, } return feed_dict def pred_fn(): outputs = [] for batch in dev_batches[:100]: x0, x1, x2, y = batch all_layers, emb_outputs = sess.run( [vars], feed_dict=batch2feed_dict(batch)) outputs.append((all_layers, emb_outputs, x0)) return outputs return pred_fn()
def __init__(self, hparam, voca_size, start_model_path): print("run_ukp_ex") tf.reset_default_graph() self.task = transformer_nli(hparam, voca_size, 5, True) self.sess = init_session() self.sess.run(tf.global_variables_initializer()) load_model_w_scope(self.sess, start_model_path, ['bert', 'cls_dense', 'aux_conflict'])
def fetch_params(hparam, vocab_size, run_name, data_loader, model_path): print("fetch_hidden_vector:", run_name) task = transformer_nli_hidden(hparam, vocab_size, 0, False) sess = init_session() sess.run(tf.global_variables_initializer()) load_model_w_scope(sess, model_path, ["bert"]) vars = tf.trainable_variables() names = list([v.name for v in vars]) vars_out, = sess.run([vars]) return names, vars_out
def init_fn_generic(sess, start_type, start_model_path): if start_type == "cls": load_model_with_blacklist(sess, start_model_path, ["explain", "explain_optimizer"]) elif start_type == "cls_new": load_model_with_blacklist( sess, start_model_path, ["explain", "explain_optimizer", "optimizer"]) elif start_type == "cls_ex": load_model(sess, start_model_path) elif start_type == "as_is": load_model(sess, start_model_path) elif start_type == "cls_ex_for_pairing": load_model_with_blacklist(sess, start_model_path, ["match_predictor", "match_optimizer"]) elif start_type == "bert": load_model_w_scope(sess, start_model_path, ["bert"]) elif start_type == "cold": pass else: assert False
def load_fn(sess, model_path): return load_model_w_scope(sess, model_path, "bert")
def load_fn(sess, model_path): if not resume: return load_model_w_scope(sess, model_path, "bert") else: return load_model(sess, model_path)