utils.py

import numpy as np
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
import sys
import os

def prepare_data_for_emb(seqs_x, opt):
    maxlen = opt.maxlen
    lengths_x = [len(s) for s in seqs_x]
    if maxlen != None:
        new_seqs_x = []
        new_lengths_x = []
        for l_x, s_x in zip(lengths_x, seqs_x):
            if l_x < maxlen:
                new_seqs_x.append(s_x)
                new_lengths_x.append(l_x)
            else:
                new_seqs_x.append(s_x[:maxlen])
                new_lengths_x.append(maxlen)
        lengths_x = new_lengths_x
        seqs_x = new_seqs_x

        if len(lengths_x) < 1:
            return None, None

    n_samples = len(seqs_x)
    maxlen_x = np.max(lengths_x)
    x = np.zeros((n_samples, maxlen)).astype('int32')
    x_mask = np.zeros((n_samples, maxlen)).astype('float32')
    for idx, s_x in enumerate(seqs_x):
        x[idx, :lengths_x[idx]] = s_x
        x_mask[idx, :lengths_x[idx]] = 1. # change to remove the real END token
    return x, x_mask

def restore_from_save(t_vars, sess, opt):
    save_keys = tensors_key_in_file(opt.save_path)
    #print(save_keys.keys()) 
    ss = set([var.name for var in t_vars])&set([s+":0" for s in save_keys.keys()])
    cc = {var.name:var for var in t_vars}
    ss_right_shape = set([s for s in ss if cc[s].get_shape() == save_keys[s[:-2]]])  # only restore variables with correct shape
    
    if opt.reuse_discrimination:
        ss2 = set([var.name[2:] for var in t_vars])&set([s+":0" for s in save_keys.keys()])
        cc2 = {var.name[2:][:-2]:var for var in t_vars if var.name[2:] in ss2 if var.get_shape() == save_keys[var.name[2:][:-2]]}
        for s_iter in ss_right_shape:
            cc2[s_iter[:-2]] = cc[s_iter]
        
        loader = tf.train.Saver(var_list=cc2)
        loader.restore(sess, opt.save_path)
        print("Loaded variables for discriminator:"+str(cc2.keys()))
    
    else:    
        loader = tf.train.Saver(var_list= [var for var in t_vars if var.name in ss_right_shape])
        loader.restore(sess, opt.save_path)
        print("Loading variables from '%s'." % opt.save_path)
        print("Loaded variables:"+str(ss_right_shape))
    
    return loader

def tensors_key_in_file(file_name):
    """Return tensors key in a checkpoint file.
    Args:
    file_name: Name of the checkpoint file.
    """
    try:
        reader = pywrap_tensorflow.NewCheckpointReader(file_name)
        return reader.get_variable_to_shape_map()
    except Exception as e:  # pylint: disable=broad-except
        print(str(e))
        return None
    
def get_minibatches_idx(n, minibatch_size, shuffle=False):
    idx_list = np.arange(n, dtype="int32")

    if shuffle:
        np.random.shuffle(idx_list)

    minibatches = []
    minibatch_start = 0
    for i in range(n // minibatch_size):
        minibatches.append(idx_list[minibatch_start:
                                    minibatch_start + minibatch_size])
        minibatch_start += minibatch_size
    return zip(range(len(minibatches)), minibatches)

def load_class_embedding( wordtoidx, opt):
    print("load class embedding")
    name_list = [ k.lower().split(' ') for k in opt.class_name]
    id_list = [ [ wordtoidx[i] for i in l] for l in name_list]
    value_list = [ [ opt.W_emb[i] for i in l]    for l in id_list]
    value_mean = [ np.mean(l,0)  for l in value_list]
    return np.asarray(value_mean)