text stringlengths 1 93.6k |
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# 分隔符
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for j in xrange(0,2):
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qcSplit = QLabel(u'|')
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qcSplit.setAlignment(QtCore.Qt.AlignCenter)
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griddown.addWidget(qcSplit, j, i)
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i+=1
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self.groupProcess.setLayout(griddown)
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# <FILESEP>
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import tensorflow as tf
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import numpy as np
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import pickle
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import random
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import re
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import os
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import sys
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import time
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import math
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from seq2seq_model_comp import *
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import data_utils
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from train_utils import *
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import args
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FLAGS = args.FLAGS
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_buckets = args._buckets
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def test():
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with tf.Session() as sess:
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# build the model
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model = Seq2Seq(
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'TEST',
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FLAGS.size,
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FLAGS.num_layers,
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FLAGS.vocab_size,
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_buckets,
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FLAGS.lr,
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FLAGS.lr_decay,
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FLAGS.grad_norm,
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critic=FLAGS.gan_type,
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critic_size=FLAGS.gan_size,
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critic_num_layers=FLAGS.gan_num_layers,
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use_attn=FLAGS.use_attn,
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output_sample=True,
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input_embed=True,
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feed_prev=True,
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batch_size=FLAGS.batch_size,
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dtype=tf.float32)
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sess.run(tf.variables_initializer(tf.global_variables()))
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ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
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print(FLAGS.model_dir)
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print(ckpt)
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model.pre_saver.restore(sess, ckpt.model_checkpoint_path)
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print ('read in model from {}'.format(ckpt.model_checkpoint_path))
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vocab_path = os.path.join(FLAGS.data_dir, "vocab%d" % FLAGS.vocab_size)
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vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
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def beam_search(encoder_inputs, encoder_len):
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beam_size = 10
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beam_len = 1
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beam_encoder_inputs = []
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for l in range(_buckets[-1][0]):
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beam_encoder_inputs.append(
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np.array([encoder_inputs[l][0] for _ in range(beam_size)], np.int32))
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beam_encoder_len = [encoder_len[0] for _ in range(beam_size)]
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decoder_inputs = [[data_utils.GO_ID]] + [[data_utils.PAD_ID] for _ in range(_buckets[-1][1]-1)]
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outs = model.stepwise_test_beam(sess, encoder_inputs, encoder_len, decoder_inputs)
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outs = outs[0][0]
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top_k_idx = np.argsort(outs[0])[-beam_size:]
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path = [[data_utils.GO_ID for _ in range(beam_size)], top_k_idx] + \
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[[data_utils.PAD_ID for _ in range(beam_size)] for _ in range(_buckets[-1][1]-beam_len-1)]
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end_path = []
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probs = np.zeros(beam_size)
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for k in range(beam_size):
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probs[k] += np.log(outs[0][top_k_idx[k]])
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end_path.append(data_utils.EOS_ID == path[-1][k])
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for _ in range(_buckets[-1][1]-1):
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beam_len += 1
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outs = model.stepwise_test_beam(sess, beam_encoder_inputs, beam_encoder_len, path)
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outs = outs[0][beam_len-1]
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top_k_idxes = [np.argsort(outs[p])[-beam_size:] for p in range(beam_size)]
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edges = []
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tmp_probs = []
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for p in range(beam_size):
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if end_path[p]:
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tmp_probs.extend([probs[p]])
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edges.append(1)
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else:
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tmp_probs.extend([probs[p]+np.log(outs[p][k]) for k in top_k_idxes[p]])
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edges.append(beam_size)
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top_k_path_idx = np.argsort(tmp_probs)[-beam_size:]
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edges_scale = [sum(edges[:i+1]) for i in range(beam_size)]
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tmp_path = []
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for l in range(beam_len):
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step = []
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