#! -*- coding: utf-8 -*- import os os.environ["TF_KERAS"] = "1" os.environ["CUDA_VISIBLE_DEVICES"] = "1" import glob from numpy import random random.seed(1001) from tqdm import tqdm import numpy as np import pandas as pd from bert4keras.backend import keras, K from bert4keras.layers import Loss from bert4keras.models import build_transformer_model from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.optimizers import Adam from bert4keras.snippets import sequence_padding, open from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder from keras.models import Model import tensorflow as tf from keras.backend import set_session config = tf.ConfigProto() config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) # 此处不同 global graph graph = tf.get_default_graph() sess = tf.Session(graph=graph) set_session(sess) # global graph,model # graph = tf.get_default_graph() # sess = tf.Session(graph=graph) # K.set_session(sess) # 基本参数 class GenerateModel(object): def __init__(self): self.epoch_acc_vel = 0 self.config_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_config.json' self.checkpoint_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_model.ckpt' self.dict_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/vocab.txt' self.maxlen = 120 def device_setup(self): token_dict, keep_tokens = load_vocab( dict_path=self.dict_path, simplified=True, startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], ) tokenizer = Tokenizer(token_dict, do_lower_case=True) model = build_transformer_model( self.config_path, self.checkpoint_path, application='unilm', keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表 ) # output = CrossEntropy(2)(model.inputs + model.outputs) # # model = Model(model.inputs, output) model = Model(model.inputs, model.outputs) path_model = './output_quan/best_model_20wan_1.weights' model.load_weights(path_model) return model, tokenizer class CrossEntropy(Loss): """交叉熵作为loss，并mask掉输入部分 """ def compute_loss(self, inputs, mask=None): y_true, y_mask, y_pred = inputs y_true = y_true[:, 1:] # 目标token_ids y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分 y_pred = y_pred[:, :-1] # 预测序列，错开一位 loss = K.sparse_categorical_crossentropy(y_true, y_pred) loss = K.sum(loss * y_mask) / K.sum(y_mask) return loss class AutoTitle(AutoRegressiveDecoder): """seq2seq解码器 """ def __init__(self, model, tokenizer, start_id, end_id, maxlen, minlen=1): super(AutoTitle, self).__init__(start_id, end_id, maxlen, minlen) self.model = model self.tokenizer = tokenizer self.start_id = start_id self.end_id = end_id self.minlen = minlen self.models = {} if start_id is None: self.first_output_ids = np.empty((1, 0), dtype=int) else: self.first_output_ids = np.array([[self.start_id]]) def data_generator(self, inputs, output_ids): batch_token_ids, batch_segment_ids = [], [] if output_ids == []: for txt in inputs: token_ids, segment_ids = self.tokenizer.encode(txt, maxlen=120) batch_token_ids.append(token_ids) batch_segment_ids.append(segment_ids) else: for txt,output_id in zip(inputs, output_ids): token_ids, segment_ids = self.tokenizer.encode(txt, output_id) batch_token_ids.append(token_ids[:-1]) batch_segment_ids.append(segment_ids[:-1]) batch_token_ids = sequence_padding(batch_token_ids) batch_segment_ids = sequence_padding(batch_segment_ids) return batch_token_ids, batch_segment_ids def random_sample_batch( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ inputs = [np.array([i for j in i]) for i in inputs] output_ids = self.first_output_ids results = [] for step in range(self.maxlen): probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n, axis=0) inputs = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n, axis=0) if topk is not None: k_indices = probas.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas = np.take_along_axis(probas, k_indices, axis=1) # topk概率 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 if topp is not None: p_indices = probas.argsort(axis=1)[:, ::-1] # 从高到低排序 probas = np.take_along_axis(probas, p_indices, axis=1) # 排序概率 cumsum_probas = np.cumsum(probas, axis=1) # 累积概率 flag = np.roll(cumsum_probas >= topp, 1, axis=1) # 标记超过topp的部分 flag[:, 0] = False # 结合上面的np.roll，实现平移一位的效果 probas[flag] = 0 # 后面的全部置零 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topp is not None: sample_ids = np.take_along_axis( p_indices, sample_ids, axis=1 ) # 对齐原id if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids[flag]: # 存好已完成序列 results.append(ids) flag = (flag == False) # 标记未完成序列 inputs = [i[flag] for i in inputs] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 end_counts = end_counts[flag] # 只保留未完成部分end计数 if len(output_ids) == 0: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results def random_sample_and_beam_search( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ whether_end_b = False results_r = [] results_b = [] # index_r = [i for i in range(n)] # index_b = [i for i in range(topk)] index_r = np.arange(n) index_b = np.arange(topk) inputs = [np.array([i]) for i in inputs] output_ids, output_scores = self.first_output_ids, np.zeros(1) results = [] for step in range(self.maxlen): beam_n = len(index_b) probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas = probas / probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n + topk, axis=0) inputs_r = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n + topk, axis=0) inputs_b = [np.repeat(i, topk, axis=0) for i in inputs] probas_b = probas[0, :] probas_r = probas[:-beam_n, :] output_ids_r = output_ids[:-beam_n, :] output_ids_b = output_ids[-beam_n:, :] else: probas_b = probas[-beam_n:, :] if whether_end_b == False: inputs_r = [i[:-beam_n, :] for i in inputs] inputs_b = [i[-beam_n:, :] for i in inputs] probas_r = probas[:-beam_n, :] output_ids_r = output_ids[:-beam_n, :] output_ids_b = output_ids[-beam_n:, :] else: inputs_r = inputs probas_r = probas output_ids_r = output_ids k_indices = probas_r.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas_r = np.take_along_axis(probas_r, k_indices, axis=1) # topk概率 probas_r /= probas_r.sum(axis=1, keepdims=True) # 重新归一化 if whether_end_b == False: scores = output_scores.reshape((-1, 1)) + probas_b # 综合累积得分 indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk indices_1 = indices // scores.shape[1] # 行索引 indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引 output_ids_b = np.concatenate([output_ids_b[indices_1], indices_2], 1) # 更新输出 output_scores = np.take_along_axis( scores, indices, axis=None ) # 更新得分 sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas_r) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids_r = np.concatenate([output_ids_r, sample_ids], 1) # 更新输出 # output_ids = np.concatenate([output_ids_r, output_ids_b], 0) if whether_end_b == False: is_end_r = output_ids_r[:, -1] == self.end_id # 标记是否以end标记结束 is_end_b = output_ids_b[:, -1] == self.end_id # 标记是否以end标记结束 end_counts_r = (output_ids_r == self.end_id).sum(1) # 统计出现的end标记 end_counts_b = (output_ids_b == self.end_id).sum(1) # 统计出现的end标记 else: is_end_r = output_ids_r[:, -1] == self.end_id end_counts_r = (output_ids_r == self.end_id).sum(1) # random_serach if output_ids_r.shape[1] >= self.minlen: # 最短长度判断 flag = is_end_r & (end_counts_r >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids_r[flag]: # 存好已完成序列 results_r.append(ids) flag = (flag == False) # 标记未完成序列 index_r = index_r[flag] inputs_r = [i[flag] for i in inputs_r] # 只保留未完成部分输入 output_ids_r = output_ids_r[flag] # 只保留未完成部分候选集 end_counts_r = end_counts_r[flag] # 只保留未完成部分end计数 # beam_serach if whether_end_b == False: if output_ids_b.shape[1] >= self.minlen: # 最短长度判断 best = output_scores.argmax() # 得分最大的那个 if is_end_b[best] and end_counts_b[best] >= min_ends: # 如果已经终止 results_b.append(output_ids_b[best]) # 直接输出 whether_end_b = True else: # 否则，只保留未完成部分 flag_b = ~is_end_b | (end_counts_b < min_ends) # 标记未完成序列 if not flag_b.all(): # 如果有已完成的 index_b = index_b[flag_b] inputs_b = [i[flag_b] for i in inputs_b] # 扔掉已完成序列 output_ids_b = output_ids_b[flag_b] # 扔掉已完成序列 output_scores = output_scores[flag_b] # 扔掉已完成序列 end_counts_b = end_counts_b[flag_b] # 扔掉已完成end计数 topk = flag_b.sum() # topk相应变化 if whether_end_b == False and len(output_ids_r) != 0: token_r = inputs_r[0] sample_ids_r = inputs_r[1] token_b = inputs_b[0] sample_ids_b = inputs_b[1] token = np.concatenate([token_r,token_b],0) sample_ids = np.concatenate([sample_ids_r,sample_ids_b],0) inputs = [token,sample_ids] output_ids = np.concatenate([output_ids_r, output_ids_b], 0) elif whether_end_b == True and len(output_ids_r) != 0: inputs = inputs_r output_ids = output_ids_r elif whether_end_b == False and len(output_ids_r) == 0: inputs = inputs_b output_ids = output_ids_b else: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results_r, results_b def random_sample_and_beam_search_batch( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ whether_end_b = False results_r = [] results_b = [] index_r = np.arange(n) index_b = np.arange(topk) inputs = [np.array([i]) for i in inputs] output_ids, output_scores = self.first_output_ids, np.zeros(1) results = [] for step in range(self.maxlen): beam_n = len(index_b) probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas = probas / probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n + topk, axis=0) inputs_r = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n + topk, axis=0) inputs_b = [np.repeat(i, topk, axis=0) for i in inputs] probas_b = probas[0, :] probas_r = probas[:-beam_n, :] output_ids_r = output_ids[:-beam_n, :] output_ids_b = output_ids[-beam_n:, :] else: probas_b = probas[-beam_n:, :] if whether_end_b == False: inputs_r = [i[:-beam_n, :] for i in inputs] inputs_b = [i[-beam_n:, :] for i in inputs] probas_r = probas[:-beam_n, :] output_ids_r = output_ids[:-beam_n, :] output_ids_b = output_ids[-beam_n:, :] else: inputs_r = inputs probas_r = probas output_ids_r = output_ids k_indices = probas_r.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas_r = np.take_along_axis(probas_r, k_indices, axis=1) # topk概率 probas_r /= probas_r.sum(axis=1, keepdims=True) # 重新归一化 if whether_end_b == False: scores = output_scores.reshape((-1, 1)) + probas_b # 综合累积得分 indices = scores.argpartition(-topk, axis=None)[-topk:] # 仅保留topk indices_1 = indices // scores.shape[1] # 行索引 indices_2 = (indices % scores.shape[1]).reshape((-1, 1)) # 列索引 output_ids_b = np.concatenate([output_ids_b[indices_1], indices_2], 1) # 更新输出 output_scores = np.take_along_axis( scores, indices, axis=None ) # 更新得分 sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas_r) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids_r = np.concatenate([output_ids_r, sample_ids], 1) # 更新输出 # output_ids = np.concatenate([output_ids_r, output_ids_b], 0) if whether_end_b == False: is_end_r = output_ids_r[:, -1] == self.end_id # 标记是否以end标记结束 is_end_b = output_ids_b[:, -1] == self.end_id # 标记是否以end标记结束 end_counts_r = (output_ids_r == self.end_id).sum(1) # 统计出现的end标记 end_counts_b = (output_ids_b == self.end_id).sum(1) # 统计出现的end标记 else: is_end_r = output_ids_r[:, -1] == self.end_id end_counts_r = (output_ids_r == self.end_id).sum(1) # random_serach if output_ids_r.shape[1] >= self.minlen: # 最短长度判断 flag = is_end_r & (end_counts_r >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids_r[flag]: # 存好已完成序列 results_r.append(ids) flag = (flag == False) # 标记未完成序列 index_r = index_r[flag] inputs_r = [i[flag] for i in inputs_r] # 只保留未完成部分输入 output_ids_r = output_ids_r[flag] # 只保留未完成部分候选集 end_counts_r = end_counts_r[flag] # 只保留未完成部分end计数 # beam_serach if whether_end_b == False: if output_ids_b.shape[1] >= self.minlen: # 最短长度判断 best = output_scores.argmax() # 得分最大的那个 if is_end_b[best] and end_counts_b[best] >= min_ends: # 如果已经终止 results_b.append(output_ids_b[best]) # 直接输出 whether_end_b = True else: # 否则，只保留未完成部分 flag_b = ~is_end_b | (end_counts_b < min_ends) # 标记未完成序列 if not flag_b.all(): # 如果有已完成的 index_b = index_b[flag_b] inputs_b = [i[flag_b] for i in inputs_b] # 扔掉已完成序列 output_ids_b = output_ids_b[flag_b] # 扔掉已完成序列 output_scores = output_scores[flag_b] # 扔掉已完成序列 end_counts_b = end_counts_b[flag_b] # 扔掉已完成end计数 topk = flag_b.sum() # topk相应变化 if whether_end_b == False and len(output_ids_r) != 0: token_r = inputs_r[0] sample_ids_r = inputs_r[1] token_b = inputs_b[0] sample_ids_b = inputs_b[1] token = np.concatenate([token_r,token_b],0) sample_ids = np.concatenate([sample_ids_r,sample_ids_b],0) inputs = [token,sample_ids] output_ids = np.concatenate([output_ids_r, output_ids_b], 0) elif whether_end_b == True and len(output_ids_r) != 0: inputs = inputs_r output_ids = output_ids_r elif whether_end_b == False and len(output_ids_r) == 0: inputs = inputs_b output_ids = output_ids_b else: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results_r, results_b def random_sample_seed( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ inputs = [np.array([i]) for i in inputs] output_ids = self.first_output_ids results = [] for step in range(self.maxlen): probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 probas = np.repeat(probas, n, axis=0) inputs = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n, axis=0) if topk is not None: k_indices = probas.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas = np.take_along_axis(probas, k_indices, axis=1) # topk概率 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 if topp is not None: p_indices = probas.argsort(axis=1)[:, ::-1] # 从高到低排序 probas = np.take_along_axis(probas, p_indices, axis=1) # 排序概率 cumsum_probas = np.cumsum(probas, axis=1) # 累积概率 flag = np.roll(cumsum_probas >= topp, 1, axis=1) # 标记超过topp的部分 flag[:, 0] = False # 结合上面的np.roll，实现平移一位的效果 probas[flag] = 0 # 后面的全部置零 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 random.seed(1001) sample_func = lambda p: np.random.choice(len(p), p=p, ) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topp is not None: sample_ids = np.take_along_axis( p_indices, sample_ids, axis=1 ) # 对齐原id if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids[flag]: # 存好已完成序列 results.append(ids) flag = (flag == False) # 标记未完成序列 inputs = [i[flag] for i in inputs] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 end_counts = end_counts[flag] # 只保留未完成部分end计数 if len(output_ids) == 0: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results # def beam_search_batch( # self, # inputs_str, # topk = 1 # temperature=1, # min_ends=1 # ): # """随机采样n个结果 # 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp # 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 # 返回：n个解码序列组成的list。 # """ # output_str = [] # # token_ids, segment_ids = self.data_generator(inputs, output_ids) # batch_nums = len(inputs_str) # output_ids = self.first_output_ids_batch = np.empty((batch_nums, 0), dtype=int) # # results = [[] for i in range(batch_nums)] # index_data = [i for i in range(batch_nums)] # for step in range(self.maxlen): # # token_ids, segment_ids = self.data_generator(inputs_str, output_str) # inputs = [token_ids, segment_ids] # probas = self.predict_batch( # inputs # ) # 计算当前概率 # # probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 # # probas_new = [] # probas_bool = np.array(token_ids, dtype=bool) # # np.array(np.where(probas_bool == True)) # for i,sentence in enumerate(probas_bool): # lie = np.array(np.where(sentence == True))[0] # probas_new.append(probas[i,lie[-1]]) # probas = np.array(probas_new) # k_indices = np.argmax(probas,axis=1) # k_indices = k_indices.reshape(-1,1) # # sample_ids = k_indices # output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 # is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 # end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 # if output_ids.shape[1] >= self.minlen: # 最短长度判断 # flag = is_end & (end_counts >= min_ends) # 标记已完成序列 # if flag.any(): # 如果有已完成的 # index = np.array(np.where(flag == True))[0] # pop_index = [] # for i in index: # results[index_data[i]] = output_ids[i] # pop_index.append(index_data[i]) # for i in pop_index: # index_data.remove(i) # # for ids in output_ids[flag]: # 存好已完成序列 # # results.append(ids) # flag = (flag == False) # 标记未完成序列 # inputs_str = [inputs_str[i] for i in index_data] # 只保留未完成部分输入 # output_ids = output_ids[flag] # 只保留未完成部分候选集 # if len(output_ids) == 0: # break # else: # output_str = [tokenizer.decode(ids) for ids in output_ids] # else: # output_str = [tokenizer.decode(ids) for ids in output_ids] # # 如果还有未完成序列，直接放入结果 # # for ids in output_ids: # # results.append(ids) # # 返回结果 # return results def random_sample_topp_gentle( self, inputs, n, topk=None, topp=None, states=None, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ inputs = [np.array([i]) for i in inputs] output_ids = self.first_output_ids results = [] for step in range(self.maxlen): probas, states = self.predict( inputs, output_ids, states, temperature, 'probas' ) # 计算当前概率 probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 if step == 0: # 第1步预测后将结果重复n次 # TODO probas = np.repeat(probas, n, axis=0) inputs = [np.repeat(i, n, axis=0) for i in inputs] output_ids = np.repeat(output_ids, n, axis=0) if topk is not None: k_indices = probas.argpartition(-topk, axis=1)[:, -topk:] # 仅保留topk probas = np.take_along_axis(probas, k_indices, axis=1) # topk概率 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 if topp is not None: p_indices = probas.argsort(axis=1)[:, ::-1] # 从高到低排序 probas = np.take_along_axis(probas, p_indices, axis=1) # 排序概率 cumsum_probas = np.cumsum(probas, axis=1) # 累积概率 flag = np.roll(cumsum_probas >= topp, 1, axis=1) # 标记超过topp的部分 flag[:, 0] = False # 结合上面的np.roll，实现平移一位的效果 probas[flag] = 0 # 后面的全部置零 probas /= probas.sum(axis=1, keepdims=True) # 重新归一化 # me = np.mean(n) # c = n + (2 * me) # c /= c.sum() sample_func = lambda p: np.random.choice(len(p), p=p) # 按概率采样函数 sample_ids = np.apply_along_axis(sample_func, 1, probas) # 执行采样 sample_ids = sample_ids.reshape((-1, 1)) # 对齐形状 if topp is not None: sample_ids = np.take_along_axis( p_indices, sample_ids, axis=1 ) # 对齐原id if topk is not None: sample_ids = np.take_along_axis( k_indices, sample_ids, axis=1 ) # 对齐原id output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 for ids in output_ids[flag]: # 存好已完成序列 results.append(ids) flag = (flag == False) # 标记未完成序列 inputs = [i[flag] for i in inputs] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 end_counts = end_counts[flag] # 只保留未完成部分end计数 if len(output_ids) == 0: break # 如果还有未完成序列，直接放入结果 for ids in output_ids: results.append(ids) # 返回结果 return results def batch( self, inputs_str, temperature=1, min_ends=1 ): """随机采样n个结果 说明：非None的topk表示每一步只从概率最高的topk个中采样；而非None的topp 表示每一步只从概率最高的且概率之和刚好达到topp的若干个token中采样。 返回：n个解码序列组成的list。 """ output_str = [] # token_ids, segment_ids = self.data_generator(inputs, output_ids) batch_nums = len(inputs_str) output_ids = self.first_output_ids_batch = np.empty((batch_nums, 0), dtype=int) results = [[] for i in range(batch_nums)] index_data = [i for i in range(batch_nums)] for step in range(self.maxlen): token_ids, segment_ids = self.data_generator(inputs_str, output_str) inputs = [token_ids, segment_ids] probas = self.predict_batch( inputs ) # 计算当前概率 # probas /= probas.sum(axis=1, keepdims=True) # 确保归一化 probas_new = [] probas_bool = np.array(token_ids, dtype=bool) # np.array(np.where(probas_bool == True)) for i,sentence in enumerate(probas_bool): lie = np.array(np.where(sentence == True))[0] probas_new.append(probas[i,lie[-1]]) probas = np.array(probas_new) k_indices = np.argmax(probas,axis=1) k_indices = k_indices.reshape(-1,1) sample_ids = k_indices output_ids = np.concatenate([output_ids, sample_ids], 1) # 更新输出 is_end = output_ids[:, -1] == self.end_id # 标记是否以end标记结束 end_counts = (output_ids == self.end_id).sum(1) # 统计出现的end标记 if output_ids.shape[1] >= self.minlen: # 最短长度判断 flag = is_end & (end_counts >= min_ends) # 标记已完成序列 if flag.any(): # 如果有已完成的 index = np.array(np.where(flag == True))[0] pop_index = [] for i in index: results[index_data[i]] = output_ids[i] pop_index.append(index_data[i]) for i in pop_index: index_data.remove(i) # for ids in output_ids[flag]: # 存好已完成序列 # results.append(ids) flag = (flag == False) # 标记未完成序列 inputs_str = [inputs_str[i] for i in index_data] # 只保留未完成部分输入 output_ids = output_ids[flag] # 只保留未完成部分候选集 if len(output_ids) == 0: break else: output_str = [tokenizer.decode(ids) for ids in output_ids] else: output_str = [tokenizer.decode(ids) for ids in output_ids] # 如果还有未完成序列，直接放入结果 # for ids in output_ids: # results.append(ids) # 返回结果 return results @AutoRegressiveDecoder.wraps(default_rtype='probas') def predict(self, inputs, output_ids, states): token_ids, segment_ids = inputs token_ids = np.concatenate([token_ids, output_ids], 1) segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) with graph.as_default(): K.set_session(sess) nodes = self.last_token(self.model).predict([token_ids, segment_ids]) return nodes # return self.last_token(self.model).predict([token_ids, segment_ids]) def predict_batch(self, inputs): token_ids, segment_ids = inputs # token_ids = np.concatenate([token_ids, output_ids], 1) # segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) with graph.as_default(): K.set_session(sess) nodes = self.model.predict([token_ids, segment_ids]) return nodes # return self.last_token(self.model).predict([token_ids, segment_ids]) def generate(self, text, topk=5): text = text[0] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) output_ids = self.beam_search([token_ids, segment_ids], topk=topk) # 基于beam search return self.tokenizer.decode(output_ids) def generate_random(self, text, n=20, topp=0.9): token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) # batch_token_ids = sequence_padding(batch_token_ids) # batch_segment_ids = sequence_padding(batch_segment_ids) # token_ids, segment_ids = self.data_generator(text) output_ids = self.random_sample([token_ids, segment_ids], n, topp=topp) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_random_sample_topp_gentle(self, text, n=20, topp=0.9): token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) # batch_token_ids = sequence_padding(batch_token_ids) # batch_segment_ids = sequence_padding(batch_segment_ids) # token_ids, segment_ids = self.data_generator(text) output_ids = self.random_sample_topp_gentle([token_ids, segment_ids], n, topp=topp) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_random_shortest(self, text, n=20, topk=5): token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) # batch_token_ids = sequence_padding(batch_token_ids) # batch_segment_ids = sequence_padding(batch_segment_ids) # token_ids, segment_ids = self.data_generator(text) output_ids = self.random_sample_seed([token_ids, segment_ids], n, topk) # 基于随机采样 return_str = [tokenizer.decode(ids) for ids in output_ids][0] return return_str def generate_top(self, text): output_ids = self.batch(text) # 基于随机采样 return [tokenizer.decode(ids) for ids in output_ids] def generate_random_sample_and_beam_search(self, text, n=20, topk=5): text = text[0] token_ids, segment_ids = self.tokenizer.encode(text, maxlen=120) output_ids_r, output_ids_b = self.random_sample_and_beam_search([token_ids, segment_ids], n=n, topk=topk) # 基于beam search output_str_r = [self.tokenizer.decode(ids) for ids in output_ids_r] output_str_b = [self.tokenizer.decode(ids) for ids in output_ids_b] return output_str_r, output_str_b generatemodel = GenerateModel() model, tokenizer = generatemodel.device_setup() autotitle = AutoTitle(model, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=60) def just_show(file): data = [] try: with open(file, 'r', encoding="utf-8") as f: lines = [x.strip() for x in f if x.strip() != ''] except: with open(file, 'r', encoding="gbk") as f: lines = [x.strip() for x in f if x.strip() != ''] # s2 = u'她只能应下来。' # lines = pd.read_csv(file,encoding="gbk").values.tolist() # random.shuffle(lines) # lines = lines[:20] for s in tqdm(lines[:2]): print(s) pre = autotitle.generate_random(s) print(s) print(pre) # data.append([s, pre]) # pd.DataFrame(data,columns=["原始文本","生成文本"]).to_csv("data/text_测试一万字_unilm_修正数据_小说预训练_全部数据_epoch72_反向训练.csv") def just_show_sentence(file): """ @param file:list """ # file = file[0] # for i in range(100): # pre = autotitle.generate_random_sample_topp_gentle(file) # print(pre) pre = autotitle.generate(file) print(pre) # print(pre) # if isinstance(pre,list): # for i in pre: # print(i, len(i)) # # # if isinstance(pre,str): # print(pre) def just_show_csv(file): data_new = [] data = pd.read_csv(file).values.tolist() for sentence in tqdm(data): sentence = sentence[1] print(sentence) data_new_dan = [] data_new_dan.extend([sentence, len(sentence)]) pre = autotitle.generate_random(sentence) for i in pre: data_new_dan.extend([i, len(i)]) data_new.append(data_new_dan) pd.DataFrame(data_new).to_csv("data/###第3章 非常尴尬_generate_random.csv") # return pre if __name__ == '__main__': # file = "train_2842.txt" # just_show(file) text = ["迈向新时代,当代青年要立鸿鹄之志,做马克思主义的坚定信仰者。"] just_show_sentence(text) # "简言之，她不好过，李四也别想好过！" # s = "张三的对话" # print(autotitle.generate(s)) # file = "data/###第3章 非常尴尬.csv" # just_show_csv(file)