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bert-base-zh/config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

bert-base-zh/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a693db616eaf647ed2bfe531e1fa446637358fc108a8bf04e8d4db17e837ee9
+size 411577189

bert-base-zh/tokenizer_config.json

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+{
+  "do_lower_case": false
+}

main_2.py

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+import transformers
+import torch
+import os
+import json
+import random
+import numpy as np
+from torch import nn
+import argparse
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+from tqdm import tqdm
+from torch.nn import DataParallel
+import logging
+from transformers import GPT2TokenizerFast, GPT2LMHeadModel, GPT2Config
+from transformers import BertTokenizerFast
+# from transformers import BertTokenizer
+from os.path import join, exists
+from itertools import zip_longest, chain
+# from chatbot.model import DialogueGPT2Model
+# from dataset import MyDataset
+from torch.utils.data import Dataset, DataLoader
+from torch.nn import CrossEntropyLoss
+from sklearn.model_selection import train_test_split
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoConfig, get_linear_schedule_with_warmup, AdamW, BertModel
+
+PAD = '[PAD]'
+pad_id = 0
+
+
+def set_args():
+    """
+    Sets up the arguments.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', default='0', type=str, required=False, help='生成设备')
+    # parser.add_argument('--model_config', default='config/model_config_dialogue_small.json', type=str, required=False,
+    #                     help='模型参数')
+    parser.add_argument('--log_path', default='interact.log', type=str, required=False, help='interact日志存放位置')
+    parser.add_argument('--model_path', default='./pathology_extra/result/12/model.pth', type=str, required=False, help='对话模型路径')
+    parser.add_argument('--vocab_path', default='D:\\transformerFileDownload\\Pytorch\\bert-base-zh\\vocab.txt', type=str, required=False,
+                        help='对话模型路径')
+    parser.add_argument('--save_samples_path', default="sample/", type=str, required=False, help="保存聊天记录的文件路径")
+    parser.add_argument('--repetition_penalty', default=1.0, type=float, required=False,
+                        help="重复惩罚参数，若生成的对话重复性较高，可适当提高该参数")
+    # parser.add_argument('--seed', type=int, default=None, help='设置种子用于生成随机数，以使得训练的结果是确定的')
+    parser.add_argument('--max_len', type=int, default=25, help='每个utterance的最大长度,超过指定长度则进行截断')
+    parser.add_argument('--max_history_len', type=int, default=3, help="dialogue history的最大长度")
+    parser.add_argument('--no_cuda', action='store_true', help='不使用GPU进行预测')
+    return parser.parse_args()
+
+
+def create_logger(args):
+    """
+    将日志输出到日志文件和控制台
+    """
+    logger = logging.getLogger(__name__)
+    logger.setLevel(logging.INFO)
+
+    formatter = logging.Formatter(
+        '%(asctime)s - %(levelname)s - %(message)s')
+
+    # 创建一个handler，用于写入日志文件
+    file_handler = logging.FileHandler(
+        filename=args.log_path)
+    file_handler.setFormatter(formatter)
+    file_handler.setLevel(logging.INFO)
+    logger.addHandler(file_handler)
+
+    # 创建一个handler，用于将日志输出到控制台
+    console = logging.StreamHandler()
+    console.setLevel(logging.DEBUG)
+    console.setFormatter(formatter)
+    logger.addHandler(console)
+
+    return logger
+
+class Word_BERT(nn.Module):
+    def __init__(self, seq_label=1,cancer_label=8,transfer_label=2,ly_transfer=2):
+        super(Word_BERT, self).__init__()
+        self.bert = BertModel.from_pretrained('D:\\transformerFileDownload\\Pytorch\\bert-base-zh')
+        # self.bert_config = self.bert.config
+        self.out = nn.Sequential(
+            # nn.Linear(768,256),
+            # nn.ReLU(),
+            nn.Dropout(0.1),
+            nn.Linear(768, seq_label)
+        )
+        self.cancer = nn.Sequential(
+            nn.Dropout(0.1),
+            nn.Linear(768, cancer_label)
+        )
+        self.transfer = nn.Sequential(
+            nn.Dropout(0.1),
+            nn.Linear(768, transfer_label)
+        )
+        self.ly_transfer = nn.Sequential(
+            nn.Dropout(0.1),
+            nn.Linear(768, ly_transfer)
+        )
+
+    def forward(self, word_input, masks):
+        # print(word_input.size())
+        output = self.bert(word_input, attention_mask=masks)
+        sequence_output = output.last_hidden_state
+        pool = output.pooler_output
+        # print(sequence_output.size())
+        # print(pool.size())
+        out = self.out(sequence_output)
+        cancer = self.cancer(pool)
+        transfer = self.transfer(pool)
+        ly_transfer = self.ly_transfer(pool)
+        return out,cancer,transfer,ly_transfer
+
+def getChat(text: str, userid: int):
+    # while True:
+    #     if True:
+    # text = input("user:")
+    # text = "你好"
+    # if args.save_samples_path:
+    #     samples_file.write("user:{}\n".format(text))
+    text = ['[CLS]']+[i for i in text]+['[SEP]']
+    # print(text)
+    text_ids = tokenizer.convert_tokens_to_ids(text)
+    # print(text_ids)
+
+    input_ids = torch.tensor(text_ids).long().to(device)
+    input_ids = input_ids.unsqueeze(0)
+    mask_input = torch.ones_like(input_ids).long().to(device)
+    # print(input_ids.size())
+    response = []  # 根据context，生成的response
+    # 最多生成max_len个token
+    with torch.no_grad():
+        out, cancer, transfer, ly_transfer = model(input_ids, mask_input)
+        out = F.sigmoid(out).squeeze(2).cpu()
+        out = out.numpy().tolist()
+        cancer = cancer.argmax(dim=-1).cpu().numpy().tolist()
+        transfer = transfer.argmax(dim=-1).cpu().numpy().tolist()
+        ly_transfer = ly_transfer.argmax(dim=-1).cpu().numpy().tolist()
+        # print(out)
+        # print(cancer,transfer,ly_transfer)
+
+    pred_thresold = [[1 if jj > 0.4 else 0 for jj in ii] for ii in out]
+    size_list = []
+    start,end = 0,0
+    for i,j in enumerate(pred_thresold[0]):
+        if j==1 and start==end:
+            start = i
+        elif j!=1 and start!=end:
+            end = i
+            size_list.append((start,end))
+            start = end
+    print(size_list)
+
+    cancer_dict = {'腺癌': 0, '肺良性疾病': 1, '鳞癌': 2, '无法判断组织分型': 3, '复合型': 4, '转移癌': 5, '小细胞癌': 6, '大细胞癌': 7}
+    id_cancer = {j:i for i,j in cancer_dict.items()}
+    transfer_id = {'无': 0, '转移': 1}
+    id_transfer = {j:i for i,j in transfer_id.items()}
+    lymph_transfer_id = {'无': 0, '淋巴转移': 1}
+    id_lymph_transfer = {j: i for i, j in lymph_transfer_id.items()}
+    # print(cancer)
+    cancer = id_cancer[cancer[0]]
+    transfer = id_transfer[transfer[0]]
+    ly_transfer = id_lymph_transfer[ly_transfer[0]]
+    print(cancer,transfer,ly_transfer)
+
+    return size_list,cancer,transfer,ly_transfer
+
+
+import requests
+
+
+def testFunc():
+    url = "http://localhost:7788/getChat"
+    payload = json.dumps({
+        "userid": 602099768,
+        "context": "我就试一下"
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    print(response.text)
+
+
+import uvicorn
+from pydantic import BaseModel
+from fastapi import FastAPI
+
+app = FastAPI()
+# import intel_extension_for_pytorch as ipex
+
+args = set_args()
+logger = create_logger(args)
+# 当用户使用GPU,并且GPU可用时
+args.cuda = torch.cuda.is_available() and not args.no_cuda
+device = 'cuda' if args.cuda else 'cpu'
+logger.info('using device:{}'.format(device))
+os.environ["CUDA_VISIBLE_DEVICES"] = args.device
+tokenizer = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
+# tokenizer = BertTokenizer(vocab_file=args.voca_path)
+model = Word_BERT()
+# model = model.load_state_dict(torch.load(args.model_path))
+model = model.to(device)
+# model = ipex.optimize(model, dtype=torch.float32)
+model.eval()
+# if args.save_samples_path:
+#     if not os.path.exists(args.save_samples_path):
+#         os.makedirs(args.save_samples_path)
+#     samples_file = open(args.save_samples_path + '/samples.txt', 'a', encoding='utf8')
+#     samples_file.write("聊天记录{}:\n".format(datetime.now()))
+# 存储聊天记录，每个utterance以token的id的形式进行存储
+# history = []
+Allhistory = {}
+print('初始化完成')
+
+if __name__ == '__main__':
+    # getChat("测试一下", 0)
+    # main()
+    uvicorn.run(app='main_2:app', host="localhost",
+                port=7788, reload=False)
+    # testFunc()
+
+
+class Items1(BaseModel):
+    context: str
+    userid: int
+    # must: bool
+
+
+import time
+
+lastReplyTime = 0
+
+
+@app.post("/getChat")
+async def get_Chat(item1: Items1):
+    global lastReplyTime
+    tempReplyTime = int(time.time() * 1000)
+    # if tempReplyTime % 10 == 0 or item1.must == True or tempReplyTime - lastReplyTime < 30000:
+    # if item1.must == True:
+    #     lastReplyTime = tempReplyTime
+    result = getChat(
+        item1.context, item1.userid)
+    return {"res": result}