test

README.md

@@ -43,7 +43,7 @@ DeepKE 提供了多种知识抽取模型。
 1. RE 
 
    ```
-   1.REGULAR
+   1.STANDARD
    
    2.FEW-SHOT
    
@@ -53,12 +53,12 @@ DeepKE 提供了多种知识抽取模型。
 2. NER
 
    ```
-   REGULAR
+   STANDARD
    ```
 
 3. AE
    ```
-   REGULAR
+   STANDARD
    ```
 
 
@@ -76,7 +76,7 @@ DeepKE 提供了多种知识抽取模型。
    
    具体流程请进入详细的README中，RE包括了以下三个子功能
    
-   **[REGULAR](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/regular/README.md)**
+   **[STANDARD](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/regular/README.md)**
    
    FEW-SHORT
    
@@ -94,7 +94,7 @@ DeepKE 提供了多种知识抽取模型。
 
    具体流程请进入详细的README中：
 
-   **[REGULAR](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ner/regular/README.md)**
+   **[STANDARD](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ner/regular/README.md)**
 
 3. AE
 
@@ -108,7 +108,7 @@ DeepKE 提供了多种知识抽取模型。
    
    具体流程请进入详细的README中:
    
-   **[REGULAR](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ae/regular/README.md)**
+   **[STANDARD](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ae/regular/README.md)**
 
 
 
@@ -127,6 +127,8 @@ Deepke的架构图如下所示
 
 1. 安装后提示 `ModuleNotFoundError: No module named 'past'`，输入命令 `pip install future` 即可解决。
 
+1. 使用语言预训练模型时，在线安装下载模型比较慢，更建议提前下载好，存放到 pretrained 文件夹内。具体存放文件要求见文件夹内的 readme.md。
+
 ## 致谢
 
 

example/ae/standard/predict.py

@@ -8,8 +8,8 @@ from deepke.ae_re_tools import Serializer
 from deepke.ae_re_tools import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_attribute_data
 import matplotlib.pyplot as plt
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-from deepke.ae_re_utils import load_pkl, load_csv
-import deepke.ae_re_models as models
+from deepke.ae_st_utils import load_pkl, load_csv
+import deepke.ae_st_models as models
 
 
 logger = logging.getLogger(__name__)

example/ae/standard/run.py

@@ -11,9 +11,9 @@ from torch.utils.tensorboard import SummaryWriter
 # self
 import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-import deepke.ae_re_models as models
-from deepke.ae_re_tools import preprocess , CustomDataset, collate_fn ,train, validate
-from deepke.ae_re_utils import manual_seed, load_pkl
+import deepke.ae_st_models as models
+from deepke.ae_st_tools import preprocess , CustomDataset, collate_fn ,train, validate
+from deepke.ae_st_utils import manual_seed, load_pkl
 
 
 logger = logging.getLogger(__name__)

example/re/standard/predict.py

@@ -4,12 +4,12 @@ import torch
 import logging
 import hydra
 from hydra import utils
-from deepke.re_re_tools import Serializer
-from deepke.re_re_tools import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_relation_data
+from deepke.re_st_tools import Serializer
+from deepke.re_st_tools import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_relation_data
 import matplotlib.pyplot as plt
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-from deepke.re_re_utils import load_pkl, load_csv
-import deepke.re_re_models as models
+from deepke.re_st_utils import load_pkl, load_csv
+import deepke.re_st_models as models
 
 
 logger = logging.getLogger(__name__)

example/re/standard/run.py

@@ -11,9 +11,9 @@ from torch.utils.tensorboard import SummaryWriter
 # self
 import sys
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-import deepke.re_re_models as models
-from deepke.re_re_tools import preprocess , CustomDataset, collate_fn ,train, validate
-from deepke.re_re_utils import manual_seed, load_pkl
+import deepke.re_st_models as models
+from deepke.re_st_tools import preprocess , CustomDataset, collate_fn ,train, validate
+from deepke.re_st_utils import manual_seed, load_pkl
 
 
 logger = logging.getLogger(__name__)

src/deepke/ae/regular/tools/main.py

@@ -1,142 +0,0 @@
-import os
-import hydra
-import torch
-import logging
-import torch.nn as nn
-from torch import optim
-from hydra import utils
-import matplotlib.pyplot as plt
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-# self
-import sys
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-import models as models
-from tools import preprocess , CustomDataset, collate_fn ,train, validate
-from utils import manual_seed, load_pkl
-
-
-logger = logging.getLogger(__name__)
-
-@hydra.main(config_path="../conf/config.yaml")
-def main(cfg):
-    cwd = utils.get_original_cwd()
-    cwd = cwd[0:-5]
-    cfg.cwd = cwd
-    cfg.pos_size = 2 * cfg.pos_limit + 2
-    logger.info(f'\n{cfg.pretty()}')
-
-    __Model__ = {
-        'cnn': models.PCNN,
-        'rnn': models.BiLSTM,
-        'transformer': models.Transformer,
-        'gcn': models.GCN,
-        'capsule': models.Capsule,
-        'lm': models.LM,
-    }
-
-    # device
-    if cfg.use_gpu and torch.cuda.is_available():
-        device = torch.device('cuda', cfg.gpu_id)
-    else:
-        device = torch.device('cpu')
-    logger.info(f'device: {device}')
-
-    # 如果不修改预处理的过程，这一步最好注释掉，不用每次运行都预处理数据一次
-    if cfg.preprocess:
-        preprocess(cfg)
-    
-    train_data_path = os.path.join(cfg.cwd, cfg.out_path, 'train.pkl')
-    valid_data_path = os.path.join(cfg.cwd, cfg.out_path, 'valid.pkl')
-    test_data_path = os.path.join(cfg.cwd, cfg.out_path, 'test.pkl')
-    vocab_path = os.path.join(cfg.cwd, cfg.out_path, 'vocab.pkl')
-
-    if cfg.model_name == 'lm':
-        vocab_size = None
-    else:
-        vocab = load_pkl(vocab_path)
-        vocab_size = vocab.count
-    cfg.vocab_size = vocab_size
-
-    train_dataset = CustomDataset(train_data_path)
-    valid_dataset = CustomDataset(valid_data_path)
-    test_dataset = CustomDataset(test_data_path)
-
-    train_dataloader = DataLoader(train_dataset, batch_size=cfg.batch_size, shuffle=True, collate_fn=collate_fn(cfg))
-    valid_dataloader = DataLoader(valid_dataset, batch_size=cfg.batch_size, shuffle=True, collate_fn=collate_fn(cfg))
-    test_dataloader = DataLoader(test_dataset, batch_size=cfg.batch_size, shuffle=True, collate_fn=collate_fn(cfg))
-
-    model = __Model__[cfg.model_name](cfg)
-    model.to(device)
-    logger.info(f'\n {model}')
-
-    optimizer = optim.Adam(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay)
-    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=cfg.lr_factor, patience=cfg.lr_patience)
-    criterion = nn.CrossEntropyLoss()
-
-    best_f1, best_epoch = -1, 0
-    es_loss, es_f1, es_epoch, es_patience, best_es_epoch, best_es_f1, es_path, best_es_path = 1e8, -1, 0, 0, 0, -1, '', ''
-    train_losses, valid_losses = [], []
-
-    if cfg.show_plot and cfg.plot_utils == 'tensorboard':
-        writer = SummaryWriter('tensorboard')
-    else:
-        writer = None
-
-    logger.info('=' * 10 + ' Start training ' + '=' * 10)
-
-    for epoch in range(1, cfg.epoch + 1):
-        manual_seed(cfg.seed + epoch)
-        train_loss = train(epoch, model, train_dataloader, optimizer, criterion, device, writer, cfg)
-        valid_f1, valid_loss = validate(epoch, model, valid_dataloader, criterion, device, cfg)
-        scheduler.step(valid_loss)
-        model_path = model.save(epoch, cfg)
-        # logger.info(model_path)
-
-        train_losses.append(train_loss)
-        valid_losses.append(valid_loss)
-        if best_f1 < valid_f1:
-            best_f1 = valid_f1
-            best_epoch = epoch
-        # 使用 valid loss 做 early stopping 的判断标准
-        if es_loss > valid_loss:
-            es_loss = valid_loss
-            es_f1 = valid_f1
-            es_epoch = epoch
-            es_patience = 0
-            es_path = model_path
-        else:
-            es_patience += 1
-            if es_patience >= cfg.early_stopping_patience:
-                best_es_epoch = es_epoch
-                best_es_f1 = es_f1
-                best_es_path = es_path
-
-    if cfg.show_plot:
-        if cfg.plot_utils == 'matplot':
-            plt.plot(train_losses, 'x-')
-            plt.plot(valid_losses, '+-')
-            plt.legend(['train', 'valid'])
-            plt.title('train/valid comparison loss')
-            plt.show()
-
-        if cfg.plot_utils == 'tensorboard':
-            for i in range(len(train_losses)):
-                writer.add_scalars('train/valid_comparison_loss', {
-                    'train': train_losses[i],
-                    'valid': valid_losses[i]
-                }, i)
-            writer.close()
-
-    logger.info(f'best(valid loss quota) early stopping epoch: {best_es_epoch}, '
-                f'this epoch macro f1: {best_es_f1:0.4f}')
-    logger.info(f'this model save path: {best_es_path}')
-    logger.info(f'total {cfg.epoch} epochs, best(valid macro f1) epoch: {best_epoch}, '
-                f'this epoch macro f1: {best_f1:.4f}')
-
-    logger.info('=====end of training====')
-    logger.info('')
-    logger.info('=====start test performance====')
-    validate(-1, model, test_dataloader, criterion, device, cfg)
-    logger.info('=====ending====')
-

src/deepke/ae/regular/tools/predict.py

@@ -1,147 +0,0 @@
-import os
-import sys
-import torch
-import logging
-import hydra
-from hydra import utils
-from serializer import Serializer
-from preprocess import _serialize_sentence, _convert_tokens_into_index, _add_pos_seq, _handle_attribute_data
-import matplotlib.pyplot as plt
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../")))
-from utils import load_pkl, load_csv
-import models as models
-
-
-logger = logging.getLogger(__name__)
-
-
-def _preprocess_data(data, cfg):
-    vocab = load_pkl(os.path.join(cfg.cwd, cfg.out_path, 'vocab.pkl'), verbose=False)
-    attribute_data = load_csv(os.path.join(cfg.cwd, cfg.data_path, 'attribute.csv'), verbose=False)
-    atts = _handle_attribute_data(attribute_data)
-    cfg.vocab_size = vocab.count
-    serializer = Serializer(do_chinese_split=cfg.chinese_split)
-    serial = serializer.serialize
-
-    _serialize_sentence(data, serial, cfg)
-    _convert_tokens_into_index(data, vocab)
-    _add_pos_seq(data, cfg)
-    logger.info('start sentence preprocess...')
-    formats = '\nsentence: {}\nchinese_split: {}\n' \
-              'tokens:    {}\ntoken2idx: {}\nlength:    {}\nentity_index:  {}\nattribute_value_index:  {}'
-    logger.info(
-        formats.format(data[0]['sentence'], cfg.chinese_split,
-                       data[0]['tokens'], data[0]['token2idx'], data[0]['seq_len'],
-                       data[0]['entity_index'], data[0]['attribute_value_index']))
-    return data, atts
-
-
-def _get_predict_instance(cfg):
-    flag = input('是否使用范例[y/n]，退出请输入: exit .... ')
-    flag = flag.strip().lower()
-    if flag == 'y' or flag == 'yes':
-        sentence = '张冬梅，女，汉族，1968年2月生，河南淇县人，1988年7月加入中国共产党，1989年9月参加工作，中央党校经济管理专业毕业，中央党校研究生学历'
-        entity = '张冬梅'
-        attribute_value = '汉族'
-    elif flag == 'n' or flag == 'no':
-        sentence = input('请输入句子：')
-        entity = input('请输入句中需要预测的实体：')
-        attribute_value = input('请输入句中需要预测的属性值：')
-    elif flag == 'exit':
-        sys.exit(0)
-    else:
-        print('please input yes or no, or exit!')
-        _get_predict_instance(cfg)
-        
-
-    instance = dict()
-    instance['sentence'] = sentence.strip()
-    instance['entity'] = entity.strip()
-    instance['attribute_value'] = attribute_value.strip()
-    instance['entity_offset'] = sentence.find(entity)
-    instance['attribute_value_offset'] = sentence.find(attribute_value)
-
-    return instance
-
-
-
-
-@hydra.main(config_path='../conf/config.yaml')
-def main(cfg):
-    cwd = utils.get_original_cwd()
-    cwd = cwd[0:-5]
-    cfg.cwd = cwd
-    cfg.pos_size = 2 * cfg.pos_limit + 2
-    print(cfg.pretty())
-
-    # get predict instance
-    instance = _get_predict_instance(cfg)
-    data = [instance]
-
-    # preprocess data
-    data, rels = _preprocess_data(data, cfg)
-
-    # model
-    __Model__ = {
-        'cnn': models.PCNN,
-        'rnn': models.BiLSTM,
-        'transformer': models.Transformer,
-        'gcn': models.GCN,
-        'capsule': models.Capsule,
-        'lm': models.LM,
-    }
-
-    # 最好在 cpu 上预测
-    cfg.use_gpu = False
-    if cfg.use_gpu and torch.cuda.is_available():
-        device = torch.device('cuda', cfg.gpu_id)
-    else:
-        device = torch.device('cpu')
-    logger.info(f'device: {device}')
-
-    model = __Model__[cfg.model_name](cfg)
-    logger.info(f'model name: {cfg.model_name}')
-    logger.info(f'\n {model}')
-    model.load(cfg.fp, device=device)
-    model.to(device)
-    model.eval()
-
-    x = dict()
-    x['word'], x['lens'] = torch.tensor([data[0]['token2idx']]), torch.tensor([data[0]['seq_len']])
-    
-    if cfg.model_name != 'lm':
-        x['entity_pos'], x['attribute_value_pos'] = torch.tensor([data[0]['entity_pos']]), torch.tensor([data[0]['attribute_value_pos']])
-        if cfg.model_name == 'cnn':
-            if cfg.use_pcnn:
-                x['pcnn_mask'] = torch.tensor([data[0]['entities_pos']])
-        if cfg.model_name == 'gcn':
-            # 没找到合适的做 parsing tree 的工具，暂时随机初始化
-            adj = torch.empty(1,data[0]['seq_len'],data[0]['seq_len']).random_(2)
-            x['adj'] = adj
-
-
-    for key in x.keys():
-        x[key] = x[key].to(device)
-
-    with torch.no_grad():
-        y_pred = model(x)
-        y_pred = torch.softmax(y_pred, dim=-1)[0]
-        prob = y_pred.max().item()
-        prob_att = list(rels.keys())[y_pred.argmax().item()]
-        logger.info(f"\"{data[0]['entity']}\" 和 \"{data[0]['attribute_value']}\" 在句中属性为：\"{prob_att}\"，置信度为{prob:.2f}。")
-
-    if cfg.predict_plot:
-        plt.rcParams["font.family"] = 'Arial Unicode MS'
-        x = list(rels.keys())
-        height = list(y_pred.cpu().numpy())
-        plt.bar(x, height)
-        for x, y in zip(x, height):
-            plt.text(x, y, '%.2f' % y, ha="center", va="bottom")
-        plt.xlabel('关系')
-        plt.ylabel('置信度')
-        plt.xticks(rotation=315)
-        plt.show()
-
-
-if __name__ == '__main__':
-    main()