修正一些不规范表达

README.md

@@ -1,9 +1,3 @@
-<!--
- * @Date: 2021-06-24 15:57:09
- * @LastEditors: GodK
- * @LastEditTime: 2021-07-27 22:33:36
--->
-
 # GlobalPointer_pytorch
 
 > 喜欢本项目的话，欢迎点击右上角的star，感谢每一个点赞的你。
@@ -66,4 +60,3 @@ python evaluate.py
 默认配置（超参数已在 `config.py` 文件中），数据集是 CLUENER
 
 * 验证集 Best F1：0.7966
-* 测试集 F1：

common/utils.py

@@ -6,7 +6,7 @@
 import torch
 
 
-def multilabel_categorical_crossentropy(y_pred, y_true):
+def multilabel_categorical_crossentropy(y_true, y_pred):
     """
     https://kexue.fm/archives/7359
     """

config.py

@@ -8,7 +8,7 @@
     "exp_name": "cluener",
     "encoder": "BERT",
     "data_home": "./datasets",
-    "bert_path": "./pretrained_models/bert-base-chinese",  # bert-base-cased， bert-base-chinese
+    "bert_path": "./pretrained_models/bert-base-chinese",  # bert-base-chinese or other plm from https://huggingface.co/models
     "run_type": "train",  # train, eval
     "f1_2_save": 0.5,  # 存模型的最低f1值
     "logger": "default"  # wandb or default，default意味着只输出日志到控制台
@@ -23,24 +23,24 @@
 train_config = {
     "train_data": "train.json",
     "valid_data": "dev.json",
-    "test_data": "test.json",
+    "test_data": "dev.json",
     "ent2id": "ent2id.json",
     "path_to_save_model": "./outputs",  # 在logger不是wandb时生效
     "hyper_parameters": {
-        "lr": 5e-5,
+        "lr": 2e-5,
         "batch_size": 64,
         "epochs": 50,
         "seed": 2333,
         "max_seq_len": 128,
-        "scheduler": "CAWR"
+        "scheduler": "CAWR"  # CAWR, Step, None
     }
 }
 
 eval_config = {
     "model_state_dir": "./outputs/cluener/",  # 预测时注意填写模型路径（时间tag文件夹）
     "run_id": "",
     "last_k_model": 1,  # 取倒数第几个model_state
-    "test_data": "test.json",
+    "predict_data": "test.json",
     "ent2id": "ent2id.json",
     "save_res_dir": "./results",
     "hyper_parameters": {
@@ -58,7 +58,7 @@
 step_scheduler = {
     # StepLR
     "decay_rate": 0.999,
-    "decay_steps": 100,
+    "decay_steps": 200,
 }
 
 # ---------------------------------------------

evaluate.py

@@ -26,8 +26,8 @@
 tokenizer = BertTokenizerFast.from_pretrained(config["bert_path"], add_special_tokens=True, do_lower_case=False)
 
 
-def load_data(data_path, data_type="test"):
-    if data_type == "test":
+def load_data(data_path, data_type="predict"):
+    if data_type == "predict":
         datas = []
         with open(data_path, encoding="utf-8") as f:
             for line in f:
@@ -43,16 +43,16 @@ def load_data(data_path, data_type="test"):
 ent_type_size = len(ent2id)
 
 
-def data_generator(data_type="test"):
+def data_generator(data_type="predict"):
     """
     读取数据，生成DataLoader。
     """
 
-    if data_type == "test":
-        test_data_path = os.path.join(config["data_home"], config["exp_name"], config["test_data"])
-        test_data = load_data(test_data_path, "test")
+    if data_type == "predict":
+        predict_data_path = os.path.join(config["data_home"], config["exp_name"], config["predict_data"])
+        predict_data = load_data(predict_data_path, "predict")
 
-    all_data = test_data
+    all_data = predict_data
 
     # TODO:句子截取
     max_tok_num = 0
@@ -65,17 +65,16 @@ def data_generator(data_type="test"):
 
     data_maker = DataMaker(tokenizer)
 
-    if data_type == "test":
-        # test_inputs = data_maker.generate_inputs(test_data, max_seq_len, ent2id, data_type="test")
-        test_dataloader = DataLoader(MyDataset(test_data),
+    if data_type == "predict":
+        predict_dataloader = DataLoader(MyDataset(predict_data),
                                      batch_size=hyper_parameters["batch_size"],
                                      shuffle=False,
                                      num_workers=config["num_workers"],
                                      drop_last=False,
                                      collate_fn=lambda x: data_maker.generate_batch(x, max_seq_len, ent2id,
-                                                                                    data_type="test")
+                                                                                    data_type="predict")
                                      )
-        return test_dataloader
+        return predict_dataloader
 
 
 def decode_ent(text, pred_matrix, tokenizer, threshold=0):
@@ -137,11 +136,11 @@ def load_model():
 
 
 def evaluate():
-    test_dataloader = data_generator(data_type="test")
+    predict_dataloader = data_generator(data_type="predict")
 
     model = load_model()
 
-    predict_res = predict(test_dataloader, model)
+    predict_res = predict(predict_dataloader, model)
 
     if not os.path.exists(os.path.join(config["save_res_dir"], config["exp_name"])):
         os.mkdir(os.path.join(config["save_res_dir"], config["exp_name"]))

models/GlobalPointer.py

@@ -56,7 +56,7 @@ def generate_inputs(self, datas, max_seq_len, ent2id, data_type="train"):
             )
 
             labels = None
-            if data_type != "test":
+            if data_type != "predict":
                 ent2token_spans = self.preprocessor.get_ent2token_spans(
                     sample["text"], sample["entity_list"]
                 )
@@ -89,13 +89,13 @@ def generate_batch(self, batch_data, max_seq_len, ent2id, data_type="train"):
             input_ids_list.append(sample[1])
             attention_mask_list.append(sample[2])
             token_type_ids_list.append(sample[3])
-            if data_type != "test":
+            if data_type != "predict":
                 labels_list.append(sample[4])
 
         batch_input_ids = torch.stack(input_ids_list, dim=0)
         batch_attention_mask = torch.stack(attention_mask_list, dim=0)
         batch_token_type_ids = torch.stack(token_type_ids_list, dim=0)
-        batch_labels = torch.stack(labels_list, dim=0) if data_type != "test" else None
+        batch_labels = torch.stack(labels_list, dim=0) if data_type != "predict" else None
 
         return sample_list, batch_input_ids, batch_attention_mask, batch_token_type_ids, batch_labels
 
@@ -173,7 +173,7 @@ def forward(self, input_ids, attention_mask, token_type_ids):
         # outputs:(batch_size, seq_len, ent_type_size, inner_dim*2)
         outputs = torch.stack(outputs, dim=-2)
         # qw,kw:(batch_size, seq_len, ent_type_size, inner_dim)
-        qw, kw = outputs[..., :self.inner_dim], outputs[..., self.inner_dim:]  # TODO:修改为Linear获取？
+        qw, kw = outputs[..., :self.inner_dim], outputs[..., self.inner_dim:]
 
         if self.RoPE:
             # pos_emb:(batch_size, seq_len, inner_dim)

train.py

@@ -38,7 +38,7 @@
 
     model_state_dict_dir = wandb.run.dir
     logger = wandb
-else:
+elif config["run_type"] == "train":
     model_state_dict_dir = os.path.join(config["path_to_save_model"], config["exp_name"],
                                         time.strftime("%Y-%m-%d_%H.%M.%S", time.gmtime()))
     if not os.path.exists(model_state_dict_dir):
@@ -160,7 +160,7 @@ def train_step(batch_train, model, optimizer, criterion):
 
     logits = model(batch_input_ids, batch_attention_mask, batch_token_type_ids)
 
-    loss = criterion(logits, batch_labels)
+    loss = criterion(batch_labels, logits)
 
     optimizer.zero_grad()
     loss.backward()
@@ -203,6 +203,8 @@ def loss_fun(y_true, y_pred):
         decay_rate = hyper_parameters["decay_rate"]
         decay_steps = hyper_parameters["decay_steps"]
         scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=decay_steps, gamma=decay_rate)
+    else:
+        scheduler = None
 
     pbar = tqdm(enumerate(dataloader), total=len(dataloader))
     total_loss = 0.
@@ -213,7 +215,8 @@ def loss_fun(y_true, y_pred):
         total_loss += loss
 
         avg_loss = total_loss / (batch_ind + 1)
-        scheduler.step()
+        if scheduler is not None:
+            scheduler.step()
 
         pbar.set_description(
             f'Project:{config["exp_name"]}, Epoch: {epoch + 1}/{hyper_parameters["epochs"]}, Step: {batch_ind + 1}/{len(dataloader)}')