diff --git a/load_dataset.py b/load_dataset.py
new file mode 100644
index 0000000..a88b6a2
--- /dev/null
+++ b/load_dataset.py
@@ -0,0 +1,161 @@
+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+"""
+@author:Marques
+@file:load_dataset.py
+@email:admin@marques22.com
+@email:2021022362@m.scnu.edu.cn
+@time:2021/12/03
+"""
+import sys
+from pathlib import Path
+import pandas as pd
+import numpy as np
+import torch
+import yaml
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+"""
+1. 读取方法
+# 无论是否提前切分，均提前转成npy格式
+# 1.1 提前预处理，切分好后生成npy，直接载入切分好的片段   内存占用多  读取简单
+使用此方法： 1.2 提前预处理，载入整夜数据，切分好后生成csv或xls，根据片段读取  内存占用少    读取较为复杂
+"""
+
+datasets = {}
+
+
+# 减少重复读取
+def read_dataset(config, augment=None):
+    data_path = Path(config["Path"]["dataset"])
+    try:
+        file_list = []
+        if data_path.is_dir():
+            dataset_list = list(data_path.rglob("*.npy_low_zscore.npy"))
+            dataset_list.sort()
+            file_list += dataset_list
+        elif data_path.is_file():
+            raise Exception(f'dataset path should be a dir')
+        else:
+            raise Exception(f'{data_path} does not exist')
+    except Exception as e:
+        raise Exception(f'Error loading data from {data_path}: {e} \n')
+
+    print("loading dataset")
+    for i in tqdm(file_list):
+        select_dataset = np.load(i, allow_pickle=True)[0]
+        # select_dataset = preprocessing.Butterworth(select_dataset, "lowpass", low_cut=20, order=3)
+        if augment is not None:
+            select_dataset = augment(select_dataset, config)
+        datasets[i.name.split("samp")[0]] = select_dataset
+
+
+# 用第二种方法读取
+class ApneaDataset(Dataset):
+    def __init__(self, config, dataset_type, select_sampno, segment_augment=None):
+        self.data_path = Path(config["Path"]["dataset"])
+        self.label_path = Path(config["Path"]["label"])
+        self.segment_augment = segment_augment
+        self.labels_info = None
+        self.labels = None
+        self.dataset_type = dataset_type
+        self.select_sampNo = select_sampno
+        self.disable_hpy = config["disable_hpy"]
+        self.apply_samplerate = config["apply_samplerate"]
+
+        # self._getAllData()
+        self._getAllLabels()
+
+    def __getitem__(self, index):
+        # PN patience number
+        # SP/EP start point, end point
+        # temp_label.append([sampNo, label[-1], i, hpy_num, csa_num, osa_num, mean_low, flow_low])
+        PN, segmentNo, label_type, new_label, SP, EP = self.labels_info[index]
+        # PN, label, SP, EP, hpy_num, csa_num, osa_num, mean_low, flow_low = self.labels_info[index]
+
+        if isinstance(datasets, dict):
+            segment = self.segment_augment(datasets[str(PN)], SP * self.apply_samplerate, EP * self.apply_samplerate)
+            return (*segment, self.labels[index], PN, segmentNo, label_type, new_label, SP, EP)
+        else:
+            raise Exception(f'dataset read failure!')
+
+    def count_SA(self):
+        # assert isinstance(self.disable_hpy, int)
+        return sum(self.labels)
+
+    def __len__(self):
+        return len(self.labels_info)
+
+    def _getAllLabels(self):
+        label_path = Path(self.label_path)
+        if not label_path.exists():
+            raise Exception(f'{self.label_path} does not exist')
+
+        try:
+            file_list = []
+            if label_path.is_dir():
+                if self.dataset_type == "train":
+                    label_list = list(label_path.rglob("*_train_label.csv"))
+                elif self.dataset_type == "valid":
+                    label_list = list(label_path.rglob("*_valid_label.csv"))
+                elif self.dataset_type == "test":
+                    label_list = list(label_path.glob("*_sa_test_label.csv"))
+                    # label_list = list(label_path.rglob("*_test_label.npy"))
+                elif self.dataset_type == "all_test":
+                    label_list = list(label_path.rglob("*_sa_all_label.csv"))
+                else:
+                    raise ValueError("self.dataset type error")
+                # label_list = list(label_path.rglob("*_label.npy"))
+                label_list.sort()
+                file_list += label_list
+            elif label_path.is_file():
+                raise Exception(f'dataset path should be a dir')
+            else:
+                raise Exception(f'{self.label_path} does not exist')
+        except Exception as e:
+            raise Exception(f'Error loading data from {self.label_path}: {e} \n')
+        print("loading labels")
+        for i in tqdm(file_list):
+            if int(i.name.split("_")[0]) not in self.select_sampNo:
+                continue
+
+            if self.labels_info is None:
+                self.labels_info = pd.read_csv(i).to_numpy(dtype=int)
+            else:
+                labels = pd.read_csv(i).to_numpy(dtype=int)
+                if len(labels) > 0:
+                    self.labels_info = np.concatenate((self.labels_info, labels))
+
+        self.labels = (self.labels_info[:, 3] > self.disable_hpy) * 1
+        self.labels = torch.from_numpy(self.labels)
+        gpu = torch.cuda.is_available()
+        self.labels = self.labels.cuda() if gpu else self.labels
+
+        # self.labels_info = self.labels_info[:10000]
+        print(f"{self.dataset_type} length is {len(self.labels_info)}")
+
+
+class TestApneaDataset2(ApneaDataset):
+    def __init__(self, config, dataset_type, select_sampno, segment_augment=None):
+        super(TestApneaDataset2, self).__init__(
+            config,
+            dataset_type=dataset_type,
+            select_sampno=select_sampno,
+            segment_augment=segment_augment,
+        )
+
+    def __getitem__(self, index):
+        PN, segmentNo, label_type, new_label, SP, EP = self.labels_info[index]
+        # PN, label, SP, EP, hpy_num, csa_num, osa_num, mean_low, flow_low = self.labels_info[index]
+
+        if isinstance(datasets, dict):
+            dataset = datasets[str(PN)]
+            segment = self.segment_augment(dataset, SP * self.apply_samplerate, EP * self.apply_samplerate)
+            return (*segment, self.labels[index], PN, segmentNo, label_type, new_label, SP, EP)
+        else:
+            raise Exception(f'dataset read failure!')
+
+
+if __name__ == '__main__':
+    pass
diff --git a/my_augment.py b/my_augment.py
new file mode 100644
index 0000000..8b78209
--- /dev/null
+++ b/my_augment.py
@@ -0,0 +1,58 @@
+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+"""
+@author:Marques
+@file:my_augment.py
+@email:admin@marques22.com
+@email:2021022362@m.scnu.edu.cn
+@time:2022/07/26
+"""
+import torch.cuda
+import yaml
+
+from utils.Preprocessing import BCG_Operation
+import numpy as np
+from scipy.signal import stft
+from torch import from_numpy
+with open("./settings.yaml") as f:
+    hyp = yaml.load(f, Loader=yaml.SafeLoader)  # load hyps
+
+apply_samplerate = hyp["apply_samplerate"]
+dataset_samplerate = hyp["dataset_samplerate"]
+preprocessing = BCG_Operation()
+preprocessing.sample_rate = dataset_samplerate
+
+
+def my_augment(dataset, config):
+    # dataset = preprocessing.Butterworth(dataset, "lowpass", low_cut=20, order=6)
+    # dataset = (dataset - dataset.mean()) / dataset.std()
+    # dataset_low = preprocessing.Butterworth(dataset, "lowpass", low_cut=0.7, order=6)
+    # dataset_high = preprocessing.Butterworth(dataset, "highpass", high_cut=1, order=6)
+    print(f"dataset sample_rate is {config['dataset_samplerate']}  down_ratio is {config['dataset_samplerate'] // config['apply_samplerate']}")
+    dataset = dataset[::config['dataset_samplerate'] // config['apply_samplerate']]
+    gpu = torch.cuda.is_available()
+    dataset = {"raw": from_numpy(dataset).float().cuda() if gpu else from_numpy(dataset).float(),
+               # "low": dataset_low,
+               # "high": dataset_high
+               }
+    return dataset
+
+
+def get_stft(x, fs, n):
+    print(len(x))
+    f, t, amp = stft(x, fs, nperseg=n)
+    z = np.abs(amp.copy())
+    return f, t, z
+
+
+def my_segment_augment(dataset, SP, EP):
+    # dataset_segment1 = dataset["low"][int(SP) * 100:int(EP) * 100].copy()
+    # dataset_segment2 = dataset["high"][int(SP) * 100:int(EP) * 100].copy()
+
+    # dataset_segment = np.concatenate(([dataset_segment1], [dataset_segment2]), axis=0)
+    dataset_segment = dataset["raw"][int(SP):int(EP)].unsqueeze(dim=0)
+    return [dataset_segment]
+
+
+if __name__ == '__main__':
+    pass