156 lines
5.6 KiB
Python
156 lines
5.6 KiB
Python
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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@author:Marques
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@file:load_dataset.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2021/12/03
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"""
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import sys
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from pathlib import Path
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import pandas as pd
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import numpy as np
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import torch.utils.data
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from utils.Preprocessing import BCG_Operation
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preprocessing = BCG_Operation()
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preprocessing.sample_rate = 100
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"""
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1. 读取方法
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# 无论是否提前切分，均提前转成npy格式
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# 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
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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
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"""
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datasets = {}
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# 减少重复读取
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def read_dataset(data_path, augment=None):
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data_path = Path(data_path)
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try:
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f = []
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if data_path.is_dir():
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dataset_list = list(data_path.rglob("*.npy"))
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dataset_list.sort()
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f += dataset_list
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elif data_path.is_file():
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raise Exception(f'dataset path should be a dir')
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else:
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raise Exception(f'{data_path} does not exist')
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except Exception as e:
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raise Exception(f'Error loading data from {data_path}: {e} \n')
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print("loading dataset")
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for i in tqdm(f):
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select_dataset = np.load(i)
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select_dataset = preprocessing.Butterworth(select_dataset, "lowpass", low_cut=20, order=3)
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if augment is not None:
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select_dataset = augment(select_dataset)
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datasets[i.name.split("samp")[0]] = select_dataset
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# 用第二种方法读取
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class ApneaDataset(Dataset):
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def __init__(self, data_path, label_path, select_sampno, dataset_type, segment_augment=None):
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self.data_path = data_path
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self.label_path = label_path
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self.segment_augment = segment_augment
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self.labels = None
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self.dataset_type = dataset_type
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self.select_sampNo = select_sampno
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# self._getAllData()
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self._getAllLabels()
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def __getitem__(self, index):
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# PN patience number
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# SP/EP start point, end point
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# temp_label.append([sampNo, label[-1], i, hpy_num, csa_num, osa_num, mean_low, flow_low])
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PN, segmentNo, label_type, new_label, SP, EP = self.labels[index]
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# PN, label, SP, EP, hpy_num, csa_num, osa_num, mean_low, flow_low = self.labels[index]
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if isinstance(datasets, dict):
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dataset = datasets[str(PN)]
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segment = self.segment_augment(dataset, SP, EP)
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return (*segment, int(float(label_type) > 1), PN, segmentNo, label_type, new_label, SP, EP)
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else:
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raise Exception(f'dataset read failure!')
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def count_SA(self):
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return sum(self.labels[:, 3] > 1)
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def __len__(self):
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return len(self.labels)
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def _getAllLabels(self):
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label_path = Path(self.label_path)
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if not label_path.exists():
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raise Exception(f'{self.label_path} does not exist')
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try:
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f = []
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if label_path.is_dir():
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if self.dataset_type == "train":
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label_list = list(label_path.rglob("*_train_label.csv"))
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elif self.dataset_type == "valid":
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label_list = list(label_path.rglob("*_valid_label.csv"))
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elif self.dataset_type == "test":
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label_list = list(label_path.glob("*_sa_test_label.csv"))
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# label_list = list(label_path.rglob("*_test_label.npy"))
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elif self.dataset_type == "all_test":
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label_list = list(label_path.rglob("*_sa_all_label.csv"))
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else:
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raise ValueError("self.dataset type error")
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# label_list = list(label_path.rglob("*_label.npy"))
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label_list.sort()
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f += label_list
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elif label_path.is_file():
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raise Exception(f'dataset path should be a dir')
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else:
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raise Exception(f'{self.label_path} does not exist')
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except Exception as e:
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raise Exception(f'Error loading data from {self.label_path}: {e} \n')
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print("loading labels")
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for i in tqdm(f):
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if int(i.name.split("_")[0]) not in self.select_sampNo:
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continue
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if self.labels is None:
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self.labels = pd.read_csv(i).to_numpy(dtype=int)
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else:
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labels = pd.read_csv(i).to_numpy(dtype=int)
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if len(labels) > 0:
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self.labels = np.concatenate((self.labels, labels))
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# self.labels = self.labels[:10000]
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print(f"{self.dataset_type} length is {len(self.labels)}")
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class TestApneaDataset2(ApneaDataset):
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def __init__(self, data_path, label_path, select_sampno, dataset_type, segment_augment=None):
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super(TestApneaDataset2, self).__init__(
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data_path=data_path,
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label_path=label_path,
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dataset_type=dataset_type,
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segment_augment=segment_augment,
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select_sampno=select_sampno
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)
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def __getitem__(self, index):
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PN, segmentNo, label_type, new_label, SP, EP = self.labels[index]
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# PN, label, SP, EP, hpy_num, csa_num, osa_num, mean_low, flow_low = self.labels[index]
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if isinstance(datasets, dict):
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dataset = datasets[str(PN)]
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segment = self.segment_augment(dataset, SP, EP)
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return (*segment, int(float(label_type) > 1), PN, segmentNo, label_type, new_label, SP, EP)
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else:
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raise Exception(f'dataset read failure!')
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if __name__ == '__main__':
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pass
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