111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
"""
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@Author: miykah
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@Email: miykah@163.com
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@FileName: load_data.py
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@DateTime: 2022/7/20 16:40
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"""
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import matplotlib.pyplot as plt
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import torch
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import numpy as np
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from torch.utils.data import Dataset, DataLoader
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import torchvision.transforms as transforms
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import random
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'''正常Dataset类'''
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class Nor_Dataset(Dataset):
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def __init__(self, datas, labels=None):
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self.datas = torch.tensor(datas)
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if labels is not None:
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self.labels = torch.tensor(labels)
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else:
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self.labels = None
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def __getitem__(self, index):
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data = self.datas[index]
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if self.labels is not None:
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label = self.labels[index]
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return data, label
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return data
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def __len__(self):
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return len(self.datas)
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'''未标记目标数据的Dataset类'''
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class Tar_U_Dataset(Dataset):
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def __init__(self, datas):
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self.datas = torch.tensor(datas)
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def __getitem__(self, index):
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data = self.datas[index]
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data_bar = data.clone().detach()
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data_bar2 = data.clone().detach()
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mu = 0
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sigma = 0.1
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'''对未标记目标数据加噪，得到data'和data'' '''
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for i, j in zip(range(data_bar[0].shape[0]), range(data_bar2[0].shape[0])):
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data_bar[0, i] += random.gauss(mu, sigma)
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data_bar2[0, j] += random.gauss(mu, sigma)
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return data, data_bar, data_bar2
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def __len__(self):
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return len(self.datas)
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def draw_signal_img(data, data_bar, data_bar2):
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pic = plt.figure(figsize=(12, 6), dpi=100)
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plt.rcParams['font.family'] = ['Arial Unicode MS', 'Microsoft YaHei', 'SimHei', 'sans-serif']
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plt.rcParams['axes.unicode_minus'] = False
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plt.subplot(3, 1, 1)
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plt.plot(data, 'b')
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plt.subplot(3, 1, 2)
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plt.plot(data_bar, 'b')
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plt.subplot(3, 1, 3)
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plt.plot(data_bar2, 'b')
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plt.show()
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def get_dataset(src_condition, tar_condition):
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if torch.cuda.is_available():
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root_dir = 'D:\\DataSet\\DDS_data\\5cls_tsne_2\\processed_data\\800EachCls_shot10\\'
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else:
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root_dir = 'E:\\DataSet\\DDS_data\\5cls_tsne_2\\processed_data\\800EachCls_shot10\\'
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src_data = np.load(root_dir + src_condition + '\\src\\' + 'data.npy')
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src_label = np.load(root_dir + src_condition + '\\src\\' + 'label.npy')
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tar_data = np.load(root_dir + tar_condition + '\\tar\\' + 'data.npy')
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val_data = np.load(root_dir + tar_condition + '\\val\\' + 'data.npy')
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val_label = np.load(root_dir + tar_condition + '\\val\\' + 'label.npy')
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test_data = np.load(root_dir + tar_condition + '\\test\\' + 'data.npy')
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test_label = np.load(root_dir + tar_condition + '\\test\\' + 'label.npy')
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src_dataset = Nor_Dataset(src_data, src_label)
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tar_dataset = Nor_Dataset(tar_data)
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val_dataset = Nor_Dataset(val_data, val_label)
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test_dataset = Nor_Dataset(test_data, test_label)
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return src_dataset, tar_dataset, val_dataset, test_dataset
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if __name__ == '__main__':
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# pass
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# tar_data = np.load("E:\\DataSet\\DDS_data_CDAC\\processed_data\\A\\tar\\data.npy")
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# tar_dataset = Nor_Dataset(datas=tar_data)
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# tar_loader = DataLoader(dataset=tar_dataset, batch_size=len(tar_dataset), shuffle=False)
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#
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# for batch_idx, (data) in enumerate(tar_loader):
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# print(data[0][0])
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# # print(data_bar[0][0])
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# # print(data_bar2[0][0])
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# if batch_idx == 0:
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# break
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src_data = np.load("E:\\DataSet\\DDS_data_CDAC\\processed_data\\A\\src\\data.npy")
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src_label = np.load("E:\\DataSet\\DDS_data_CDAC\\processed_data\\A\\src\\label.npy")
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src_dataset = Nor_Dataset(datas=src_data, labels=src_label)
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src_loader = DataLoader(dataset=src_dataset, batch_size=len(src_dataset), shuffle=False)
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for batch_idx, (data, label) in enumerate(src_loader):
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print(data[10][0])
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print(label[10])
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# print(data_bar[0][0])
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# print(data_bar2[0][0])
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if batch_idx == 0:
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break |