def data_preprare(self, test=False):
if len(os.path.basename(self.data_dir).split('.')) == 2:
with open(self.data_dir, 'rb') as fo:
list_data = pickle.load(fo, encoding='bytes')
else:
list_data = get_files(self.data_dir, test)
with open(os.path.join(self.data_dir, "MFPTCWT.pkl"), 'wb') as fo:
pickle.dump(list_data, fo)
if test:
test_dataset = dataset(list_data=list_data,
test=True,
transform=data_transforms(
'val', self.normalise_type))
return test_dataset
else:
print(len(list_data[0]))
print(len(list_data[1]))
data_pd = pd.DataFrame({
"data": list_data[0],
"label": list_data[1]
})
train_pd, val_pd = train_test_split(data_pd,
test_size=0.2,
random_state=40,
stratify=data_pd["label"])
train_dataset = dataset(list_data=train_pd,
transform=data_transforms(
'train', self.normalise_type))
val_dataset = dataset(list_data=val_pd,
transform=data_transforms(
'val', self.normalise_type))
return train_dataset, val_dataset
def data_preprare(self, test=False):
if len(os.path.basename(self.data_dir).split('.')) == 2:
with open(self.data_dir, 'rb') as fo:
list_data = pickle.load(fo, encoding='bytes')
else:
list_data = get_files(self.data_dir, test)
with open(os.path.join(self.data_dir, "CWRUCWT.pkl"), 'wb') as fo:
pickle.dump(list_data, fo)
if test:
test_dataset = dataset(list_data=list_data,
test=True,
transform=None)
return test_dataset
else:
data_pd = pd.DataFrame({
"data": list_data[0],
"label": list_data[1]
})
train_pd, val_pd = train_test_split_order(data_pd,
test_size=0.2,
num_classes=10)
train_dataset = dataset(list_data=train_pd,
transform=data_transforms(
'train', self.normlizetype))
val_dataset = dataset(list_data=val_pd,
transform=data_transforms(
'val', self.normlizetype))
return train_dataset, val_dataset
def data_preprare(self, test=False):
if len(os.path.basename(self.data_dir).split('.')) == 2:
with open(self.data_dir, 'rb') as fo:
list_data = pickle.load(fo, encoding='bytes')
else:
list_data = get_files(self.data_dir, test)
with open(os.path.join(self.data_dir, "CWRUSlice.pkl"),
'wb') as fo:
pickle.dump(list_data, fo)
if test:
test_dataset = dataset(list_data=list_data,
test=True,
transform=None)
return test_dataset
else:
data_pd = pd.DataFrame({
"data": list_data[0],
"label": list_data[1]
})
train_pd, val_pd = train_test_split_order(data_pd,
test_size=0.6,
num_classes=10)
train_dataset = dataset(list_data=train_pd,
transform=data_transforms(
'train', self.normlizetype))
val_dataset = dataset(list_data=val_pd,
transform=data_transforms(
'val', self.normlizetype))
return train_dataset, val_dataset
# path = r'/media/ubuntu/Data/hcy/dl_based_bearing_diagnosis/tmp'
# data = CWRUSlice(path, 'mean-std')
# datasets = {}
# datasets['train'], datasets['val'] = data.data_preprare()
# dataloaders = {x: torch.utils.data.DataLoader(datasets[x], batch_size=64,
# shuffle=(True if x == 'train' else False),
# num_workers=0)
# for x in ['train', 'val']}
# a = next(iter(dataloaders['train']))
#
# print(a[0].shape)
# path = r'E:\learning\现代信号处理\dl_based_bearing_diagnosis\tmp'
# data = CWRUSlice(path, 'mean-std')
#
# # print(max(train_dataset[600][0][0]), len(val_dataset))
# # plt.plot(train_dataset[600][0][0])
# # plt.show()
#
# datasets = {}
# datasets['train'], datasets['val'] = data.data_preprare()
# dataloaders = {x: torch.utils.data.DataLoader(datasets[x], batch_size=64,
# shuffle=(True if x == 'train' else False),
# num_workers=0)
# for x in ['train', 'val']}
# a = next(iter(dataloaders['train']))
#
# print(a[0][5][0],a[0].shape)
# print(len(datasets['train']), len(datasets['val']))
# plt.imshow(a[0][8][0])
# plt.show()
