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transform.py
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transform.py
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import torch
import random
import numpy as np
from scipy import signal
from scipy.ndimage.interpolation import shift
class TwoTransform:
def __init__(self, transform):
self.transform = transform
def __call__(self, x):
return [self.transform(x), self.transform(x)]
class Compose:
def __init__(self, transforms, mode='full'):
self.transforms = transforms
self.mode = mode
def __call__(self, x):
if self.mode == 'random':
index = random.randint(0, len(self.transforms) - 1)
x = self.transforms[index](x)
elif self.mode == 'full':
for t in self.transforms:
x = t(x)
elif self.mode == 'shuffle':
transforms = np.random.choice(self.transforms, len(self.transforms), replace=False)
for t in transforms:
x = t(x)
else:
raise NotImplementedError
return x
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
class RandomAmplitudeScale:
def __init__(self, range=(0.5, 2.0), p=0.5):
self.range = range
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
scale = random.uniform(self.range[0], self.range[1])
return x * scale
return x
def __repr__(self):
return self.__class__.__name__ + '()'
class RandomDCShift:
def __init__(self, range=(-10.0, 10.0), p=0.5):
self.range = range
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
shift = random.uniform(self.range[0], self.range[1])
return x + shift
return x
def __repr__(self):
return self.__class__.__name__ + '()'
class RandomTimeShift:
def __init__(self, range=(-300, 300), mode='constant', cval=0.0, p=0.5):
self.range = range
self.mode = mode
self.cval = cval
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
t_shift = random.randint(self.range[0], self.range[1])
if len(x.shape) == 2:
x = x[0]
x = shift(input=x, shift=t_shift, mode=self.mode, cval=self.cval)
x = np.expand_dims(x, axis=0)
return x
def __repr__(self):
return self.__class__.__name__ + '()'
class RandomZeroMasking:
def __init__(self, range=(0, 300), p=0.5):
self.range = range
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
mask_len = random.randint(self.range[0], self.range[1])
random_pos = random.randint(0, x.shape[1] - mask_len)
mask = np.concatenate([np.ones((1, random_pos)), np.zeros((1, mask_len)), np.ones((1, x.shape[1] - mask_len - random_pos))], axis=1)
return x * mask
return x
def __repr__(self):
return self.__class__.__name__ + '()'
class RandomAdditiveGaussianNoise:
def __init__(self, range=(0.0, 0.2), p=0.5):
self.range = range
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
sigma = random.uniform(self.range[0], self.range[1])
return x + np.random.normal(0, sigma, x.shape)
return x
def __repr__(self):
return self.__class__.__name__ + '()'
class RandomBandStopFilter:
def __init__(self, range=(0.5, 30.0), band_width=2.0, sampling_rate=100.0, p=0.5):
self.range = range
self.band_width = band_width
self.sampling_rate = sampling_rate
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
low_freq = random.uniform(self.range[0], self.range[1])
center_freq = low_freq + self.band_width / 2.0
b, a = signal.iirnotch(center_freq, center_freq / self.band_width, fs=self.sampling_rate)
x = signal.lfilter(b, a, x)
return x
def __repr__(self):
return self.__class__.__name__ + '()'