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TICNN_weight_init.py
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TICNN_weight_init.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class Laplace_fast(nn.Module):
def __init__(self, out_channels, kernel_size, frequency, eps=0.3, mode='sigmoid'):
super(Laplace_fast, self).__init__()
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.fre = frequency
self.a_ = torch.linspace(0, self.out_channels, self.out_channels).view(-1, 1)
self.b_ = torch.linspace(0, self.out_channels, self.out_channels).view(-1, 1)
self.time_disc = torch.linspace(0, self.kernel_size - 1, steps=int(self.kernel_size))
def Laplace(self, p):
# m = 1000
# ep = 0.03
# # tal = 0.1
# f = 80
w = 2 * torch.pi * self.fre
# A = 0.08
q = torch.tensor(1 - pow(0.03, 2))
if self.mode == 'vanilla':
return ((1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1)))) * (torch.sin(w * (p - 0.1))))
if self.mode == 'maxmin':
a = (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).sigmoid()) * (
torch.sin(w * (p - 0.1)))
return (a-a.min())/(a.max()-a.min())
if self.mode == 'sigmoid':
return (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).sigmoid()) * (
torch.sin(w * (p - 0.1)))
if self.mode == 'softmax':
return (1/math.e) * torch.exp(F.softmax((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1)), dim=-1)) * (
torch.sin(w * (p - 0.1)))
if self.mode == 'tanh':
return (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).tanh()) * (torch.sin(w * (p - 0.1)))
if self.mode == 'atan':
return (1/math.e) * torch.exp((2 / torch.pi) * (((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1)))).atan()) * (
torch.sin(w * (p - 0.1)))
def forward(self):
p1 = (self.time_disc - self.b_) / (self.a_ + self.eps)
return self.Laplace(p1).view(self.out_channels, 1, self.kernel_size)
class Harmonice_fast(nn.Module):
def __init__(self, out_channels, kernel_size, frequency, eps=0.3, mode='sigmoid'):
super(Laplace_fast, self).__init__()
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.fre = frequency
self.a_ = torch.linspace(0, self.out_channels, self.out_channels).view(-1, 1)
self.b_ = torch.linspace(0, self.out_channels, self.out_channels).view(-1, 1)
self.time_disc = torch.linspace(0, self.kernel_size - 1, steps=int(self.kernel_size))
def Laplace(self, p):
# m = 1000
# ep = 0.03
# # tal = 0.1
# f = 80
w = torch.pi * self.fre
# A = 0.08
q = torch.tensor(1 - pow(0.03, 2))
if self.mode == 'vanilla':
return (torch.sin(0.03/q*2*w(p-0.1))-torch.sin(0.03/q*w(p-0.1)))/ (0.03/q*w(p-0.1))
if self.mode == 'maxmin':
a = (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).sigmoid()) * (
torch.sin(w * (p - 0.1)))
return (a-a.min())/(a.max()-a.min())
if self.mode == 'sigmoid':
return (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).sigmoid()) * (
torch.sin(w * (p - 0.1)))
if self.mode == 'softmax':
return (1/math.e) * torch.exp(F.softmax((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1)), dim=-1)) * (
torch.sin(w * (p - 0.1)))
if self.mode == 'tanh':
return (1/math.e) * torch.exp(((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1))).tanh()) * (torch.sin(w * (p - 0.1)))
if self.mode == 'atan':
return (1/math.e) * torch.exp((2 / torch.pi) * (((-0.03 / (torch.sqrt(q))) * (w * (p - 0.1)))).atan()) * (
torch.sin(w * (p - 0.1)))
def forward(self):
p1 = (self.time_disc - self.b_) / (self.a_ + self.eps)
return self.Laplace(p1).view(self.out_channels, 1, self.kernel_size)
class Morlet_fast(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3, mode='sigmoid'):
super(Morlet_fast, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc_right = torch.linspace(0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)))
self.time_disc_left = torch.linspace(-(self.kernel_size / 2) + 1, -1, steps=int((self.kernel_size / 2)))
def Morlet(self, p, c):
if self.mode == 'vanilla':
return c * torch.exp((-torch.pow(p, 2) / 2)) * torch.cos(5 * p)
if self.mode == 'sigmoid':
return c * torch.exp((-torch.pow(p, 2) / 2).sigmoid()) * torch.cos(5 * p)
if self.mode == 'maxmin':
a = c * torch.exp((-torch.pow(p, 2) / 2).sigmoid()) * torch.cos(5 * p)
return (a-a.min())/(a.max()-a.min())
if self.mode == 'softmax':
return c * torch.exp(F.softmax(-torch.pow(p, 2) / 2, dim=-1)) * torch.cos(5 * p)
if self.mode == 'tanh':
return c * torch.exp((-torch.pow(p, 2) / 2).tanh()) * torch.cos(5 * p)
if self.mode == 'atan':
return c * torch.exp((2 / torch.pi) * ((-torch.pow(p, 2) / 2).atan())) * torch.cos(5 * p)
def forward(self):
p1 = (self.time_disc_right - self.b_) / (self.a_ + self.eps)
p2 = (self.time_disc_left - self.b_) / (self.a_ + self.eps)
c = (pow(torch.pi, 0.25)) / torch.sqrt(self.a_ + 0.01) # worth exploring 1e-3 & D = C / self.a_.cuda()
# C = (pow(pi, 0.25)) / (torch.sqrt(self.a_) + 1e-12)
# C = (pow(pi, 0.25)) / (torch.sqrt(self.a_) + self.eps)
Morlet_right = self.Morlet(p1, c)
Morlet_left = self.Morlet(p2, c)
return torch.cat([Morlet_left, Morlet_right], dim=1).view(self.out_channels, 1, self.kernel_size)
class Mexh_fast(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3, mode='sigmoid'):
super(Mexh_fast, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc_right = torch.linspace(0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)))
self.time_disc_left = torch.linspace(-(self.kernel_size / 2) + 1, -1, steps=int((self.kernel_size / 2)))
def Mexh(self, p):
# p = 0.04 * p # 将时间转化为在[-5,5]这个区间内
if self.mode == 'vanilla':
return ((2 / pow(3, 0.5) * (pow(torch.pi, -0.25))) * (1 - torch.pow(p, 2)) * torch.exp((-torch.pow(p, 2) / 2)))
if self.mode == 'sigmoid':
return ((2 / pow(3, 0.5) * (pow(torch.pi, -0.25))) * (1 - torch.pow(p, 2)) * torch.exp(
(-torch.pow(p, 2) / 2).sigmoid()))
if self.mode == 'softmax':
return ((2 / pow(3, 0.5) * (pow(torch.pi, -0.25))) * (1 - torch.pow(p, 2)) * torch.exp(
F.softmax(-torch.pow(p, 2) / 2, dim=-1)))
if self.mode == 'tanh':
return ((2 / pow(3, 0.5) * (pow(torch.pi, -0.25))) * (1 - torch.pow(p, 2)) * torch.exp(
(-torch.pow(p, 2) / 2).tanh()))
if self.mode == 'atan':
return (2 / torch.pi) * ((2 / pow(3, 0.5) * (pow(torch.pi, -0.25))) * (1 - torch.pow(p, 2)) * torch.exp(
(-torch.pow(p, 2) / 2))).atan()
def forward(self):
p1 = (self.time_disc_right - self.b_) / (self.a_ + self.eps)
p2 = (self.time_disc_left - self.b_) / (self.a_ + self.eps)
Mexh_right = self.Mexh(p1)
Mexh_left = self.Mexh(p2)
return torch.cat([Mexh_left, Mexh_right], dim=1).view(self.out_channels, 1, self.kernel_size) # 40x1x250
class Gaussian_fast(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3, mode='sigmoid'):
super(Gaussian_fast, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc_right = torch.linspace(1, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)))
self.time_disc_left = torch.linspace(-(self.kernel_size / 2) + 1, -1, steps=int((self.kernel_size / 2)))
def Gaussian(self, p):
if self.mode == 'vanilla':
return -((1 / (pow(2 * torch.pi, 0.5))) * p * (torch.exp(((-torch.pow(p, 2)) / 2))))
if self.mode == 'sigmoid':
return -((1 / (pow(2 * torch.pi, 0.5))) * p * (torch.exp(((-torch.pow(p, 2)) / 2).sigmoid())))
if self.mode == 'softmax':
return -((1 / (pow(2 * torch.pi, 0.5))) * p * (torch.exp(F.softmax((-torch.pow(p, 2)) / 2, dim=-1))))
if self.mode == 'tanh':
return -((1 / (pow(2 * torch.pi, 0.5))) * p * (torch.exp(((-torch.pow(p, 2)) / 2).tanh())))
if self.mode == 'atan':
return -((1 / (pow(2 * torch.pi, 0.5))) * p * (torch.exp((2 / torch.pi) * (((-torch.pow(p, 2)) / 2).atan()))))
# y = D * torch.exp(-torch.pow(p, 2))
# F0 = (2./torch.pi)**(1./4.) * torch.exp(-torch.pow(p, 2))
# y = -2 / (3 ** (1 / 2)) * (-1 + 2 * p ** 2) * F0
# y = (2./torch.pi)**(1./4.) * torch.exp(-torch.pow(p, 2))
# y = -2 / (3 ** (1 / 2)) * (-1 + 2 * p ** 2) * y
# y = -((1 / (pow(2 * torch.pi, 0.5))) * p * torch.exp((-torch.pow(p, 2)) / 2))
def forward(self):
p1 = (self.time_disc_right - self.b_) / (self.a_ + self.eps)
p2 = (self.time_disc_left - self.b_) / (self.a_ + self.eps)
Gaussian_right = self.Gaussian(p1)
Gaussian_left = self.Gaussian(p2)
return torch.cat([Gaussian_left, Gaussian_right], dim=1).view(self.out_channels, 1,
self.kernel_size)
class Shannon_fast(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3):
super(Shannon_fast, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc_right = torch.linspace(0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)))
self.time_disc_left = torch.linspace(-(self.kernel_size / 2) + 1, -1, steps=int((self.kernel_size / 2)))
def Shannon(self, p):
return (torch.sin(torch.pi * (p - 0.5)) - torch.sin(2 * torch.pi * (p - 0.5))) / (torch.pi * (p - 0.5))
def forward(self):
p1 = (self.time_disc_right - self.b_) / (self.a_ + self.eps)
p2 = (self.time_disc_left - self.b_) / (self.a_ + self.eps)
Shannon_right = self.Shannon(p1)
Shannon_left = self.Shannon(p2)
return torch.cat([Shannon_left, Shannon_right], dim=1).view(self.out_channels, 1,
self.kernel_size)
class Sin_fast(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3, mode='sigmoid'):
super(Sin_fast, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc_right = torch.linspace(0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)))
self.time_disc_left = torch.linspace(-(self.kernel_size / 2) + 1, -1, steps=int((self.kernel_size / 2)))
# self.time_disc_right = torch.linspace(0, 1, steps=int((self.kernel_size / 2)))
# self.time_disc_left = torch.linspace(-1, 0, steps=int((self.kernel_size / 2)))
def Sin(self, p):
# y = (torch.sin(2 * torch.pi * (p - 0.5)) - torch.sin(torch.pi * (p - 0.5))) / (torch.pi * (p - 0.5))
return torch.sin(p) / (p + 1e-12)
def forward(self):
p1 = (self.time_disc_right - self.b_) / (self.a_ + self.eps) #
p2 = (self.time_disc_left - self.b_) / (self.a_ + self.eps) #
Shannon_right = self.Sin(p1)
Shannon_left = self.Sin(p2)
return torch.cat([Shannon_left, Shannon_right], dim=1).view(self.out_channels, 1,
self.kernel_size)
class Morlet_fast1(nn.Module):
def __init__(self, out_channels, kernel_size, eps=0.3, mode='sigmoid'):
super(Morlet_fast1, self).__init__()
if kernel_size % 2 != 0:
kernel_size = kernel_size - 1
self.out_channels = out_channels
self.kernel_size = kernel_size
self.eps = eps
self.mode = mode
self.a_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.b_ = torch.linspace(0, out_channels, out_channels).view(-1, 1)
self.time_disc = torch.linspace((-self.kernel_size / 2), (self.kernel_size / 2) - 1, steps=int(self.kernel_size))
def Morlet(self, p, c):
if self.mode == 'vanilla':
return c * torch.exp((-torch.pow(p, 2) / 2)) * torch.cos(5 * p)
if self.mode == 'sigmoid':
return c * torch.exp((-torch.pow(p, 2) / 2).sigmoid()) * torch.cos(5 * p)
if self.mode == 'maxmin':
a = c * torch.exp((-torch.pow(p, 2) / 2).sigmoid()) * torch.cos(5 * p)
return (a-a.min())/(a.max()-a.min())
if self.mode == 'softmax':
return c * torch.exp(F.softmax(-torch.pow(p, 2) / 2, dim=-1)) * torch.cos(5 * p)
if self.mode == 'tanh':
return c * torch.exp((-torch.pow(p, 2) / 2).tanh()) * torch.cos(5 * p)
if self.mode == 'atan':
return c * torch.exp((2 / torch.pi) * ((-torch.pow(p, 2) / 2).atan())) * torch.cos(5 * p)
def forward(self):
# a = self.time_disc - self.b_
# print(a.size())
p = (self.time_disc - self.b_) / (self.a_ + self.eps)
c = (pow(torch.pi, 0.25)) / torch.sqrt(self.a_ + 0.01) # worth exploring 1e-3 & D = C / self.a_.cuda()
# C = (pow(pi, 0.25)) / (torch.sqrt(self.a_) + 1e-12)
# C = (pow(pi, 0.25)) / (torch.sqrt(self.a_) + self.eps)
Morlet = self.Morlet(p, c)
return Morlet.view(self.out_channels, 1, self.kernel_size)
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
input = torch.randn(2, 1, 1024).cuda()
weight = Morlet_fast1(out_channels=64, kernel_size=300, eps=0.3, mode='vanilla').forward().cuda()
weight_t = weight.cpu().detach().numpy()
y = weight_t[20, :, :].squeeze()
x = np.linspace(0, 300, 300)
plt.plot(x, y)
plt.savefig('wahaha.tiff', format='tiff', dpi=600)
plt.show()