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score_models.py
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score_models.py
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import torch
import torch.nn.functional as F
from kans import KAN, FastKAN, ReLUKAN, BottleNeckKAGN, WavKAN, KABN, KACN, KAGN, KAJN, KALN
from kan_convs import KANConv2DLayer, FastKANConv2DLayer, ReLUKANConv2DLayer, BottleNeckKAGNConv2DLayer, WavKANConv2DLayer, KABNConv2DLayer, KACNConv2DLayer, KAGNConv2DLayer, KAJNConv2DLayer, KALNConv2DLayer, BottleNeckSelfKAGNtention2D
from functions import get_timestep_embedding
##----------------------------------------------------------------------------------------------------------##
## SQuIRELS ##
##----------------------------------------------------------------------------------------------------------##
class MLP(torch.nn.Module):
def __init__(self, input_dim, layer_widths, activate_final = False, activation_fn=F.relu):
super(MLP, self).__init__()
layers = []
prev_width = input_dim
for layer_width in layer_widths:
layers.append(torch.nn.Linear(prev_width, layer_width))
# # same init for everyone
# torch.nn.init.constant_(layers[-1].weight, 0)
prev_width = layer_width
self.input_dim = input_dim
self.layer_widths = layer_widths
self.layers = torch.nn.ModuleList(layers)
self.activate_final = activate_final
self.activation_fn = activation_fn
def forward(self, x):
for i, layer in enumerate(self.layers[:-1]):
x = self.activation_fn(layer(x))
x = self.layers[-1](x)
if self.activate_final:
x = self.activation_fn(x)
return x
class SquirelsScoreNetwork(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = MLP(3 * t_enc_dim + 1,
layer_widths=decoder_layers +[x_dim],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
self.t_encoder = MLP(pos_dim,
layer_widths=encoder_layers +[t_enc_dim],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
self.x_encoder = MLP(x_dim,
layer_widths=encoder_layers +[t_enc_dim],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
self.e_encoder = MLP(1,
layer_widths=encoder_layers +[t_enc_dim],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class SquirelsScoreNetworkConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers=encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = MLP(self.temb_dim,
layer_widths=self.encoder_layers +[self.t_enc_dim],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
self.con_x_encoder1 = torch.nn.Conv2d(1, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_encoder2 = torch.nn.Conv2d(self.conv_dof, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_encoder3 = torch.nn.Conv2d(self.conv_dof, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_encoder4 = torch.nn.Conv2d(self.conv_dof, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_decoder1 = torch.nn.Conv2d(self.conv_dof*2+self.n_cond+1, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_decoder2 = torch.nn.Conv2d(self.conv_dof, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_decoder3 = torch.nn.Conv2d(self.conv_dof, self.conv_dof, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.con_x_decoder4 = torch.nn.Conv2d(self.conv_dof, 1, (3, 3), stride=(1, 1),
padding=(1, 1),bias=self.bias)
self.leakyReLU = torch.nn.LeakyReLU()
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.leakyReLU(self.con_x_encoder1(x))
xemb = self.leakyReLU(self.con_x_encoder2(xemb))
xemb = self.leakyReLU(self.con_x_encoder3(xemb))
xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.leakyReLU(self.con_x_decoder1(h))
out = self.leakyReLU(self.con_x_decoder2(out))
out = self.leakyReLU(self.con_x_decoder3(out))
out = self.con_x_decoder4(out)
return out
class SquirelsScoreNetworkLinear(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.t_hl_dim=encoder_layers[0]
self.t_nh_layers=len(encoder_layers)
self.p_hl_dim=conv_dof
self.p_nh_layers=4
self.x_dim = x_dim
self.temb_dim = temb_dim
self.t_enc_layers = [self.temb_dim] + [self.t_hl_dim] * self.t_nh_layers + [self.p_hl_dim]
self.p_enc_layers = [self.x_dim] + [self.p_hl_dim] * self.p_nh_layers
self.p_dec_layers = [2 * self.p_hl_dim + self.x_dim + 3] + [self.p_hl_dim] * (self.p_nh_layers-1) + [self.x_dim]
self.n_cond = n_cond
self.bias = False
self.t_encoder = MLP(self.t_enc_layers[0],
layer_widths=self.t_enc_layers[1:],
activate_final = False,
activation_fn=torch.nn.LeakyReLU())
self.con_x_encoder1 = MLP(self.p_enc_layers[0], [self.p_enc_layers[1]], activate_final = False)
self.con_x_encoder2 = MLP(self.p_enc_layers[1], [self.p_enc_layers[2]], activate_final = False)
self.con_x_encoder3 = MLP(self.p_enc_layers[2], [self.p_enc_layers[3]], activate_final = False)
self.con_x_encoder4 = MLP(self.p_enc_layers[3], [self.p_enc_layers[4]], activate_final = False)
self.con_x_decoder1 = MLP(self.p_dec_layers[0], [self.p_dec_layers[1]], activate_final = False)
self.con_x_decoder2 = MLP(self.p_dec_layers[1], [self.p_dec_layers[2]], activate_final = False)
self.con_x_decoder3 = MLP(self.p_dec_layers[2], [self.p_dec_layers[3]], activate_final = False)
self.con_x_decoder4 = MLP(self.p_dec_layers[3], [self.p_dec_layers[4]], activate_final = False)
self.leakyReLU = torch.nn.LeakyReLU()
def forward(self, x, t, cond=None, selfcond=None):
x_shape = x.shape
x = x.reshape((x_shape[0], x_shape[1], x_shape[2]*x_shape[3]))
if len(cond) > 0:
cond = cond.unsqueeze(1)
if len(selfcond) > 0:
selfcond = selfcond.reshape((selfcond.shape[0], selfcond.shape[1], selfcond.shape[2]*selfcond.shape[3]))
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb).unsqueeze(1)
xemb = self.leakyReLU(self.con_x_encoder1(x))
xemb = self.leakyReLU(self.con_x_encoder2(xemb))
xemb = self.leakyReLU(self.con_x_encoder3(xemb))
xemb = self.con_x_encoder4(xemb)
# print(f"xemb: {xemb.shape}, temb: {temb.shape}, selfcond: {selfcond.shape}, cond: {cond.shape}")
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], -1)
else:
h = torch.cat([xemb, temb, selfcond], -1)
out = self.leakyReLU(self.con_x_decoder1(h))
out = self.leakyReLU(self.con_x_decoder2(out))
out = self.leakyReLU(self.con_x_decoder3(out))
out = self.con_x_decoder4(out)
out = out.reshape(x_shape)
return out
##----------------------------------------------------------------------------------------------------------##
## KAN ##
##----------------------------------------------------------------------------------------------------------##
class ScoreKAN(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = KAN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers +[x_dim])
self.t_encoder = KAN(layers_hidden = [pos_dim] + encoder_layers +[t_enc_dim])
self.x_encoder = KAN(layers_hidden = [x_dim] + encoder_layers +[t_enc_dim])
self.e_encoder = KAN(layers_hidden = [1] + encoder_layers +[t_enc_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class ScoreKANConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = KAN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder1 = KANConv2DLayer(1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder2 = KANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder3 = KANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder4 = KANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder1 = KANConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder2 = KANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder3 = KANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder4 = KANConv2DLayer(self.conv_dof, 1, (3, 3), stride=(1, 1), padding=1)
self.leakyReLU = torch.nn.LeakyReLU()
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.leakyReLU(self.con_x_encoder1(x))
xemb = self.leakyReLU(self.con_x_encoder2(xemb))
xemb = self.leakyReLU(self.con_x_encoder3(xemb))
xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.leakyReLU(self.con_x_decoder1(h))
out = self.leakyReLU(self.con_x_decoder2(out))
out = self.leakyReLU(self.con_x_decoder3(out))
out = self.con_x_decoder4(out)
return out
##----------------------------------------------------------------------------------------------------------##
## FAST KAN ##
##----------------------------------------------------------------------------------------------------------##
class FastScoreKAN(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.t_encoder = FastKAN(layers_hidden = [pos_dim] + encoder_layers + [t_enc_dim])
self.x_encoder = FastKAN(layers_hidden = [x_dim] + encoder_layers + [t_enc_dim])
self.e_encoder = FastKAN(layers_hidden = [1] + encoder_layers + [t_enc_dim])
self.net = FastKAN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers + [x_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class FastScoreKANConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = FastKAN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder1 = FastKANConv2DLayer(1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder2 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder3 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder4 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder1 = FastKANConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder2 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder3 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder4 = FastKANConv2DLayer(self.conv_dof, 1, (3, 3), stride=(1, 1), padding=1)
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.con_x_encoder1(x)
xemb = self.con_x_encoder2(xemb)
xemb = self.con_x_encoder3(xemb)
xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.con_x_decoder1(h)
out = self.con_x_decoder2(out)
out = self.con_x_decoder3(out)
out = self.con_x_decoder4(out)
return out
class FastScoreKANLinear(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.t_hl_dim=encoder_layers[0]
self.t_nh_layers=len(encoder_layers)
self.p_hl_dim=conv_dof
self.p_nh_layers=4
self.x_dim = x_dim
self.temb_dim = temb_dim
self.t_enc_layers = [self.temb_dim] + [self.t_hl_dim] * self.t_nh_layers + [self.p_hl_dim]
self.p_enc_layers = [self.x_dim] + [self.p_hl_dim] * self.p_nh_layers
self.p_dec_layers = [2 * self.p_hl_dim + self.x_dim + 3] + [self.p_hl_dim] * (self.p_nh_layers-1) + [self.x_dim]
self.n_cond = n_cond
self.bias = False
self.t_encoder = FastKAN(layers_hidden = self.t_enc_layers)
self.con_x_encoder = FastKAN(layers_hidden = self.p_enc_layers)
self.con_x_decoder = FastKAN(layers_hidden = self.p_dec_layers)
def forward(self, x, t, cond=None, selfcond=None):
x_shape = x.shape
x = x.reshape((x_shape[0], x_shape[1], x_shape[2]*x_shape[3]))
if len(cond) > 0:
cond = cond.unsqueeze(1)
if len(selfcond) > 0:
selfcond = selfcond.reshape((selfcond.shape[0], selfcond.shape[1], selfcond.shape[2]*selfcond.shape[3]))
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb).unsqueeze(1)
xemb = self.con_x_encoder(x)
# print(f"xemb: {xemb.shape}, temb: {temb.shape}, selfcond: {selfcond.shape}, cond: {cond.shape}")
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], -1)
else:
h = torch.cat([xemb, temb, selfcond], -1)
out = self.con_x_decoder(h)
out = out.reshape(x_shape)
return out
##----------------------------------------------------------------------------------------------------------##
## FAST KAN WIDE ##
##----------------------------------------------------------------------------------------------------------##
class FastScoreKANWide(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = FastKAN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers +[x_dim])
self.t_encoder = FastKAN(layers_hidden = [pos_dim] + encoder_layers +[t_enc_dim])
self.x_encoder = FastKAN(layers_hidden = [x_dim] + encoder_layers +[t_enc_dim])
self.e_encoder = FastKAN(layers_hidden = [1] + encoder_layers +[t_enc_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class FastScoreKANConvWide(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = FastKAN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder = FastKANConv2DLayer(1, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_encoder1 = FastKANConv2DLayer(1, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_encoder2 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_encoder3 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_encoder4 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder = FastKANConv2DLayer(self.conv_dof*2+self.n_cond+1, 1, (3, 3), stride=1, padding=1)
# self.con_x_decoder1 = FastKANConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_decoder2 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_decoder3 = FastKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
# self.con_x_decoder4 = FastKANConv2DLayer(self.conv_dof, 1, (3, 3), stride=(1, 1), padding=1)
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.con_x_encoder(x)
# xemb = self.con_x_encoder1(x)
# xemb = self.con_x_encoder2(xemb)
# xemb = self.con_x_encoder3(xemb)
# xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.con_x_decoder(h)
# out = self.con_x_decoder1(h)
# out = self.con_x_decoder2(out)
# out = self.con_x_decoder3(out)
# out = self.con_x_decoder4(out)
return out
#----------------------------------------------------------------------------------------------------------#
# RELU KAN #
#----------------------------------------------------------------------------------------------------------#
class ReluScoreKAN(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = ReLUKAN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers +[x_dim])
self.t_encoder = ReLUKAN(layers_hidden = [pos_dim] + encoder_layers +[t_enc_dim])
self.x_encoder = ReLUKAN(layers_hidden = [x_dim] + encoder_layers +[t_enc_dim])
self.e_encoder = ReLUKAN(layers_hidden = [1] + encoder_layers +[t_enc_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class ReluScoreKANConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = ReLUKAN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder1 = ReLUKANConv2DLayer(1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder2 = ReLUKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder3 = ReLUKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_encoder4 = ReLUKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder1 = ReLUKANConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder2 = ReLUKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder3 = ReLUKANConv2DLayer(self.conv_dof, self.conv_dof, (3, 3), stride=1, padding=1)
self.con_x_decoder4 = ReLUKANConv2DLayer(self.conv_dof, 1, (3, 3), stride=(1, 1), padding=1)
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.con_x_encoder1(x)
xemb = self.con_x_encoder2(xemb)
xemb = self.con_x_encoder3(xemb)
xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.con_x_decoder1(h)
out = self.con_x_decoder2(out)
out = self.con_x_decoder3(out)
out = self.con_x_decoder4(out)
return out
class ReluScoreKANLinear(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.t_hl_dim=encoder_layers[0]
self.t_nh_layers=len(encoder_layers)
self.p_hl_dim=conv_dof
self.p_nh_layers=4
self.x_dim = x_dim
self.temb_dim = temb_dim
self.t_enc_layers = [self.temb_dim] + [self.t_hl_dim] * self.t_nh_layers + [self.p_hl_dim]
self.p_enc_layers = [self.x_dim] + [self.p_hl_dim] * self.p_nh_layers
self.p_dec_layers = [2 * self.p_hl_dim + self.x_dim + 3] + [self.p_hl_dim] * (self.p_nh_layers-1) + [self.x_dim]
self.n_cond = n_cond
self.bias = False
self.t_encoder = ReLUKAN(layers_hidden = self.t_enc_layers)
self.con_x_encoder = ReLUKAN(layers_hidden = self.p_enc_layers)
self.con_x_decoder = ReLUKAN(layers_hidden = self.p_dec_layers)
def forward(self, x, t, cond=None, selfcond=None):
x_shape = x.shape
x = x.reshape((x_shape[0], x_shape[1], x_shape[2]*x_shape[3]))
if len(cond) > 0:
cond = cond.unsqueeze(1)
if len(selfcond) > 0:
selfcond = selfcond.reshape((selfcond.shape[0], selfcond.shape[1], selfcond.shape[2]*selfcond.shape[3]))
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb).unsqueeze(1)
xemb = self.con_x_encoder(x).unsqueeze(1)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], -1)
else:
h = torch.cat([xemb, temb, selfcond], -1)
out = self.con_x_decoder(h)
out = out.reshape(x_shape)
return out
#----------------------------------------------------------------------------------------------------------#
# BOTTLENECK KAN #
#----------------------------------------------------------------------------------------------------------#
class BottleneckScoreKAGN(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = BottleNeckKAGN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers +[x_dim])
self.t_encoder = BottleNeckKAGN(layers_hidden = [pos_dim] + encoder_layers +[t_enc_dim])
self.x_encoder = BottleNeckKAGN(layers_hidden = [x_dim] + encoder_layers +[t_enc_dim])
self.e_encoder = BottleNeckKAGN(layers_hidden = [1] + encoder_layers +[t_enc_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)
else:
h = torch.cat([xemb ,temb, selfcond], -1)
out = self.net(h)
return out
class BottleneckScoreKAGNConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = BottleNeckKAGN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder1 = BottleNeckKAGNConv2DLayer(1, self.conv_dof, 3, stride=1, padding=1)
self.con_x_encoder2 = BottleNeckKAGNConv2DLayer(self.conv_dof, self.conv_dof, 3, stride=1, padding=1)
self.con_x_encoder3 = BottleNeckKAGNConv2DLayer(self.conv_dof, self.conv_dof, 3, stride=1, padding=1)
self.con_x_encoder4 = BottleNeckKAGNConv2DLayer(self.conv_dof, self.conv_dof, 3, stride=1, padding=1)
self.con_x_decoder1 = BottleNeckKAGNConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, 3, stride=1, padding=1)
self.con_x_decoder2 = BottleNeckKAGNConv2DLayer(self.conv_dof, self.conv_dof, 3, stride=1, padding=1)
self.con_x_decoder3 = BottleNeckKAGNConv2DLayer(self.conv_dof, self.conv_dof, 3, stride=1, padding=1)
self.con_x_decoder4 = BottleNeckKAGNConv2DLayer(self.conv_dof, 1, 3, stride=(1, 1), padding=1)
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.con_x_encoder1(x)
xemb = self.con_x_encoder2(xemb)
xemb = self.con_x_encoder3(xemb)
xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.con_x_decoder1(h)
out = self.con_x_decoder2(out)
out = self.con_x_decoder3(out)
out = self.con_x_decoder4(out)
return out
class BottleneckScoreKAGNLinear(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.t_hl_dim=encoder_layers[0]
self.t_nh_layers=len(encoder_layers)
self.p_hl_dim=conv_dof
self.p_nh_layers=4
self.x_dim = x_dim
self.temb_dim = temb_dim
self.t_enc_layers = [self.temb_dim] + [self.t_hl_dim] * self.t_nh_layers + [self.p_hl_dim]
self.p_enc_layers = [self.x_dim] + [self.p_hl_dim] * self.p_nh_layers
self.p_dec_layers = [2 * self.p_hl_dim + self.x_dim + 3] + [self.p_hl_dim] * (self.p_nh_layers-1) + [self.x_dim]
self.n_cond = n_cond
self.bias = False
self.t_encoder = BottleNeckKAGN(layers_hidden = self.t_enc_layers)
self.con_x_encoder = BottleNeckKAGN(layers_hidden = self.p_enc_layers)
self.con_x_decoder = BottleNeckKAGN(layers_hidden = self.p_dec_layers)
def forward(self, x, t, cond=None, selfcond=None):
x_shape = x.shape
x = x.reshape((x_shape[0], x_shape[1], x_shape[2]*x_shape[3]))
x = torch.squeeze(x)
# if len(cond) > 0:
# cond = cond.unsqueeze(1)
if len(selfcond) > 0:
selfcond = selfcond.reshape((selfcond.shape[0], selfcond.shape[2]*selfcond.shape[3]))
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb).unsqueeze(1)
temb = torch.squeeze(temb)
xemb = self.con_x_encoder(x)
# print(f"xemb: {xemb.shape}, temb: {temb.shape}, selfcond: {selfcond.shape}, cond: {cond.shape}")
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], -1)
else:
h = torch.cat([xemb, temb, selfcond], -1)
out = self.con_x_decoder(h)
out = out.reshape(x_shape)
return out
class BottleneckScoreKAGNAttentionConv(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], temb_dim=128, conv_dof=32, x_dim=100, n_cond=0):
super().__init__()
self.conv_dof = conv_dof
self.n_cond = n_cond
self.temb_dim = temb_dim
self.encoder_layers = encoder_layers
self.t_enc_dim = self.conv_dof
self.bias = False
self.t_encoder = BottleNeckKAGN(layers_hidden = [self.temb_dim] + self.encoder_layers +[self.t_enc_dim])
self.con_x_encoder1 = BottleNeckKAGNConv2DLayer(1, self.conv_dof, kernel_size=1, stride=1, padding=0)
self.con_x_encoder2 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
self.con_x_encoder3 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
# self.con_x_encoder4 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
self.con_x_decoder0 = BottleNeckKAGNConv2DLayer(self.conv_dof*2+self.n_cond+1, self.conv_dof, 1, stride=1, padding=0)
# self.con_x_decoder1 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
self.con_x_decoder2 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
self.con_x_decoder3 = BottleNeckSelfKAGNtention2D(self.conv_dof, kernel_size=3, stride=1, padding=1)
self.con_x_decoder4 = BottleNeckKAGNConv2DLayer(self.conv_dof, 1, 1, stride=1, padding=0)
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if self.n_cond > 0:
cond = cond.view(-1, self.n_cond, 1, 1).expand(-1, -1, 10, 10)
temb = get_timestep_embedding(t, self.temb_dim)
#print(t.size(), temb.size())
temb = self.t_encoder(temb).view(-1, self.t_enc_dim, 1, 1).expand(-1, -1, 10, 10)
xemb = self.con_x_encoder1(x)
xemb = self.con_x_encoder2(xemb)
xemb = self.con_x_encoder3(xemb)
# xemb = self.con_x_encoder4(xemb)
if self.n_cond > 0:
h = torch.cat([xemb, temb, selfcond, cond], 1)
else:
h = torch.cat([xemb, temb, selfcond], 1)
out = self.con_x_decoder0(h)
# out = self.con_x_decoder1(out)
out = self.con_x_decoder2(out)
out = self.con_x_decoder3(out)
out = self.con_x_decoder4(out)
return out
#----------------------------------------------------------------------------------------------------------#
# WAVELET KAN #
#----------------------------------------------------------------------------------------------------------#
class WavScoreKAN(torch.nn.Module):
def __init__(self, encoder_layers=[256,256], pos_dim=128, decoder_layers=[256,256], x_dim=1, n_cond=1):
super().__init__()
self.temb_dim = pos_dim
t_enc_dim = pos_dim *2
self.locals = [encoder_layers, pos_dim, decoder_layers, x_dim]
self.n_cond = n_cond
self.net = WavKAN(layers_hidden = [3 * t_enc_dim + 1] + decoder_layers +[x_dim])
self.t_encoder = WavKAN(layers_hidden = [pos_dim] + encoder_layers +[t_enc_dim])
self.x_encoder = WavKAN(layers_hidden = [x_dim] + encoder_layers +[t_enc_dim])
self.e_encoder = WavKAN(layers_hidden = [1] + encoder_layers +[t_enc_dim])
def forward(self, x, t, cond=None, selfcond=None):
if len(x.shape) == 1:
x = x.unsqueeze(0)
if len(selfcond.shape) == 1:
selfcond = selfcond.unsqueeze(0)
temb = get_timestep_embedding(t, self.temb_dim)
temb = self.t_encoder(temb)
xemb = self.x_encoder(x)
if self.n_cond > 0:
eemb = cond
eemb = self.e_encoder(eemb)
h = torch.cat([xemb, temb, selfcond, eemb], -1)