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###########################################################################
# NLP demo software by HyperbeeAI. #
# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. [email protected] #
###########################################################################
license_statement = "NLP demo software by HyperbeeAI. Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. [email protected]"
print("imported models.py")
print(license_statement)
print("")
import torch
import torch.nn as nn
import layers
class encoder_ai85cnn(nn.Module):
def __init__(
self,
device,
**kwargs
):
super().__init__()
self.cc0 = layers.ai85_conv1d( 64, 112, 1, 0, activation=None)
self.cc1 = layers.ai85_conv1d( 112, 112, 3, 1, activation='relu')
self.res1 = layers.ai85_add()
self.cc2 = layers.ai85_conv1d( 112, 112, 3, 1, activation='relu')
self.res2 = layers.ai85_add()
self.cc3 = layers.ai85_conv1d( 112, 112, 3, 1, activation='relu')
self.res3 = layers.ai85_add()
self.cc4 = layers.ai85_conv1d( 112, 112, 3, 1, activation='relu')
self.res4 = layers.ai85_add()
self.cc5 = layers.ai85_conv1d( 112, 64 , 1, 0, activation=None)
self.resg = layers.ai85_add()
self.device = device
def forward(self, x):
r = self.cc0(x)
t = self.cc1( r )
r = self.res1(t, r)
t = self.cc2( r )
r = self.res2(t, r)
t = self.cc3( r )
r = self.res3(t, r)
t = self.cc4( r )
r = self.res4(t, r)
t = self.cc5(r)
y = self.resg(t, x)
return y
class encoder(nn.Module):
def __init__(
self,
device,
**kwargs
):
super().__init__()
self.pre = layers.lpre()
self.cnn = encoder_ai85cnn(device = device);
self.device = device
def forward(self, x):
ssb = x.shape[0]
sl = x.shape[1]
pre_d = self.pre(x, 0, sl, ssb)
out = self.cnn(pre_d)
return out, pre_d
class decoder_ai85cnn_ccf(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 64 , 1, 0, activation=None, output_width_30b=True)
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_cpr(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.layer1 = layers.ai85_conv1d( 64*2, 64, 1, 0, activation='relu')
self.layer2 = layers.ai85_conv1d( 64, 64, 1, 0, activation='relu')
def forward(self, x):
x = self.layer1(x)
y = self.layer2(x)
return y
class decoder_ai85cnn_cl1(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 112, 3, 0, activation='relu')
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_cma(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 64, 112, 1, 0, activation=None)
self.res= layers.ai85_add()
def forward(self, x, res):
t = self.op(x)
y = self.res(t, res)
return y
class decoder_ai85cnn_claa(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 112, 3, 0, activation='relu')
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_cl0(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 64, 112, 1, 0, activation=None)
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_clfa(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 112, 3, 0, activation='relu')
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_ccac(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 112, 3, 0, activation='relu')
def forward(self, x):
y = self.op(x)
return y
class decoder_ai85cnn_cib(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.op = layers.ai85_conv1d( 112, 64 , 1, 0, activation=None)
def forward(self, x):
y = self.op(x)
return y
class decoder(nn.Module):
def __init__(
self,
device,
tpi,
**kwargs
):
super().__init__()
self.device = device
self.tpi = tpi
self.pre = layers.lpre()
self.fff = nn.Linear(64, 16384)
self.fff.weight = self.pre.ee1.weight # i.e., fff is not a layer, this is just an easy way of doing reverse embedding on pytorch
self.cl0 = decoder_ai85cnn_cl0();
self.ccf = decoder_ai85cnn_ccf();
self.cib = decoder_ai85cnn_cib();
self.cma = decoder_ai85cnn_cma();
self.cpr = decoder_ai85cnn_cpr();
self.cl1 = decoder_ai85cnn_cl1();
self.claa = decoder_ai85cnn_claa();
self.clfa = decoder_ai85cnn_clfa();
self.ccac = decoder_ai85cnn_ccac();
def forward(self, x, ees , pss=0):
ssb = x.shape[0]
sst = x.shape[1]
sl = ees.shape[2]
pre_d = self.pre(x, pss, sst + pss, ssb)
t = self.cl0(pre_d)
cl0_out = t
ssb, ts1, _ = t.shape
tp = torch.zeros(ssb, ts1, 2).fill_(self.tpi).to(t.device)
t = torch.cat((tp, t), dim = 2)
xconv = self.cl1(t)
t = self.cib(xconv)
ssb, ss_p, sst = t.shape
x2 = ees.unsqueeze(3).repeat(1, 1, 1, sst).view(ssb, ss_p, -1)
t = t.unsqueeze(2).repeat(1, 1, sl, 1).view(ssb, ss_p, -1)
t = torch.cat([t, x2], dim=1)
t = self.cpr(t)
t = t.view(ssb, ss_p, sl, sst)
t = torch.max(t, dim=2).values
t = self.cma(t, xconv)
t = torch.cat((tp, t), dim = 2)
xconv = self.claa(t)
t = self.cib(xconv)
t = t.unsqueeze(2).repeat(1, 1, sl, 1).view(ssb, ss_p, -1)
t = torch.cat([t, x2], dim=1)
t = self.cpr(t)
t = t.view(ssb, ss_p, sl, sst)
t = torch.max(t, dim=2).values
t = self.cma(t, xconv)
t = torch.cat((tp, t), dim = 2)
xconv = self.clfa(t)
t = self.cib(xconv)
t = t.unsqueeze(2).repeat(1, 1, sl, 1).view(ssb, ss_p, -1)
t = torch.cat([t, x2], dim=1)
t = self.cpr(t)
t = t.view(ssb, ss_p, sl, sst)
t = torch.max(t, dim=2).values
t = self.cma(t, xconv)
t = torch.cat((tp, t), dim = 2)
xconv = self.ccac(t)
t = self.cib(xconv)
t = t.unsqueeze(2).repeat(1, 1, sl, 1).view(ssb, ss_p, -1)
t = torch.cat([t, x2], dim=1)
t = self.cpr(t)
t = t.view(ssb, ss_p, sl, sst)
t = torch.max(t, dim=2).values
t = self.cma(t, xconv)
pss = t + sst
ccf_out = self.ccf(t)
output = self.fff(ccf_out.permute(0, 2, 1))
return output, pre_d, ccf_out
class seq2seq(nn.Module):
def __init__(self, encoder, decoder):
super().__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, src, trg):
enc_out, _ = self.encoder(src)
output, _, _ = self.decoder(trg, enc_out)
return output
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