def forward(self, k, x, logposterior):
'''
k: number of samples
x: [B,X]
logposterior(z) -> [P,B]
'''
self.B = x.size()[0]
self.P = k
# print (self.B, 'B')
# print (k)
# fsdaf
#q(v|x)
out = x
for i in range(len(self.qv_weights)-1):
out = self.act_func(self.qv_weights[i](out))
out = self.qv_weights[-1](out)
mean = out[:,:self.z_size]
logvar = out[:,self.z_size:]
#Sample v0
eps = Variable(torch.FloatTensor(k, self.B, self.z_size).normal_().type(self.dtype)) #[P,B,Z]
v = eps.mul(torch.exp(.5*logvar)) + mean #[P,B,Z]
logqv0 = lognormal(v, mean, logvar) #[P,B]
#[PB,Z]
v = v.view(-1,self.z_size)
#[PB,X]
x_tiled = x.repeat(k,1)
#[PB,X+Z]
# print (x_tiled.size())
# print (v.size())
xv = torch.cat((x_tiled, v),1)
#q(z|x,v)
out = xv
for i in range(len(self.qz_weights)-1):
out = self.act_func(self.qz_weights[i](out))
out = self.qz_weights[-1](out)
mean = out[:,:self.z_size]
logvar = out[:,self.z_size:]
self.B = x.size()[0]
# print (self.B, 'B')
#Sample z0
eps = Variable(torch.FloatTensor(k, self.B, self.z_size).normal_().type(self.dtype)) #[P,B,Z]
# print (eps.size(),'eps')
# print (mean.size(),'mean')
# print (self.P, 'P')
# print (mean)
mean = mean.contiguous().view(self.P,self.B,self.z_size)
logvar = logvar.contiguous().view(self.P,self.B,self.z_size)
# print (mean)
# mean = mean.contiguous().view(self.P,1,self.z_size)
# logvar = logvar.contiguous().view(self.P,1,self.z_size)
# print (mean.size(),'mean')
z = eps.mul(torch.exp(.5*logvar)) + mean #[P,B,Z]
# print (z.size(),'z')
# mean = mean.contiguous().view(self.P*self.B,self.z_size)
# logvar = logvar.contiguous().view(self.P*self.B,self.z_size)
logqz0 = lognormal333(z, mean, logvar) #[P,B]
#[PB,Z]
z = z.view(-1,self.z_size)
# print (z.size())
logdetsum = 0.
for i in range(self.n_flows):
z, v, logdet = self.norm_flow(self.params[i],z,v)
logdetsum += logdet
xz = torch.cat((x_tiled,z),1)
#r(vT|x,zT)
out = xz
for i in range(len(self.rv_weights)-1):
out = self.act_func(self.rv_weights[i](out))
out = self.rv_weights[-1](out)
mean = out[:,:self.z_size]
logvar = out[:,self.z_size:]
mean = mean.contiguous().view(self.P,self.B,self.z_size)
logvar = logvar.contiguous().view(self.P,self.B,self.z_size)
v = v.view(k,self.B,self.z_size)
logrvT = lognormal333(v, mean, logvar) #[P,B]
z = z.view(k,self.B,self.z_size)
# print(logqz0.size(), 'here')
# print(logqv0.size())
# print(logdetsum.size())
# print(logrvT.size())
logdetsum = logdetsum.view(k,self.B)
# print (logqz0+logqv0-logdetsum-logrvT)
# fadfdsa
return z, logqz0+logqv0-logdetsum-logrvT
def sample(k):
P = k
# #Sample
# eps = Variable(torch.FloatTensor(P, B, model.z_size).normal_().type(model.dtype)) #[P,B,Z]
# z = eps.mul(torch.exp(.5*logvar)) + mean #[P,B,Z]
# logqz = lognormal(z, mean, logvar) #[P,B]
# logpx = logposterior(z)
# optimizer.zero_grad()
#q(v|x)
# out = x
# for i in range(len(self.qv_weights)-1):
# out = self.act_func(self.qv_weights[i](out))
# out = self.qv_weights[-1](out)
# mean = out[:,:self.z_size]
# logvar = out[:,self.z_size:]
#Sample v0
eps = Variable(
torch.FloatTensor(k, B,
z_size).normal_().type(model.dtype)) #[P,B,Z]
# print (eps)
v = eps.mul(torch.exp(.5 * logvar_v)) + mean_v #[P,B,Z]
logqv0 = lognormal(v, mean_v, logvar_v) #[P,B]
#[PB,Z]
v = v.view(-1, model.z_size)
# print (v)
# fsaf
# print(v)
# fasd
#[PB,X]
# x_tiled = x.repeat(k,1)
#[PB,X+Z]
# xv = torch.cat((x_tiled, v),1)
#q(z|x,v)
out = v
for i in range(len(qz_weights) - 1):
out = act_func(qz_weights[i](out))
out = qz_weights[-1](out)
mean = out[:, :z_size]
logvar = out[:, z_size:] + 5.
# print (mean)
# B = x.size()[0]
# print (self.B, 'B')
#Sample z0
eps = Variable(
torch.FloatTensor(k, B,
z_size).normal_().type(model.dtype)) #[P,B,Z]
# print (mean)
mean = mean.contiguous().view(P, B, model.z_size)
logvar = logvar.contiguous().view(P, B, model.z_size)
z = eps.mul(torch.exp(.5 * logvar)) + mean #[P,B,Z]
# print (z.size(),'z')
# mean = mean.contiguous().view(P*B,model.z_size)
# logvar = logvar.contiguous().view(P*B,model.z_size)
# print (z)
# fad
logqz0 = lognormal333(z, mean, logvar) #[P,B]
#[PB,Z]
z = z.view(-1, z_size)
logdetsum = 0.
for i in range(n_flows):
z, v, logdet = norm_flow(params[i], z, v)
logdetsum += logdet
# xz = torch.cat((x_tiled,z),1)
#r(vT|x,zT)
out = z
for i in range(len(rv_weights) - 1):
out = act_func(rv_weights[i](out))
out = rv_weights[-1](out)
mean = out[:, :model.z_size]
logvar = out[:, model.z_size:]
mean = mean.contiguous().view(P, B, model.z_size)
logvar = logvar.contiguous().view(P, B, model.z_size)
v = v.view(k, B, model.z_size)
logrvT = lognormal333(v, mean, logvar) #[P,B]
z = z.view(k, B, model.z_size)
logq = logqz0 + logqv0 - logdetsum - logrvT
# print (torch.mean(logqz0),torch.mean(logqv0),torch.mean(logdetsum),torch.mean(logrvT))
return z, logq
def sample(self, mean, logvar, k, logposterior):
self.P = k
self.B = mean.size()[0]
if torch.cuda.is_available():
self.grad_outputs = torch.ones(k, self.B).cuda()
else:
self.grad_outputs = torch.ones(k, self.B)
gaus = Gaussian(self.hyper_config)
# q(z0)
z, logqz0 = gaus.sample(mean, logvar, k)
# q(v0)
zeros = Variable(torch.zeros(self.B, self.z_size)).cuda()
v, logqv0 = gaus.sample(zeros, zeros, k)
#[PB,Z]
z = z.view(-1,self.z_size)
v = v.view(-1,self.z_size)
#Transform
logdetsum = 0.
for i in range(self.n_flows):
params = self.flow_params[i]
# z, v, logdet = self.norm_flow([self.flow_params[i]],z,v)
z, v, logdet = self.norm_flow(params,z,v, logposterior)
logdetsum += logdet
logdetsum = logdetsum.view(k,self.B)
#r(vT|x,zT)
#r(vT|zT) try that
out = z #[PB,Z]
# print (out.size())
# fasda
for i in range(len(self.rv_weights)-1):
out = self.act_func(self.rv_weights[i](out))
out = self.rv_weights[-1](out)
# print (out)
mean = out[:,:self.z_size]
logvar = out[:,self.z_size:]
# r_vt = Gaussian(self.hyper_config, mean, logvar)
v = v.view(k, self.B, self.z_size)
z = z.view(k, self.B, self.z_size)
mean = mean.contiguous().view(k, self.B, self.z_size)
logvar = logvar.contiguous().view(k, self.B, self.z_size)
# print (mean.size()) #[PB,Z]
# print (v.size()) #[P,B,Z]
# print (self.B)
# print (k)
# logrvT = gaus.logprob(v, mean, logvar)
logrvT = lognormal333(v, mean, logvar)
# print (logqz0.size())
# print (logqv0.size())
# print (logdetsum.size())
# print (logrvT.size())
# fadsf
logpz = logqz0+logqv0-logdetsum-logrvT
return z, logpz
def sample(self, mean, logvar, k):
self.B = mean.size()[0]
gaus = Gaussian(self.hyper_config)
# q(z0)
z, logqz0 = gaus.sample(mean, logvar, k)
# q(v0)
zeros = Variable(torch.zeros(self.B, self.z_size)).cuda()
v, logqv0 = gaus.sample(zeros, zeros, k)
#[PB,Z]
z = z.view(-1, self.z_size)
v = v.view(-1, self.z_size)
#Transform
logdetsum = 0.
for i in range(self.n_flows):
params = self.flow_params[i]
# z, v, logdet = self.norm_flow([self.flow_params[i]],z,v)
z, v, logdet = self.norm_flow(params, z, v)
logdetsum += logdet
logdetsum = logdetsum.view(k, self.B)
#r(vT|x,zT)
#r(vT|zT) try that
out = z #[PB,Z]
# print (out.size())
# fasda
for i in range(len(self.rv_weights) - 1):
out = self.act_func(self.rv_weights[i](out))
out = self.rv_weights[-1](out)
# print (out)
mean = out[:, :self.z_size]
logvar = out[:, self.z_size:]
# r_vt = Gaussian(self.hyper_config, mean, logvar)
v = v.view(k, self.B, self.z_size)
z = z.view(k, self.B, self.z_size)
mean = mean.contiguous().view(k, self.B, self.z_size)
logvar = logvar.contiguous().view(k, self.B, self.z_size)
# print (mean.size()) #[PB,Z]
# print (v.size()) #[P,B,Z]
# print (self.B)
# print (k)
# logrvT = gaus.logprob(v, mean, logvar)
logrvT = lognormal333(v, mean, logvar)
# print (logqz0.size())
# print (logqv0.size())
# print (logdetsum.size())
# print (logrvT.size())
# fadsf
logpz = logqz0 + logqv0 - logdetsum - logrvT
return z, logpz
def sample(k):
P = k
# #Sample
# eps = Variable(torch.FloatTensor(P, B, model.z_size).normal_().type(model.dtype)) #[P,B,Z]
# z = eps.mul(torch.exp(.5*logvar)) + mean #[P,B,Z]
# logqz = lognormal(z, mean, logvar) #[P,B]
# logpx = logposterior(z)
# optimizer.zero_grad()
#q(v|x)
# out = x
# for i in range(len(self.qv_weights)-1):
# out = self.act_func(self.qv_weights[i](out))
# out = self.qv_weights[-1](out)
# mean = out[:,:self.z_size]
# logvar = out[:,self.z_size:]
#Sample v0
eps = Variable(torch.FloatTensor(k, B, z_size).normal_().type(model.dtype)) #[P,B,Z]
# print (eps)
v = eps.mul(torch.exp(.5*logvar_v)) + mean_v #[P,B,Z]
logqv0 = lognormal(v, mean_v, logvar_v) #[P,B]
#[PB,Z]
v = v.view(-1,model.z_size)
# print (v)
# fsaf
# print(v)
# fasd
#[PB,X]
# x_tiled = x.repeat(k,1)
#[PB,X+Z]
# xv = torch.cat((x_tiled, v),1)
#q(z|x,v)
out = v
for i in range(len(qz_weights)-1):
out = act_func(qz_weights[i](out))
out = qz_weights[-1](out)
mean = out[:,:z_size]
logvar = out[:,z_size:] + 5.
# print (mean)
# B = x.size()[0]
# print (self.B, 'B')
#Sample z0
eps = Variable(torch.FloatTensor(k, B, z_size).normal_().type(model.dtype)) #[P,B,Z]
# print (mean)
mean = mean.contiguous().view(P,B,model.z_size)
logvar = logvar.contiguous().view(P,B,model.z_size)
z = eps.mul(torch.exp(.5*logvar)) + mean #[P,B,Z]
# print (z.size(),'z')
# mean = mean.contiguous().view(P*B,model.z_size)
# logvar = logvar.contiguous().view(P*B,model.z_size)
# print (z)
# fad
logqz0 = lognormal333(z, mean, logvar) #[P,B]
#[PB,Z]
z = z.view(-1,z_size)
logdetsum = 0.
for i in range(n_flows):
z, v, logdet = norm_flow(params[i],z,v)
logdetsum += logdet
# xz = torch.cat((x_tiled,z),1)
#r(vT|x,zT)
out = z
for i in range(len(rv_weights)-1):
out = act_func(rv_weights[i](out))
out = rv_weights[-1](out)
mean = out[:,:model.z_size]
logvar = out[:,model.z_size:]
mean = mean.contiguous().view(P,B,model.z_size)
logvar = logvar.contiguous().view(P,B,model.z_size)
v = v.view(k,B,model.z_size)
logrvT = lognormal333(v, mean, logvar) #[P,B]
z = z.view(k,B,model.z_size)
logq = logqz0+logqv0-logdetsum-logrvT
# print (torch.mean(logqz0),torch.mean(logqv0),torch.mean(logdetsum),torch.mean(logrvT))
return z, logq
def forward(self, k, x, logposterior):
'''
k: number of samples
x: [B,X]
logposterior(z) -> [P,B]
'''
self.B = x.size()[0]
self.P = k
# print (self.B, 'B')
# print (k)
# fsdaf
#q(v|x)
out = x
for i in range(len(self.qv_weights) - 1):
out = self.act_func(self.qv_weights[i](out))
out = self.qv_weights[-1](out)
mean = out[:, :self.z_size]
logvar = out[:, self.z_size:]
#Sample v0
eps = Variable(
torch.FloatTensor(k, self.B, self.z_size).normal_().type(
self.dtype)) #[P,B,Z]
v = eps.mul(torch.exp(.5 * logvar)) + mean #[P,B,Z]
logqv0 = lognormal(v, mean, logvar) #[P,B]
#[PB,Z]
v = v.view(-1, self.z_size)
#[PB,X]
x_tiled = x.repeat(k, 1)
#[PB,X+Z]
# print (x_tiled.size())
# print (v.size())
xv = torch.cat((x_tiled, v), 1)
#q(z|x,v)
out = xv
for i in range(len(self.qz_weights) - 1):
out = self.act_func(self.qz_weights[i](out))
out = self.qz_weights[-1](out)
mean = out[:, :self.z_size]
logvar = out[:, self.z_size:]
self.B = x.size()[0]
# print (self.B, 'B')
#Sample z0
eps = Variable(
torch.FloatTensor(k, self.B, self.z_size).normal_().type(
self.dtype)) #[P,B,Z]
# print (eps.size(),'eps')
# print (mean.size(),'mean')
# print (self.P, 'P')
# print (mean)
mean = mean.contiguous().view(self.P, self.B, self.z_size)
logvar = logvar.contiguous().view(self.P, self.B, self.z_size)
# print (mean)
# mean = mean.contiguous().view(self.P,1,self.z_size)
# logvar = logvar.contiguous().view(self.P,1,self.z_size)
# print (mean.size(),'mean')
z = eps.mul(torch.exp(.5 * logvar)) + mean #[P,B,Z]
# print (z.size(),'z')
# mean = mean.contiguous().view(self.P*self.B,self.z_size)
# logvar = logvar.contiguous().view(self.P*self.B,self.z_size)
logqz0 = lognormal333(z, mean, logvar) #[P,B]
#[PB,Z]
z = z.view(-1, self.z_size)
# print (z.size())
logdetsum = 0.
for i in range(self.n_flows):
z, v, logdet = self.norm_flow(self.params[i], z, v)
logdetsum += logdet
xz = torch.cat((x_tiled, z), 1)
#r(vT|x,zT)
out = xz
for i in range(len(self.rv_weights) - 1):
out = self.act_func(self.rv_weights[i](out))
out = self.rv_weights[-1](out)
mean = out[:, :self.z_size]
logvar = out[:, self.z_size:]
mean = mean.contiguous().view(self.P, self.B, self.z_size)
logvar = logvar.contiguous().view(self.P, self.B, self.z_size)
v = v.view(k, self.B, self.z_size)
logrvT = lognormal333(v, mean, logvar) #[P,B]
z = z.view(k, self.B, self.z_size)
# print(logqz0.size(), 'here')
# print(logqv0.size())
# print(logdetsum.size())
# print(logrvT.size())
logdetsum = logdetsum.view(k, self.B)
# print (logqz0+logqv0-logdetsum-logrvT)
# fadfdsa
return z, logqz0 + logqv0 - logdetsum - logrvT