def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def encode(self, x, y=None, c=None):
xy = x if y is None else torch.cat((x, y), dim=-1)
xyc = xy if c is None else torch.cat((xy, c), dim=-1)
states = self.elu(self.initializer(xyc)).view(
x.size()[0], self.NUM_LAYERS * (1 + self.BIDIRECTIONAL), 2 * self.HIDDEN_DIM)
(h_0, c_0) = states.split(self.HIDDEN_DIM, dim=-1)
x = x.unsqueeze(2)
position = torch.arange(self.x_dim, dtype=torch.float) / (0.5 * (self.x_dim - 1)) - 1.0
position = position.view(1, self.x_dim).expand(x.size()[0], self.x_dim).unsqueeze(2)
x = torch.cat((x, position), dim=2)
if y is not None:
y = y.unsqueeze(1).expand(x.size()[0], self.x_dim, self.y_dim)
x = torch.cat((x, y), dim=2)
if c is not None:
c = c.unsqueeze(1).expand(x.size()[0], self.x_dim, self.c_dim)
x = torch.cat((x, c), dim=2)
input = self.elu(self.embedder(x))
# print(x.size())
# print(input.size())
_, (h_n, _) = self.rnn(input.transpose(1, 0), (h_0.transpose(1, 0), c_0.transpose(1, 0)))
# h_n of shape (num_layers * num_directions, batch, hidden_size)
# reshape to (batch, last_hidden_outputs)
h = h_n.transpose(1, 0).reshape(-1, self.NUM_LAYERS * (1+self.BIDIRECTIONAL) * self.HIDDEN_DIM)
out = self.regressor(h)
m, v = ut.gaussian_parameters(out, dim=-1)
return m, v
def encode(self, x, y=None):
xy = x if y is None else torch.cat((x, y), dim=1)
xy = xy.view(-1, self.channel * 96 * 96)
h = self.net(xy)
m, v = ut.gaussian_parameters(h, dim=1)
#print(self.z_dim,m.size(),v.size())
return m, v
def decode(self, z, y=None, c=None):
# Note: not designed for IW!!
assert len(z.size()) <= 2
zy = z if y is None else torch.cat((z, y), dim=-1)
zyc = zy if c is None else torch.cat((zy, c), dim=-1)
states = self.elu(self.initializer(zyc)).view(
z.size()[0], self.NUM_LAYERS * (1 + self.BIDIRECTIONAL),
2 * self.HIDDEN_DIM)
(h_0, c_0) = states.split(self.HIDDEN_DIM, dim=-1)
input = zyc.unsqueeze(1).expand(*zyc.size()[0:-1], self.x_dim,
zyc.size()[-1])
position = torch.arange(
self.x_dim, dtype=torch.float) / (0.5 * (self.x_dim - 1)) - 1.0
position = position.view(1, self.x_dim).expand(z.size()[0],
self.x_dim).unsqueeze(2)
input = torch.cat((input, position), dim=2)
output, (_, _) = self.rnn(input.transpose(1, 0),
(h_0.transpose(1, 0), c_0.transpose(1, 0)))
out = self.regressor(output.transpose(1, 0)).transpose(-2, -1).reshape(
z.size()[0], -1)
m, v = ut.gaussian_parameters(out, dim=-1)
return m, v
def mse_rnn(model, inputs, targets, n_samples):
pred = model.forward(inputs).detach()
if not model.constant_var:
mean, var = ut.gaussian_parameters(pred, dim=-1)
else:
mean = pred
var = model.pred_var
return ((targets - mean) ** 2).sum(-1).sum(0)
def wse_rnn(model, inputs, targets):
pred = model.forward(inputs).detach()
if not model.constant_var:
mean, var = ut.gaussian_parameters(pred, dim=-1)
else:
mean = pred
var = model.pred_var
sample_trajs = ut.sample_gaussian(mean, var)
return ((targets - sample_trajs) ** 2).sum(-1).sum(0)
def conditional_encode(self, x, l):
x = x.view(-1, self.channel * 96 * 96)
x = F.elu(self.fc1(x))
l = l.view(-1, 4)
x = F.elu(self.fc2(torch.cat([x, l], dim=1)))
x = F.elu(self.fc3(x))
x = self.fc4(x)
m, v = ut.gaussian_parameters(x, dim=1)
return m, v
def get_mse(model, full_true_trajs, n_samples=100):
""" root-weighted square error (RWSE) captures
the deviation of a model’s probability
mass from real-world trajectories
"""
n_seqs = full_true_trajs.shape[1]
inputs = full_true_trajs[:model.n_input_steps, :, :].detach()
targets = full_true_trajs[model.n_input_steps:, :, :2].detach()
if model.BBB:
for i in range(n_samples):
# not using sharpening
pred = model.forward(inputs) # one output sample
pred = pred.detach()
if i == 0:
pred_list = pred.unsqueeze(-1)
else:
pred = pred.unsqueeze(-1)
pred_list = torch.cat((pred_list, pred), dim=-1)
if model.constant_var:
mean_pred = pred_list.mean(dim=-1)
std_pred = pred_list.std(dim=-1)
else:
mean_pred = pred_list[:, :, :-1, :].mean(dim=-1)
std_pred = pred_list[:, :, :-1, :].std(dim=-1)
else:
pred = model.forward(inputs)
pred = pred.detach()
if not model.constant_var:
mean, var = ut.gaussian_parameters(pred, dim=-1)
else:
mean = pred
var = model.pred_var
for i in range(n_samples):
sample_trajs = ut.sample_gaussian(mean, var)
sample_trajs = mean
if i == 0:
pred_list = sample_trajs.unsqueeze(-1)
else:
sample_trajs = sample_trajs.unsqueeze(-1)
pred_list = torch.cat((pred_list, sample_trajs), dim=-1)
if model.constant_var:
mean_pred = pred_list.mean(dim=-1)
std_pred = pred_list.std(dim=-1)
else:
mean_pred = pred_list[:, :, :-1, :].mean(dim=-1)
std_pred = pred_list[:, :, :-1, :].std(dim=-1)
mse = ((mean_pred - targets)**2).sum() / n_seqs
return mse
def sample_z(self, batch):
m, v = ut.gaussian_parameters(self.z_pre.squeeze(0), dim=0)
# Among all the mix Gaussian distribution, sample batch size z
# For each a, which distribution it belongs to is sampled by a categorical distribution.
idx = torch.distributions.categorical.Categorical(self.pi).sample(
(batch, ))
m, v = m[idx], v[idx]
return ut.sample_gaussian(m, v)
def kl_elem(self, z, qm, qv):
# Compute the mixture of Gaussian prior
prior_m, prior_v = ut.gaussian_parameters(self.z_pre, dim=1)
log_prob_net = ut.log_normal(z, qm, qv)
log_prob_prior = ut.log_normal_mixture(z, prior_m, prior_v)
# print("log_prob_net:", log_prob_net.mean(), "log_prob_prior:", log_prob_prior.mean())
kl_elem = log_prob_net - log_prob_prior
return kl_elem
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
m, v = self.enc.encode(x)
dist = Normal(loc=m, scale=torch.sqrt(v))
z_sample = dist.rsample(sample_shape=torch.Size([iw]))
log_batch_z_sample_prob = []
kl_batch_z_sample = []
for i in range(iw):
recon_logits = self.dec.decode(z_sample[i])
log_batch_z_sample_prob.append(
ut.log_bernoulli_with_logits(
x, recon_logits)) # [batch, z_sample]
kl_batch_z_sample.append(
ut.log_normal(z_sample[i], m, v) -
ut.log_normal_mixture(z_sample[i], prior[0], prior[1]))
log_batch_z_sample_prob = torch.stack(log_batch_z_sample_prob, dim=1)
kl_batch_z_sample = torch.stack(kl_batch_z_sample, dim=1)
niwae = -ut.log_mean_exp(log_batch_z_sample_prob - kl_batch_z_sample,
dim=1).mean(dim=0)
rec = -torch.mean(log_batch_z_sample_prob, dim=0)
kl = torch.mean(kl_batch_z_sample, dim=0)
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
pm, pv = ut.gaussian_parameters(self.z_pre, dim=1)
#
# Generate samples.
qm, qv = self.enc.encode(x)
niwaes = []
recs = []
kls = []
for i in range(iw):
z_sample = ut.sample_gaussian(qm, qv).view(-1, qm.shape[1])
rec = self.dec.decode(z_sample)
logptheta_x_g_z = ut.log_bernoulli_with_logits(x, rec)
logptheta_z = ut.log_normal_mixture(z_sample, pm, pv)
logqphi_z_g_x = ut.log_normal(z_sample, qm, qv)
niwae = logptheta_x_g_z + logptheta_z - logqphi_z_g_x
#
# Normal variables.
rec = -ut.log_bernoulli_with_logits(x, rec)
kl = ut.log_normal(z_sample, qm, qv) - ut.log_normal_mixture(
z_sample, pm, pv)
niwaes.append(niwae)
recs.append(rec)
kls.append(kl)
niwaes = torch.stack(niwaes, -1)
niwae = ut.log_mean_exp(niwaes, -1)
kl = torch.stack(kls, -1)
rec = torch.stack(recs, -1)
################################################################################
# End of code modification
################################################################################
return -niwae.mean(), kl.mean(), rec.mean()
def negative_elbo_bound(self, x):
"""
Computes the Evidence Lower Bound, KL and, Reconstruction costs
Args:
x: tensor: (batch, dim): Observations
Returns:
nelbo: tensor: (): Negative evidence lower bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute negative Evidence Lower Bound and its KL and Rec decomposition
#
# To help you start, we have computed the mixture of Gaussians prior
# prior = (m_mixture, v_mixture) for you, where
# m_mixture and v_mixture each have shape (1, self.k, self.z_dim)
#
# Note that nelbo = kl + rec
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
(m, v) = self.enc.encode(x) # compute the encoder output
#print(" ***** \n")
#print("x xhape ", x.shape)
#print("m and v shapes = ", m.shape, v.shape)
prior = ut.gaussian_parameters(self.z_pre, dim=1)
#print("prior shapes = ", prior[0].shape, prior[1].shape)
z = ut.sample_gaussian(m, v) # sample a point from the multivariate Gaussian
#print("shape of z = ",z.shape)
logits = self.dec.decode(z) # pass the sampled "Z" through the decoder
#print("logits shape = ", logits.shape)
rec = -torch.mean(ut.log_bernoulli_with_logits(x, logits), -1) # Calculate log Prob of the output
log_prob = ut.log_normal(z, m, v)
log_prob -= ut.log_normal_mixture(z, prior[0], prior[1])
kl = torch.mean(log_prob)
rec = torch.mean(rec)
nelbo = kl + rec
################################################################################
# End of code modification
################################################################################
return nelbo, kl, rec
def negative_elbo_bound(self, x):
"""
Computes the Evidence Lower Bound, KL and, Reconstruction costs
Args:
x: tensor: (batch, dim): Observations
Returns:
nelbo: tensor: (): Negative evidence lower bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute negative Evidence Lower Bound and its KL and Rec decomposition
#
# To help you start, we have computed the mixture of Gaussians prior
# prior = (m_mixture, v_mixture) for you, where
# m_mixture and v_mixture each have shape (1, self.k, self.z_dim)
#
# Note that nelbo = kl + rec
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
q_m, q_v = self.enc.encode(x)
#print("q_m", q_m.size())
z_given_x = ut.sample_gaussian(q_m, q_v)
decoded_bernoulli_logits = self.dec.decode(z_given_x)
rec = -ut.log_bernoulli_with_logits(x, decoded_bernoulli_logits)
#rec = -torch.mean(rec)
#terms for KL divergence
log_q_phi = ut.log_normal(z_given_x, q_m, q_v)
#print("log_q_phi", log_q_phi.size())
log_p_theta = ut.log_normal_mixture(z_given_x, prior[0], prior[1])
#print("log_p_theta", log_p_theta.size())
kl = log_q_phi - log_p_theta
#print("kl", kl.size())
nelbo = torch.mean(kl + rec)
rec = torch.mean(rec)
kl = torch.mean(kl)
################################################################################
# End of code modification
################################################################################
return nelbo, kl, rec
def get_nll(self, outputs, targets):
"""
:return: negative log-likelihood of a minibatch
"""
if self.likelihood_cost_form == 'mse':
# This method is not validated
return self.mse_fn(outputs, targets)
elif self.likelihood_cost_form == 'gaussian':
if not self.constant_var:
mean, var = ut.gaussian_parameters(outputs, dim=-1)
return -torch.mean(ut.log_normal(targets, mean, var))
else:
var = self.pred_var * torch.ones_like(outputs)
return -torch.mean(ut.log_normal(targets, outputs, var))
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
q_m, q_v = self.enc.encode(x)
q_m_, q_v_ = ut.duplicate(q_m, rep=iw), ut.duplicate(q_v, rep=iw)
z_given_x = ut.sample_gaussian(q_m_, q_v_)
decoded_bernoulli_logits = self.dec.decode(z_given_x)
#duplicate x
x_dup = ut.duplicate(x, rep=iw)
rec = ut.log_bernoulli_with_logits(x_dup, decoded_bernoulli_logits)
log_p_theta = ut.log_normal_mixture(z_given_x, prior[0], prior[1])
log_q_phi = ut.log_normal(z_given_x, q_m_, q_v_)
kl = log_q_phi - log_p_theta
niwae = rec - kl
niwae = ut.log_mean_exp(niwae.reshape(iw, -1), dim=0)
niwae = -torch.mean(niwae)
#yay!
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def negative_elbo_bound(self, x):
"""
Computes the Evidence Lower Bound, KL and, Reconstruction costs
Args:
x: tensor: (batch, dim): Observations
Returns:
nelbo: tensor: (): Negative evidence lower bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute negative Evidence Lower Bound and its KL and Rec decomposition
#
# To help you start, we have computed the mixture of Gaussians prior
# prior = (m_mixture, v_mixture) for you, where
# m_mixture and v_mixture each have shape (1, self.k, self.z_dim)
#
# Note that nelbo = kl + rec
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
prior_m, prior_v = prior
batch = x.shape[0]
qm, qv = self.enc.encode(x)
# Now draw Zs from the posterior qm/qv
z = ut.sample_gaussian(qm, qv)
l_posterior = ut.log_normal(z, qm, qv)
multi_m = prior_m.expand(batch, *prior_m.shape[1:])
multi_v = prior_v.expand(batch, *prior_v.shape[1:])
l_prior = ut.log_normal_mixture(z, multi_m, multi_v)
kls = l_posterior - l_prior
kl = torch.mean(kls)
probs = self.dec.decode(z)
recs = ut.log_bernoulli_with_logits(x, probs)
rec = -1.0 * torch.mean(recs)
nelbo = kl + rec
################################################################################
# End of code modification
################################################################################
return nelbo, kl, rec
def negative_elbo_bound(self, x):
"""
Computes the Evidence Lower Bound, KL and, Reconstruction costs
Args:
x: tensor: (batch, dim): Observations
Returns:
nelbo: tensor: (): Negative evidence lower bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute negative Evidence Lower Bound and its KL and Rec decomposition
#
# To help you start, we have computed the mixture of Gaussians prior
# prior = (m_mixture, v_mixture) for you, where
# m_mixture and v_mixture each have shape (1, self.k, self.z_dim)
#
# Note that nelbo = kl + rec
#
# Outputs should all be scalar
################################################################################
#
# Compute the mixture of Gaussian prior
pm, pv = ut.gaussian_parameters(self.z_pre, dim=1)
#
# Generate samples.
qm, qv = self.enc.encode(x)
z_sample = ut.sample_gaussian(qm, qv)
rec = self.dec.decode(z_sample)
#
# Compute loss.
# KL divergence between the latent distribution and the prior.
rec = -ut.log_bernoulli_with_logits(x, rec)
# kl = ut.kl_normal(qm, qv, pm, pv)
kl = ut.log_normal(z_sample, qm, qv) - ut.log_normal_mixture(
z_sample, pm, pv)
#
# The liklihood of reproducing the sample image given the parameters.
# Would need to take the average of this otherwise.
nelbo = (kl + rec).mean()
# NELBO: 89.24684143066406. KL: 10.346451759338379. Rec: 78.90038299560547
################################################################################
# End of code modification
################################################################################
return nelbo, kl.mean(), rec.mean()
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
N_batches, dims = x.size()
x = ut.duplicate(x, iw)
q_mu, q_var = self.enc.encode(x)
z_samp = ut.sample_gaussian(q_mu, q_var)
logits = self.dec.decode(z_samp)
probs = ut.log_bernoulli_with_logits(x, logits)
kl_vals = -ut.log_normal(z_samp, q_mu, q_var) + ut.log_normal_mixture(z_samp, *prior)
probs = probs + kl_vals
niwae = torch.mean(-ut.log_mean_exp(probs.reshape(N_batches, iw), 1))
kl = torch.tensor(0)
rec = torch.tensor(0)
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def get_rwse(model, full_true_trajs, n_samples=100):
""" root-weighted square error (RWSE) captures
the deviation of a model’s probability
mass from real-world trajectories
"""
n_seqs = full_true_trajs.shape[1]
inputs = full_true_trajs[:model.n_input_steps, :, :].detach()
targets = full_true_trajs[model.n_input_steps:, :, :2].detach()
if model.BBB:
for i in range(n_samples):
# not using sharpening
pred = model.forward(inputs)
pred = pred.detach()
if not model.constant_var:
pred = pred[:, :, :-1]
mean_sq_err = ((targets - pred)**2).sum() / n_seqs
if i == 0:
mean_sq_err_list = mean_sq_err.unsqueeze(-1)
else:
mean_sq_err = mean_sq_err.unsqueeze(-1)
mean_sq_err_list = torch.cat((mean_sq_err_list, mean_sq_err),
dim=-1)
else:
pred = model.forward(inputs)
pred = pred.detach()
if not model.constant_var:
mean, var = ut.gaussian_parameters(pred, dim=-1)
else:
mean = pred
var = model.pred_var
for i in range(n_samples):
sample_trajs = ut.sample_gaussian(mean, var)
mean_sq_err = ((targets - sample_trajs)**2).sum() / n_seqs
if i == 0:
mean_sq_err_list = mean_sq_err.unsqueeze(-1)
else:
mean_sq_err = mean_sq_err.unsqueeze(-1)
mean_sq_err_list = torch.cat((mean_sq_err_list, mean_sq_err),
dim=-1)
mean_rwse = mean_sq_err_list.mean().sqrt()
return mean_rwse
def negative_elbo_bound(self, x):
"""
Computes the Evidence Lower Bound, KL and, Reconstruction costs
Args:
x: tensor: (batch, dim): Observations
Returns:
nelbo: tensor: (): Negative evidence lower bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute negative Evidence Lower Bound and its KL and Rec decomposition
#
# To help you start, we have computed the mixture of Gaussians prior
# prior = (m_mixture, v_mixture) for you, where
# m_mixture and v_mixture each have shape (1, self.k, self.z_dim)
#
# Note that nelbo = kl + rec
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
N_samp, dim = x.size()
q_mu, q_var = self.enc.encode(x)
z_samp = ut.sample_gaussian(q_mu, q_var)
logits = self.dec.decode(z_samp)
rec = -torch.mean(ut.log_bernoulli_with_logits(x, logits))
kl = torch.mean(ut.log_normal(z_samp, q_mu, q_var) - ut.log_normal_mixture(z_samp, *prior))
nelbo = kl + rec
################################################################################
# End of code modification
################################################################################
return nelbo, kl, rec
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
m, v = self.enc.encode(x)
m = ut.duplicate(m, iw)
v = ut.duplicate(v, iw)
x = ut.duplicate(x, iw)
z = ut.sample_gaussian(m, v)
logits = self.dec.decode(z)
kl = ut.log_normal(z, m, v) - ut.log_normal_mixture(z, *prior)
rec = -ut.log_bernoulli_with_logits(x, logits)
nelbo = kl + rec
niwae = -ut.log_mean_exp(-nelbo.reshape(iw, -1), dim=0)
niwae, kl, rec = niwae.mean(), kl.mean(), rec.mean()
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def decode(self, z, y=None, c=None):
zy = z if y is None else torch.cat((z, y), dim=-1)
zyc = zy if c is None else torch.cat((zy, c), dim=-1)
h = self.net(zyc)
m, v = ut.gaussian_parameters(h, dim=-1)
return m, v
def encode(self, x, y=None, c=None):
xy = x if y is None else torch.cat((x, y), dim=-1)
xyc = xy if c is None else torch.cat((xy, c), dim=-1)
h = self.net(xyc)
m, v = ut.gaussian_parameters(h, dim=-1)
return m, v
def encode(self, x, y=None):
xy = x if y is None else torch.cat((x, y), dim=1)
h = self.net(xy.float())
m, v = ut.gaussian_parameters(h, dim=1)
return m, v
def encode_simple(self, x):
x = self.conv6(x)
m, v = ut.gaussian_parameters(x, dim=1)
#print(m.size())
return m, v
def sample_z(self, batch):
m, v = ut.gaussian_parameters(self.z_pre.squeeze(0), dim=0)
idx = torch.distributions.categorical.Categorical(self.pi).sample(
(batch, ))
m, v = m[idx], v[idx]
return ut.sample_gaussian(m, v)
def train(model, train_data, batch_size, n_batches,
lr=1e-3,
clip_grad=None,
iter_max=np.inf,
iter_save=np.inf,
iter_plot=np.inf,
reinitialize=False,
kernel=None):
# Optimization
if reinitialize:
model.apply(ut.reset_weights)
optimizer = optim.Adam(model.parameters(), lr=lr)
mse = nn.MSELoss()
# # Model
# hidden = model.init_hidden(batch_size)
i = 0 # i is num of gradient steps taken by end of loop iteration
loss_list = []
mse_list = []
with tqdm.tqdm(total=iter_max) as pbar:
while True:
for batch in train_data:
i += 1
# print(psutil.virtual_memory())
optimizer.zero_grad()
inputs = batch[:model.n_input_steps, :, :]
targets = batch[model.n_input_steps:, :, :2]
# Since the data is not continued from batch to batch,
# reinit hidden every batch. (using zeros)
outputs = model.forward(inputs, targets=targets)
batch_mean_nll, KL, KL_sharp = model.get_loss(outputs, targets)
# print(batch_mean_nll, KL, KL_sharp)
# # Re-weighting for minibatches
NLL_term = batch_mean_nll * model.n_pred_steps
# Here B = n_batchs, C = 1 (since each sequence is complete)
KL_term = KL / n_batches
loss = NLL_term + KL_term
if model.sharpen:
KL_sharp /= n_batches
loss += KL_sharp
loss_list.append(loss.cpu().detach())
if clip_grad is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad)
# Print progress
if model.likelihood_cost_form == 'gaussian':
if model.constant_var:
mse_val = mse(outputs, targets) * model.n_pred_steps
else:
if model.rnn_cell_type == 'FF':
mean, var = ut.gaussian_parameters_ff(outputs, dim=0)
else:
mean, var = ut.gaussian_parameters(outputs, dim=-1)
mse_val = mse(mean, targets) * model.n_pred_steps
elif model.likelihood_cost_form == 'mse':
mse_val = batch_mean_nll * model.n_pred_steps
mse_list.append(mse_val.cpu().detach())
if i % iter_plot == 0:
with torch.no_grad():
model.eval()
if model.input_feat_dim <= 2:
ut.test_plot(model, i, kernel)
elif model.input_feat_dim == 4:
rand_idx = random.sample(range(batch.shape[1]), 4)
full_true_traj = batch[:, rand_idx, :]
if not model.BBB:
if model.constant_var:
pred_traj = outputs[:, rand_idx, :]
std_pred = None
else:
pred_traj = mean[:, rand_idx, :]
std_pred = var.sqrt()
ut.plot_highd_traj(model, i, full_true_traj,
pred_traj, std_pred=std_pred)
else:
# resample a few forward passes
ut.plot_highd_traj_BBB(model, i, full_true_traj,
n_resample_weights=10)
ut.plot_history(model, loss_list, i, obj='loss')
ut.plot_history(model, mse_list, i, obj='mse')
model.train()
# loss.backward(retain_graph=True)
loss.backward()
optimizer.step()
pbar.set_postfix(loss='{:.2e}'.format(loss),
mse='{:.2e}'.format(mse_val))
pbar.update(1)
# Save model
if i % iter_save == 0:
ut.save_model_by_name(model, i, only_latest=True)
if i == iter_max:
return
def negative_iwae_bound(self, x, iw):
"""
Computes the Importance Weighted Autoencoder Bound
Additionally, we also compute the ELBO KL and reconstruction terms
Args:
x: tensor: (batch, dim): Observations
iw: int: (): Number of importance weighted samples
Returns:
niwae: tensor: (): Negative IWAE bound
kl: tensor: (): ELBO KL divergence to prior
rec: tensor: (): ELBO Reconstruction term
"""
################################################################################
# TODO: Modify/complete the code here
# Compute niwae (negative IWAE) with iw importance samples, and the KL
# and Rec decomposition of the Evidence Lower Bound
#
# Outputs should all be scalar
################################################################################
# Compute the mixture of Gaussian prior
prior = ut.gaussian_parameters(self.z_pre, dim=1)
prior_m, prior_v = prior
batch = x.shape[0]
multi_x = ut.duplicate(x, iw)
qm, qv = self.enc.encode(x)
multi_qm = ut.duplicate(qm, iw)
multi_qv = ut.duplicate(qv, iw)
# z will be (batch*iw x z_dim)
# with sampled z's for a given x non-contiguous!
z = ut.sample_gaussian(multi_qm, multi_qv)
probs = self.dec.decode(z)
recs = ut.log_bernoulli_with_logits(multi_x, probs)
rec = -1.0 * torch.mean(recs)
multi_m = prior_m.expand(batch * iw, *prior_m.shape[1:])
multi_v = prior_v.expand(batch * iw, *prior_v.shape[1:])
z_priors = ut.log_normal_mixture(z, multi_m, multi_v)
x_posteriors = recs
z_posteriors = ut.log_normal(z, multi_qm, multi_qv)
kls = z_posteriors - z_priors
kl = torch.mean(kls)
log_ratios = z_priors + x_posteriors - z_posteriors
# Should be (batch*iw, z_dim), batch ratios non contiguous
unflat_log_ratios = log_ratios.reshape(iw, batch)
niwaes = ut.log_mean_exp(unflat_log_ratios, 0)
niwae = -1.0 * torch.mean(niwaes)
################################################################################
# End of code modification
################################################################################
return niwae, kl, rec
def encode(self, x):
h = self.net(x)
m, v = ut.gaussian_parameters(h, dim=1)
return m, v
