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unsupervised_vae_test.py
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unsupervised_vae_test.py
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import inspect
import os
import shutil
from functools import partial
from typing import Sequence
import numpy as np
import tensorflow as tf
from tensorflow_probability.python.distributions import Normal, Gamma, \
Bernoulli, Independent, MixtureSameFamily, \
MultivariateNormalDiag, Categorical
from odin.bay import VariationalModel, VariationalAutoencoder, \
DistributionDense, BetaVAE, AnnealingVAE, DisentanglementGym, RVconf, \
BetaCapacityVAE, BetaGammaVAE
from odin.bay.distributions import QuantizedLogistic
from odin.bay.layers import MixtureNormalLatents
from odin.fuel import get_dataset, ImageDataset
from odin.networks import get_networks
from odin.utils import as_tuple
from utils import *
# ===========================================================================
# Classes
# ===========================================================================
def make_normal(p: tf.Tensor) -> Normal:
loc, scale = tf.split(p, 2, axis=-1)
scale = tf.math.softplus(scale)
return Normal(loc=loc, scale=scale)
def make_gamma(p: tf.Tensor) -> Gamma:
log_rate, concentration = tf.split(p, 2, -1)
concentration = tf.nn.softplus(concentration) + tf.math.exp(-7.)
return Gamma(log_rate=log_rate, concentration=concentration)
def make_gaussian_out(p: tf.Tensor,
event_shape: Sequence[int]) -> Independent:
loc, scale = tf.split(p, 2, -1)
loc = tf.reshape(loc, (-1,) + tuple(event_shape))
scale = tf.reshape(scale, (-1,) + tuple(event_shape))
scale = tf.nn.softplus(scale)
return Independent(Normal(loc=loc, scale=scale), len(event_shape))
class MultiCapacity(BetaVAE):
def __init__(self, args: Arguments, **kwargs):
networks = get_networks(args.ds, zdim=args.zdim,
is_hierarchical=False,
is_semi_supervised=False)
zdim = args.zdim
prior = Normal(loc=tf.zeros([zdim]), scale=tf.ones([zdim]))
latents = [
DistributionDense(units=zdim * 2, posterior=make_normal, prior=prior,
name='latents1'),
DistributionDense(units=zdim * 2, posterior=make_normal, prior=prior,
name='latents2')
]
networks['latents'] = latents
super().__init__(**networks, **kwargs)
def encode(self, inputs, training=None, **kwargs):
h = self.encoder(inputs, training=training)
return [qz(h, training=training, sample_shape=self.sample_shape)
for qz in self.latents]
def decode(self, latents, training=None, **kwargs):
z = tf.concat(latents, -1)
h = self.decoder(z, training=training)
return self.observation(h, training=training)
def elbo_components(self, inputs, training=None, mask=None, **kwargs):
px, (qz1, qz2) = self(inputs, training=training)
llk = dict(llk=px.log_prob(inputs))
kl1 = tf.reduce_sum(qz1.KL_divergence(analytic=self.analytic), -1)
kl2 = tf.reduce_sum(qz2.KL_divergence(analytic=self.analytic), -1)
zdim = int(np.prod(qz1.event_shape))
C = tf.constant(self.free_bits * zdim, dtype=self.dtype)
kl = dict(latents1=self.beta * kl1,
latents2=tf.abs(kl2 - C))
return llk, kl
class Freebits(BetaVAE):
def __init__(self, args: Arguments, free_bits=None, beta=1, **kwargs):
networks = get_networks(args.ds, zdim=args.zdim,
is_hierarchical=False, is_semi_supervised=False)
zdim = args.zdim
prior = Normal(loc=tf.zeros([zdim]), scale=tf.ones([zdim]))
networks['latents'] = DistributionDense(units=zdim * 2,
posterior=make_normal, prior=prior,
name=networks['latents'].name)
super().__init__(free_bits=free_bits, beta=beta, **networks, **kwargs)
def elbo_components(self, inputs, training=None, mask=None, **kwargs):
free_bits = self.free_bits
self.free_bits = None
llk, kl = super(BetaVAE, self).elbo_components(
inputs, training=training, mask=mask)
self.free_bits = free_bits
kl_new = {}
for k, v in kl.items():
if free_bits is not None:
v = tf.maximum(self.free_bits, v)
v = tf.reduce_sum(v, axis=-1)
kl_new[k] = self.beta * v
return llk, kl_new
class EquilibriumVAE(BetaVAE):
def __init__(self, R: float = 0., C: float = 0.,
random_capacity: bool = False,
dropout: float = 0., beta=1.0, **kwargs):
kwargs.pop('free_bits', None)
super().__init__(beta=beta, free_bits=None, **kwargs)
self.R = float(R)
self.C = float(C)
self.dropout = float(dropout)
self.random_capacity = bool(random_capacity)
def encode(self, inputs, training=None, **kwargs):
if self.dropout > 0 and training:
inputs = tf.nn.dropout(inputs, rate=self.dropout)
return super().encode(inputs, training=training, **kwargs)
def elbo_components(self, inputs, training=None, mask=None, **kwargs):
px, qz = self(inputs, training=training, mask=mask)
# === 1. reconstructed information
llk = {}
for p, x in zip(as_tuple(px), as_tuple(inputs)):
name = p.name.split('_')[1]
if hasattr(p, 'distribution'):
p = p.distribution
if isinstance(p, Bernoulli):
p = Bernoulli(logits=p.logits)
elif isinstance(px, Normal):
p = Normal(loc=p.loc, scale=p.scale)
elif isinstance(p, QuantizedLogistic):
p = QuantizedLogistic(loc=p.loc, scale=p.scale,
low=p.low, high=p.high,
inputs_domain=p.inputs_domain,
reinterpreted_batch_ndims=None)
lk = p.log_prob(x)
if self.R != 0.:
lk = tf.minimum(lk, self.R)
lk = tf.reduce_sum(lk, tf.range(1, x.shape.rank))
llk[f'llk_{name}'] = lk
# === 2. latent capacity
kl = {}
for q in as_tuple(qz):
name = q.name.split('_')[1]
kl_q = q.KL_divergence(analytic=self.analytic)
if self.C > 0:
zdim = int(np.prod(q.event_shape))
C = tf.constant(self.C * zdim, dtype=self.dtype)
if self.random_capacity:
C = C * tf.random.uniform(shape=[], minval=0., maxval=1.,
dtype=self.dtype)
kl_q = tf.math.abs(kl_q - C)
kl_q = self.beta * kl_q
kl[f'kl_{name}'] = kl_q
return llk, kl
class GammaVAE(AnnealingVAE):
def __init__(self, args: Arguments, **kwargs):
networks = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
zdim = args.zdim
prior = Gamma(rate=tf.fill([zdim], 0.3),
concentration=tf.fill([zdim], 0.3))
networks['latents'] = DistributionDense(units=zdim * 2,
posterior=make_gamma, prior=prior,
name=networks['latents'].name)
super().__init__(**networks, **kwargs)
def elbo_components(self, inputs, training=None, mask=None, **kwargs):
llk, kl = super().elbo_components(inputs, training=training, mask=mask)
kl = {k: tf.reduce_sum(v, -1) for k, v in kl.items()}
return llk, kl
# prior N(0, 2)
class Normal2VAE(Freebits):
def __init__(self, args: Arguments, free_bits=None, beta=1, **kwargs):
networks = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
zdim = args.zdim
prior = Normal(loc=tf.zeros([zdim]), scale=tf.fill([zdim], 2.))
networks['latents'] = DistributionDense(units=zdim * 2,
posterior=make_normal, prior=prior,
name=networks['latents'].name)
super(Freebits, self).__init__(free_bits=free_bits, beta=beta, **networks,
**kwargs)
class GaussianOut(BetaVAE):
def __init__(self, args: Arguments, free_bits=None, beta=1., **kwargs):
networks = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
obs: DistributionDense = networks['observation']
event_shape = obs.event_shape
obs_new = DistributionDense(
event_shape=event_shape,
units=int(np.prod(event_shape)) * 2,
posterior=partial(make_gaussian_out, event_shape=event_shape),
name='image')
networks['observation'] = obs_new
super().__init__(free_bits=free_bits, beta=beta, **networks,
**kwargs)
# Gaussian mixture posterior
class GMMVAE(BetaVAE):
def __init__(self, n_components=10, prior=None, **kwargs):
latents = kwargs.pop('latents')
latents = MixtureNormalLatents(units=np.prod(latents.event_shape),
n_components=n_components,
prior=prior,
name=latents.name)
# p: MixtureSameFamily = latents.prior
# mix: Categorical = p.mixture_distribution
# print(mix.probs_parameter())
# print(p.components_distribution.distribution.loc)
# print(p.components_distribution.distribution.scale)
super(GMMVAE, self).__init__(latents=latents, **kwargs)
class IWGammaVAE(VariationalAutoencoder):
def __init__(self, gamma: float = 2.0, n_iw: int = 10, **kwargs):
super().__init__(**kwargs)
self.gamma = gamma
self.n_iw = n_iw
def elbo_components(self, inputs, training=None, mask=None, **kwargs):
llk, kl = super().elbo_components(inputs, training, mask, **kwargs)
llk = {k: self.gamma * v for k, v in llk.items()}
# D(q(z)||p(z))
ids = tf.range(inputs.shape[0], dtype=tf.int32)
tf.random.shuffle(ids)
ids = ids[:self.n_iw]
x = tf.gather(inputs, ids, axis=0)
qz_x = self.encode(x, training=training, mask=mask)
pz = qz_x.KL_divergence.prior
qx = tf.constant(1. / inputs.shape[0], dtype=self.dtype)
z = tf.convert_to_tensor(qz_x)
kl[f'kl_iw'] = tf.reduce_mean(
qz_x.log_prob(z) - pz.log_prob(z) + tf.math.log(qx), 0)
return llk, kl
# ===========================================================================
# Extra models
# ===========================================================================
# === 1. VAE with free-bits
def model_rvae(args: Arguments):
return VariationalAutoencoder(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
reverse=True)
def model_vae1(args: Arguments):
return VariationalAutoencoder(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
free_bits=0.5)
def model_vae2(args: Arguments):
return VariationalAutoencoder(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
free_bits=1.0)
def model_vae3(args: Arguments):
return VariationalAutoencoder(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
free_bits=1.5)
def model_fullcov(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
zdims = int(np.prod(nets['latents'].event_shape))
nets['latents'] = RVconf(
event_shape=zdims,
projection=True,
posterior='mvntril',
prior=Independent(Normal(tf.zeros([zdims]), tf.ones([zdims])), 1),
name='latents').create_posterior()
return VariationalAutoencoder(**nets, name='FullCov')
def model_gmmprior(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
latent_size = np.prod(nets['latents'].event_shape)
n_components = 100
loc = tf.compat.v1.get_variable(name="loc", shape=[n_components, latent_size])
raw_scale_diag = tf.compat.v1.get_variable(
name="raw_scale_diag", shape=[n_components, latent_size])
mixture_logits = tf.compat.v1.get_variable(
name="mixture_logits", shape=[n_components])
nets['latents'].prior = MixtureSameFamily(
components_distribution=MultivariateNormalDiag(
loc=loc,
scale_diag=tf.nn.softplus(raw_scale_diag) + tf.math.exp(-7.)),
mixture_distribution=Categorical(logits=mixture_logits),
name="prior")
return VariationalAutoencoder(**nets, name='GMMPrior')
def model_fullcovgmm(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
latent_size = int(np.prod(nets['latents'].event_shape))
n_components = 100
loc = tf.compat.v1.get_variable(name="loc", shape=[n_components, latent_size])
raw_scale_diag = tf.compat.v1.get_variable(
name="raw_scale_diag", shape=[n_components, latent_size])
mixture_logits = tf.compat.v1.get_variable(
name="mixture_logits", shape=[n_components])
nets['latents'] = RVconf(
event_shape=latent_size,
projection=True,
posterior='mvntril',
prior=MixtureSameFamily(
components_distribution=MultivariateNormalDiag(
loc=loc,
scale_diag=tf.nn.softplus(raw_scale_diag) + tf.math.exp(-7.)),
mixture_distribution=Categorical(logits=mixture_logits),
name="prior"),
name='latents').create_posterior()
return VariationalAutoencoder(**nets, name='FullCov')
# === 1.1. Beta-Capacity VAE (Bugress 2018)
# beta=10 C=[0.01, 25]
def model_bcvae1(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaCapacityVAE(**nets, c_min=0.01, c_max=25, gamma=10, n_steps=60000)
# beta=5 C=[0.01, 25]
def model_bcvae2(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaCapacityVAE(**nets, c_min=0.01, c_max=25, gamma=5, n_steps=60000)
# beta = 0.05
def model_bvae1(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaVAE(**nets, beta=0.05)
# beta = 0.1
def model_bvae2(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaVAE(**nets, beta=0.1)
# beta = 0.5
def model_bvae3(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaVAE(**nets, beta=0.5)
# beta = 2
def model_bvae4(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaVAE(**nets, beta=2)
# gamma=2; beta = 1.0
def model_gvae1(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaGammaVAE(**nets, beta=1.0, gamma=2.0)
# gamma=5; beta = 1.0
def model_gvae2(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaGammaVAE(**nets, beta=1.0, gamma=5.0)
# gamma=2.0; beta=2.0
def model_gvae3(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaGammaVAE(**nets, beta=2.0, gamma=2.0)
# gamma=5.0; beta=2.0
def model_gvae4(args: Arguments):
nets = get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False)
return BetaGammaVAE(**nets, beta=2.0, gamma=5.0)
# === 2. equilibrium VAE
# beta=1 C=0.5
def model_equilibriumvae1(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
C=0.5)
# beta=1 C=1
def model_equilibriumvae2(args: Arguments):
return EquilibriumVAE(**get_networks(args.ds, zdim=args.zdim),
C=1.0)
# beta=1 C=1.5
def model_equilibriumvae3(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
C=1.5)
# beta=1 C=0.5, dropout=0.3
def model_equilibriumvae4(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
C=0.5, dropout=0.3)
# beta=5 C=0.5, dropout=0.
def model_equilibriumvae5(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
C=0.5, dropout=0., beta=5)
# beta=1 R=-0.1 C=0., dropout=0.
def model_equilibriumvae6(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
R=-0.1, C=0., dropout=0., beta=1.)
# beta=1 R=-0.1 C=0.5, dropout=0.
def model_equilibriumvae7(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
R=-0.1, C=0.5, dropout=0., beta=1.)
# R = -0.5
def model_equilibriumvae9(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
R=-0.5, C=0., dropout=0., beta=1.)
# beta=1 C=1.0, random_capacity=True
def model_equilibriumvae8(args: Arguments):
return EquilibriumVAE(
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False),
R=0.0, C=1.0, random_capacity=True, dropout=0.0,
beta=1.)
# === 4. IWGamma
# gamma=1.0, iw=10
def model_iwgamma1(args: Arguments):
return IWGammaVAE(gamma=1.0, n_iw=10, **get_networks(args.ds, zdim=args.zdim,
is_hierarchical=False,
is_semi_supervised=False))
# gamma=2.0, iw=10
def model_iwgamma2(args: Arguments):
return IWGammaVAE(gamma=2.0, n_iw=10, **get_networks(args.ds, zdim=args.zdim,
is_hierarchical=False,
is_semi_supervised=False))
# === 5. others
def model_multicapacity(args: Arguments):
return MultiCapacity(args, beta=5, free_bits=1.0)
# beta=1, free_bits=None
def model_freebits(args: Arguments):
return Freebits(args, beta=1, free_bits=None)
# beta=1, free_bits=0.5
def model_freebits1(args: Arguments):
return Freebits(args, beta=1, free_bits=0.5)
# beta=5, free_bits=0.5
def model_freebits2(args: Arguments):
return Freebits(args, beta=5, free_bits=0.5)
def model_gamma1(args: Arguments):
return GammaVAE(args)
def model_normal2(args: Arguments):
return Normal2VAE(args)
def model_gaussianout(args: Arguments):
return GaussianOut(args)
# n_components = 10
def model_gmmvae1(args: Arguments):
return GMMVAE(n_components=10,
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False))
# n_components = 50
def model_gmmvae2(args: Arguments):
return GMMVAE(n_components=50,
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False))
# n_components = 10, prior=N(0, 1)
def model_gmmvae3(args: Arguments):
zdim = args.zdim
prior = Independent(Normal(loc=tf.zeros([zdim]), scale=tf.ones([zdim])), 1)
return GMMVAE(n_components=10, prior=prior, analytic=False,
**get_networks(args.ds, zdim=args.zdim, is_hierarchical=False,
is_semi_supervised=False))
# ===========================================================================
# Main
# ===========================================================================
def evaluate(model: VariationalModel, ds: ImageDataset, args: Arguments):
# === 1. prepare path
path = get_results_path(args)
if args.override and os.path.exists(path):
print('Override results at path:', path)
shutil.rmtree(path)
os.makedirs(path)
# === 2. run the Gym
gym = DisentanglementGym(dataset=args.ds, model=model)
with gym.run_model(n_samples=-1, partition='test'):
# should be max here
stddev = np.max(gym.qz_x[0].stddev(), 0)
gym.plot_distortion()
for i in range(3):
gym.plot_latents_traverse(n_top_latents=20, title=f'_x{i}',
max_val=3 * stddev,
min_val=-3 * stddev,
seed=i)
gym.plot_latents_stats()
gym.plot_reconstruction()
gym.plot_latents_sampling()
gym.plot_latents_factors()
gym.plot_latents_tsne()
gym.plot_correlation(method='spearman')
gym.plot_correlation(method='pearson')
gym.save_figures(path, verbose=True)
def main(args: Arguments):
# === 0. set configs
ds = get_dataset(args.ds)
# === 1. get model
model = None
for k, v in globals().items():
if inspect.isfunction(v) and 'model_' == k[:6] and \
k.split('_')[-1] == args.vae:
model = v(args)
model.build(ds.full_shape)
break
if model is None:
model = get_model(args, return_dataset=False)
# === 2. eval
if args.eval:
model.load_weights(get_model_path(args), raise_notfound=True, verbose=True)
evaluate(model, ds, args)
# === 3. train
else:
train(model, ds, args)
if __name__ == '__main__':
set_cfg(root_path=os.path.expanduser('~/exp/unsupervised'))
run_multi(main, args=get_args())