def main(self):
import os
import tensorflow as tf
from gan.load_data import load_dSprites
from gan.latent import UniformLatent, JointLatent
from gan.network import Decoder, InfoGANDiscriminator, \
CrDiscriminator, MetricRegresser
from gan.infogan_cr import INFOGAN_CR
from gan.metric import FactorVAEMetric, DSpritesInceptionScore, \
DHSICMetric
data, metric_data, latent_values, metadata = \
load_dSprites("data/dSprites")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["uniform_reg_dim"]):
latent_list.append(
UniformLatent(in_dim=1,
out_dim=1,
low=-1.0,
high=1.0,
q_std=1.0,
apply_reg=True))
if self._config["uniform_not_reg_dim"] > 0:
latent_list.append(
UniformLatent(in_dim=self._config["uniform_not_reg_dim"],
out_dim=self._config["uniform_not_reg_dim"],
low=-1.0,
high=1.0,
q_std=1.0,
apply_reg=False))
latent = JointLatent(latent_list=latent_list)
decoder = Decoder(output_width=width,
output_height=height,
output_depth=depth)
infoGANDiscriminator = \
InfoGANDiscriminator(
output_length=latent.reg_out_dim,
q_l_dim=self._config["q_l_dim"])
crDiscriminator = CrDiscriminator(output_length=latent.num_reg_latent)
shape_network = MetricRegresser(
output_length=3, scope_name="dSpritesSampleQualityMetric_shape")
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
dSpritesInceptionScore = DSpritesInceptionScore(
sess=sess,
do_training=False,
data=data,
metadata=metadata,
latent_values=latent_values,
network_path="metric_model/DSprites",
shape_network=shape_network,
sample_dir=sample_dir)
dHSICMetric = DHSICMetric(sess=sess, data=data)
metric_callbacks = [
factorVAEMetric, dSpritesInceptionScore, dHSICMetric
]
gan = INFOGAN_CR(
sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
infoGANDiscriminator=infoGANDiscriminator,
crDiscriminator=crDiscriminator,
gap_start=self._config["gap_start"],
gap_decrease_times=self._config["gap_decrease_times"],
gap_decrease=self._config["gap_decrease"],
gap_decrease_batch=self._config["gap_decrease_batch"],
cr_coe_start=self._config["cr_coe_start"],
cr_coe_increase_times=self._config["cr_coe_increase_times"],
cr_coe_increase=self._config["cr_coe_increase"],
cr_coe_increase_batch=self._config["cr_coe_increase_batch"],
info_coe_de=self._config["info_coe_de"],
info_coe_infod=self._config["info_coe_infod"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"],
de_lr=self._config["de_lr"],
infod_lr=self._config["infod_lr"],
crd_lr=self._config["crd_lr"],
summary_freq=self._config["summary_freq"])
gan.build()
gan.train()
def main(self):
import os
import tensorflow as tf
from gan.load_data import load_dSprites
from gan.latent import GaussianLatent, JointLatent
from gan.network import VAEDecoder, VAEEncoder, TCDiscriminator, \
MetricRegresser
from gan.factorVAE import FactorVAE
from gan.metric import FactorVAEMetric, DSpritesInceptionScore, \
DHSICMetric
data, metric_data, latent_values, metadata = \
load_dSprites("data/dSprites")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["gaussian_dim"]):
latent_list.append(GaussianLatent(
in_dim=1, out_dim=1, loc=0.0, scale=1.0, q_std=1.0,
apply_reg=True))
latent = JointLatent(latent_list=latent_list)
decoder = VAEDecoder(
output_width=width, output_height=height, output_depth=depth)
encoder = VAEEncoder(output_length=latent.reg_in_dim)
tcDiscriminator = TCDiscriminator()
shape_network = MetricRegresser(
output_length=3,
scope_name="dSpritesSampleQualityMetric_shape")
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
dSpritesInceptionScore = DSpritesInceptionScore(
sess=sess,
do_training=False,
data=data,
metadata=metadata,
latent_values=latent_values,
network_path="metric_model/DSprites",
shape_network=shape_network,
sample_dir=sample_dir)
dHSICMetric = DHSICMetric(
sess=sess,
data=data)
metric_callbacks = [factorVAEMetric,
dSpritesInceptionScore,
dHSICMetric]
vae = FactorVAE(
sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
encoder=encoder,
tcDiscriminator=tcDiscriminator,
tc_coe=self._config["tc_coe"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"])
vae.build()
vae.train()
def main(self):
import os
import tensorflow as tf
import pickle
from gan.load_data import load_dSprites
from gan.latent import GaussianLatent, JointLatent
from gan.network import VAEDecoder, VAEEncoder, TCDiscriminator, \
MetricRegresser
from gan.factorVAE import FactorVAE
from gan.metric import FactorVAEMetric, DSpritesInceptionScore, \
DHSICMetric
data, metric_data, latent_values, metadata = \
load_dSprites("data/dSprites")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["gaussian_dim"]):
latent_list.append(GaussianLatent(
in_dim=1, out_dim=1, loc=0.0, scale=1.0, q_std=1.0,
apply_reg=True))
latent = JointLatent(latent_list=latent_list)
decoder = VAEDecoder(
output_width=width, output_height=height, output_depth=depth)
encoder = VAEEncoder(output_length=latent.reg_in_dim)
tcDiscriminator = TCDiscriminator()
shape_network = MetricRegresser(
output_length=3,
scope_name="dSpritesSampleQualityMetric_shape")
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
dSpritesInceptionScore = DSpritesInceptionScore(
sess=sess,
do_training=False,
data=data,
metadata=metadata,
latent_values=latent_values,
network_path="metric_model/DSprites",
shape_network=shape_network,
sample_dir=sample_dir)
dHSICMetric = DHSICMetric(
sess=sess,
data=data)
metric_callbacks = [factorVAEMetric,
dSpritesInceptionScore,
dHSICMetric]
vae = FactorVAE(
sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
encoder=encoder,
tcDiscriminator=tcDiscriminator,
tc_coe=self._config["tc_coe"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"])
vae.build()
vae.load()
metric_data_groups = []
L = 100
M = 1000
for i in range(M):
fixed_latent_id = i % 10
latents_sampled = vae.latent.sample(L)
latents_sampled[:, fixed_latent_id] = \
latents_sampled[0, fixed_latent_id]
imgs_sampled = vae.sample_from(latents_sampled)
metric_data_groups.append(
{"img": imgs_sampled,
"label": fixed_latent_id})
latents_sampled = vae.latent.sample(data.shape[0] / 10)
metric_data_eval_std = vae.sample_from(latents_sampled)
metric_data = {
"groups": metric_data_groups,
"img_eval_std": metric_data_eval_std}
metric_data_path = os.path.join(self._work_dir, "metric_data.pkl")
with open(metric_data_path, "wb") as f:
pickle.dump(metric_data, f, protocol=2)
def main(self):
import os
import tensorflow as tf
from gan.load_data import load_3Dpots
from gan.latent import UniformLatent, JointLatent
from gan.network import Decoder, InfoGANDiscriminator, \
CrDiscriminator
from gan.infogan_cr import INFOGAN_CR
from gan.metric import FactorVAEMetric, \
BetaVAEMetric, SAPMetric, FStatMetric, MIGMetric, DCIMetric
import pickle
data, metric_data, latent_values = load_3Dpots("data/3Dpots")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["uniform_reg_dim"]):
latent_list.append(
UniformLatent(in_dim=1,
out_dim=1,
low=-1.0,
high=1.0,
q_std=1.0,
apply_reg=True))
if self._config["uniform_not_reg_dim"] > 0:
latent_list.append(
UniformLatent(in_dim=self._config["uniform_not_reg_dim"],
out_dim=self._config["uniform_not_reg_dim"],
low=-1.0,
high=1.0,
q_std=1.0,
apply_reg=False))
latent = JointLatent(latent_list=latent_list)
decoder = Decoder(output_width=width,
output_height=height,
output_depth=depth)
infoGANDiscriminator = \
InfoGANDiscriminator(
output_length=latent.reg_out_dim,
q_l_dim=self._config["q_l_dim"])
crDiscriminator = CrDiscriminator(output_length=latent.num_reg_latent)
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
metric_callbacks = [factorVAEMetric]
gan = INFOGAN_CR(
sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
infoGANDiscriminator=infoGANDiscriminator,
crDiscriminator=crDiscriminator,
gap_start=self._config["gap_start"],
gap_decrease_times=self._config["gap_decrease_times"],
gap_decrease=self._config["gap_decrease"],
gap_decrease_batch=self._config["gap_decrease_batch"],
cr_coe_start=self._config["cr_coe_start"],
cr_coe_increase_times=self._config["cr_coe_increase_times"],
cr_coe_increase=self._config["cr_coe_increase"],
cr_coe_increase_batch=self._config["cr_coe_increase_batch"],
info_coe_de=self._config["info_coe_de"],
info_coe_infod=self._config["info_coe_infod"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"],
de_lr=self._config["de_lr"],
infod_lr=self._config["infod_lr"],
crd_lr=self._config["crd_lr"],
summary_freq=self._config["summary_freq"])
gan.build()
gan.load()
results = {}
factorVAEMetric_f = FactorVAEMetric(metric_data, sess=sess)
factorVAEMetric_f.set_model(gan)
results["FactorVAE"] = factorVAEMetric_f.evaluate(-1, -1, -1)
betaVAEMetric_f = BetaVAEMetric(metric_data, sess=sess)
betaVAEMetric_f.set_model(gan)
results["betaVAE"] = betaVAEMetric_f.evaluate(-1, -1, -1)
sapMetric_f = SAPMetric(metric_data, sess=sess)
sapMetric_f.set_model(gan)
results["SAP"] = sapMetric_f.evaluate(-1, -1, -1)
fStatMetric_f = FStatMetric(metric_data, sess=sess)
fStatMetric_f.set_model(gan)
results["FStat"] = fStatMetric_f.evaluate(-1, -1, -1)
migMetric_f = MIGMetric(metric_data, sess=sess)
migMetric_f.set_model(gan)
results["MIG"] = migMetric_f.evaluate(-1, -1, -1)
for regressor in [
"Lasso", "LassoCV", "RandomForest", "RandomForestIBGAN",
"RandomForestCV"
]:
dciVAEMetric_f = DCIMetric(metric_data,
sess=sess,
regressor=regressor)
dciVAEMetric_f.set_model(gan)
results["DCI_{}".format(regressor)] = dciVAEMetric_f.evaluate(
-1, -1, -1)
with open(os.path.join(self._work_dir, "final_metrics.pkl"),
"wb") as f:
pickle.dump(results, f)
def main(self):
import os
import tensorflow as tf
import pickle
from gan.load_data import load_dSprites
from gan.latent import GaussianLatent, JointLatent
from gan.network import VAEDecoder, VAEEncoder, TCDiscriminator, \
MetricRegresser
from gan.factorVAE import FactorVAE
from gan.metric import FactorVAEMetric, DSpritesInceptionScore, \
DHSICMetric
from gpu_task_scheduler.config_manager import ConfigManager
from config_mc import config
data, metric_data, latent_values, metadata = \
load_dSprites("data/dSprites")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["gaussian_dim"]):
latent_list.append(
GaussianLatent(in_dim=1,
out_dim=1,
loc=0.0,
scale=1.0,
q_std=1.0,
apply_reg=True))
latent = JointLatent(latent_list=latent_list)
decoder = VAEDecoder(output_width=width,
output_height=height,
output_depth=depth)
encoder = VAEEncoder(output_length=latent.reg_in_dim)
tcDiscriminator = TCDiscriminator()
shape_network = MetricRegresser(
output_length=3, scope_name="dSpritesSampleQualityMetric_shape")
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
dSpritesInceptionScore = DSpritesInceptionScore(
sess=sess,
do_training=False,
data=data,
metadata=metadata,
latent_values=latent_values,
network_path="metric_model/DSprites",
shape_network=shape_network,
sample_dir=sample_dir)
dHSICMetric = DHSICMetric(sess=sess, data=data)
metric_callbacks = [
factorVAEMetric, dSpritesInceptionScore, dHSICMetric
]
vae = FactorVAE(sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
encoder=encoder,
tcDiscriminator=tcDiscriminator,
tc_coe=self._config["tc_coe"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"])
vae.build()
vae.load()
results = []
configs = []
sub_config_manager = ConfigManager(config)
while sub_config_manager.get_num_left_config() > 0:
sub_config = sub_config_manager.get_next_config()
metric_data_path = os.path.join(sub_config["work_dir"],
"metric_data.pkl")
with open(metric_data_path, "rb") as f:
sub_metric_data = pickle.load(f)
sub_factorVAEMetric = FactorVAEMetric(sub_metric_data,
sess=sess)
sub_factorVAEMetric.set_model(vae)
sub_result = sub_factorVAEMetric.evaluate(-1, -1, -1)
results.append(sub_result)
configs.append(sub_config)
with open(os.path.join(self._work_dir, "cross_evaluation.pkl"),
"wb") as f:
pickle.dump({
"results": results,
"configs": configs
},
f,
protocol=2)
def main(self):
import os
import tensorflow as tf
from gan.load_data import load_3Dpots
from gan.latent import GaussianLatent, JointLatent
from gan.network import VAEDecoder, VAEEncoder, TCDiscriminator
from gan.factorVAE import FactorVAE
from gan.metric import FactorVAEMetric, \
BetaVAEMetric, SAPMetric, FStatMetric, MIGMetric, DCIMetric
import pickle
data, metric_data, latent_values = load_3Dpots("data/3Dpots")
_, height, width, depth = data.shape
latent_list = []
for i in range(self._config["gaussian_dim"]):
latent_list.append(GaussianLatent(
in_dim=1, out_dim=1, loc=0.0, scale=1.0, q_std=1.0,
apply_reg=True))
latent = JointLatent(latent_list=latent_list)
decoder = VAEDecoder(
output_width=width, output_height=height, output_depth=depth)
encoder = VAEEncoder(output_length=latent.reg_in_dim)
tcDiscriminator = TCDiscriminator()
checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sample_dir = os.path.join(self._work_dir, "sample")
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
time_path = os.path.join(self._work_dir, "time.txt")
metric_path = os.path.join(self._work_dir, "metric.csv")
run_config = tf.ConfigProto()
with tf.Session(config=run_config) as sess:
factorVAEMetric = FactorVAEMetric(metric_data, sess=sess)
metric_callbacks = [factorVAEMetric]
vae = FactorVAE(
sess=sess,
checkpoint_dir=checkpoint_dir,
sample_dir=sample_dir,
time_path=time_path,
epoch=self._config["epoch"],
batch_size=self._config["batch_size"],
data=data,
vis_freq=self._config["vis_freq"],
vis_num_sample=self._config["vis_num_sample"],
vis_num_rep=self._config["vis_num_rep"],
latent=latent,
decoder=decoder,
encoder=encoder,
tcDiscriminator=tcDiscriminator,
tc_coe=self._config["tc_coe"],
metric_callbacks=metric_callbacks,
metric_freq=self._config["metric_freq"],
metric_path=metric_path,
output_reverse=self._config["output_reverse"])
vae.build()
vae.load()
results = {}
factorVAEMetric_f = FactorVAEMetric(metric_data, sess=sess)
factorVAEMetric_f.set_model(vae)
results["FactorVAE"] = factorVAEMetric_f.evaluate(-1, -1, -1)
betaVAEMetric_f = BetaVAEMetric(metric_data, sess=sess)
betaVAEMetric_f.set_model(vae)
results["betaVAE"] = betaVAEMetric_f.evaluate(-1, -1, -1)
sapMetric_f = SAPMetric(metric_data, sess=sess)
sapMetric_f.set_model(vae)
results["SAP"] = sapMetric_f.evaluate(-1, -1, -1)
fStatMetric_f = FStatMetric(metric_data, sess=sess)
fStatMetric_f.set_model(vae)
results["FStat"] = fStatMetric_f.evaluate(-1, -1, -1)
migMetric_f = MIGMetric(metric_data, sess=sess)
migMetric_f.set_model(vae)
results["MIG"] = migMetric_f.evaluate(-1, -1, -1)
for regressor in ["Lasso", "LassoCV", "RandomForest", "RandomForestIBGAN", "RandomForestCV"]:
dciVAEMetric_f = DCIMetric(metric_data, sess=sess, regressor=regressor)
dciVAEMetric_f.set_model(vae)
results["DCI_{}".format(regressor)] = dciVAEMetric_f.evaluate(-1, -1, -1)
with open(os.path.join(self._work_dir, "final_metrics.pkl"), "wb") as f:
pickle.dump(results, f)