def main_per_model(unused_argv):
base_path = "backbone"
model_path = os.path.join(base_path, "pca")
if not os.path.exists(model_path):
os.makedirs(model_path)
print("\n\n*- Preprocessing '%s' \n\n" % (FLAGS.dataset))
preproces_gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset),
"preprocess.preprocess_fn = @split_train_and_validation",
"split_train_and_validation.random_seed = %d" % (FLAGS.rng)
]
preprocess.preprocess_with_gin(FLAGS.dataset,
"dummy_name",
overwrite=FLAGS.overwrite,
gin_config_files=None,
gin_bindings=preproces_gin_bindings)
print("\n\n*- Preprocessing DONE \n\n")
print("\n\n*- Training PCA.")
gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset + '_' + str(FLAGS.rng)),
"train_pca.random_seed = 0",
"train_pca.num_pca_components = [10, 30]", #[100, 500, 1000, 2000, 4000]",
]
pca.train_pca_with_gin(model_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
preprocess.destroy_train_and_validation_splits(FLAGS.dataset + '_' +
str(FLAGS.rng))
print("\n\n*- Training COMPLETED \n\n")
def main(unused_argv):
base_path = "3dshapes_models"
print("\n\n*- Preprocessing '%s' \n\n" % (FLAGS.dataset))
preproces_gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset),
"preprocess.preprocess_fn = @split_train_and_validation_per_model",
"split_train_and_validation_per_model.random_seed = %d" % (FLAGS.rng)
]
preprocess.preprocess_with_gin(FLAGS.dataset,
FLAGS.model,
overwrite=FLAGS.overwrite,
gin_config_files=None,
gin_bindings=preproces_gin_bindings)
print("\n\n*- Preprocessing DONE \n\n")
vae_path = os.path.join(base_path,
FLAGS.model + FLAGS.dataset + '_' + str(FLAGS.rng))
train_vae_path = os.path.join(vae_path, 'model')
visualize_model.visualize(train_vae_path, vae_path + "/vis",
FLAGS.overwrite)
preprocess.destroy_train_and_validation_splits(FLAGS.dataset + '_' +
FLAGS.model + '_' +
str(FLAGS.rng))
def create_pca_holdout_split(unused_argv):
print("\n\n*- Preprocessing '%s' \n\n" % (FLAGS.dataset))
preproces_gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset),
"preprocess.preprocess_fn = @pca_split_holdout",
"pca_split_holdout.random_seed = 0",
"pca_split_holdout.split_size = 5000"
]
preprocess.preprocess_with_gin(FLAGS.dataset,
"dummy_name",
overwrite=FLAGS.overwrite,
gin_config_files=None,
gin_bindings=preproces_gin_bindings)
print("\n\n*- Preprocessing DONE \n\n")
def main_per_model(unused_argv):
base_path = "3dshapes_models"
print("\n\n*- Evaluating '%s' \n\n" % (FLAGS.model))
vae_path = os.path.join(base_path,
FLAGS.model + FLAGS.dataset + '_' + str(FLAGS.rng))
model_path = os.path.join(vae_path, "model")
print(vae_path, model_path)
print("\n\n*- Preprocessing '%s' \n\n" % (FLAGS.dataset))
preproces_gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset),
"preprocess.preprocess_fn = @split_train_and_validation_per_model",
"split_train_and_validation_per_model.random_seed = %d" % (FLAGS.rng)
]
preprocess.preprocess_with_gin(FLAGS.dataset,
FLAGS.model,
overwrite=FLAGS.overwrite,
gin_config_files=None,
gin_bindings=preproces_gin_bindings)
print("\n\n*- Preprocessing DONE \n\n")
print("\n\n*- Evaluating Recall.")
gin_bindings = [
"evaluate_with_decodings.evaluation_fn = @recall",
"evaluate_with_decodings.postprocess_fn = @mean_representation",
"evaluate_with_decodings.random_seed = 0",
"dataset.name = '%s'" %
(FLAGS.dataset + '_' + FLAGS.model + '_' + str(FLAGS.rng)),
# "recall.num_recall_samples = 100",
"recall.nhood_sizes = [3, 5]",
"recall.num_interventions_per_latent_dim = 20",
"recall.num_pca_components = [2000]"
]
result_path = os.path.join(vae_path, "metrics",
"recall_20interventions_pca2000")
evaluate_with_decodings.evaluate_with_gin(model_path,
result_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
preprocess.destroy_train_and_validation_splits(FLAGS.dataset + '_' +
FLAGS.model + '_' +
str(FLAGS.rng))
print("\n\n*- Evaluation COMPLETED \n\n")
def main(unused_argv):
base_path = "3dshapes_models"
done = False
while not done:
try:
print("\n\n*- Preprocessing '%s' \n\n" % (FLAGS.dataset))
preproces_gin_bindings = [
"dataset.name = '%s'" % (FLAGS.dataset),
"preprocess.preprocess_fn = @split_train_and_validation_per_model",
"split_train_and_validation_per_model.random_seed = %d" %
(FLAGS.rng)
]
preprocess.preprocess_with_gin(FLAGS.dataset,
FLAGS.model,
overwrite=FLAGS.overwrite,
gin_config_files=None,
gin_bindings=preproces_gin_bindings)
print("\n\n*- Preprocessing DONE \n\n")
done = True
except:
time.sleep(30)
if FLAGS.model == "vae":
gin_file = "3d_shape_vae.gin"
if FLAGS.model == "bvae":
gin_file = "3d_shape_bvae.gin"
if FLAGS.model == "b8vae":
gin_file = "3d_shape_b8vae.gin"
if FLAGS.model == "fvae":
gin_file = "3d_shape_fvae.gin"
if FLAGS.model == "btcvae":
gin_file = "3d_shape_btcvae.gin"
if FLAGS.model == "annvae":
gin_file = "3d_shape_annvae.gin"
if FLAGS.model == "randomvae":
gin_file = "3d_shape_randomvae.gin"
print("\n\n*- Training '%s' \n\n" % (FLAGS.model))
vae_gin_bindings = [
"model.random_seed = %d" % (FLAGS.rng),
"dataset.name = '%s'" %
(FLAGS.dataset + '_' + FLAGS.model + '_' + str(FLAGS.rng))
]
vae_path = os.path.join(base_path,
FLAGS.model + FLAGS.dataset + '_' + str(FLAGS.rng))
train_vae_path = os.path.join(vae_path, 'model')
unsupervised_train_partial.train_with_gin(train_vae_path, FLAGS.overwrite,
[gin_file], vae_gin_bindings)
visualize_model.visualize(train_vae_path, vae_path + "/vis",
FLAGS.overwrite)
preprocess.destroy_train_and_validation_splits(FLAGS.dataset + '_' +
FLAGS.model + '_' +
str(FLAGS.rng))
print("\n\n*- Training DONE \n\n")
print("\n\n*- Postprocessing '%s' \n\n" % (FLAGS.model))
postprocess_gin_bindings = [
"postprocess.postprocess_fn = @mean_representation",
"dataset.name='dummy_data'",
"postprocess.random_seed = %d" % (FLAGS.rng)
]
representation_path = os.path.join(vae_path, "representation")
model_path = os.path.join(vae_path, "model")
postprocess.postprocess_with_gin(model_path,
representation_path,
FLAGS.overwrite,
gin_config_files=None,
gin_bindings=postprocess_gin_bindings)
print("\n\n*- Postprocessing DONE \n\n")
# --- Evaluate disentanglement metrics
print("\n\n*- Evaluating MIG.")
gin_bindings = [
"evaluation.evaluation_fn = @mig", "dataset.name='3dshapes'",
"evaluation.random_seed = 0", "mig.num_train = 10000",
"discretizer.discretizer_fn = @histogram_discretizer",
"discretizer.num_bins = 20"
]
result_path = os.path.join(vae_path, "metrics", "mig")
evaluate.evaluate_with_gin(representation_path,
result_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
print("\n\n*- Evaluating BetaVEA.")
gin_bindings = [
"evaluation.evaluation_fn = @beta_vae_sklearn",
"dataset.name='3dshapes'", "evaluation.random_seed = 0",
"beta_vae_sklearn.batch_size = 16",
"beta_vae_sklearn.num_train = 10000",
"beta_vae_sklearn.num_eval = 5000",
"discretizer.discretizer_fn = @histogram_discretizer",
"discretizer.num_bins = 20"
]
result_path = os.path.join(vae_path, "metrics", "bvae")
evaluate.evaluate_with_gin(representation_path,
result_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
print("\n\n*- Evaluating FactorVAE.")
gin_bindings = [
"evaluation.evaluation_fn = @factor_vae_score",
"dataset.name='3dshapes'", "evaluation.random_seed = 0",
"factor_vae_score.batch_size = 16",
"factor_vae_score.num_train = 10000",
"factor_vae_score.num_eval = 5000",
"factor_vae_score.num_variance_estimate = 10000",
"discretizer.discretizer_fn = @histogram_discretizer",
"discretizer.num_bins = 20"
]
result_path = os.path.join(vae_path, "metrics", "fvae")
evaluate.evaluate_with_gin(representation_path,
result_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
print("\n\n*- Evaluating DCI.")
gin_bindings = [
"evaluation.evaluation_fn = @dci", "dataset.name='3dshapes'",
"evaluation.random_seed = 0", "dci.batch_size = 16",
"dci.num_train = 10000", "dci.num_test = 5000",
"discretizer.discretizer_fn = @histogram_discretizer",
"discretizer.num_bins = 20"
]
result_path = os.path.join(vae_path, "metrics", "dci")
evaluate.evaluate_with_gin(representation_path,
result_path,
FLAGS.overwrite,
gin_bindings=gin_bindings)
print("\n\n*- Evaluation COMPLETED \n\n")
# --- Downstream tasks
print("\n\n*- Training downstream factor regression '%s' \n\n" %
(FLAGS.model))
downstream_regression_train_gin_bindings = [
"evaluation.evaluation_fn = @downstream_regression_on_representations",
"dataset.name = '3dshapes_task'", "evaluation.random_seed = 0",
"downstream_regression_on_representations.holdout_dataset_name = '3dshapes_holdout'",
"downstream_regression_on_representations.num_train = [127500]",
"downstream_regression_on_representations.num_test = 22500",
"downstream_regression_on_representations.num_holdout = 80000",
"predictor.predictor_fn = @mlp_regressor",
"mlp_regressor.hidden_layer_sizes = [16, 8]",
"mlp_regressor.activation = 'logistic'", "mlp_regressor.max_iter = 50",
"mlp_regressor.random_state = 0"
]
result_path = os.path.join(vae_path, "metrics", "factor_regression")
evaluate.evaluate_with_gin(
representation_path,
result_path,
FLAGS.overwrite,
gin_config_files=None,
gin_bindings=downstream_regression_train_gin_bindings)
print("\n\n*- Training downstream factor regression DONE \n\n")
print("\n\n*- Training downstream reconstruction '%s' \n\n" %
(FLAGS.model))
downstream_reconstruction_train_gin_bindings = [
"supervised_model.model = @downstream_decoder()",
"supervised_model.batch_size = 64",
"supervised_model.training_steps = 30000",
"supervised_model.eval_steps = 1000",
"supervised_model.random_seed = 0",
"supervised_model.holdout_dataset_name = '3dshapes_holdout'",
"dataset.name='3dshapes_task'",
"decoder_optimizer.optimizer_fn = @AdamOptimizer",
"AdamOptimizer.beta1 = 0.9", "AdamOptimizer.beta2 = 0.999",
"AdamOptimizer.epsilon = 1e-08",
"AdamOptimizer.learning_rate = 0.0001", "AdamOptimizer.name = 'Adam'",
"AdamOptimizer.use_locking = False",
"decoder.decoder_fn = @deconv_decoder",
"reconstruction_loss.loss_fn = @l2_loss"
]
result_path = os.path.join(vae_path, "metrics", "reconstruction")
supervised_train_partial.train_with_gin(
result_path,
representation_path,
FLAGS.overwrite,
gin_bindings=downstream_reconstruction_train_gin_bindings)
visualize_model.visualize_supervised(result_path, representation_path,
result_path + "/vis", FLAGS.overwrite)
print("\n\n*- Training downstream reconstruction DONE \n\n")
print("\n\n*- Training & evaluation COMPLETED \n\n")
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 5 20:32:58 2020
@author: petrapoklukar
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
sys.path.append('..')
from disentanglement_lib.preprocessing import preprocess
preproces_task_gin_bindings = [
"preprocess.preprocess_fn = @split_train_and_validation",
"split_train_and_validation.random_seed = 0",
"split_train_and_validation.unit_labels = True"
]
preprocess.preprocess_with_gin('3dshapes_task',
'',
overwrite=False,
gin_config_files=None,
gin_bindings=preproces_task_gin_bindings)