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run_training_vaes_wI.py
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run_training_vaes_wI.py
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#!/usr/bin/python
#-*- coding: utf-8 -*-
# >.>.>.>.>.>.>.>.>.>.>.>.>.>.>.>.
# Licensed under the Apache License, Version 2.0 (the "License")
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# --- File Name: run_training_vaes_wI.py
# --- Creation Date: 16-10-2020
# --- Last Modified: Fri 16 Oct 2020 21:04:39 AEDT
# --- Author: Xinqi Zhu
# .<.<.<.<.<.<.<.<.<.<.<.<.<.<.<.<
"""
Run training file Variational Autoencoders with I.
Code borrowed from run_training.py from NVIDIA.
"""
import argparse
import copy
import os
import sys
import dnnlib
from dnnlib import EasyDict
from metrics.metric_defaults import metric_defaults
from training.vc_modular_networks2 import split_module_names, LATENT_MODULES
#----------------------------------------------------------------------------
def run(dataset,
data_dir,
result_dir,
num_gpus,
total_kimg,
mirror_augment,
metrics,
resume_pkl,
model_type='vc_gan2',
latent_type='uniform',
batch_size=32,
batch_per_gpu=16,
random_seed=1000,
G_fmap_base=8,
module_G_list=None,
G_nf_scale=4,
E_fmap_base=8,
module_E_list=None,
E_nf_scale=4,
D_fmap_base=9,
module_D_list=None,
D_nf_scale=4,
I_fmap_base=9,
module_I_list=None,
I_nf_scale=4,
fmap_decay=0.15,
fmap_min=16,
fmap_max=512,
n_samples_per=10,
topk_dims_to_show=20,
hy_beta=1,
hy_gamma=0,
hy_dcp=40,
hy_ncut=1,
hy_rec=20,
hy_hes=20,
hy_lin=20,
hy_mat=80,
hy_gmat=0,
hy_oth=80,
hy_det=0,
hessian_type='no_act_points',
n_act_points=10,
lie_alg_init_type='oth',
lie_alg_init_scale=0.1,
G_lrate_base=0.002,
D_lrate_base=None,
lambda_d_factor=10.,
lambda_od=1.,
group_loss_type='_rec_mat_',
group_feats_size=400,
temp=0.67,
n_discrete=0,
drange_net=[-1, 1],
recons_type='bernoulli_loss',
use_group_decomp=False,
snapshot_ticks=10):
train = EasyDict(
run_func_name='training.training_loop_vae.training_loop_vae'
) # Options for training loop.
if not (module_G_list is None):
module_G_list = _str_to_list(module_G_list)
key_G_ls, size_G_ls, count_dlatent_G_size = split_module_names(
module_G_list)
if not (module_E_list is None):
module_E_list = _str_to_list(module_E_list)
key_E_ls, size_E_ls, count_dlatent_E_size = split_module_names(
module_E_list)
if not (module_D_list is None):
module_D_list = _str_to_list(module_D_list)
key_D_ls, size_D_ls, count_dlatent_D_size = split_module_names(
module_D_list)
if not (module_I_list is None):
module_I_list = _str_to_list(module_I_list)
key_I_ls, size_I_ls, count_dlatent_I_size = split_module_names(
module_I_list)
D = D_opt = D_loss = None
E = EasyDict(func_name='training.vae_networks.E_main_modular',
fmap_min=fmap_min,
fmap_max=fmap_max,
fmap_decay=fmap_decay,
latent_size=count_dlatent_E_size,
group_feats_size=group_feats_size,
module_E_list=module_E_list,
nf_scale=E_nf_scale,
n_discrete=n_discrete,
fmap_base=2 << E_fmap_base) # Options for encoder network.
G = EasyDict(func_name='training.vae_networks.G_main_modular',
fmap_min=fmap_min,
fmap_max=fmap_max,
fmap_decay=fmap_decay,
latent_size=count_dlatent_G_size,
group_feats_size=group_feats_size,
module_G_list=module_G_list,
nf_scale=G_nf_scale,
n_discrete=n_discrete,
recons_type=recons_type,
n_act_points=n_act_points,
lie_alg_init_type=lie_alg_init_type,
lie_alg_init_scale=lie_alg_init_scale,
fmap_base=2 << G_fmap_base) # Options for generator network.
I = EasyDict(func_name='training.vae_I_networks.I_main_modular',
fmap_min=fmap_min,
fmap_max=fmap_max,
fmap_decay=fmap_decay,
latent_size=count_dlatent_I_size,
module_I_list=module_I_list,
nf_scale=I_nf_scale,
fmap_base=2 << I_fmap_base) # Options for I network.
G_opt = EasyDict(beta1=0.9, beta2=0.999,
epsilon=1e-8) # Options for generator optimizer.
if model_type == 'factor_vae' or model_type == 'factor_sindis_vae': # Factor-VAE
D = EasyDict(
func_name='training.vae_networks.D_factor_vae_modular',
fmap_min=fmap_min,
fmap_max=fmap_max,
fmap_decay=fmap_decay,
latent_size=count_dlatent_D_size,
module_D_list=module_D_list,
nf_scale=D_nf_scale,
fmap_base=2 << D_fmap_base) # Options for generator network.
D_opt = EasyDict(beta1=0.5, beta2=0.9,
epsilon=1e-8) # Options for discriminator optimizer.
desc = model_type + '_modular'
if model_type == 'beta_vae': # Beta-VAE
G_loss = EasyDict(
func_name='training.loss_vae.beta_vae',
latent_type=latent_type,
hy_beta=hy_beta,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'betatc_vae': # BetaTC-VAE
G_loss = EasyDict(
func_name='training.loss_vae.betatc_vae',
latent_type=latent_type,
hy_beta=hy_beta,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'lie_vae': # LieVAE
G_loss = EasyDict(
func_name='training.loss_vae_lie.lie_vae',
latent_type=latent_type,
hy_rec=hy_rec,
hy_dcp=hy_dcp,
hy_hes=hy_hes,
hy_lin=hy_lin,
hy_ncut=hy_ncut,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'lie_vae_with_split': # LieVAE with split loss
G_loss = EasyDict(
func_name='training.loss_vae_lie.lie_vae_with_split',
latent_type=latent_type,
hy_rec=hy_rec,
hy_dcp=hy_dcp,
hy_hes=hy_hes,
hy_lin=hy_lin,
hy_ncut=hy_ncut,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'group_vae_v2': # GroupVAE-v2
G_loss = EasyDict(
func_name='training.loss_vae_group_v2.group_act_vae',
latent_type=latent_type,
hy_beta=hy_beta,
hy_rec=hy_rec,
hy_gmat=hy_gmat,
hy_dcp=hy_dcp,
hy_hes=hy_hes,
hy_lin=hy_lin,
hy_ncut=hy_ncut,
hessian_type=hessian_type,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'group_vae_spl_v2': # GroupVAE-v2
G_loss = EasyDict(
func_name='training.loss_vae_group_v2.group_act_spl_vae',
latent_type=latent_type,
hy_beta=hy_beta,
hy_rec=hy_rec,
hy_gmat=hy_gmat,
hy_dcp=hy_dcp,
hy_hes=hy_hes,
hy_lin=hy_lin,
hy_ncut=hy_ncut,
hessian_type=hessian_type,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'group_vae': # Group-VAE
G_loss = EasyDict(
func_name='training.loss_vae.group_vae',
latent_type=latent_type,
hy_beta=hy_beta,
hy_dcp=hy_dcp,
hy_ncut=hy_ncut,
hy_rec=hy_rec,
hy_mat=hy_mat,
hy_gmat=hy_gmat,
hy_oth=hy_oth,
hy_det=hy_det,
use_group_decomp=use_group_decomp,
group_loss_type=group_loss_type,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'group_vae_wc': # Group-VAE-with_Cat
G_loss = EasyDict(
func_name='training.loss_vae.group_vae_wc',
latent_type=latent_type,
hy_beta=hy_beta,
hy_gamma=hy_gamma,
temp=temp,
use_group_decomp=use_group_decomp,
group_loss_type=group_loss_type,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'dip_vae_i' or model_type == 'dip_vae_ii': # DIP-VAE
G_loss = EasyDict(
func_name='training.loss_vae.dip_vae',
lambda_d_factor=lambda_d_factor,
lambda_od=lambda_od,
latent_type=latent_type,
dip_type=model_type,
recons_type=recons_type) # Options for generator loss.
elif model_type == 'factor_vae': # Factor-VAE
G_loss = EasyDict(
func_name='training.loss_vae.factor_vae_G',
latent_type=latent_type,
hy_gamma=hy_gamma,
recons_type=recons_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vae.factor_vae_D',
latent_type=latent_type) # Options for discriminator loss.
elif model_type == 'factor_sindis_vae': # Factor-VAE
G_loss = EasyDict(
func_name='training.loss_vae.factor_vae_sindis_G',
latent_type=latent_type,
hy_gamma=hy_gamma,
recons_type=recons_type) # Options for generator loss.
D_loss = EasyDict(
func_name='training.loss_vae.factor_vae_sindis_D',
latent_type=latent_type) # Options for discriminator loss.
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='1080p',
layout='random') # Options for setup_snapshot_image_grid().
sc = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {
'rnd.np_random_seed': random_seed,
'allow_soft_placement': True
} # Options for tflib.init_tf().
train.data_dir = data_dir
train.total_kimg = total_kimg
train.mirror_augment = mirror_augment
train.image_snapshot_ticks = train.network_snapshot_ticks = snapshot_ticks
sched.G_lrate_base = G_lrate_base
sched.D_lrate_base = D_lrate_base
sched.minibatch_size_base = batch_size
sched.minibatch_gpu_base = batch_per_gpu
metrics = [metric_defaults[x] for x in metrics]
desc += '-' + dataset
dataset_args = EasyDict(tfrecord_dir=dataset, max_label_size='full')
assert num_gpus in [1, 2, 4, 8]
sc.num_gpus = num_gpus
desc += '-%dgpu' % num_gpus
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
kwargs = EasyDict(train)
kwargs.update(G_args=G,
E_args=E,
D_args=D,
G_opt_args=G_opt,
D_opt_args=D_opt,
G_loss_args=G_loss,
D_loss_args=D_loss,
traversal_grid=True)
kwargs.update(dataset_args=dataset_args,
sched_args=sched,
grid_args=grid,
n_continuous=count_dlatent_G_size,
n_discrete=n_discrete,
drange_net=drange_net,
metric_arg_list=metrics,
tf_config=tf_config,
resume_pkl=resume_pkl,
n_samples_per=n_samples_per,
topk_dims_to_show=topk_dims_to_show)
kwargs.submit_config = copy.deepcopy(sc)
kwargs.submit_config.run_dir_root = result_dir
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
#----------------------------------------------------------------------------
def _str_to_bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def _str_to_list(v):
v_values = v.strip()[1:-1]
module_G_list = [x.strip() for x in v_values.split(',')]
return module_G_list
def _str_to_list_of_int(v):
v_values = v.strip()[1:-1]
step_list = [int(x.strip()) for x in v_values.split(',')]
return step_list
def _parse_comma_sep(s):
if s is None or s.lower() == 'none' or s == '':
return []
return s.split(',')
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(
description='Train VAEs.',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'--result-dir',
help='Root directory for run results (default: %(default)s)',
default='results',
metavar='DIR')
parser.add_argument('--data-dir',
help='Dataset root directory',
required=True)
parser.add_argument('--dataset', help='Training dataset', required=True)
parser.add_argument('--num-gpus',
help='Number of GPUs (default: %(default)s)',
default=1,
type=int,
metavar='N')
parser.add_argument(
'--total-kimg',
help='Training length in thousands of images (default: %(default)s)',
metavar='KIMG',
default=25000,
type=int)
parser.add_argument('--mirror-augment',
help='Mirror augment (default: %(default)s)',
default=False,
metavar='BOOL',
type=_str_to_bool)
parser.add_argument(
'--metrics',
help='Comma-separated list of metrics or "none" (default: %(default)s)',
default='None',
type=_parse_comma_sep)
parser.add_argument('--model_type',
help='Type of model to train',
default='beta_vae',
type=str,
metavar='MODEL_TYPE',
choices=[
'beta_vae', 'factor_vae', 'factor_sindis_vae',
'dip_vae_i', 'dip_vae_ii', 'betatc_vae',
'group_vae', 'group_vae_wc', 'group_vae_v2', 'group_vae_spl_v2',
'lie_vae', 'lie_vae_with_split'
])
parser.add_argument('--resume_pkl',
help='Continue training using pretrained pkl.',
default=None,
metavar='RESUME_PKL',
type=str)
parser.add_argument('--snapshot_ticks',
help='Network and image snapshot tick.',
metavar='SNAPSHOT_TICKS',
default=10,
type=int)
parser.add_argument(
'--n_samples_per',
help=
'Number of samples for each line in traversal (default: %(default)s)',
metavar='N_SHOWN_SAMPLES_PER_LINE',
default=10,
type=int)
parser.add_argument('--batch_size',
help='N batch.',
metavar='N_BATCH',
default=32,
type=int)
parser.add_argument('--batch_per_gpu',
help='N batch per gpu.',
metavar='N_BATCH_PER_GPU',
default=16,
type=int)
parser.add_argument('--latent_type',
help='What type of latent priori to use.',
metavar='LATENT_TYPE',
default='normal',
choices=['uniform', 'normal', 'trunc_normal'],
type=str)
parser.add_argument('--fmap_decay',
help='fmap decay for network building.',
metavar='FMAP_DECAY',
default=0.15,
type=float)
parser.add_argument('--G_fmap_base',
help='Fmap base for G.',
metavar='G_FMAP_BASE',
default=8,
type=int)
parser.add_argument('--E_fmap_base',
help='Fmap base for E.',
metavar='E_FMAP_BASE',
default=8,
type=int)
parser.add_argument('--D_fmap_base',
help='Fmap base for D.',
metavar='D_FMAP_BASE',
default=9,
type=int)
parser.add_argument('--random_seed',
help='TF random seed.',
metavar='RANDOM_SEED',
default=9,
type=int)
parser.add_argument('--module_G_list',
help='Module list for G modular network.',
default=None,
metavar='MODULE_G_LIST',
type=str)
parser.add_argument('--module_E_list',
help='Module list for E modular network.',
default=None,
metavar='MODULE_E_LIST',
type=str)
parser.add_argument('--module_D_list',
help='Module list for D modular network.',
default=None,
metavar='MODULE_D_LIST',
type=str)
parser.add_argument('--fmap_min',
help='FMAP min.',
metavar='FMAP_MIN',
default=16,
type=int)
parser.add_argument('--fmap_max',
help='FMAP max.',
metavar='FMAP_MAX',
default=512,
type=int)
parser.add_argument('--G_nf_scale',
help='N feature map scale for G.',
metavar='G_NF_SCALE',
default=4,
type=int)
parser.add_argument('--E_nf_scale',
help='N feature map scale for E.',
metavar='E_NF_SCALE',
default=4,
type=int)
parser.add_argument('--D_nf_scale',
help='N feature map scale for D.',
metavar='D_NF_SCALE',
default=4,
type=int)
parser.add_argument('--arch',
help='Architecture for vc2_gan_style2_noI.',
metavar='ARCH',
default='resnet',
type=str)
parser.add_argument(
'--topk_dims_to_show',
help='Number of top disentant dimensions to show in a snapshot.',
metavar='TOPK_DIMS_TO_SHOW',
default=20,
type=int)
parser.add_argument('--hy_beta',
help='Hyper-param for beta-vae.',
metavar='HY_BETA',
default=1,
type=float)
parser.add_argument('--hy_gamma',
help='Hyper-param for factor-vae.',
metavar='HY_GAMMA',
default=0,
type=float)
parser.add_argument('--hy_dcp',
help='Hyper-param for decompose in GroupVAE and LieVAE.',
metavar='HY_DCP',
default=1,
type=float)
parser.add_argument('--hy_hes',
help='Hyper-param for Hessian in LieVAE.',
metavar='HY_HES',
default=1,
type=float)
parser.add_argument('--hy_lin',
help='Hyper-param for linear dependency in LieVAE.',
metavar='HY_LIN',
default=1,
type=float)
parser.add_argument('--hy_ncut',
help='Hyper-param for number of cuts in GroupVAE.',
metavar='HY_NCUT',
default=1,
type=int)
parser.add_argument('--n_act_points',
help='Hyper-param for number of points for act on in GroupVAEv2.',
metavar='N_ACT_POINTS',
default=10,
type=int)
parser.add_argument('--hessian_type',
help='Hessian type in GroupVAEv2.',
metavar='HESSIAN_TYPE',
default='no_act_points',
type=str)
parser.add_argument('--lie_alg_init_type',
help='Hyper-param for lie_alg_init_type.',
metavar='LIE_ALG_INIT_TYPE',
default='oth',
type=str)
parser.add_argument('--lie_alg_init_scale',
help='Hyper-param for lie_alg_init_scale.',
metavar='LIE_ALG_INIT_SCALE',
default=0.1,
type=float)
parser.add_argument(
'--hy_rec',
help='Hyper-param for gfeats reconstruction in GroupVAE and LieVAE.',
metavar='HY_REC',
default=1,
type=float)
parser.add_argument('--hy_mat',
help='Hyper-param for mat_mul in GroupVAE.',
metavar='HY_MAT',
default=1,
type=float)
parser.add_argument('--hy_gmat',
help='Hyper-param for G mat_mul in GroupVAE.',
metavar='HY_GMAT',
default=0,
type=float)
parser.add_argument('--hy_oth',
help='Hyper-param for G mat_mul in GroupVAE.',
metavar='HY_OTH',
default=1,
type=float)
parser.add_argument('--hy_det',
help='Hyper-param for determinant in GroupVAE.',
metavar='HY_DET',
default=0,
type=float)
parser.add_argument('--G_lrate_base',
help='G learning rate.',
metavar='G_LRATE_BASE',
default=0.002,
type=float)
parser.add_argument('--D_lrate_base',
help='D learning rate.',
metavar='D_LRATE_BASE',
default=0.002,
type=float)
parser.add_argument('--drange_net',
help='Dynamic range used in networks.',
default=[-1, 1],
metavar='DRANGE_NET',
type=_str_to_list_of_int)
parser.add_argument('--recons_type',
help='Reconstruction loss type.',
default='bernoulli_loss',
metavar='RECONS_TYPE',
type=str,
choices=['l2_loss', 'bernoulli_loss'])
parser.add_argument('--lambda_d_factor',
help='DIP vae lambda_d_factor.',
metavar='LAMBDA_D_FACTOR',
default=10.,
type=float)
parser.add_argument('--lambda_od',
help='DIP vae lambda_od.',
metavar='LAMBDA_OD',
default=1.,
type=float)
parser.add_argument('--group_loss_type',
help='Group vae loss type.',
metavar='GROUP_LOSS_TYPE',
default='_rec_mat_',
type=str)
parser.add_argument('--group_feats_size',
help='Group vae group_feats_size.',
metavar='GROUP_FEATS_SIZE',
default=400,
type=int)
parser.add_argument('--temp',
help='Group vae with discrete latents. Gumbel temp.',
metavar='TEMP',
default=0.67,
type=float)
parser.add_argument('--n_discrete',
help='Number of discrete categories in model.',
metavar='N_DISCRETE',
default=0,
type=int)
parser.add_argument('--use_group_decomp',
help='If use group decomposition loss in group vae',
default=True,
metavar='USE_GROUP_DECOMP',
type=_str_to_bool)
args = parser.parse_args()
if not os.path.exists(args.data_dir):
print('Error: dataset root directory does not exist.')
sys.exit(1)
for metric in args.metrics:
if metric not in metric_defaults:
print('Error: unknown metric \'%s\'' % metric)
sys.exit(1)
run(**vars(args))
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------