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msdistgan_cifar.py
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msdistgan_cifar.py
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'''
************************************************************************
Implementation of SS/MS-DistGAN model by the authors of the paper:
"Self-supervised GAN: Analysis and Improvement with Multi-class Minimax
Game", NeurIPS 2019.
************************************************************************
'''
import os, sys
import numpy as np
import argparse
from msdistgan import MSDistGAN
from modules.dataset import Dataset
from modules.eval import compute_fid_score
if __name__ == '__main__':
'''
********************************************************************
* Command-line arguments
********************************************************************
'''
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=0, help='The ID of the specified GPU')
parser.add_argument('--is_train', type=int, default=1, help='train: 1, test: 0')
parser.add_argument('--db_name', type=str, default='cifar10', help='Database options: cifar10 or cifar100')
parser.add_argument('--nnet_type', type=str, default='resnet', help='Network architectures: dcgan, sngan and resnet')
parser.add_argument('--loss_type', type=str, default='hinge', help='Loss types: log or hinge')
parser.add_argument('--out_dir', type=str, default='./output/', help='The output path of the program')
parser.add_argument('--data_source', type=str, default='./data/cifar10/', help='The dataset path to store (if data is unavailable, it is downloaded automatically)')
parser.add_argument('--ss_task', type=int, default=2, help='0: no SS, 1: SS task, 2: MS task (self-supervised task)')
parser.add_argument('--n_steps', type=int, default=300000, help='The number of training iterations')
parser.add_argument('--noise_dim', type=int, default=128, help='The dimension of latent noise')
parser.add_argument('--batch_size', type=int, default=64, help='Mini-batch size')
parser.add_argument('--nb_test_real', type=int, default=10000, help='Number of real samples to compute FID')
parser.add_argument('--nb_test_fake', type=int, default=5000, help='Number of fake samples to compute FID')
opt = parser.parse_args()
'''
********************************************************************
* Database and outputs
********************************************************************
'''
db_name = opt.db_name # 'cifar10' or 'cifar100'
out_dir = opt.out_dir
data_source = opt.data_source
if db_name not in ['cifar10', 'cifar100']:
print('\n[msdisgan_cifar.py -- __main__] %s is not supported.' % (db_name))
exit()
is_train = opt.is_train # 1 (train model and compute FID after training)
# 0 (compute FID of pre-trained model)
'''
Number of real or generated samples to compute FID scores
'''
nb_test_real = opt.nb_test_real
nb_test_fake = opt.nb_test_fake
'''
********************************************************************
* Network architectures and objective losses
********************************************************************
'''
'''
To apply SS and SS into Dist-GAN model: Refer to Dist-GAN paper
for more details: https://arxiv.org/abs/1803.08887
'''
model = 'distgan'
'''
network architecture supports: 'dcgan', 'sngan', 'resnet'
'''
nnet_type = opt.nnet_type
'''
objective loss type supports: 'log' or 'hinge'
'''
loss_type = opt.loss_type
'''
0: no use ss/ms task (original dist-gan)
1: the original self-supervised task (SS task)
2: the multi-class minimax self-supervised task (MS task)
To select suggested @lambda_d and @lambda_g for SS or MS tasks
'''
ss_task = opt.ss_task
'''
Selected pamraeters for SS and MS tasks
@lambda_d: SS/MS for discriminator learning
@lambda_g: SS/MS for generator learning
'''
if nnet_type == 'dcgan':
if ss_task == 1:
lambda_d = 1.0 # 1.0 for SS
lambda_g = 0.0 # 0.0 for SS
elif ss_task == 2:
lambda_d = 1.0 # 1.0 for MS
lambda_g = 0.1 # 0.1 for MS
else:
lambda_d = 0.0
lambda_g = 0.0
elif nnet_type == 'sngan':
if ss_task == 1:
lambda_d = 1.0 # 1.0 for SS
lambda_g = 0.0 # 0.0 for SS
elif ss_task == 2:
lambda_d = 1.0 # 1.0 for MS
lambda_g = 0.01 # 0.0 for MS
else:
lambda_d = 0.0
lambda_g = 0.0
elif nnet_type == 'resnet':
if ss_task == 1:
lambda_d = 0.5 # 0.5 for SS
lambda_g = 0.0 # 0.0 for SS
elif ss_task == 2:
lambda_d = 0.5 # 0.5 for MS
lambda_g = 0.1 # 0.1 for MS
else:
lambda_d = 0.0
lambda_g = 0.0
else:
print('\n[msdisgan_cifar.py -- __main__] %s is not supported.' % (nnet_type))
exit()
'''
********************************************************************
* Training, network architectures and model parameters
********************************************************************
'''
n_steps = opt.n_steps # the number of iterations
noise_dim = opt.noise_dim # the noise dimension
'''
The dimension of feature size for original dist-gan model.
If you're using our pre-defined datasets, keep it!
dcgan: 2048
sngan: 8192
resnet 8192
Otherwise, adapt to new feature_dim for your new dataset.
'''
if nnet_type == 'sngan' or nnet_type == 'resnet':
feature_dim = 8192
elif nnet_type == 'dcgan':
feature_dim = 2048
'''
The unit dimensions for network architectures.
@df_dim: feature map unit for discriminator.
@gf_dim: feature map unit for generator.
@ef_dim: feature map unit for encoder.
@lr: learning rate
@beta1, beta2 parameters for Adam optimizer
'''
if nnet_type == 'resnet':
df_dim = 128
gf_dim = 128
ef_dim = 128
lr = 2e-4
beta1 = 0.0
beta2 = 0.9
else:
df_dim = 64
gf_dim = 64
ef_dim = 64
lr = 2e-4
beta1 = 0.5
beta2 = 0.9
batch_size = opt.batch_size # bach size for each iteration
'''
The fixed parameters for original Dist-GAN model. Refer to Dist-GAN
paper for more details: https://arxiv.org/abs/1803.08887
'''
lambda_p = 0.5 # gradient-penalty term
lambda_r = 1.0 # data-latent distance term
lambda_w = np.sqrt(noise_dim * 1.0/feature_dim)
'''
********************************************************************
* Training and testing
********************************************************************
'''
ext_name = 'sstask_%d_ld_%.02f_lg_%.02f_batch_%d_niters_%d' \
% (ss_task, lambda_d, lambda_g , batch_size, n_steps)
#output dir
model_dir = db_name + '_' + model + '_' \
+ nnet_type + '_' \
+ loss_type + '_' \
+ ext_name
base_dir = os.path.join(out_dir, model_dir, db_name)
if not os.path.exists(base_dir):
os.makedirs(base_dir)
# setup dataset
dataset = Dataset(name=db_name, source=data_source,\
batch_size=batch_size)
# setup gan model and train
msdistgan = MSDistGAN(model=model, \
is_train = is_train, \
ss_task = ss_task, \
loss_type = loss_type, \
lambda_p = lambda_p, \
lambda_r = lambda_r, \
lambda_w = lambda_w, \
lambda_d = lambda_d, \
lambda_g = lambda_g, \
noise_dim = noise_dim, \
lr = lr, \
beta1 = beta1, \
beta2 = beta2, \
nnet_type = nnet_type, \
df_dim = df_dim, \
gf_dim = gf_dim, \
ef_dim = ef_dim, \
dataset=dataset, \
n_steps = n_steps, \
out_dir=base_dir)
if is_train == 1:
msdistgan.train()
# compute fid score
compute_fid_score(dbname = db_name, \
input_dir = out_dir, \
model = model_dir, \
nb_train = nb_test_real,\
nb_test = nb_test_fake)
elif is_train == 0:
# compute fid score
compute_fid_score(dbname = db_name, \
input_dir = out_dir, \
model = model_dir, \
nb_train = nb_test_real,\
nb_test = nb_test_fake)