def main():
# dataset = 'shuffle_mnist'
dataset = 'disjoint_mnist'
# dataset = 'cifar_10'
# Load configs
opts = configs.config_mnist
# opts = configs.config_cifar
task_num = opts['task_num']
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
# Load data
data = setdata.set_data(dataset, task_num)
logging.error(opts)
# print (data[0].data.shape)
# print (data[0].labels.shape)
# print (data[0].labels[:5])
# print (data[1].labels[:5])
# print (data[0].test_labels[:5])
# print (data[1].test_labels[:5])
# print (data[0].test_data.shape)
# print (data[0].test_labels.shape)
# print (data[0].data_shape)
# images = []
# for i in range(3):
# img = np.reshape(data[i].data[:10], (10, 28, 28))
# images.append(np.concatenate(img, axis = 1))
# img = np.concatenate([images[0], images[1], images[2]], axis = 0)
# plt.imshow(img, cmap='Greys_r',interpolation='none', vmin=0., vmax=1)
# plt.savefig("dataset_1.jpg")
# tf.set_random_seed(1233)
with tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))) as sess:
# Initialize model
wae = WAE(opts, sess)
# Initialize variables
sess.run(wae.init)
logging.error(tf.trainable_variables())
logging.error(len(tf.trainable_variables()))
# logging.error(wae.t_network_vars)
# Initialize/Set former parameters as zeros
random_z, pseudo_G_z, pseudo_T_z, w_old, b_old, f_g_z, pred = wae.former_init(
)
for i in range(len(data)):
if i == 0:
# Consider no trans loss and regularization loss in Task 0
lambda_list = {
'wae_lambda': 1,
'rec_lambda': 0.1,
'trans_lambda': 0.0,
'reg_lambda': 0.0,
'f_lambda': 0.0,
'main_lambda': 1.0
}
logging.error("task " + str(i) + ":")
logging.error(lambda_list)
wae.train(data, i, random_z, pseudo_G_z, pseudo_T_z, w_old,
b_old, f_g_z, pred, lambda_list)
wae.test(data, True)
# Update/Save former parameters
random_z, pseudo_G_z, pseudo_T_z, w_old, b_old, f_g_z, pred = wae.former(
)
print(type(random_z))
print(type(pseudo_G_z))
print(type(pseudo_T_z[-1]))
print(pseudo_G_z.shape)
# Print images generated by random z
# idx = np.random.choice(400, 20, replace=False)
# img_gen = np.reshape(pseudo_G_z[idx], (20, 28, 28))
# img_gen = np.concatenate(img_gen, axis = 1)
# plt.imshow(img_gen, cmap='Greys_r',interpolation='none', vmin=0., vmax=1)
# plt.savefig("img_gen_after_task_%d_0.jpg" % i)
# break
else:
lambda_list = {
'wae_lambda': 1.0,
'rec_lambda': 0.5 * opts['task_num'],
'trans_lambda': 0.01 * opts['task_num'],
'reg_lambda': 0.01,
'f_lambda': 1.0,
'main_lambda': 1.0
}
logging.error("task " + str(i) + ":")
logging.error(lambda_list)
wae.train(data, i, random_z, pseudo_G_z, pseudo_T_z, w_old,
b_old, f_g_z, pred, lambda_list)
wae.test(data, True)
random_z, pseudo_G_z, pseudo_T_z, w_old, b_old, f_g_z, pred = wae.former(
)
def main():
if FLAGS.exp == 'celebA':
opts = configs.config_celebA
elif FLAGS.exp == 'celebA_small':
opts = configs.config_celebA_small
elif FLAGS.exp == 'celebA_ae_patch_var':
opts = configs.config_celebA_ae_patch_var
elif FLAGS.exp == 'celebA_sylvain_adv':
opts = configs.config_celebA_sylvain_adv
elif FLAGS.exp == 'celebA_adv':
opts = configs.config_celebA_adv
elif FLAGS.exp == 'mnist':
opts = configs.config_mnist
elif FLAGS.exp == 'mnist_small':
opts = configs.config_mnist_small
elif FLAGS.exp == 'dsprites':
opts = configs.config_dsprites
elif FLAGS.exp == 'grassli':
opts = configs.config_grassli
elif FLAGS.exp == 'grassli_small':
opts = configs.config_grassli_small
elif FLAGS.exp == 'cifar':
opts = configs.config_cifar
else:
assert False, 'Unknown experiment configuration'
opts['mode'] = FLAGS.mode
if opts['mode'] == 'test':
assert FLAGS.checkpoint is not None, 'Checkpoint must be provided'
opts['checkpoint'] = FLAGS.checkpoint
if FLAGS.zdim is not None:
opts['zdim'] = FLAGS.zdim
if FLAGS.pz is not None:
opts['pz'] = FLAGS.pz
if FLAGS.lr is not None:
opts['lr'] = FLAGS.lr
if FLAGS.w_aef is not None:
opts['w_aef'] = FLAGS.w_aef
if FLAGS.z_test is not None:
opts['z_test'] = FLAGS.z_test
if FLAGS.lambda_schedule is not None:
opts['lambda_schedule'] = FLAGS.lambda_schedule
if FLAGS.work_dir is not None:
opts['work_dir'] = FLAGS.work_dir
if FLAGS.wae_lambda is not None:
opts['lambda'] = FLAGS.wae_lambda
if FLAGS.celebA_crop is not None:
opts['celebA_crop'] = FLAGS.celebA_crop
if FLAGS.enc_noise is not None:
opts['e_noise'] = FLAGS.enc_noise
if FLAGS.e_num_filters is not None:
opts['e_num_filters'] = FLAGS.e_num_filters
if FLAGS.g_num_filters is not None:
opts['g_num_filters'] = FLAGS.g_num_filters
if FLAGS.smart_cost is True:
opts['cost'] = []
if FLAGS.patch_var_w is not None:
opts['cost'].append(('patch_variances', FLAGS.patch_var_w))
if FLAGS.l2sq_w is not None:
opts['cost'].append(('l2sq', FLAGS.l2sq_w))
if FLAGS.sylvain_adv_c_w is not None and FLAGS.sylvain_emb_c_w is not None:
adv_c_w = FLAGS.sylvain_adv_c_w
emb_c_w = FLAGS.sylvain_emb_c_w
opts['cost'].append(
('_sylvain_recon_loss_using_disc_conv', [adv_c_w, emb_c_w]))
opts['cross_p_w'] = 0
opts['diag_p_w'] = 0
if FLAGS.adv_c_num_units is not None:
opts['adv_c_num_units'] = FLAGS.adv_c_num_units
if FLAGS.adv_c_patches_size is not None:
opts['adv_c_patches_size'] = FLAGS.adv_c_patches_size
if FLAGS.adv_use_sq is not None:
opts['adv_use_sq'] = FLAGS.adv_use_sq
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
utils.create_dir(opts['work_dir'])
utils.create_dir(os.path.join(opts['work_dir'], 'checkpoints'))
if opts['e_noise'] == 'gaussian' and opts['pz'] != 'normal':
assert False, 'Gaussian encoders compatible only with Gaussian prior'
return
# Dumping all the configs to the text file
with utils.o_gfile((opts['work_dir'], 'params.txt'), 'w') as text:
text.write('Parameters:\n')
for key in opts:
text.write('%s : %s\n' % (key, opts[key]))
# Loading the dataset
data = DataHandler(opts)
assert data.num_points >= opts['batch_size'], 'Training set too small'
# Creating WAE model
wae = WAE(opts)
if opts['mode'] == 'train':
# Training WAE
wae.train(data)
elif opts['mode'] == 'test':
# Do something else
wae.test()
def main():
# Select dataset to use
if FLAGS.exp == 'celebA':
opts = configs.config_celebA
elif FLAGS.exp == 'celebA_small':
opts = configs.config_celebA_small
elif FLAGS.exp == 'mnist':
opts = configs.config_mnist
elif FLAGS.exp == 'mnist_small':
opts = configs.config_mnist_small
elif FLAGS.exp == 'dsprites':
opts = configs.config_dsprites
elif FLAGS.exp == 'grassli':
opts = configs.config_grassli
elif FLAGS.exp == 'grassli_small':
opts = configs.config_grassli_small
else:
assert False, 'Unknown experiment configuration'
# Select training method
if FLAGS.method:
opts['method'] = FLAGS.method
# Working directory
if FLAGS.work_dir:
opts['work_dir'] = FLAGS.work_dir
# Verbose
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
# Create directories
utils.create_dir(opts['method'])
work_dir = os.path.join(opts['method'], opts['work_dir'])
utils.create_dir(work_dir)
utils.create_dir(os.path.join(work_dir, 'checkpoints'))
# Dumping all the configs to the text file
with utils.o_gfile((work_dir, 'params.txt'), 'w') as text:
text.write('Parameters:\n')
for key in opts:
text.write('%s : %s\n' % (key, opts[key]))
# Loading the dataset
data = DataHandler(opts)
assert data.num_points >= opts['batch_size'], 'Training set too small'
#Reset tf graph
tf.reset_default_graph()
# build WAE
wae = WAE(opts)
# Training/testing/vizu
if FLAGS.mode == "train":
wae.train(data, opts['work_dir'], FLAGS.weights_file)
elif FLAGS.mode == "test":
wae.test(data, opts['work_dir'], FLAGS.weights_file)
elif FLAGS.mode == "reg":
wae.reg(data, opts['work_dir'], FLAGS.weights_file)
elif FLAGS.mode == "vizu":
wae.vizu(data, opts['work_dir'], FLAGS.weights_file)