params['net']['shape'] = [signal_length, 1] # Shape of the image
params['net']['inpainting'] = dict()
params['net']['inpainting']['split'] = signal_split
params['net']['gamma_gp'] = 10 # Gradient penalty
params['net']['fs'] = 14700 // downscale
params['net']['loss_type'] = 'wasserstein'
params['optimization'] = params_optimization
params['summary_every'] = 100 # Tensorboard summaries every ** iterations
params['print_every'] = 50 # Console summaries every ** iterations
params['save_every'] = 1000 # Save the model every ** iterations
params['summary_dir'] = os.path.join(global_path, name + '_summary/')
params['save_dir'] = os.path.join(global_path, name + '_checkpoints/')
params['Nstats'] = 0
resume, params = utils.test_resume(False, params)
#%%
# # Build the model
wgan = GANsystem(InpaintingGAN, params)
# # Train the model
wgan.train(dataset, resume=resume)
end = time.time()
print('Elapse time: {} minutes'.format((end - start) / 60))
# =============================================================================
# #%%
params['net']['inpainting'] = dict()
params['net']['inpainting']['split'] = signal_split
params['net']['gamma_gp'] = 10 # Gradient penalty
params['net']['fs'] = fs
params['net']['loss_type'] = 'wasserstein'
params['optimization'] = params_optimization
params[
'summary_every'] = 100 # Tensorboard summaries every ** iterations
params['print_every'] = 50 # Console summaries every ** iterations
params['save_every'] = 1000 # Save the model every ** iterations
params['summary_dir'] = os.path.join(global_path, name + '_summary/')
params['save_dir'] = os.path.join(global_path, name + '_checkpoints/')
params['Nstats'] = 0
resume, params = utils.test_resume(True, params)
# Build the model
print('Load the model')
wgan = GANsystem(InpaintingGAN, params)
# Generate new samples
print('Generate new samples')
real_signals = dataset.get_samples(N=N_f)
if model == 'extend':
border1 = real_signals[:, signal_split[0]:(signal_split[0] +
signal_split[1])]
border2 = real_signals[:, -(signal_split[3] +
signal_split[4]):-signal_split[4]]
border3 = real_signals[:, :(signal_split[0] + signal_split[1])]
border4 = real_signals[:, -(signal_split[3] + signal_split[4]):]
params['net']['cosmology'] = params_cosmology # Parameters for the cosmological summaries
params['net']['prior_distribution'] = 'gaussian'
params['net']['shape'] = [ns, ns, ns, 8] # Shape of the image
params['net']['loss_type'] = 'normalized_wasserstein' # loss ('hinge' or 'wasserstein')
params['net']['gamma_gp'] = 10 # Gradient penalty
params['net']['upscaling'] = 4
params['optimization'] = params_optimization
params['summary_every'] = 500 # Tensorboard summaries every ** iterations
params['print_every'] = 50 # Console summaries every ** iterations
params['save_every'] = 1000 # Save the model every ** iterations
params['summary_dir'] = os.path.join(global_path, name +'_summary/')
params['save_dir'] = os.path.join(global_path, name + '_checkpoints/')
resume, params = utils.test_resume(try_resume, params)
params['Nstats'] = 30
params['Nstats_cubes'] = 10
class CosmoUpscalePatchWGAN(UpscalePatchWGAN, CosmoWGAN):
pass
wgan = UpscaleGANsystem(CosmoUpscalePatchWGAN, params)
dataset = load.load_nbody_dataset(
spix=ns,
scaling=1,
resolution=256,
Mpch=350,
patch=True,
