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,