'loss': 'l1_plus', 'loss_params': loss_params, 'g_optimizer': 'adam', 'd_optimizer': 'adam', 'g_optim_opts': paper_opts, 'd_optim_opts': paper_opts, 'sample_interval': 205, 'batch_size': 2, 'epochs': 201, 'save_model_interval': None, 'save_img_interval': None, 'save_dir': os.getenv('SDIR') + '/patchgan_variations/patch_70_l1_1_res1/', 'save_summary': { 'epochs': np.arange(10, 210, 10).tolist(), 'box_size': (100,100), 'transform': FCS(k=4, inverse=True), 'n': 4, 'grid_size': (2,2) } } train_loader = BahamasLoaderPaired([os.getenv('D32'), os.getenv('G32')], batch_size=schedule['batch_size'], ntest=10, transform=transform_fcs, train_set=True) test_loader = BahamasLoaderPaired([os.getenv('D32'), os.getenv('G32')],
paper_opts = adam_opts paper_opts['betas'] = (0.5, 0.999) paper_opts['lr'] = 1e-3 from src.configs.resnet.b6relu import g_structure from src.configs.patchgan.nobn_nosig_bfalse import d_structure epoch_end = 45 ntest = 36 sets = [os.getenv('D32'), os.getenv('G32')] grouping = [[0], [1]] transforms = [] for i, val in enumerate(sets): transforms.append(FCS(k=4, inverse=False, totorch=True)) schedule = { 'type': 'translator', 'subtype': 'wgp', 'warm_start': True, 'loss': 'l1_plus', 'loss_params': loss_params, 'g_optimizer': 'adam', 'd_optimizer': 'adam', 'g_optim_opts': paper_opts, 'd_optim_opts': paper_opts, 'g_decay': torch.optim.lr_scheduler.StepLR, 'd_decay': torch.optim.lr_scheduler.StepLR, 'lrdecay_opts': { 'step_size': 10
os.getenv('D32Z00'), os.getenv('D32Z05'), os.getenv('D32Z10'), os.getenv('D32Z20'), os.getenv('D32V2Z00'), os.getenv('G32Z00'), os.getenv('G32Z05'), os.getenv('G32Z10'), os.getenv('G32Z20'), os.getenv('G32V2Z00') ] grouping = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] transforms = [] for i, val in enumerate(sets): transforms.append(FCS(k=4, inverse=False, totorch=True, scale=1.75)) def Schedule(name): schedule = { 'type': 'translator', 'subtype': 'wgp', 'warm_start': True, 'loss': 'l1_plus', 'loss_params': loss_params, 'g_optimizer': 'adam', 'd_optimizer': 'adam', 'g_optim_opts': paper_opts, 'd_optim_opts': paper_opts, 'g_decay': torch.optim.lr_scheduler.StepLR, 'd_decay': torch.optim.lr_scheduler.StepLR,
os.getenv('D32Z10'), os.getenv('D32Z20'), os.getenv('D32V2Z00'), os.getenv('G32Z00'), os.getenv('G32Z05'), os.getenv('G32Z10'), os.getenv('G32Z20'), os.getenv('G32V2Z00') ] grouping = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] transforms = [] scales = [1, 1, 1, 1, 1, -1, -1, -1, -1, -1] for i, val in enumerate(sets): transforms.append( FCS(k=4, inverse=False, totorch=True, scale=scales[i], shift=0)) def Schedule(name): schedule = { 'type': 'translator', 'subtype': 'wgp', 'warm_start': True, 'loss': 'l1_plus', 'loss_params': loss_params, 'g_optimizer': 'adam', 'd_optimizer': 'adam', 'g_optim_opts': paper_opts, 'd_optim_opts': paper_opts, 'g_decay': torch.optim.lr_scheduler.StepLR, 'd_decay': torch.optim.lr_scheduler.StepLR,