'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,