create_range_compress_transforms, chain_transformations, \
atleast_3d, squeeze
from src.features.transforms import create_fcs
folder = os.path.basename(os.path.dirname(__file__))
subfolder = os.path.splitext(os.path.basename(__file__))[0]
name = '/' + folder + '/' + subfolder + '/'
from src.configs.schedules.round_16.stock import Schedule
from src.configs.resnet.dim256x1 import g_structure
from src.configs.patchgan.dim256x2_70_nobn_nosig import d_structure
fc_transform, fc_transform_inv = create_fcs(k_values={
'dm': 2,
'pressure': 4
},
scale=1.75,
shift=-1)
transform = chain_transformations([fc_transform, atleast_3d])
inv_transform = chain_transformations([squeeze, fc_transform_inv])
schedule = Schedule(name)
schedule['sample_interval'] = 100
schedule['batch_size'] = 4
schedule['decay_iter'] = 10
schedule['g_optim_opts']['lr'] = 0.0002
schedule['d_optim_opts']['lr'] = 0.0002
schedule['save_summary']['iters'] = [1] + np.arange(0, 10000, 50).tolist()
adam_opts = {
'lr': 0.001,
'betas': (0.9, 0.999),
'eps': 1e-08,
'weight_decay': 0,
'amsgrad': False
}
loss_params = {'n_critic': 5, 'grad_lambda': 10, 'l1_lambda': (1e4) / 0.05}
paper_opts = adam_opts
paper_opts['betas'] = (0.5, 0.999)
paper_opts['lr'] = 0.0002
fc_transform, fc_transform_inv = create_fcs(k=4, scale=1.75, shift=-1)
transform = chain_transformations([fc_transform, atleast_3d])
inv_transform = chain_transformations([squeeze, fc_transform_inv])
def Schedule(name,
transform=transform,
inv_transform=inv_transform,
loss_params=loss_params,
paper_opts=paper_opts,
epoch_end=100,
n_test=64):
schedule = {
'type': 'translator',