def use_type(x): return bool(parsing.strtobool(x))
else: __C.num_workers[0] = 4 # Add custom arguments __C.train.lnorm = ("smooth_l1", edict(choices=["l1", "l2", "smooth_l1"], type=str)) __C.train.weight_decay = edict() __C.train.weight_decay.default = 2e-6 __C.train.weight_decay.discriminator = 10 * __C.train.weight_decay.default __C.train.lnorm_mult = (0.996, edict(type=float)) __C.train.adv_mult = (0.004, edict(type=float)) __C.train.task_lambdas = edict() __C.train.task_lambdas.depths = 1 __C.train.task_lambdas.normals = 1 __C.train.task_lambdas.autoencoder = 1 __C.train.adv_skip = (True, edict(type=lambda x: bool(parsing.strtobool(x)))) # Model args __C.model = edict() __C.model.arch = ("rn", edict(type=str, choices=["rn"])) # Encoder args # __C.model.backbone = edict() # __C.model.backbone.use = True # __C.model.backbone.kwargs = edict(lightweight=True, layers=None) __C.model.encoder = edict(kwargs=edict(out_nc=512)) # Midreps args # __C.model.midreps = edict() # __C.model.midreps.use = ( # True,
def allowed_type(x): return bool(parsing.strtobool(x))
import torch from pytorch_lightning.utilities import parsing from base_config import __C, parse_args_and_set_config, edict, _to_values_only parse_bool = lambda x: bool(parsing.strtobool(x)) if torch.cuda.is_available(): __C.orig_dir = ( "/storage1/samenabar/code/CLMAC/clevr-dataset-gen/datasets/CLEVR_v1.2", edict(type=str), ) __C.uni_dir = ( "/storage1/samenabar/code/CLMAC/clevr-dataset-gen/datasets/CLEVR_Uni_v1.2", edict(type=str), ) else: __C.orig_dir = ( "/Users/sebamenabar/Documents/datasets/tmp/CLEVR_v1.2", edict(type=str), ) __C.uni_dir = ( "/Users/sebamenabar/Documents/datasets/tmp/CLEVR_Uni_v1.2", edict(type=str), ) __C.train.num_plot_samples = (32, edict(type=int)) __C.train.augment = (False, edict(type=parse_bool)) __C.train.dataset = ("orig", edict(choices=["orig", "uni", "gqa"])) __C.train.gradient_clip_val = (8.0, edict(type=float)) __C.train.optimizers = edict()