def load_experiment(data_folder="../data/data_cost/files"):
manual_seed(42)
np.random.seed(42)
filterwarnings(action="ignore", category=DeprecationWarning, module=r".*")
filterwarnings(action="ignore", module=r"torch.quantization")
filterwarnings(action="ignore", category=UserWarning)
datasets, n_classes = prepare_cifar2(folder=data_folder)
sspace = search_space()
quant_params = None
collate_fn = None
args = configuration("TRAIN")
if not path.exists(args["ROOT"]):
mkdir(args["ROOT"])
time_init = time()
sparse_exp = SparseExperiment(
name=str(args["NAME"]),
root=args["ROOT"],
objectives=int(args["OBJECTIVES"]),
pruning=bool_converter(args["PRUNING"]),
epochs=args["epochs1"],
datasets=datasets,
classes=n_classes,
search_space=sspace,
net=Net,
flops=int(args["FLOPS"]),
quant_scheme=str(args["QUANT_SCHEME"]),
quant_params=quant_params,
collate_fn=collate_fn,
splitter=bool_converter(args["SPLITTER"]),
models_path=path.join(args["ROOT"], "models"),
)
exp, data = sparse_exp.create_load_experiment()
return exp, data
if __name__ == "__main__":
manual_seed(42)
np.random.seed(42)
filterwarnings(action="ignore", category=DeprecationWarning, module=r".*")
filterwarnings(action="ignore", module=r"torch.quantization")
filterwarnings(action="ignore", category=UserWarning)
datasets, n_classes = prepare_cost(folder="../data/data_cost/files",
image=False)
search_space = search_space()
quant_params = {nn.LSTM, nn.Linear, nn.GRU}
collate_fn = split_pad_n_pack
if bool_converter(configuration("DEFAULT")["TRAIN"]):
args = configuration("TRAIN")
if not path.exists(args["ROOT"]):
mkdir(args["ROOT"])
time_init = time()
sparse_instance = Sparse(r1=int(args["R1"]),
r2=int(args["R2"]),
r3=int(args["R3"]),
epochs1=int(args["EPOCHS1"]),
epochs2=int(args["EPOCHS2"]),
epochs3=int(args["EPOCHS3"]),
name=str(args["NAME"]),
root=args["ROOT"],
objectives=str_to_list(args["OBJECTIVES"]),
batch_size=int(args["BATCH_SIZE"]),
morphisms=bool_converter(args["MORPHISMS"]),