def leave_one_out(self):
n_subjects = 32
results = []
for i in tqdm(range(1, n_subjects)):
datasets, n_classes = prepare_cost(
test_subjects=[i], folder="../data/data_cost/files/")
params = self.arm_parameters()
net = copy.copy(self.net)
AccMetric = AccuracyMetric(self.epochs, "Accuracy Test",
self.pruning, datasets, n_classes,
self.net, self.quant_scheme,
self.quant_params, self.collate_fn,
self.splitter, self.models_path)
AccMetric.parametrization = self.arm_parameters()
AccMetric.reload = True
collate_fn = copy.copy(self.collate_fn)
if self.splitter:
collate_fn = partial(collate_fn, max_len=params.get("max_len"))
AccMetric.trainer.load_dataloaders(params.get("batch_size", 4),
collate_fn=collate_fn)
input_shape = get_shape_from_dataloader(
AccMetric.trainer.dataloader["train"], params)
old_net = self.reload_net(params, n_classes, input_shape, net)
AccMetric.old_net = old_net
acc = AccMetric.train_evaluate(str(self.arm) + "_test_result")
results.append(acc)
save(str(self.arm) + "_test_result.npy", asarray(results))
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_cost(folder=data_folder, image=True)
sspace = search_space()
quant_params = None
collate_fn = split_arrange_pad_n_pack
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
from sparsemod.utils_data import configuration, bool_converter, str_to_list
from sparsemod.sparse import Sparse
from rnn import search_space, Net, operations, split_pad_n_pack
from load_data import prepare_cost
from test import ModelTester
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"]),