# SPDX-License-Identifier: LGPL-3.0-or-later
from dargs import (
Argument,
)
[docs]
def nvnmd_args():
doc_version = (
"configuration the nvnmd version (0 | 1), 0 for 4 types, 1 for 32 types"
)
doc_max_nnei = "configuration the max number of neighbors, 128|256 for version 0, 128 for version 1"
doc_net_size_file = (
"configuration the number of nodes of fitting_net, just can be set as 128"
)
doc_map_file = "A file containing the mapping tables to replace the calculation of embedding nets"
doc_config_file = "A file containing the parameters about how to implement the model in certain hardware"
doc_weight_file = "a *.npy file containing the weights of the model"
doc_enable = "enable the nvnmd training"
doc_restore_descriptor = (
"enable to restore the parameter of embedding_net from weight.npy"
)
doc_restore_fitting_net = (
"enable to restore the parameter of fitting_net from weight.npy"
)
doc_quantize_descriptor = "enable the quantizatioin of descriptor"
doc_quantize_fitting_net = "enable the quantizatioin of fitting_net"
args = [
Argument("version", int, optional=False, default=0, doc=doc_version),
Argument("max_nnei", int, optional=False, default=128, doc=doc_max_nnei),
Argument("net_size", int, optional=False, default=128, doc=doc_net_size_file),
Argument("map_file", str, optional=False, default="none", doc=doc_map_file),
Argument(
"config_file", str, optional=False, default="none", doc=doc_config_file
),
Argument(
"weight_file", str, optional=False, default="none", doc=doc_weight_file
),
Argument("enable", bool, optional=False, default=False, doc=doc_enable),
Argument(
"restore_descriptor",
bool,
optional=False,
default=False,
doc=doc_restore_descriptor,
),
Argument(
"restore_fitting_net",
bool,
optional=False,
default=False,
doc=doc_restore_fitting_net,
),
Argument(
"quantize_descriptor",
bool,
optional=False,
default=False,
doc=doc_quantize_descriptor,
),
Argument(
"quantize_fitting_net",
bool,
optional=False,
default=False,
doc=doc_quantize_fitting_net,
),
]
doc_nvnmd = "The nvnmd options."
return Argument("nvnmd", dict, args, [], optional=True, doc=doc_nvnmd)