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import json
from pathlib import Path
from typing import Dict
from typing import List
from typing import NamedTuple
from typing import Union
from become_yukarin.param import Param
class SRDatasetConfig(NamedTuple):
param: Param
input_glob: Path
train_crop_size: int
input_global_noise: float
input_local_noise: float
blur_size_factor: float
seed: int
num_test: int
class SRModelConfig(NamedTuple):
pass
class SRLossConfig(NamedTuple):
mse: float
adversarial: float
class SRTrainConfig(NamedTuple):
batchsize: int
gpu: int
log_iteration: int
snapshot_iteration: int
class SRProjectConfig(NamedTuple):
name: str
tags: List[str]
class SRConfig(NamedTuple):
dataset: SRDatasetConfig
model: SRModelConfig
loss: SRLossConfig
train: SRTrainConfig
project: SRProjectConfig
def save_as_json(self, path):
d = _namedtuple_to_dict(self)
json.dump(d, open(path, 'w'), indent=2, sort_keys=True, default=_default_path)
def _default_path(o):
if isinstance(o, Path):
return str(o)
raise TypeError(repr(o) + " is not JSON serializable")
def _namedtuple_to_dict(o: NamedTuple):
return {
k: v if not hasattr(v, '_asdict') else _namedtuple_to_dict(v)
for k, v in o._asdict().items()
}
def create_from_json(s: Union[str, Path]):
try:
d = json.loads(s)
except TypeError:
d = json.load(open(s))
backward_compatible(d)
return SRConfig(
dataset=SRDatasetConfig(
param=Param(),
input_glob=Path(d['dataset']['input_glob']),
train_crop_size=d['dataset']['train_crop_size'],
input_global_noise=d['dataset']['input_global_noise'],
input_local_noise=d['dataset']['input_local_noise'],
blur_size_factor=d['dataset']['blur_size_factor'],
seed=d['dataset']['seed'],
num_test=d['dataset']['num_test'],
),
model=SRModelConfig(
),
loss=SRLossConfig(
mse=d['loss']['mse'],
adversarial=d['loss']['adversarial'],
),
train=SRTrainConfig(
batchsize=d['train']['batchsize'],
gpu=d['train']['gpu'],
log_iteration=d['train']['log_iteration'],
snapshot_iteration=d['train']['snapshot_iteration'],
),
project=SRProjectConfig(
name=d['project']['name'],
tags=d['project']['tags'],
)
)
def backward_compatible(d: Dict):
if 'blur_size_factor' not in d['dataset']:
d['dataset']['blur_size_factor'] = 0
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