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import json
from pathlib import Path
from typing import List
from typing import NamedTuple
from typing import Union
from .param import Param
class DatasetConfig(NamedTuple):
param: Param
input_glob: Path
target_glob: Path
input_mean_path: Path
input_var_path: Path
target_mean_path: Path
target_var_path: Path
features: List[str]
train_crop_size: int
global_noise: float
local_noise: float
seed: int
num_test: int
class DiscriminatorModelConfig(NamedTuple):
in_channels: int
hidden_channels_list: List[int]
last_channels: int
class ModelConfig(NamedTuple):
in_channels: int
conv_bank_out_channels: int
conv_bank_k: int
max_pooling_k: int
conv_projections_hidden_channels: int
highway_layers: int
out_channels: int
out_size: int
aligner_out_time_length: int
disable_last_rnn: bool
enable_aligner: bool
discriminator: DiscriminatorModelConfig
class LossConfig(NamedTuple):
l1: float
predictor_fake: float
discriminator_true: float
discriminator_fake: float
discriminator_grad: float
class TrainConfig(NamedTuple):
batchsize: int
gpu: int
log_iteration: int
snapshot_iteration: int
class ProjectConfig(NamedTuple):
name: str
tags: List[str]
class Config(NamedTuple):
dataset: DatasetConfig
model: ModelConfig
loss: LossConfig
train: TrainConfig
project: ProjectConfig
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))
discriminator_model_config = DiscriminatorModelConfig(
in_channels=d['model']['discriminator']['in_channels'],
hidden_channels_list=d['model']['discriminator']['hidden_channels_list'],
last_channels=d['model']['discriminator']['last_channels'],
)
return Config(
dataset=DatasetConfig(
param=Param(),
input_glob=Path(d['dataset']['input_glob']),
target_glob=Path(d['dataset']['target_glob']),
input_mean_path=Path(d['dataset']['input_mean_path']),
input_var_path=Path(d['dataset']['input_var_path']),
target_mean_path=Path(d['dataset']['target_mean_path']),
target_var_path=Path(d['dataset']['target_var_path']),
features=d['dataset']['features'],
train_crop_size=d['dataset']['train_crop_size'],
global_noise=d['dataset']['global_noise'],
local_noise=d['dataset']['local_noise'],
seed=d['dataset']['seed'],
num_test=d['dataset']['num_test'],
),
model=ModelConfig(
in_channels=d['model']['in_channels'],
conv_bank_out_channels=d['model']['conv_bank_out_channels'],
conv_bank_k=d['model']['conv_bank_k'],
max_pooling_k=d['model']['max_pooling_k'],
conv_projections_hidden_channels=d['model']['conv_projections_hidden_channels'],
highway_layers=d['model']['highway_layers'],
out_channels=d['model']['out_channels'],
out_size=d['model']['out_size'],
aligner_out_time_length=d['model']['aligner_out_time_length'],
disable_last_rnn=d['model']['disable_last_rnn'],
enable_aligner=d['model']['enable_aligner'],
discriminator=discriminator_model_config,
),
loss=LossConfig(
l1=d['loss']['l1'],
predictor_fake=d['loss']['predictor_fake'],
discriminator_true=d['loss']['discriminator_true'],
discriminator_fake=d['loss']['discriminator_fake'],
discriminator_grad=d['loss']['discriminator_grad'],
),
train=TrainConfig(
batchsize=d['train']['batchsize'],
gpu=d['train']['gpu'],
log_iteration=d['train']['log_iteration'],
snapshot_iteration=d['train']['snapshot_iteration'],
),
project=ProjectConfig(
name=d['project']['name'],
tags=d['project']['tags'],
)
)
|
