超解像

1 files changed, 0 insertions, 170 deletions

diff --git a/become_yukarin/config.py b/become_yukarin/config.py
deleted file mode 100644
index 4ba953e..0000000
--- a/become_yukarin/config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import json
-from pathlib import Path
-from typing import Dict
-from typing import List
-from typing import NamedTuple
-from typing import Optional
-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
-    input_global_noise: float
-    input_local_noise: float
-    target_global_noise: float
-    target_local_noise: float
-    seed: int
-    num_test: int
-
-
-class DiscriminatorModelConfig(NamedTuple):
-    in_channels: int
-    hidden_channels_list: List[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: Optional[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))
-
-    backward_compatible(d)
-
-    if d['model']['discriminator'] is not None:
-        discriminator_model_config = DiscriminatorModelConfig(
-            in_channels=d['model']['discriminator']['in_channels'],
-            hidden_channels_list=d['model']['discriminator']['hidden_channels_list'],
-        )
-    else:
-        discriminator_model_config = None
-
-    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'],
-            input_global_noise=d['dataset']['input_global_noise'],
-            input_local_noise=d['dataset']['input_local_noise'],
-            target_global_noise=d['dataset']['target_global_noise'],
-            target_local_noise=d['dataset']['target_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'],
-        )
-    )
-
-
-def backward_compatible(d: Dict):
-    if 'input_global_noise' not in d['dataset']:
-        d['dataset']['input_global_noise'] = d['dataset']['global_noise']
-        d['dataset']['input_local_noise'] = d['dataset']['local_noise']
-
-    if 'target_global_noise' not in d['dataset']:
-        d['dataset']['target_global_noise'] = d['dataset']['global_noise']
-        d['dataset']['target_local_noise'] = d['dataset']['local_noise']