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from typing import TypedDict, Optional, Union
from utils.dataset import ClipClasses, ClipConditions, ClipViews
class SystemConfiguration(TypedDict):
disable_acc: bool
CUDA_VISIBLE_DEVICES: str
save_dir: str
image_log_on: bool
class DatasetConfiguration(TypedDict):
name: str
root_dir: str
train_size: int
num_sampled_frames: int
truncate_threshold: int
discard_threshold: int
selector: Optional[dict[str, Union[ClipClasses, ClipConditions, ClipViews]]]
num_input_channels: int
frame_size: tuple[int, int]
cache_on: bool
class DataloaderConfiguration(TypedDict):
batch_size: tuple[int, int]
num_workers: int
pin_memory: bool
class ModelHPConfiguration(TypedDict):
ae_feature_channels: int
f_a_c_p_dims: tuple[int, int, int]
hpm_scales: tuple[int, ...]
hpm_use_1x1conv: bool
hpm_use_avg_pool: bool
hpm_use_max_pool: bool
fpfe_feature_channels: int
fpfe_kernel_sizes: tuple[tuple, ...]
fpfe_paddings: tuple[tuple, ...]
fpfe_halving: tuple[int, ...]
tfa_squeeze_ratio: int
tfa_num_parts: int
embedding_dims: int
triplet_is_hard: bool
triplet_is_mean: bool
triplet_margins: tuple[float, float]
class SubOptimizerHPConfiguration(TypedDict):
lr: int
betas: tuple[float, float]
eps: float
weight_decay: float
amsgrad: bool
class OptimizerHPConfiguration(TypedDict):
lr: int
betas: tuple[float, float]
eps: float
weight_decay: float
amsgrad: bool
auto_encoder: SubOptimizerHPConfiguration
part_net: SubOptimizerHPConfiguration
hpm: SubOptimizerHPConfiguration
fc: SubOptimizerHPConfiguration
class SchedulerHPConfiguration(TypedDict):
start_step: int
final_gamma: float
class HyperparameterConfiguration(TypedDict):
model: ModelHPConfiguration
optimizer: OptimizerHPConfiguration
scheduler: SchedulerHPConfiguration
class ModelConfiguration(TypedDict):
name: str
restore_iter: int
total_iter: int
restore_iters: tuple[int, ...]
total_iters: tuple[int, ...]
class Configuration(TypedDict):
system: SystemConfiguration
dataset: DatasetConfiguration
dataloader: DataloaderConfiguration
hyperparameter: HyperparameterConfiguration
model: ModelConfiguration
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