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from dataclasses import dataclass, field
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from typing import Any, Dict, Optional
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@dataclass
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class LossConfiguration:
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num_classes: int
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xent_weight: float = 1.0
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dice_weight: float = 1.0
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focal_loss: bool = False
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focal_loss_gamma: float = 2.0
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requires_frustrum: bool = True
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requires_flood_mask: bool = False
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class_weights: Optional[Any] = None
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label_smoothing: float = 0.1
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@dataclass
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class BackboneConfigurationBase:
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pretrained: bool
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frozen: bool
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output_dim: bool
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@dataclass
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class DINOConfiguration(BackboneConfigurationBase):
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pretrained: bool = True
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frozen: bool = False
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output_dim: int = 128
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@dataclass
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class ResNetConfiguration(BackboneConfigurationBase):
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input_dim: int
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encoder: str
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remove_stride_from_first_conv: bool
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num_downsample: Optional[int]
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decoder_norm: str
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do_average_pooling: bool
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checkpointed: bool
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@dataclass
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class ImageEncoderConfiguration:
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name: str
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backbone: Any
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@dataclass
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class ModelConfiguration:
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segmentation_head: Dict[str, Any]
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image_encoder: ImageEncoderConfiguration
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name: str
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num_classes: int
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latent_dim: int
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z_max: int
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x_max: int
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pixel_per_meter: int
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num_scale_bins: int
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loss: LossConfiguration
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scale_range: list[int] = field(default_factory=lambda: [0, 9])
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z_min: Optional[int] = None |
