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from abc import ABC, abstractmethod |
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from dataclasses import dataclass |
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from typing import Generic, Literal, TypeVar |
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from jaxtyping import Float |
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from torch import Tensor, nn |
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from ..types import Gaussians |
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DepthRenderingMode = Literal[ |
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"depth", |
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"log", |
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"disparity", |
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"relative_disparity", |
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] |
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@dataclass |
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class DecoderOutput: |
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color: Float[Tensor, "batch view 3 height width"] |
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depth: Float[Tensor, "batch view height width"] | None |
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alpha: Float[Tensor, "batch view height width"] | None |
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lod_rendering: dict | None |
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T = TypeVar("T") |
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class Decoder(nn.Module, ABC, Generic[T]): |
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cfg: T |
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def __init__(self, cfg: T) -> None: |
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super().__init__() |
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self.cfg = cfg |
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@abstractmethod |
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def forward( |
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self, |
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gaussians: Gaussians, |
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extrinsics: Float[Tensor, "batch view 4 4"], |
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intrinsics: Float[Tensor, "batch view 3 3"], |
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near: Float[Tensor, "batch view"], |
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far: Float[Tensor, "batch view"], |
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image_shape: tuple[int, int], |
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depth_mode: DepthRenderingMode | None = None, |
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) -> DecoderOutput: |
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pass |
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