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78d2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import json
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
import torch
from dacite import Config, from_dict
from jaxtyping import Float, Int64
from torch import Tensor
from ...evaluation.evaluation_index_generator import IndexEntry
from ...misc.step_tracker import StepTracker
from ...paths import asset_path
from ..data_types import Stage
from .view_sampler import ViewSampler
@dataclass
class ViewSamplerEvaluationCfg:
name: Literal["evaluation"]
index_path: Path
num_context_views: int
class ViewSamplerEvaluation(ViewSampler[ViewSamplerEvaluationCfg]):
index: dict[str, IndexEntry | None]
def __init__(
self,
cfg: ViewSamplerEvaluationCfg,
stage: Stage,
is_overfitting: bool,
cameras_are_circular: bool,
step_tracker: StepTracker | None,
) -> None:
super().__init__(cfg, stage, is_overfitting, cameras_are_circular, step_tracker)
dacite_config = Config(cast=[tuple])
with asset_path(cfg.index_path).open("r") as f:
self.index = {
k: None if v is None else from_dict(IndexEntry, v, dacite_config)
for k, v in json.load(f).items()
}
def _sample_impl(
self,
scene: str,
extrinsics: Float[Tensor, "view 4 4"],
intrinsics: Float[Tensor, "view 3 3"],
device: torch.device = torch.device("cpu"),
**kwargs,
) -> tuple[
Int64[Tensor, " context_view"], # indices for context views
Int64[Tensor, " target_view"], # indices for target views
]:
entry = self.index.get(scene)
if entry is None:
raise ValueError(f"No indices available for scene {scene}.")
context_indices = torch.tensor(entry.context, dtype=torch.int64, device=device)
target_indices = torch.tensor(entry.target, dtype=torch.int64, device=device)
return context_indices, target_indices
@property
def num_context_views(self) -> int:
return 0
@property
def num_target_views(self) -> int:
return 0
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