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Running
on
Zero
File size: 2,212 Bytes
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from typing import Any
import torch
from unik3d.datasets.pipelines import Compose, PanoCrop, PanoRoll
from unik3d.datasets.sequence_dataset import SequenceDataset
class d2D3DS(SequenceDataset):
min_depth = 0.01
max_depth = 10.0
depth_scale = 512.0
test_split = "train.txt"
train_split = "train.txt"
sequences_file = "sequences.json"
hdf5_paths = [f"2D3DS.hdf5"]
def __init__(
self,
image_shape: tuple[int, int],
split_file: str,
test_mode: bool,
normalize: bool,
augmentations_db: dict[str, Any],
resize_method: str,
mini: float = 1.0,
num_frames: int = 1,
benchmark: bool = False,
decode_fields: list[str] = ["image", "depth"],
inplace_fields: list[str] = ["cam2w", "camera_params"],
**kwargs,
) -> None:
super().__init__(
image_shape=image_shape,
split_file=split_file,
test_mode=test_mode,
benchmark=benchmark,
normalize=normalize,
augmentations_db=augmentations_db,
resize_method=resize_method,
mini=mini,
num_frames=num_frames,
decode_fields=decode_fields,
inplace_fields=inplace_fields,
**kwargs,
)
self.resizer = Compose(
[PanoCrop(), PanoRoll(test_mode=test_mode), self.resizer]
)
def preprocess(self, results):
self.resizer.ctx = None
if self.test_mode:
for i, seq in enumerate(results["sequence_fields"]):
results[seq]["points"] = results[seq]["camera"].reconstruct(
results[seq]["depth"]
)
results[seq]["depth"] = results[seq]["points"][:, -1:]
results[seq]["gt_fields"].add("points")
return super().preprocess(results)
def pre_pipeline(self, results):
results = super().pre_pipeline(results)
results["dense"] = [True] * self.num_frames * self.num_copies
results["synthetic"] = [False] * self.num_frames * self.num_copies
results["quality"] = [1] * self.num_frames * self.num_copies
return results
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