↓ 6 callersFunctionpad_cube Args: cube: torch.Tensor, [b*m, c, h, w] or [b, m, c, h, w] padding: int
utils/cube.py:18
↓ 3 callersFunctionimages_to_equi_and_dice Args: images: np.ndarray (B, 6, H, W, C) | torch.Tensor (B, 6, C, H, W) Returns: equis: np.ndarray, (B, H, W, C) dice
app.py:21
↓ 2 callersMethodfrom_cubediffusion Args: faces: np.ndarray, (6, H, W, C), uint8 cube_format: str, 'list', 'dict', 'dice', 'horizon'
utils/cube.py:310
↓ 2 callersMethodfrom_perspective Args: images: np.ndarray, (M, H, W, C), uint8 fovs: np.ndarray, (M,), float thetas: np.ndarray, (M,), flo
utils/cube.py:323
↓ 2 callersFunctionnpers2pers(images: List[np.ndarray], fovs, thetas, phis, fov2, theta2, phi2, height2, width2, overlap=False)
utils/pers.py:129
↓ 2 callersFunctionprep_matrices(
height: int,
width: int,
batch: int,
fov_x: float,
skew: float = 0.0,
dtype: torch.d
external/equilib/equi2pers/torch.py:42
↓ 2 callersFunctionprep_matrices(
height: int,
width: int,
batch: int,
fov_x: float,
skew: float = 0.0,
dtype: np.dtyp
external/equilib/equi2pers/numpy.py:36