def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
if torch.jit.isinstance(v, List[torch.Tensor]):
v = torch.stack(v)
if not torch.jit.isinstance(v, torch.Tensor):
raise ValueError(f"Unsupported input: {v}")
v = torch.nan_to_num(v, nan=self.computed_fill_value)
v = v.long()
outputs: List[torch.Tensor] = []
for v_i in v:
components = h3_to_components(v_i)
header: List[int] = [
components.mode,
components.edge,
components.resolution,
components.base_cell,
]
cells_padding: List[int] = [self.h3_padding_value] * (
self.max_h3_resolution - len(components.cells))
output = torch.tensor(header + components.cells + cells_padding,
dtype=torch.uint8,
device=v.device)
outputs.append(output)
return torch.stack(outputs)
def h3_to_list(h3_int):
components = h3_to_components(h3_int)
header = [
components['mode'], components['edge'], components['resolution'],
components['base_cell']
]
cells_padding = [H3_PADDING_VALUE
] * (MAX_H3_RESOLUTION - len(components['cells']))
return header + components['cells'] + cells_padding
def h3_to_list(h3_int):
components = h3_to_components(h3_int)
header = [
components.mode, components.edge, components.resolution,
components.base_cell
]
cells_padding = [H3_PADDING_VALUE
] * (MAX_H3_RESOLUTION - len(components.cells))
return header + components.cells + cells_padding
def h3_to_list(h3_int):
components = h3_to_components(h3_int)
header = [components["mode"], components["edge"], components["resolution"], components["base_cell"]]
cells_padding = [H3_PADDING_VALUE] * (MAX_H3_RESOLUTION - len(components["cells"]))
return header + components["cells"] + cells_padding
