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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| import pickle | |
| from functools import lru_cache | |
| from typing import Dict, Optional, Tuple | |
| import torch | |
| from detectron2.utils.file_io import PathManager | |
| from densepose.data.meshes.catalog import MeshCatalog, MeshInfo | |
| def _maybe_copy_to_device( | |
| attribute: Optional[torch.Tensor], device: torch.device | |
| ) -> Optional[torch.Tensor]: | |
| if attribute is None: | |
| return None | |
| return attribute.to(device) | |
| class Mesh: | |
| def __init__( | |
| self, | |
| vertices: Optional[torch.Tensor] = None, | |
| faces: Optional[torch.Tensor] = None, | |
| geodists: Optional[torch.Tensor] = None, | |
| symmetry: Optional[Dict[str, torch.Tensor]] = None, | |
| texcoords: Optional[torch.Tensor] = None, | |
| mesh_info: Optional[MeshInfo] = None, | |
| device: Optional[torch.device] = None, | |
| ): | |
| """ | |
| Args: | |
| vertices (tensor [N, 3] of float32): vertex coordinates in 3D | |
| faces (tensor [M, 3] of long): triangular face represented as 3 | |
| vertex indices | |
| geodists (tensor [N, N] of float32): geodesic distances from | |
| vertex `i` to vertex `j` (optional, default: None) | |
| symmetry (dict: str -> tensor): various mesh symmetry data: | |
| - "vertex_transforms": vertex mapping under horizontal flip, | |
| tensor of size [N] of type long; vertex `i` is mapped to | |
| vertex `tensor[i]` (optional, default: None) | |
| texcoords (tensor [N, 2] of float32): texture coordinates, i.e. global | |
| and normalized mesh UVs (optional, default: None) | |
| mesh_info (MeshInfo type): necessary to load the attributes on-the-go, | |
| can be used instead of passing all the variables one by one | |
| device (torch.device): device of the Mesh. If not provided, will use | |
| the device of the vertices | |
| """ | |
| self._vertices = vertices | |
| self._faces = faces | |
| self._geodists = geodists | |
| self._symmetry = symmetry | |
| self._texcoords = texcoords | |
| self.mesh_info = mesh_info | |
| self.device = device | |
| assert self._vertices is not None or self.mesh_info is not None | |
| all_fields = [self._vertices, self._faces, self._geodists, self._texcoords] | |
| if self.device is None: | |
| for field in all_fields: | |
| if field is not None: | |
| self.device = field.device | |
| break | |
| if self.device is None and symmetry is not None: | |
| for key in symmetry: | |
| self.device = symmetry[key].device | |
| break | |
| self.device = torch.device("cpu") if self.device is None else self.device | |
| assert all([var.device == self.device for var in all_fields if var is not None]) | |
| if symmetry: | |
| assert all(symmetry[key].device == self.device for key in symmetry) | |
| if texcoords and vertices: | |
| assert len(vertices) == len(texcoords) | |
| def to(self, device: torch.device): | |
| device_symmetry = self._symmetry | |
| if device_symmetry: | |
| device_symmetry = {key: value.to(device) for key, value in device_symmetry.items()} | |
| return Mesh( | |
| _maybe_copy_to_device(self._vertices, device), | |
| _maybe_copy_to_device(self._faces, device), | |
| _maybe_copy_to_device(self._geodists, device), | |
| device_symmetry, | |
| _maybe_copy_to_device(self._texcoords, device), | |
| self.mesh_info, | |
| device, | |
| ) | |
| def vertices(self): | |
| if self._vertices is None and self.mesh_info is not None: | |
| self._vertices = load_mesh_data(self.mesh_info.data, "vertices", self.device) | |
| return self._vertices | |
| def faces(self): | |
| if self._faces is None and self.mesh_info is not None: | |
| self._faces = load_mesh_data(self.mesh_info.data, "faces", self.device) | |
| return self._faces | |
| def geodists(self): | |
| if self._geodists is None and self.mesh_info is not None: | |
| self._geodists = load_mesh_auxiliary_data(self.mesh_info.geodists, self.device) | |
| return self._geodists | |
| def symmetry(self): | |
| if self._symmetry is None and self.mesh_info is not None: | |
| self._symmetry = load_mesh_symmetry(self.mesh_info.symmetry, self.device) | |
| return self._symmetry | |
| def texcoords(self): | |
| if self._texcoords is None and self.mesh_info is not None: | |
| self._texcoords = load_mesh_auxiliary_data(self.mesh_info.texcoords, self.device) | |
| return self._texcoords | |
| def get_geodists(self): | |
| if self.geodists is None: | |
| self.geodists = self._compute_geodists() | |
| return self.geodists | |
| def _compute_geodists(self): | |
| # TODO: compute using Laplace-Beltrami | |
| geodists = None | |
| return geodists | |
| def load_mesh_data( | |
| mesh_fpath: str, field: str, device: Optional[torch.device] = None | |
| ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]: | |
| with PathManager.open(mesh_fpath, "rb") as hFile: | |
| # pyre-fixme[7]: Expected `Tuple[Optional[Tensor], Optional[Tensor]]` but | |
| # got `Tensor`. | |
| return torch.as_tensor(pickle.load(hFile)[field], dtype=torch.float).to(device) | |
| return None | |
| def load_mesh_auxiliary_data( | |
| fpath: str, device: Optional[torch.device] = None | |
| ) -> Optional[torch.Tensor]: | |
| fpath_local = PathManager.get_local_path(fpath) | |
| with PathManager.open(fpath_local, "rb") as hFile: | |
| return torch.as_tensor(pickle.load(hFile), dtype=torch.float).to(device) | |
| return None | |
| def load_mesh_symmetry( | |
| symmetry_fpath: str, device: Optional[torch.device] = None | |
| ) -> Optional[Dict[str, torch.Tensor]]: | |
| with PathManager.open(symmetry_fpath, "rb") as hFile: | |
| symmetry_loaded = pickle.load(hFile) | |
| symmetry = { | |
| "vertex_transforms": torch.as_tensor( | |
| symmetry_loaded["vertex_transforms"], dtype=torch.long | |
| ).to(device), | |
| } | |
| return symmetry | |
| return None | |
| def create_mesh(mesh_name: str, device: Optional[torch.device] = None) -> Mesh: | |
| return Mesh(mesh_info=MeshCatalog[mesh_name], device=device) | |