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# -*- coding: utf-8 -*-
import torch.nn as nn
from typing import Tuple, List, Optional
# Base class for output of Point to Mesh transformation
class Point2MeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
self.center = None # Center of the mesh
self.pc = None # Point cloud data
# Base class for output of Latent to Mesh transformation
class Latent2MeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
# Base class for output of Aligned Mesh transformation
class AlignedMeshOutput(object):
def __init__(self):
self.mesh_v = None # Vertices of the mesh
self.mesh_f = None # Faces of the mesh
self.surface = None # Surface data
self.image = None # Aligned image data
self.text: Optional[str] = None # Aligned text data
self.shape_text_similarity: Optional[float] = None # Similarity between shape and text
self.shape_image_similarity: Optional[float] = None # Similarity between shape and image
# Base class for Shape as Latent with Point to Mesh transformation module
class ShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int] # Shape of the latent space
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
# Base class for Shape as Latent module
class ShapeAsLatentModule(nn.Module):
latent_shape: Tuple[int, int] # Shape of the latent space
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
# Base class for Aligned Shape as Latent with Point to Mesh transformation module
class AlignedShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int] # Shape of the latent space
def set_shape_model_only(self):
raise NotImplementedError
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
# Base class for Aligned Shape as Latent module
class AlignedShapeAsLatentModule(nn.Module):
shape_model: ShapeAsLatentModule # Shape model module
latent_shape: Tuple[int, int] # Shape of the latent space
def __init__(self, *args, **kwargs):
super().__init__()
def set_shape_model_only(self):
raise NotImplementedError
def encode_image_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_text_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_shape_embed(self, *args, **kwargs):
raise NotImplementedError
# Base class for Textured Shape as Latent module
class TexturedShapeAsLatentModule(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
def query_color(self, *args, **kwargs):
raise NotImplementedError
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