init

primitive_anything/michelangelo/models/tsal/tsal_base.py

@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+
+import torch.nn as nn
+from typing import Tuple, List, Optional
+import pytorch_lightning as pl
+
+
+class Point2MeshOutput(object):
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+        self.center = None
+        self.pc = None
+
+
+class Latent2MeshOutput(object):
+
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+
+
+class AlignedMeshOutput(object):
+
+    def __init__(self):
+        self.mesh_v = None
+        self.mesh_f = None
+        self.surface = None
+        self.image = None
+        self.text: Optional[str] = None
+        self.shape_text_similarity: Optional[float] = None
+        self.shape_image_similarity: Optional[float] = None
+
+
+class ShapeAsLatentPLModule(pl.LightningModule):
+    latent_shape: Tuple[int]
+
+    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
+
+
+class ShapeAsLatentModule(nn.Module):
+    latent_shape: Tuple[int, int]
+
+    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
+
+
+class AlignedShapeAsLatentPLModule(pl.LightningModule):
+    latent_shape: Tuple[int]
+
+    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
+
+
+class AlignedShapeAsLatentModule(nn.Module):
+    shape_model: ShapeAsLatentModule
+    latent_shape: Tuple[int, int]
+
+    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
+
+
+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