i

apps/third_party/Wonder3D/mvdiffusion/data/objaverse_dataset.py

@@ -25,7 +25,7 @@ class ObjaverseDataset(Dataset):
     def __init__(self,
         root_dir: str,
         num_views: int,
-        bg_color: Any,
+        bg_color,
         img_wh: Tuple[int, int],
         object_list: str,
         groups_num: int=1,

craftsman/data/objaverse.py

@@ -101,7 +101,7 @@ class ObjaverseDataset(Dataset):
     def __len__(self):
         return len(self.uids)
 
-    def _load_shape(self, index: int) -> Dict[str, Any]:
+    def _load_shape(self, index: int):
         if self.cfg.data_type == "occupancy":
             # for input point cloud
             pointcloud = np.load(f'{self.cfg.root_dir}/{self.uids[index]}/pointcloud.npz')
@@ -130,7 +130,7 @@ class ObjaverseDataset(Dataset):
 
         return ret
 
-    def _load_shape_supervision(self, index: int) -> Dict[str, Any]:
+    def _load_shape_supervision(self, index: int):
         # for supervision
         ret = {}
         if self.cfg.data_type == "occupancy":
@@ -166,7 +166,7 @@ class ObjaverseDataset(Dataset):
 
         return ret
 
-    def _load_image(self, index: int) -> Dict[str, Any]:
+    def _load_image(self, index: int):
         def _load_single_image(img_path):
             img = torch.from_numpy(
                 np.asarray(
@@ -209,7 +209,7 @@ class ObjaverseDataset(Dataset):
         
         return ret
 
-    def _load_caption(self, index: int, drop_text_embed: bool = False) -> Dict[str, Any]:
+    def _load_caption(self, index: int, drop_text_embed: bool = False):
         ret = {}
         if self.cfg.caption_type == "text":
             caption = eval(json.load(open(f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f'/annotation.json')))

craftsman/models/conditional_encoders/clip/modeling_clip.py

@@ -149,7 +149,7 @@ class CLIPOutput(ModelOutput):
     text_model_output: BaseModelOutputWithPooling = None
     vision_model_output: BaseModelOutputWithPooling = None
 
-    def to_tuple(self) -> Tuple[Any]:
+    def to_tuple(self):
         return tuple(
             self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
             for k in self.keys()

craftsman/models/geometry/base.py

@@ -32,7 +32,7 @@ class BaseGeometry(BaseModule):
             f"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}"
         )
 
-    def export(self, *args, **kwargs) -> Dict[str, Any]:
+    def export(self, *args, **kwargs):
         return {}
 
 

craftsman/systems/shape_diffusion.py

@@ -163,7 +163,7 @@ class ShapeDiffusionSystem(BaseSystem):
 
         self.z_scale_factor = self.cfg.z_scale_factor
 
-    def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
+    def forward(self, batch: Dict[str, Any]):
         # encode shape latents
         shape_embeds, kl_embed, posterior = self.shape_model.encode(
             batch["surface"][..., :3 + self.cfg.shape_model.point_feats], 

craftsman/utils/base.py

@@ -57,12 +57,12 @@ class Updateable:
         pass
 
 
-def update_if_possible(module: Any, epoch: int, global_step: int) -> None:
+def update_if_possible(module, epoch: int, global_step: int) -> None:
     if isinstance(module, Updateable):
         module.do_update_step(epoch, global_step)
 
 
-def update_end_if_possible(module: Any, epoch: int, global_step: int) -> None:
+def update_end_if_possible(module, epoch: int, global_step: int) -> None:
     if isinstance(module, Updateable):
         module.do_update_step_end(epoch, global_step)
 

craftsman/utils/config.py

@@ -28,7 +28,7 @@ OmegaConf.register_new_resolver(
 # ======================================================= #
 
 
-def C_max(value: Any) -> float:
+def C_max(value) -> float:
     if isinstance(value, int) or isinstance(value, float):
         pass
     else:
@@ -101,7 +101,7 @@ class ExperimentConfig:
         os.makedirs(self.trial_dir, exist_ok=True)
 
 
-def load_config(*yamls: str, cli_args: list = [], from_string=False, **kwargs) -> Any:
+def load_config(*yamls: str, cli_args: list = [], from_string=False, **kwargs):
     if from_string:
         yaml_confs = [OmegaConf.create(s) for s in yamls]
     else:
@@ -114,7 +114,7 @@ def load_config(*yamls: str, cli_args: list = [], from_string=False, **kwargs) -
     return scfg
 
 
-def config_to_primitive(config, resolve: bool = True) -> Any:
+def config_to_primitive(config, resolve: bool = True):
     return OmegaConf.to_container(config, resolve=resolve)
 
 
@@ -123,6 +123,6 @@ def dump_config(path: str, config) -> None:
         OmegaConf.save(config=config, f=fp)
 
 
-def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
+def parse_structured(fields, cfg: Optional[Union[dict, DictConfig]] = None):
     scfg = OmegaConf.structured(fields(**cfg))
     return scfg

craftsman/utils/misc.py

@@ -70,7 +70,7 @@ def load_module_weights(
     return state_dict_to_load, ckpt["epoch"], ckpt["global_step"]
 
 
-def C(value: Any, epoch: int, global_step: int) -> float:
+def C(value, epoch: int, global_step: int) -> float:
     if isinstance(value, int) or isinstance(value, float):
         pass
     else:

gradio_app.py

@@ -257,8 +257,8 @@ if __name__=="__main__":
                     with gr.Row():
                         background_choice = gr.Dropdown(label="Backgroud Choice", value="Auto Remove Background",choices=list(background_choice.keys()))
                         rmbg_type = gr.Dropdown(label="Backgroud Remove Type", value="rembg",choices=['sam', "rembg"])
-                        backgroud_color = gr.ColorPicker(label="Background Color", value="#FFFFFF", interactive=True)
-                        # backgroud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=True)
+                        # backgroud_color = gr.ColorPicker(label="Background Color", value="#FFFFFF", interactive=True)
+                        backgroud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=True)
                         
                     with gr.Row():
                         mvimg_guidance_scale = gr.Number(value=3.5, minimum=3, maximum=10, label="2D Guidance Scale")