Delete unused code.

2023-10-15 12:43:32 +09:00
parent 953f24a1d4
commit adf30b12f9
1 changed files with 0 additions and 87 deletions
@@ -100,93 +100,6 @@ class Interrogator:
    ]:
        raise NotImplementedError()

-
-class DeepDanbooruInterrogator(Interrogator):
-    def __init__(self, name: str, project_path: os.PathLike) -> None:
-        super().__init__(name)
-        self.project_path = project_path
-
-    def load(self) -> None:
-        print(f'Loading {self.name} from {str(self.project_path)}')
-
-        import tensorflow as tf
-
-        # tensorflow maps nearly all vram by default, so we limit this
-        # https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
-        # TODO: only run on the first run
-        for device in tf.config.experimental.list_physical_devices('GPU'):
-            tf.config.experimental.set_memory_growth(device, True)
-
-        with tf.device(tf_device_name):
-            import deepdanbooru.project as ddp
-
-            self.model = ddp.load_model_from_project(
-                project_path=self.project_path,
-                compile_model=False
-            )
-
-            print(f'Loaded {self.name} model from {str(self.project_path)}')
-
-            self.tags = ddp.load_tags_from_project(
-                project_path=self.project_path
-            )
-
-    def unload(self) -> bool:
-        # unloaded = super().unload()
-
-        # if unloaded:
-        #     # tensorflow suck
-        #     # https://github.com/keras-team/keras/issues/2102
-        #     import tensorflow as tf
-        #     tf.keras.backend.clear_session()
-        #     gc.collect()
-
-        # return unloaded
-
-        # There is a bug in Keras where it is not possible to release a model that has been loaded into memory.
-        # Downgrading to keras==2.1.6 may solve the issue, but it may cause compatibility issues with other packages.
-        # Using subprocess to create a new process may also solve the problem, but it can be too complex (like Automatic1111 did).
-        # It seems that for now, the best option is to keep the model in memory, as most users use the Waifu Diffusion model with onnx.
-
-        return False
-
-    def interrogate(
-        self,
-        image: Image
-    ) -> Tuple[
-        Dict[str, float],  # rating confidents
-        Dict[str, float]  # tag confidents
-    ]:
-        # init model
-        if not hasattr(self, 'model') or self.model is None:
-            self.load()
-
-        import deepdanbooru.data as ddd
-
-        # convert an image to fit the model
-        image_bufs = BytesIO()
-        image.save(image_bufs, format='PNG')
-        image = ddd.load_image_for_evaluate(
-            image_bufs,
-            self.model.input_shape[2],
-            self.model.input_shape[1]
-        )
-
-        image = image.reshape((1, *image.shape[0:3]))
-
-        # evaluate model
-        result = self.model.predict(image)
-
-        confidents = result[0].tolist()
-        ratings = {}
-        tags = {}
-
-        for i, tag in enumerate(self.tags):
-            tags[tag] = confidents[i]
-
-        return ratings, tags
-
-
 class WaifuDiffusionInterrogator(Interrogator):
    def __init__(
        self,