update

kornia/__init__.py

@@ -10,6 +10,7 @@
     color,
     contrib,
     core,
+    config,
     enhance,
     feature,
     io,

kornia/config.py

@@ -0,0 +1,51 @@
+from enum import Enum
+from dataclasses import dataclass, field
+
+__all__ = ["config", "InstallationMode"]
+
+
+class InstallationMode(str, Enum):
+    # Ask the user if to install the dependencies
+    ASK = "ASK"
+    # Install the dependencies
+    AUTO = "AUTO"
+    # Raise an error if the dependencies are not installed
+    RAISE = "RAISE"
+
+    def __eq__(self, other):
+        if isinstance(other, str):
+            return self.value.lower() == other.lower()  # Case-insensitive comparison
+        return super().__eq__(other)
+
+
+class LazyLoaderConfig:
+    _installation_mode: InstallationMode = InstallationMode.ASK
+
+    @property
+    def installation_mode(self) -> InstallationMode:
+        return self._installation_mode
+
+    @installation_mode.setter
+    def installation_mode(self, value: str):
+        # Allow setting via string by converting to the Enum
+        if isinstance(value, str):
+            try:
+                self._installation_mode = InstallationMode(value.upper())
+            except ValueError:
+                raise ValueError(f"{value} is not a valid InstallationMode. Choose from: {list(InstallationMode)}")
+        elif isinstance(value, InstallationMode):
+            self._installation_mode = value
+        else:
+            raise TypeError("installation_mode must be a string or InstallationMode Enum.")
+
+
+@dataclass
+class KorniaConfig:
+    output_dir: str = "kornia_outputs"
+    hub_cache_dir: str = ".kornia_hub"
+    hub_models_dir: str = ".kornia_hub/models"
+    hub_onnx_dir: str = ".kornia_hub/onnx_models"
+    lazyloader: LazyLoaderConfig = field(default_factory=LazyLoaderConfig)
+
+
+kornia_config = KorniaConfig()

kornia/contrib/models/rt_detr/post_processor.py

@@ -7,7 +7,7 @@
 import torch
 
 from kornia.core import Module, Tensor, concatenate, tensor
-from kornia.models.detector.utils import BoxFiltering
+from kornia.models.detection.utils import BoxFiltering
 
 
 def mod(a: Tensor, b: int) -> Tensor:

kornia/contrib/object_detection.py

@@ -1,18 +1,18 @@
 import warnings
 
-from kornia.models.detector.base import (
+from kornia.models.detection.base import (
     BoundingBox as BoundingBoxBase,
 )
-from kornia.models.detector.base import (
+from kornia.models.detection.base import (
     BoundingBoxDataFormat,
 )
-from kornia.models.detector.base import (
+from kornia.models.detection.base import (
     ObjectDetector as ObjectDetectorBase,
 )
-from kornia.models.detector.base import (
+from kornia.models.detection.base import (
     ObjectDetectorResult as ObjectDetectorResultBase,
 )
-from kornia.models.detector.base import (
+from kornia.models.detection.base import (
     results_from_detections as results_from_detections_base,
 )
 from kornia.models.utils import ResizePreProcessor as ResizePreProcessorBase

kornia/core/external.py

@@ -5,6 +5,8 @@
 from types import ModuleType
 from typing import List, Optional
 
+from kornia.config import kornia_config, InstallationMode
+
 logger = logging.getLogger(__name__)
 
 
@@ -45,23 +47,36 @@ def _load(self) -> None:
             try:
                 self.module = importlib.import_module(self.module_name)
             except ImportError as e:
-                if self.auto_install:
+                if kornia_config.lazyloader.installation_mode == InstallationMode.AUTO or self.auto_install:
                     self._install_package(self.module_name)
-                else:
+                elif kornia_config.lazyloader.installation_mode == InstallationMode.ASK:
+                    to_ask = True
                     if_install = input(
                         f"Optional dependency '{self.module_name}' is not installed. "
+                        "You may silent this prompt by `kornia_config.lazyloader.installation_mode = 'auto'`. "
                         "Do you wish to install the dependency? [Y]es, [N]o, [A]ll."
                     )
-                    if if_install.lower() == "y":
-                        self._install_package(self.module_name)
-                    elif if_install.lower() == "a":
-                        self.auto_install = True
-                        self._install_package(self.module_name)
-                    else:
-                        raise ImportError(
-                            f"Optional dependency '{self.module_name}' is not installed. "
-                            f"Please install it to use this functionality."
-                        ) from e
+                    while to_ask:
+                        if if_install.lower() == "y" or if_install.lower() == "yes":
+                            self._install_package(self.module_name)
+                            to_ask = False
+                        elif if_install.lower() == "a" or if_install.lower() == "all":
+                            self.auto_install = True
+                            self._install_package(self.module_name)
+                            to_ask = False
+                        elif if_install.lower() == "n" or if_install.lower() == "no":
+                            raise ImportError(
+                                f"Optional dependency '{self.module_name}' is not installed. "
+                                f"Please install it to use this functionality."
+                            ) from e
+                        else:
+                            if_install = input("Invalid input. Please enter 'Y', 'N', or 'A'.")
+
+                elif kornia_config.lazyloader.installation_mode == InstallationMode.RAISE:
+                    raise ImportError(
+                        f"Optional dependency '{self.module_name}' is not installed. "
+                        f"Please install it to use this functionality."
+                    ) from e
 
     def __getattr__(self, item: str) -> object:
         """Loads the module (if not already loaded) and returns the requested attribute.

kornia/models/__init__.py

@@ -1 +1 @@
-from . import detector, segmentor, tracker
+from . import detection, segmentation, tracking

kornia/models/detector/base.py → kornia/models/detection/base.py

@@ -15,6 +15,7 @@
 from kornia.core.external import numpy as np
 from kornia.io import write_image
 from kornia.utils.draw import draw_rectangle
+from kornia.utils.image import tensor_to_image
 
 __all__ = [
     "BoundingBoxDataFormat",
@@ -156,7 +157,7 @@ def draw(
             if output_type == "torch":
                 output.append(out_img[0])
             elif output_type == "pil":
-                output.append(Image.fromarray((out_img[0] * 255).permute(1, 2, 0).numpy().astype(np.uint8)))  # type: ignore
+                output.append(Image.fromarray((tensor_to_image(out_img[0]) * 255).astype(np.uint8)))  # type: ignore
             else:
                 raise RuntimeError(f"Unsupported output type `{output_type}`.")
         return output
@@ -171,8 +172,8 @@ def save(
             n_row: Number of images displayed in each row of the grid.
         """
         if directory is None:
-            name = f"detection-{datetime.datetime.now(tz=datetime.timezone.utc).strftime('%Y%m%d%H%M%S')!s}"
-            directory = os.path.join("Kornia_outputs", name)
+            name = f"detection_{datetime.datetime.now(tz=datetime.timezone.utc).strftime('%Y%m%d%H%M%S')!s}"
+            directory = os.path.join("kornia_outputs", name)
         outputs = self.draw(images, detections)
         os.makedirs(directory, exist_ok=True)
         for i, out_image in enumerate(outputs):

kornia/models/detector/rtdetr.py → kornia/models/detection/rtdetr.py

@@ -7,7 +7,7 @@
 from kornia.contrib.models.rt_detr import DETRPostProcessor
 from kornia.contrib.models.rt_detr.model import RTDETR, RTDETRConfig
 from kornia.core import rand
-from kornia.models.detector.base import ObjectDetector
+from kornia.models.detection.base import ObjectDetector
 from kornia.models.utils import ResizePreProcessor
 
 __all__ = ["RTDETRDetectorBuilder"]
@@ -128,10 +128,10 @@ def to_onnx(
         if onnx_name is None:
             _model_name = model_name
             if model_name is None and config is not None:
-                _model_name = "rtdetr-customized"
+                _model_name = "rtdetr_customized"
             elif model_name is None and config is None:
                 _model_name = "rtdetr_r18vd"
-            onnx_name = f"Kornia-RTDETR-{_model_name}-{image_size}.onnx"
+            onnx_name = f"kornia_{_model_name}_{image_size}.onnx"
 
         if image_size is None:
             val_image = rand(1, 3, 640, 640)

kornia/models/edge_detector/dexined.py → kornia/models/edge_detection/dexined.py

@@ -5,17 +5,18 @@
 
 from kornia.core import rand
 from kornia.filters.dexined import DexiNed
-from kornia.models.edge_detector.base import EdgeDetector
+from kornia.models.edge_detection.base import EdgeDetector
 from kornia.models.utils import ResizePostProcessor, ResizePreProcessor
 
 
 class DexiNedBuilder:
+
     @staticmethod
     def build(pretrained: bool = True, image_size: Optional[int] = 352) -> EdgeDetector:
         model = DexiNed(pretrained=pretrained)
         return EdgeDetector(
             model,
-            ResizePreProcessor((image_size, image_size)) if image_size is not None else nn.Identity(),
+            ResizePreProcessor(image_size, image_size) if image_size is not None else nn.Identity(),
             ResizePostProcessor() if image_size is not None else nn.Identity(),
         )
 
@@ -27,7 +28,7 @@ def to_onnx(
     ) -> Tuple[str, EdgeDetector]:
         edge_detector = DexiNedBuilder.build(pretrained, image_size)
         if onnx_name is None:
-            onnx_name = f"Kornia-DexiNed-{image_size}.onnx"
+            onnx_name = f"kornia_dexined_{image_size}.onnx"
 
         if image_size is None:
             val_image = rand(1, 3, 352, 352)

kornia/models/tracker/boxmot_tracker.py → kornia/models/tracking/boxmot_tracker.py

@@ -5,8 +5,8 @@
 from kornia.core import Tensor
 from kornia.core.external import boxmot
 from kornia.core.external import numpy as np
-from kornia.models.detector.base import ObjectDetector
-from kornia.models.detector.rtdetr import RTDETRDetectorBuilder
+from kornia.models.detection.base import ObjectDetector
+from kornia.models.detection.rtdetr import RTDETRDetectorBuilder
 from kornia.utils.image import tensor_to_image
 
 __all__ = ["BoxMotTracker"]

kornia/models/utils.py

@@ -14,16 +14,16 @@ class ResizePreProcessor(Module):
     Additionally, also returns the original image sizes for further post-processing.
     """
 
-    def __init__(self, size: tuple[int, int], interpolation_mode: str = "bilinear") -> None:
+    def __init__(self, height: int, width: int, interpolation_mode: str = "bilinear") -> None:
         """
         Args:
-            size: images will be resized to this value. If a 2-integer tuple is given, it is interpreted as
-                (height, width).
+            height: height of the resized image.
+            width: width of the resized image.
             interpolation_mode: interpolation mode for image resizing. Supported values: ``nearest``, ``bilinear``,
                 ``bicubic``, ``area``, and ``nearest-exact``.
         """
         super().__init__()
-        self.size = size
+        self.size = (height, width)
         self.interpolation_mode = interpolation_mode
 
     def forward(self, imgs: Union[Tensor, list[Tensor]]) -> tuple[Tensor, Tensor]:

kornia/onnx/sequential.py

@@ -3,14 +3,15 @@
 from kornia.core.external import numpy as np
 from kornia.core.external import onnx
 from kornia.core.external import onnxruntime as ort
+from kornia.config import kornia_config
 
 from .utils import ONNXLoader
 
 __all__ = ["ONNXSequential", "load"]
 
 
 class ONNXSequential:
-    """ONNXSequential to chain multiple ONNX operators together.
+    f"""ONNXSequential to chain multiple ONNX operators together.
 
     Args:
         *args: A variable number of ONNX models (either ONNX ModelProto objects or file paths).
@@ -24,7 +25,7 @@ class ONNXSequential:
             only one input and output node for each graph.
             If not None, `io_maps[0]` shall represent the `io_map` for combining the first and second ONNX models.
         cache_dir: The directory where ONNX models are cached locally (only for downloading from HuggingFace).
-            Defaults to None, which will use a default `.kornia_hub/onnx_models` directory.
+            Defaults to None, which will use a default `{kornia_config.hub_onnx_dir}` directory.
     """
 
     def __init__(

kornia/onnx/utils.py

@@ -7,18 +7,18 @@
 import requests
 
 from kornia.core.external import onnx
-
+from kornia.config import kornia_config
 __all__ = ["ONNXLoader"]
 
 logger = logging.getLogger(__name__)
 
 
 class ONNXLoader:
-    """Manages ONNX models, handling local caching, downloading from Hugging Face, and loading models.
+    f"""Manages ONNX models, handling local caching, downloading from Hugging Face, and loading models.
 
     Attributes:
         cache_dir: The directory where ONNX models are cached locally.
-            Defaults to None, which will use a default `.kornia_hub/onnx_models` directory.
+            Defaults to None, which will use a default `{kornia_config.hub_onnx_dir}` directory.
     """
 
     def __init__(self, cache_dir: Optional[str] = None):
@@ -39,7 +39,7 @@ def _get_file_path(self, model_name: str, cache_dir: Optional[str]) -> str:
             if self.cache_dir is not None:
                 cache_dir = self.cache_dir
             else:
-                cache_dir = ".kornia_hub/onnx_models"
+                cache_dir = kornia_config.hub_onnx_dir
 
         # The filename is the model name (without directory path)
         file_name = f"{model_name.split('/')[-1]}.onnx"
@@ -61,7 +61,7 @@ def load_model(self, model_name: str, download: bool = True, **kwargs) -> "onnx.
         """
         if model_name.startswith("hf://"):
             model_name = model_name[len("hf://") :]
-            cache_dir = kwargs.get("cache_dir", None) or self.cache_dir
+            cache_dir = kwargs.get(kornia_config.hub_onnx_dir, None) or self.cache_dir
             file_path = self._get_file_path(model_name, cache_dir)
             if not os.path.exists(file_path):
                 # Construct the raw URL for the ONNX file