Detection IoU

.devcontainer/devcontainer.json

@@ -27,6 +27,9 @@
  "editor.rulers": [
  120
  ],
+ "files.exclude": {
+ "**/__pycache__": true
+ },
  "python.pythonPath": "/usr/local/bin/python",
  "python.defaultInterpreterPath": "/usr/local/bin/python",
  "python.languageServer": "Pylance",
@@ -37,7 +40,7 @@
  "python.analysis.useImportHeuristic": true,
  "python.sortImports": true,
  "python.sortImports.args": [
- "--settings-path=${workspaceFolder}/pyproject.toml",
+ "--settings-path=${workspaceFolder}/pyproject.toml"
  ],
  "python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
  "python.formatting.blackPath": "/usr/local/py-utils/bin/black",
@@ -63,12 +66,11 @@
  ],
  "python.testing.unittestEnabled": false,
  "python.testing.pytestEnabled": true,
- "restructuredtext.confPath": "${workspaceFolder}/docs/source",
- "restructuredtext.builtDocumentationPath": "${workspaceFolder}/docs/build",
- "restructuredtext.languageServer.disabled": false,
+ "esbonio.sphinx.confDir": "${workspaceFolder}/docs/source",
+ "esbonio.sphinx.buildDir": "${workspaceFolder}/docs/build",
  "[python]": {
  "editor.codeActionsOnSave": {
- "source.organizeImports": true,
+ "source.organizeImports": true
  }
  }
  },
@@ -84,7 +86,7 @@
  "shardulm94.trailing-spaces",
  "njqdev.vscode-python-typehint",
  "lextudio.restructuredtext",
- "trond-snekvik.simple-rst",
+ "trond-snekvik.simple-rst"
  ],
  // Use 'forwardPorts' to make a list of ports inside the container available locally.
  // "forwardPorts": [],

.pre-commit-config.yaml

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 default_language_version:
- python: python3.8
+ python: python3.9
 
 ci:
  autofix_prs: true
@@ -38,7 +38,7 @@ repos:
  - id: detect-private-key
 
  - repo: https://github.com/asottile/pyupgrade
- rev: v2.38.2
+ rev: v3.1.0
  hooks:
  - id: pyupgrade
  args: [--py36-plus]
@@ -58,7 +58,7 @@ repos:
  require_serial: false
 
  - repo: https://github.com/psf/black
- rev: 22.8.0
+ rev: 22.10.0
  hooks:
  - id: black
  name: Format code

src/torchmetrics/detection/__init__.py

@@ -11,7 +11,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8
+from torchmetrics.utilities.imports import (
+ _TORCHVISION_AVAILABLE,
+ _TORCHVISION_GREATER_EQUAL_0_8,
+ _TORCHVISION_GREATER_EQUAL_0_13,
+)
 
-if _TORCHVISION_GREATER_EQUAL_0_8:
+if _TORCHVISION_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_13:
+ from torchmetrics.detection.ciou import CompleteIntersectionOverUnion # noqa: F401
+ from torchmetrics.detection.diou import DistanceIntersectionOverUnion # noqa: F401
+
+if _TORCHVISION_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_8:
+ from torchmetrics.detection.giou import GeneralizedIntersectionOverUnion # noqa: F401
+ from torchmetrics.detection.iou import IntersectionOverUnion # noqa: F401
  from torchmetrics.detection.mean_ap import MeanAveragePrecision # noqa: F401

src/torchmetrics/detection/ciou.py

@@ -0,0 +1,57 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional
+
+from torch import Tensor
+
+from torchmetrics.detection.iou import IntersectionOverUnion
+from torchmetrics.functional.detection.ciou import complete_intersection_over_union
+from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8, _TORCHVISION_GREATER_EQUAL_0_13
+
+if _TORCHVISION_GREATER_EQUAL_0_8:
+ from torchvision.ops import box_convert
+else:
+ box_convert = None
+ __doctest_skip__ = ["CompleteIntersectionOverUnion"]
+
+
+class CompleteIntersectionOverUnion(IntersectionOverUnion):
+ r"""
+ Computes Complete Intersection Over Union (CIoU) <https://arxiv.org/abs/2005.03572>`_
+ Args:
+ box_format:
+ Input format of given boxes. Supported formats are ``[`xyxy`, `xywh`, `cxcywh`]``.
+ iou_thresholds:
+ Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
+ class_metrics:
+ Option to enable per-class metrics for IoU. Has a performance impact.
+ """
+
+ iou_fn: Callable[[Tensor, Tensor, bool], Tensor] = complete_intersection_over_union
+ type: str = "ciou"
+
+ def __init__(
+ self,
+ box_format: str = "xyxy",
+ iou_threshold: Optional[float] = None,
+ class_metrics: bool = False,
+ **kwargs: Any,
+ ) -> None:
+ super().__init__(box_format, iou_threshold, class_metrics, **kwargs)
+
+ if not _TORCHVISION_GREATER_EQUAL_0_13:
+ raise ModuleNotFoundError(
+ f"Metric `{self.type.upper()}` requires that `torchvision` version 0.13.0 or newer is installed."
+ " Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`."
+ )

src/torchmetrics/detection/diou.py

@@ -0,0 +1,57 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional
+
+from torch import Tensor
+
+from torchmetrics.detection.iou import IntersectionOverUnion
+from torchmetrics.functional.detection.diou import distance_intersection_over_union
+from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8, _TORCHVISION_GREATER_EQUAL_0_13
+
+if _TORCHVISION_GREATER_EQUAL_0_8:
+ from torchvision.ops import box_convert
+else:
+ box_convert = None
+ __doctest_skip__ = ["DistanceIntersectionOverUnion"]
+
+
+class DistanceIntersectionOverUnion(IntersectionOverUnion):
+ r"""
+ Computes Distance Intersection Over Union (DIoU) <https://arxiv.org/abs/1911.08287v1>`_
+ Args:
+ box_format:
+ Input format of given boxes. Supported formats are ``[`xyxy`, `xywh`, `cxcywh`]``.
+ iou_thresholds:
+ Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
+ class_metrics:
+ Option to enable per-class metrics for IoU. Has a performance impact.
+ """
+
+ iou_fn: Callable[[Tensor, Tensor, bool], Tensor] = distance_intersection_over_union
+ type: str = "diou"
+
+ def __init__(
+ self,
+ box_format: str = "xyxy",
+ iou_threshold: Optional[float] = None,
+ class_metrics: bool = False,
+ **kwargs: Any,
+ ) -> None:
+ super().__init__(box_format, iou_threshold, class_metrics, **kwargs)
+
+ if not _TORCHVISION_GREATER_EQUAL_0_13:
+ raise ModuleNotFoundError(
+ f"Metric `{self.type.upper()}` requires that `torchvision` version 0.13.0 or newer is installed."
+ " Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`."
+ )

src/torchmetrics/detection/giou.py

@@ -0,0 +1,51 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional
+
+from torch import Tensor
+
+from torchmetrics.detection.iou import IntersectionOverUnion
+from torchmetrics.functional.detection.giou import generalized_intersection_over_union
+from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8
+
+if _TORCHVISION_GREATER_EQUAL_0_8:
+ from torchvision.ops import box_convert
+else:
+ box_convert = None
+ __doctest_skip__ = ["GeneralizedIntersectionOverUnion"]
+
+
+class GeneralizedIntersectionOverUnion(IntersectionOverUnion):
+ r"""
+ Computes Generalized Intersection Over Union (GIoU) <https://arxiv.org/abs/1902.09630>`_
+ Args:
+ box_format:
+ Input format of given boxes. Supported formats are ``[`xyxy`, `xywh`, `cxcywh`]``.
+ iou_thresholds:
+ Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
+ class_metrics:
+ Option to enable per-class metrics for IoU. Has a performance impact.
+ """
+
+ iou_fn: Callable[[Tensor, Tensor, bool], Tensor] = generalized_intersection_over_union
+ type: str = "giou"
+
+ def __init__(
+ self,
+ box_format: str = "xyxy",
+ iou_threshold: Optional[float] = None,
+ class_metrics: bool = False,
+ **kwargs: Any,
+ ) -> None:
+ super().__init__(box_format, iou_threshold, class_metrics, **kwargs)

src/torchmetrics/detection/helpers.py

@@ -0,0 +1,66 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict, Sequence
+
+from torch import Tensor
+
+
+def _input_validator(preds: Sequence[Dict[str, Tensor]], targets: Sequence[Dict[str, Tensor]]) -> None:
+ """Ensure the correct input format of `preds` and `targets`"""
+ if not isinstance(preds, Sequence):
+ raise ValueError("Expected argument `preds` to be of type Sequence")
+ if not isinstance(targets, Sequence):
+ raise ValueError("Expected argument `target` to be of type Sequence")
+ if len(preds) != len(targets):
+ raise ValueError("Expected argument `preds` and `target` to have the same length")
+
+ for k in ["boxes", "scores", "labels"]:
+ if any(k not in p for p in preds):
+ raise ValueError(f"Expected all dicts in `preds` to contain the `{k}` key")
+
+ for k in ["boxes", "labels"]:
+ if any(k not in p for p in targets):
+ raise ValueError(f"Expected all dicts in `target` to contain the `{k}` key")
+
+ if any(type(pred["boxes"]) is not Tensor for pred in preds):
+ raise ValueError("Expected all boxes in `preds` to be of type Tensor")
+ if any(type(pred["scores"]) is not Tensor for pred in preds):
+ raise ValueError("Expected all scores in `preds` to be of type Tensor")
+ if any(type(pred["labels"]) is not Tensor for pred in preds):
+ raise ValueError("Expected all labels in `preds` to be of type Tensor")
+ if any(type(target["boxes"]) is not Tensor for target in targets):
+ raise ValueError("Expected all boxes in `target` to be of type Tensor")
+ if any(type(target["labels"]) is not Tensor for target in targets):
+ raise ValueError("Expected all labels in `target` to be of type Tensor")
+
+ for i, item in enumerate(targets):
+ if item["boxes"].size(0) != item["labels"].size(0):
+ raise ValueError(
+ f"Input boxes and labels of sample {i} in targets have a"
+ f" different length (expected {item['boxes'].size(0)} labels, got {item['labels'].size(0)})"
+ )
+ for i, item in enumerate(preds):
+ if not (item["boxes"].size(0) == item["labels"].size(0) == item["scores"].size(0)):
+ raise ValueError(
+ f"Input boxes, labels and scores of sample {i} in predictions have a"
+ f" different length (expected {item['boxes'].size(0)} labels and scores,"
+ f" got {item['labels'].size(0)} labels and {item['scores'].size(0)})"
+ )
+
+
+def _fix_empty_tensors(boxes: Tensor) -> Tensor:
+ """Empty tensors can cause problems in DDP mode, this methods corrects them."""
+ if boxes.numel() == 0 and boxes.ndim == 1:
+ return boxes.unsqueeze(0)
+ return boxes