Implemented GIoU

CHANGELOG.md

@@ -17,6 +17,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `VisionDataModule` as parent class for `BinaryMNISTDataModule`, `CIFAR10DataModule`, `FashionMNISTDataModule`, 
  and `MNISTDataModule` ([#400](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/400))
 
+- Added GIoU loss ([#347](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/347))
+
 ### Changed
 
 - Decoupled datamodules from models ([#332](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/332), [#270](https://github.com/PyTorchLightning/pytorch-lightning-bolts/pull/270))

docs/source/losses.rst

@@ -13,6 +13,20 @@ We're cleaning up many of our losses, but in the meantime, submit a PR to add yo
 
 -------------
 
+Object Detection
+======================
+These are common losses used in object detection.
+
+---------------
+
+GIoU Loss
+---------
+
+.. autofunction:: pl_bolts.losses.object_detection.giou_loss
+ :noindex:
+
+---------------
+
 Reinforcement Learning
 ======================
 These are common losses used in RL.

pl_bolts/losses/object_detection.py

@@ -0,0 +1,34 @@
+"""
+Generalized Intersection over Union (GIoU) loss (Rezatofighi et. al)
+"""
+
+import torch
+
+from pl_bolts.metrics.object_detection import giou
+
+
+def giou_loss(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+ """
+ Calculates the generalized intersection over union loss.
+
+ It has been proposed in `Generalized Intersection over Union: A Metric and A
+ Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_.
+
+ Args:
+ preds: batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+ target: batch of target bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+
+ Example:
+
+ >>> import torch
+ >>> from pl_bolts.losses.object_detection import giou_loss
+ >>> preds = torch.tensor([[100, 100, 200, 200]])
+ >>> target = torch.tensor([[150, 150, 250, 250]])
+ >>> giou_loss(preds, target)
+ tensor([[1.0794]])
+
+ Returns:
+ GIoU loss
+ """
+ loss = 1 - giou(preds, target)
+ return loss

pl_bolts/metrics/object_detection.py

@@ -0,0 +1,42 @@
+import torch
+
+
+def giou(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+ """
+ Calculates the generalized intersection over union.
+
+ It has been proposed in `Generalized Intersection over Union: A Metric and A
+ Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_.
+
+ Args:
+ preds: batch of prediction bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+ target: batch of target bounding boxes with representation ``[x_min, y_min, x_max, y_max]``
+
+ Example:
+
+ >>> import torch
+ >>> from pl_bolts.metrics.object_detection import giou
+ >>> preds = torch.tensor([[100, 100, 200, 200]])
+ >>> target = torch.tensor([[150, 150, 250, 250]])
+ >>> giou(preds, target)
+ tensor([[-0.0794]])
+
+ Returns:
+ GIoU value
+ """
+ x_min = torch.max(preds[:, None, 0], target[:, 0])
+ y_min = torch.max(preds[:, None, 1], target[:, 1])
+ x_max = torch.min(preds[:, None, 2], target[:, 2])
+ y_max = torch.min(preds[:, None, 3], target[:, 3])
+ intersection = (x_max - x_min).clamp(min=0) * (y_max - y_min).clamp(min=0)
+ pred_area = (preds[:, 2] - preds[:, 0]) * (preds[:, 3] - preds[:, 1])
+ target_area = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
+ union = pred_area[:, None] + target_area - intersection
+ C_x_min = torch.min(preds[:, None, 0], target[:, 0])
+ C_y_min = torch.min(preds[:, None, 1], target[:, 1])
+ C_x_max = torch.max(preds[:, None, 2], target[:, 2])
+ C_y_max = torch.max(preds[:, None, 3], target[:, 3])
+ C_area = (C_x_max - C_x_min).clamp(min=0) * (C_y_max - C_y_min).clamp(min=0)
+ iou = torch.true_divide(intersection, union)
+ giou = iou - torch.true_divide((C_area - union), C_area)
+ return giou

tests/losses/test_object_detection.py

@@ -0,0 +1,41 @@
+"""
+Test Object Detection Loss Functions
+"""
+
+import pytest
+import torch
+
+from pl_bolts.losses.object_detection import giou_loss
+
+
+@pytest.mark.parametrize(
+ "preds, target, expected_loss",
+ [
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([0.0]),
+ )
+ ],
+)
+def test_complete_overlap(pred, target, expected_loss):
+ torch.testing.assert_allclose(giou_loss(pred, target), expected_loss)
+
+
+@pytest.mark.parametrize(
+ "preds, target, expected_loss",
+ [
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[100, 200, 200, 300]]),
+ torch.tensor([1.0]),
+ ),
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[200, 200, 300, 300]]),
+ torch.tensor([1.5]),
+ ),
+ ],
+)
+def test_no_overlap(pred, target, expected_loss):
+ torch.testing.assert_allclose(giou_loss(pred, target), expected_loss)

tests/metrics/test_object_detection.py

@@ -0,0 +1,41 @@
+"""
+Test Object Detection Metric Functions
+"""
+
+import pytest
+import torch
+
+from pl_bolts.metrics.object_detection import giou
+
+
+@pytest.mark.parametrize(
+ "preds, target, expected_giou",
+ [
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([1.0]),
+ )
+ ],
+)
+def test_complete_overlap(pred, target, expected_giou):
+ torch.testing.assert_allclose(giou(pred, target), expected_giou)
+
+
+@pytest.mark.parametrize(
+ "preds, target, expected_giou",
+ [
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[100, 200, 200, 300]]),
+ torch.tensor([0.0]),
+ ),
+ (
+ torch.tensor([[100, 100, 200, 200]]),
+ torch.tensor([[200, 200, 300, 300]]),
+ torch.tensor([-0.5]),
+ ),
+ ],
+)
+def test_no_overlap(pred, target, expected_giou):
+ torch.testing.assert_allclose(giou(pred, target), expected_giou)