add FCOS

docs/source/models.rst

@@ -594,6 +594,7 @@ The models subpackage contains definitions for the following model
 architectures for detection:
 
 - `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_
+- `FCOS <https://arxiv.org/abs/1904.01355>`_
 - `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_
 - `RetinaNet <https://arxiv.org/abs/1708.02002>`_
 - `SSD <https://arxiv.org/abs/1512.02325>`_
@@ -639,6 +640,7 @@ Network                                 box AP   mask AP   keypoint AP
 Faster R-CNN ResNet-50 FPN              37.0     -         -
 Faster R-CNN MobileNetV3-Large FPN      32.8     -         -
 Faster R-CNN MobileNetV3-Large 320 FPN  22.8     -         -
+FCOS ResNet-50 FPN                      39.1     -         -
 RetinaNet ResNet-50 FPN                 36.4     -         -
 SSD300 VGG16                            25.1     -         -
 SSDlite320 MobileNetV3-Large            21.3     -         -
@@ -699,6 +701,7 @@ Network                                 train time (s / it)  test time (s / it)
 Faster R-CNN ResNet-50 FPN              0.2288               0.0590              5.2
 Faster R-CNN MobileNetV3-Large FPN      0.1020               0.0415              1.0
 Faster R-CNN MobileNetV3-Large 320 FPN  0.0978               0.0376              0.6
+FCOS ResNet-50 FPN                      0.1450               0.0539              3.3
 RetinaNet ResNet-50 FPN                 0.2514               0.0939              4.1
 SSD300 VGG16                            0.2093               0.0744              1.5
 SSDlite320 MobileNetV3-Large            0.1773               0.0906              1.5
@@ -718,6 +721,15 @@ Faster R-CNN
     torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn
     torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn
 
+FCOS
+----
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    torchvision.models.detection.fcos_resnet50_fpn
+
 
 RetinaNet
 ---------

mypy.ini

@@ -70,6 +70,10 @@ ignore_errors = True
 
 ignore_errors = True
 
+[mypy-torchvision.models.detection.fcos]
+
+ignore_errors = True
+
 [mypy-torchvision.ops.*]
 
 ignore_errors = True

references/detection/README.md

@@ -41,6 +41,13 @@ torchrun --nproc_per_node=8 train.py\
     --lr-steps 16 22 --aspect-ratio-group-factor 3
 ```
 
+### FCOS ResNet-50 FPN
+```
+torchrun --nproc_per_node=8 train.py\
+    --dataset coco --model fcos_resnet50_fpn --epochs 26\
+    --lr-steps 16 22 --aspect-ratio-group-factor 3  --lr 0.01 --amp
+```
+
 ### RetinaNet
 ```
 torchrun --nproc_per_node=8 train.py\

test/test_models.py

@@ -218,6 +218,7 @@ def _check_input_backprop(model, inputs):
     "retinanet_resnet50_fpn": lambda x: x[1],
     "ssd300_vgg16": lambda x: x[1],
     "ssdlite320_mobilenet_v3_large": lambda x: x[1],
+    "fcos_resnet50_fpn": lambda x: x[1],
 }
 
 
@@ -274,6 +275,13 @@ def _check_input_backprop(model, inputs):
         "max_size": 224,
         "input_shape": (3, 224, 224),
     },
+    "fcos_resnet50_fpn": {
+        "num_classes": 2,
+        "score_thresh": 0.05,
+        "min_size": 224,
+        "max_size": 224,
+        "input_shape": (3, 224, 224),
+    },
     "maskrcnn_resnet50_fpn": {
         "num_classes": 10,
         "min_size": 224,
@@ -325,6 +333,10 @@ def _check_input_backprop(model, inputs):
         "max_trainable": 6,
         "n_trn_params_per_layer": [96, 99, 138, 200, 239, 257, 266],
     },
+    "fcos_resnet50_fpn": {
+        "max_trainable": 5,
+        "n_trn_params_per_layer": [54, 64, 83, 96, 106, 107],
+    },
 }
 
 

test/test_models_detection_utils.py

@@ -22,6 +22,19 @@ def test_balanced_positive_negative_sampler(self):
         assert neg[0].sum() == 3
         assert neg[0][0:6].sum() == 3
 
+    def test_box_linear_coder(self):
+        box_coder = _utils.BoxLinearCoder(normalize_by_size=True)
+        # Generate a random 10x4 boxes tensor, with coordinates < 50.
+        boxes = torch.rand(10, 4) * 50
+        boxes.clamp_(min=1.0)  # tiny boxes cause numerical instability in box regression
+        boxes[:, 2:] += boxes[:, :2]
+
+        proposals = torch.tensor([0, 0, 101, 101] * 10).reshape(10, 4).float()
+
+        rel_codes = box_coder.encode_single(boxes, proposals)
+        pred_boxes = box_coder.decode_single(rel_codes, boxes)
+        torch.allclose(proposals, pred_boxes)
+
     @pytest.mark.parametrize("train_layers, exp_froz_params", [(0, 53), (1, 43), (2, 24), (3, 11), (4, 1), (5, 0)])
     def test_resnet_fpn_backbone_frozen_layers(self, train_layers, exp_froz_params):
         # we know how many initial layers and parameters of the network should

torchvision/models/detection/__init__.py

@@ -4,3 +4,4 @@
 from .retinanet import *
 from .ssd import *
 from .ssdlite import *
+from .fcos import *

torchvision/models/detection/_utils.py

@@ -217,6 +217,83 @@ def decode_single(self, rel_codes: Tensor, boxes: Tensor) -> Tensor:
         return pred_boxes
 
 
+class BoxLinearCoder:
+    """
+    The linear box-to-box transform defined in FCOS. The transformation is parameterized
+    by the distance from the center of (square) src box to 4 edges of the target box.
+    """
+
+    def __init__(self, normalize_by_size: bool = True) -> None:
+        """
+        Args:
+            normalize_by_size (bool): normalize deltas by the size of src (anchor) boxes.
+        """
+        self.normalize_by_size = normalize_by_size
+
+    def encode_single(self, reference_boxes: Tensor, proposals: Tensor) -> Tensor:
+        """
+        Encode a set of proposals with respect to some reference boxes
+
+        Args:
+            reference_boxes (Tensor): reference boxes
+            proposals (Tensor): boxes to be encoded
+
+        Returns:
+            Tensor: the encoded relative box offsets that can be used to
+            decode the boxes.
+        """
+        # get the center of reference_boxes
+        reference_boxes_ctr_x = 0.5 * (reference_boxes[:, 0] + reference_boxes[:, 2])
+        reference_boxes_ctr_y = 0.5 * (reference_boxes[:, 1] + reference_boxes[:, 3])
+
+        # get box regression transformation deltas
+        target_l = reference_boxes_ctr_x - proposals[:, 0]
+        target_t = reference_boxes_ctr_y - proposals[:, 1]
+        target_r = proposals[:, 2] - reference_boxes_ctr_x
+        target_b = proposals[:, 3] - reference_boxes_ctr_y
+
+        targets = torch.stack((target_l, target_t, target_r, target_b), dim=1)
+        if self.normalize_by_size:
+            reference_boxes_w = reference_boxes[:, 2] - reference_boxes[:, 0]
+            reference_boxes_h = reference_boxes[:, 3] - reference_boxes[:, 1]
+            reference_boxes_size = torch.stack(
+                (reference_boxes_w, reference_boxes_h, reference_boxes_w, reference_boxes_h), dim=1
+            )
+            targets = targets / reference_boxes_size
+
+        return targets
+
+    def decode_single(self, rel_codes: Tensor, boxes: Tensor) -> Tensor:
+        """
+        From a set of original boxes and encoded relative box offsets,
+        get the decoded boxes.
+
+        Args:
+            rel_codes (Tensor): encoded boxes
+            boxes (Tensor): reference boxes.
+
+        Returns:
+            Tensor: the predicted boxes with the encoded relative box offsets.
+        """
+
+        boxes = boxes.to(rel_codes.dtype)
+
+        ctr_x = 0.5 * (boxes[:, 0] + boxes[:, 2])
+        ctr_y = 0.5 * (boxes[:, 1] + boxes[:, 3])
+        if self.normalize_by_size:
+            boxes_w = boxes[:, 2] - boxes[:, 0]
+            boxes_h = boxes[:, 3] - boxes[:, 1]
+            boxes_size = torch.stack((boxes_w, boxes_h, boxes_w, boxes_h), dim=1)
+            rel_codes = rel_codes * boxes_size
+
+        pred_boxes1 = ctr_x - rel_codes[:, 0]
+        pred_boxes2 = ctr_y - rel_codes[:, 1]
+        pred_boxes3 = ctr_x + rel_codes[:, 2]
+        pred_boxes4 = ctr_y + rel_codes[:, 3]
+        pred_boxes = torch.stack((pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=1)
+        return pred_boxes
+
+
 class Matcher:
     """
     This class assigns to each predicted "element" (e.g., a box) a ground-truth