diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index ccc4c10391..32180c5e5b 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -5,6 +5,8 @@
name: Ultralytics Actions
on:
+ issues:
+ types: [opened, edited]
pull_request_target:
branches: [main]
types: [opened, closed, synchronize]
@@ -17,6 +19,7 @@ jobs:
uses: ultralytics/actions@main
with:
token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, do not modify
+ labels: true # autolabel issues and PRs
python: true # format Python code and docstrings
markdown: true # format Markdown
prettier: true # format YAML
diff --git a/docs/en/reference/models/fastsam/utils.md b/docs/en/reference/models/fastsam/utils.md
index 43c5617c21..14695908d9 100644
--- a/docs/en/reference/models/fastsam/utils.md
+++ b/docs/en/reference/models/fastsam/utils.md
@@ -13,8 +13,4 @@ keywords: FastSAM, bounding boxes, IoU, Ultralytics, image processing, computer
## ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border
-
-
-## ::: ultralytics.models.fastsam.utils.bbox_iou
-
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 3a4ab20152..362d4eead9 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-__version__ = "8.2.62"
+__version__ = "8.2.63"
import os
diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py
index f7ffb2faa3..023c1f9ab8 100644
--- a/ultralytics/models/fastsam/predict.py
+++ b/ultralytics/models/fastsam/predict.py
@@ -1,84 +1,31 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-
import torch
-from ultralytics.engine.results import Results
-from ultralytics.models.fastsam.utils import bbox_iou
-from ultralytics.models.yolo.detect.predict import DetectionPredictor
-from ultralytics.utils import DEFAULT_CFG, ops
+from ultralytics.models.yolo.segment import SegmentationPredictor
+from ultralytics.utils.metrics import box_iou
+
+from .utils import adjust_bboxes_to_image_border
-class FastSAMPredictor(DetectionPredictor):
+class FastSAMPredictor(SegmentationPredictor):
"""
FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics
YOLO framework.
- This class extends the DetectionPredictor, customizing the prediction pipeline specifically for fast SAM.
- It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing
- for single-class segmentation.
-
- Attributes:
- cfg (dict): Configuration parameters for prediction.
- overrides (dict, optional): Optional parameter overrides for custom behavior.
- _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
+ This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It
+ adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single-
+ class segmentation.
"""
- def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
- """
- Initializes the FastSAMPredictor class, inheriting from DetectionPredictor and setting the task to 'segment'.
-
- Args:
- cfg (dict): Configuration parameters for prediction.
- overrides (dict, optional): Optional parameter overrides for custom behavior.
- _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
- """
- super().__init__(cfg, overrides, _callbacks)
- self.args.task = "segment"
-
def postprocess(self, preds, img, orig_imgs):
- """
- Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image
- size, and returns the final results.
-
- Args:
- preds (list): The raw output predictions from the model.
- img (torch.Tensor): The processed image tensor.
- orig_imgs (list | torch.Tensor): The original image or list of images.
-
- Returns:
- (list): A list of Results objects, each containing processed boxes, masks, and other metadata.
- """
- p = ops.non_max_suppression(
- preds[0],
- self.args.conf,
- self.args.iou,
- agnostic=self.args.agnostic_nms,
- max_det=self.args.max_det,
- nc=1, # set to 1 class since SAM has no class predictions
- classes=self.args.classes,
- )
- full_box = torch.zeros(p[0].shape[1], device=p[0].device)
- full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0
- full_box = full_box.view(1, -1)
- critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:])
- if critical_iou_index.numel() != 0:
- full_box[0][4] = p[0][critical_iou_index][:, 4]
- full_box[0][6:] = p[0][critical_iou_index][:, 6:]
- p[0][critical_iou_index] = full_box
-
- if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
- orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
-
- results = []
- proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
- for i, (pred, orig_img, img_path) in enumerate(zip(p, orig_imgs, self.batch[0])):
- if not len(pred): # save empty boxes
- masks = None
- elif self.args.retina_masks:
- pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
- masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
- else:
- masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
- pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
- results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+ """Applies box postprocess for FastSAM predictions."""
+ results = super().postprocess(preds, img, orig_imgs)
+ for result in results:
+ full_box = torch.tensor(
+ [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32
+ )
+ boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape)
+ idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten()
+ if idx.numel() != 0:
+ result.boxes.xyxy[idx] = full_box
return results
diff --git a/ultralytics/models/fastsam/utils.py b/ultralytics/models/fastsam/utils.py
index 480e903942..5427083e35 100644
--- a/ultralytics/models/fastsam/utils.py
+++ b/ultralytics/models/fastsam/utils.py
@@ -1,7 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
-import torch
-
def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
"""
@@ -25,43 +23,3 @@ def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
boxes[boxes[:, 2] > w - threshold, 2] = w # x2
boxes[boxes[:, 3] > h - threshold, 3] = h # y2
return boxes
-
-
-def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
- """
- Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
-
- Args:
- box1 (torch.Tensor): (4, )
- boxes (torch.Tensor): (n, 4)
- iou_thres (float): IoU threshold
- image_shape (tuple): (height, width)
- raw_output (bool): If True, return the raw IoU values instead of the indices
-
- Returns:
- high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
- """
- boxes = adjust_bboxes_to_image_border(boxes, image_shape)
- # Obtain coordinates for intersections
- x1 = torch.max(box1[0], boxes[:, 0])
- y1 = torch.max(box1[1], boxes[:, 1])
- x2 = torch.min(box1[2], boxes[:, 2])
- y2 = torch.min(box1[3], boxes[:, 3])
-
- # Compute the area of intersection
- intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
-
- # Compute the area of both individual boxes
- box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
- box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
- # Compute the area of union
- union = box1_area + box2_area - intersection
-
- # Compute the IoU
- iou = intersection / union # Should be shape (n, )
- if raw_output:
- return 0 if iou.numel() == 0 else iou
-
- # return indices of boxes with IoU > thres
- return torch.nonzero(iou > iou_thres).flatten()