ultralytics 8.2.63 refactor FastSAMPredictor (#14582)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.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

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
 
-<br><br><hr><br>
-
-## ::: ultralytics.models.fastsam.utils.bbox_iou
-
 <br><br>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.62"
+__version__ = "8.2.63"
 
 import os
 

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.yolo.segment import SegmentationPredictor
-from ultralytics.models.fastsam.utils import bbox_iou
+from ultralytics.utils.metrics import box_iou
-from ultralytics.models.yolo.detect.predict import DetectionPredictor
+
-from ultralytics.utils import DEFAULT_CFG, ops
+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.
+    This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It
-    It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing
+    adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single-
-    for single-class segmentation.
+    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.
     """
 
-    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):
-        """
+        """Applies box postprocess for FastSAM predictions."""
-        Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image
+        results = super().postprocess(preds, img, orig_imgs)
-        size, and returns the final results.
+        for result in results:
-
+            full_box = torch.tensor(
-        Args:
+                [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32
-            preds (list): The raw output predictions from the model.
+            )
-            img (torch.Tensor): The processed image tensor.
+            boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape)
-            orig_imgs (list | torch.Tensor): The original image or list of images.
+            idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten()
-
+            if idx.numel() != 0:
-        Returns:
+                result.boxes.xyxy[idx] = full_box
-            (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))
         return results

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()