ultralytics 8.0.171 new SAHI guide and callbacks fix (#4748)

Co-authored-by: chuzihang <49548797+Aria-Leo@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sanghyun Choi <farewell518@gmail.com>
Co-authored-by: Awsome <1579093407@qq.com>
Co-authored-by: ConanQZ <49194386+ConanQZ@users.noreply.github.com>

ultralytics/utils/callbacks/base.py

@@ -198,7 +198,8 @@ def add_integration_callbacks(instance):
     """
 
     # Load HUB callbacks
-    from .hub import callbacks
+    from .hub import callbacks as hub_cb
+    callbacks_list = [hub_cb]
 
     # Load training callbacks
     if 'Trainer' in instance.__class__.__name__:
@@ -210,13 +211,15 @@ def add_integration_callbacks(instance):
         from .raytune import callbacks as tune_cb
         from .tensorboard import callbacks as tb_cb
         from .wb import callbacks as wb_cb
-        callbacks.update({**clear_cb, **comet_cb, **dvc_cb, **mlflow_cb, **neptune_cb, **tune_cb, **tb_cb, **wb_cb})
+        callbacks_list.extend([clear_cb, comet_cb, dvc_cb, mlflow_cb, neptune_cb, tune_cb, tb_cb, wb_cb])
 
     # Load export callbacks (patch to avoid CoreML protobuf error)
     if 'Exporter' in instance.__class__.__name__:
         from .tensorboard import callbacks as tb_cb
-        callbacks.update(tb_cb)
+        callbacks_list.append(tb_cb)
 
-    for k, v in callbacks.items():
-        if v not in instance.callbacks[k]:  # prevent duplicate callbacks addition
-            instance.callbacks[k].append(v)  # callback[name].append(func)
+    # Add the callbacks to the callbacks dictionary
+    for callbacks in callbacks_list:
+        for k, v in callbacks.items():
+            if v not in instance.callbacks[k]:
+                instance.callbacks[k].append(v)

ultralytics/utils/metrics.py

@@ -189,7 +189,7 @@ class ConfusionMatrix:
         self.task = task
         self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == 'detect' else np.zeros((nc, nc))
         self.nc = nc  # number of classes
-        self.conf = conf
+        self.conf = 0.25 if conf is None else conf  # argument may be None from default cfg
         self.iou_thres = iou_thres
 
     def process_cls_preds(self, preds, targets):

ultralytics/utils/plotting.py

@@ -12,11 +12,10 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
 
-from ultralytics.utils import LOGGER, TryExcept, plt_settings, threaded
+from ultralytics.utils import LOGGER, TryExcept, ops, plt_settings, threaded
 
 from .checks import check_font, check_version, is_ascii
 from .files import increment_path
-from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
 
 
 class Colors:
@@ -163,7 +162,7 @@ class Annotator:
         im_gpu = im_gpu * inv_alph_masks[-1] + mcs
         im_mask = (im_gpu * 255)
         im_mask_np = im_mask.byte().cpu().numpy()
-        self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
+        self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape)
         if self.pil:
             # Convert im back to PIL and update draw
             self.fromarray(self.im)
@@ -268,8 +267,9 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
     import pandas as pd
     import seaborn as sn
 
-    # Filter matplotlib>=3.7.2 warning
+    # Filter matplotlib>=3.7.2 warning and Seaborn use_inf and is_categorical FutureWarnings
     warnings.filterwarnings('ignore', category=UserWarning, message='The figure layout has changed to tight')
+    warnings.filterwarnings('ignore', category=FutureWarning)
 
     # Plot dataset labels
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
@@ -285,8 +285,8 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
     # Matplotlib labels
     ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
     y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    with contextlib.suppress(Exception):  # color histogram bars by class
-        [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)]  # known issue #3195
+    for i in range(nc):
+        y[2].patches[i].set_color([x / 255 for x in colors(i)])
     ax[0].set_ylabel('instances')
     if 0 < len(names) < 30:
         ax[0].set_xticks(range(len(names)))
@@ -298,7 +298,7 @@ def plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None):
 
     # Rectangles
     boxes[:, 0:2] = 0.5  # center
-    boxes = xywh2xyxy(boxes) * 1000
+    boxes = ops.xywh2xyxy(boxes) * 1000
     img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
     for cls, box in zip(cls[:500], boxes[:500]):
         ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
@@ -348,12 +348,12 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
 
     if not isinstance(xyxy, torch.Tensor):  # may be list
         xyxy = torch.stack(xyxy)
-    b = xyxy2xywh(xyxy.view(-1, 4))  # boxes
+    b = ops.xyxy2xywh(xyxy.view(-1, 4))  # boxes
     if square:
         b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
     b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
-    xyxy = xywh2xyxy(b).long()
-    clip_boxes(xyxy, im.shape)
+    xyxy = ops.xywh2xyxy(b).long()
+    ops.clip_boxes(xyxy, im.shape)
     crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
     if save:
         file.parent.mkdir(parents=True, exist_ok=True)  # make directory
@@ -425,7 +425,7 @@ def plot_images(images,
             classes = cls[idx].astype('int')
 
             if len(bboxes):
-                boxes = xywh2xyxy(bboxes[idx, :4]).T
+                boxes = ops.xywh2xyxy(bboxes[idx, :4]).T
                 labels = bboxes.shape[1] == 4  # labels if no conf column
                 conf = None if labels else bboxes[idx, 4]  # check for confidence presence (label vs pred)
 
@@ -554,7 +554,7 @@ def output_to_target(output, max_det=300):
     for i, o in enumerate(output):
         box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
         j = torch.full((conf.shape[0], 1), i)
-        targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
+        targets.append(torch.cat((j, cls, ops.xyxy2xywh(box), conf), 1))
     targets = torch.cat(targets, 0).numpy()
     return targets[:, 0], targets[:, 1], targets[:, 2:]
 

ultralytics/utils/tal.py

@@ -14,7 +14,7 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
     Select the positive anchor center in gt.
 
     Args:
-        xy_centers (Tensor): shape(h*w, 4)
+        xy_centers (Tensor): shape(h*w, 2)
         gt_bboxes (Tensor): shape(b, n_boxes, 4)
 
     Returns:
@@ -228,7 +228,7 @@ class TaskAlignedAssigner(nn.Module):
         target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes  # (b, h*w)
         target_labels = gt_labels.long().flatten()[target_gt_idx]  # (b, h*w)
 
-        # Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w)
+        # Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w, 4)
         target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
 
         # Assigned target scores