Update Actions with Lychee and GitHub Token (#12592)

* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/segment/plots.py

@@ -14,7 +14,7 @@ from ..plots import Annotator, colors
 
 
 @threaded
-def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):
+def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None):
     # Plot image grid with labels
     if isinstance(images, torch.Tensor):
         images = images.cpu().float().numpy()
@@ -27,7 +27,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
     max_subplots = 16  # max image subplots, i.e. 4x4
     bs, _, h, w = images.shape  # batch size, _, height, width
     bs = min(bs, max_subplots)  # limit plot images
-    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
+    ns = np.ceil(bs**0.5)  # number of subplots (square)
     if np.max(images[0]) <= 1:
         images *= 255  # de-normalise (optional)
 
@@ -38,7 +38,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
             break
         x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
         im = im.transpose(1, 2, 0)
-        mosaic[y:y + h, x:x + w, :] = im
+        mosaic[y : y + h, x : x + w, :] = im
 
     # Resize (optional)
     scale = max_size / ns / max(h, w)
@@ -60,7 +60,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
             ti = targets[idx]  # image targets
 
             boxes = xywh2xyxy(ti[:, 2:6]).T
-            classes = ti[:, 1].astype('int')
+            classes = ti[:, 1].astype("int")
             labels = ti.shape[1] == 6  # labels if no conf column
             conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
 
@@ -77,7 +77,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                 color = colors(cls)
                 cls = names[cls] if names else cls
                 if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+                    label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}"
                     annotator.box_label(box, label, color=color)
 
             # Plot masks
@@ -103,41 +103,44 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
                         else:
                             mask = image_masks[j].astype(bool)
                         with contextlib.suppress(Exception):
-                            im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
+                            im[y : y + h, x : x + w, :][mask] = (
+                                im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
+                            )
                 annotator.fromarray(im)
     annotator.im.save(fname)  # save
 
 
-def plot_results_with_masks(file='path/to/results.csv', dir='', best=True):
+def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
     # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
     save_dir = Path(file).parent if file else Path(dir)
     fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
     ax = ax.ravel()
-    files = list(save_dir.glob('results*.csv'))
-    assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
+    files = list(save_dir.glob("results*.csv"))
+    assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
     for f in files:
         try:
             data = pd.read_csv(f)
-            index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
-                              0.1 * data.values[:, 11])
+            index = np.argmax(
+                0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11]
+            )
             s = [x.strip() for x in data.columns]
             x = data.values[:, 0]
             for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
                 y = data.values[:, j]
                 # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2)
+                ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
                 if best:
                     # best
-                    ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3)
-                    ax[i].set_title(s[j] + f'\n{round(y[index], 5)}')
+                    ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
+                    ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
                 else:
                     # last
-                    ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3)
-                    ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}')
+                    ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
+                    ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
         except Exception as e:
-            print(f'Warning: Plotting error for {f}: {e}')
+            print(f"Warning: Plotting error for {f}: {e}")
     ax[1].legend()
-    fig.savefig(save_dir / 'results.png', dpi=200)
+    fig.savefig(save_dir / "results.png", dpi=200)
     plt.close()