ultralytics 8.0.90 actions and docs improvements (#2326)

Co-authored-by: calmisential <xinyu_std@163.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: triple Mu <gpu@163.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: ran xiao <ben.xiao@me.com>
Co-authored-by: rxiao <ran.xiao@silverpond.com.au>

ultralytics/yolo/utils/metrics.py

@@ -205,7 +205,7 @@ class FocalLoss(nn.Module):
             return loss.mean()
         elif self.reduction == 'sum':
             return loss.sum()
-        else:  # 'none'
+        else:  # 'None'
             return loss
 
 

ultralytics/yolo/utils/ops.py

@@ -148,7 +148,7 @@ def non_max_suppression(
     Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
 
     Arguments:
-        prediction (torch.Tensor): A tensor of shape (batch_size, num_boxes, num_classes + 4 + num_masks)
+        prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
             containing the predicted boxes, classes, and masks. The tensor should be in the format
             output by a model, such as YOLO.
         conf_thres (float): The confidence threshold below which boxes will be filtered out.

ultralytics/yolo/utils/plotting.py

@@ -469,3 +469,39 @@ def output_to_target(output, max_det=300):
         targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
     targets = torch.cat(targets, 0).numpy()
     return targets[:, 0], targets[:, 1], targets[:, 2:]
+
+
+def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
+    """
+    Visualize feature maps of a given model module during inference.
+
+    Args:
+        x (torch.Tensor): Features to be visualized.
+        module_type (str): Module type.
+        stage (int): Module stage within the model.
+        n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
+        save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
+
+    Returns:
+        None: This function does not return any value; it saves the visualization to the specified directory.
+    """
+    for m in ['Detect', 'Pose', 'Segment']:
+        if m in module_type:
+            return
+    batch, channels, height, width = x.shape  # batch, channels, height, width
+    if height > 1 and width > 1:
+        f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png"  # filename
+
+        blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels
+        n = min(n, channels)  # number of plots
+        fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols
+        ax = ax.ravel()
+        plt.subplots_adjust(wspace=0.05, hspace=0.05)
+        for i in range(n):
+            ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
+            ax[i].axis('off')
+
+        LOGGER.info(f'Saving {f}... ({n}/{channels})')
+        plt.savefig(f, dpi=300, bbox_inches='tight')
+        plt.close()
+        np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy())  # npy save