Add docstrings to YOLOv5 functions (#12760)

* Add docstrings to top level files * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Update activations.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-02-25 14:04:01 +01:00
1 parent ca00c34c2a
commit 41603da162
@@ -19,6 +19,7 @@ class Sum(nn.Module):
            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights

    def forward(self, x):
+        """Processes input through a customizable weighted sum of `n` inputs, optionally applying learned weights."""
        y = x[0]  # no weight
        if self.weight:
            w = torch.sigmoid(self.w) * 2
@@ -53,15 +54,21 @@ class MixConv2d(nn.Module):
        self.act = nn.SiLU()

    def forward(self, x):
+        """Performs forward pass by applying SiLU activation on batch-normalized concatenated convolutional layer
+        outputs.
+        """
        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))


 class Ensemble(nn.ModuleList):
-    # Ensemble of models
+    """Ensemble of models."""
+
    def __init__(self):
+        """Initializes an ensemble of models to be used for aggregated predictions."""
        super().__init__()

    def forward(self, x, augment=False, profile=False, visualize=False):
+        """Performs forward pass aggregating outputs from an ensemble of models.."""
        y = [module(x, augment, profile, visualize)[0] for module in self]
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.stack(y).mean(0)  # mean ensemble
@@ -70,7 +77,11 @@ class Ensemble(nn.ModuleList):


 def attempt_load(weights, device=None, inplace=True, fuse=True):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    """
+    Loads and fuses an ensemble or single YOLOv5 model from weights, handling device placement and model adjustments.
+
+    Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a.
+    """
    from models.yolo import Detect, Model

    model = Ensemble()