Add docstrings and improve comments (#11229)

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

docs/en/yolov5/tutorials/architecture_description.md

@@ -38,12 +38,14 @@ import torch.nn as nn
 
 class SPP(nn.Module):
     def __init__(self):
+        """Initializes an SPP module with three different sizes of max pooling layers."""
         super().__init__()
         self.maxpool1 = nn.MaxPool2d(5, 1, padding=2)
         self.maxpool2 = nn.MaxPool2d(9, 1, padding=4)
         self.maxpool3 = nn.MaxPool2d(13, 1, padding=6)
 
     def forward(self, x):
+        """Applies three max pooling layers on input `x` and concatenates results along channel dimension."""
         o1 = self.maxpool1(x)
         o2 = self.maxpool2(x)
         o3 = self.maxpool3(x)
@@ -52,10 +54,12 @@ class SPP(nn.Module):
 
 class SPPF(nn.Module):
     def __init__(self):
+        """Initializes an SPPF module with a specific configuration of MaxPool2d layer."""
         super().__init__()
         self.maxpool = nn.MaxPool2d(5, 1, padding=2)
 
     def forward(self, x):
+        """Applies sequential max pooling and concatenates results with input tensor; expects input tensor x of any shape."""
         o1 = self.maxpool(x)
         o2 = self.maxpool(o1)
         o3 = self.maxpool(o2)
@@ -63,6 +67,7 @@ class SPPF(nn.Module):
 
 
 def main():
+    """Compares outputs and performance of SPP and SPPF on a random tensor (8, 32, 16, 16)."""
     input_tensor = torch.rand(8, 32, 16, 16)
     spp = SPP()
     sppf = SPPF()

docs/en/yolov5/tutorials/hyperparameter_evolution.md

@@ -65,7 +65,7 @@ Fitness is the value we seek to maximize. In YOLOv5 we define a default fitness
 
 ```python
 def fitness(x):
-    # Model fitness as a weighted combination of metrics
+    """Evaluates model fitness by summing weighted metrics [P, R, mAP@0.5, mAP@0.5:0.95], x is a numpy array of shape (n, 4)."""
     w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
     return (x[:, :4] * w).sum(1)
 ```

docs/en/yolov5/tutorials/pytorch_hub_model_loading.md

@@ -179,6 +179,7 @@ import threading
 
 
 def run(model, im):
+    """Performs inference on an image using a given model and saves the output; model must support `.save()` method."""
     results = model(im)
     results.save()