[Docs]: Link buttons, add autobackend, BaseModel and ops (#130)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/nn/tasks.py

@@ -17,11 +17,36 @@ from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_wei
 
 
 class BaseModel(nn.Module):
-    # YOLOv5 base model
+    '''
+     The BaseModel class is a base class for all the models in the Ultralytics YOLO family.
+    '''
+
     def forward(self, x, profile=False, visualize=False):
-        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+        """
+        > `forward` is a wrapper for `_forward_once` that runs the model on a single scale
+
+        Args:
+          x: the input image
+          profile: whether to profile the model. Defaults to False
+          visualize: if True, will return the intermediate feature maps. Defaults to False
+
+        Returns:
+          The output of the network.
+        """
+        return self._forward_once(x, profile, visualize)
 
     def _forward_once(self, x, profile=False, visualize=False):
+        """
+        > Forward pass of the network
+
+        Args:
+          x: input to the model
+          profile: if True, the time taken for each layer will be printed. Defaults to False
+          visualize: If True, it will save the feature maps of the model. Defaults to False
+
+        Returns:
+          The last layer of the model.
+        """
         y, dt = [], []  # outputs
         for m in self.model:
             if m.f != -1:  # if not from previous layer
@@ -36,6 +61,15 @@ class BaseModel(nn.Module):
         return x
 
     def _profile_one_layer(self, m, x, dt):
+        """
+        It takes a model, an input, and a list of times, and it profiles the model on the input, appending
+        the time to the list
+
+        Args:
+          m: the model
+          x: the input image
+          dt: list of time taken for each layer
+        """
         c = m == self.model[-1]  # is final layer, copy input as inplace fix
         o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
         t = time_sync()
@@ -48,7 +82,13 @@ class BaseModel(nn.Module):
         if c:
             LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
 
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+    def fuse(self):
+        """
+        > It takes a model and fuses the Conv2d() and BatchNorm2d() layers into a single layer
+
+        Returns:
+          The model is being returned.
+        """
         LOGGER.info('Fusing layers... ')
         for m in self.model.modules():
             if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
@@ -58,11 +98,27 @@ class BaseModel(nn.Module):
         self.info()
         return self
 
-    def info(self, verbose=False, imgsz=640):  # print model information
+    def info(self, verbose=False, imgsz=640):
+        """
+        Prints model information
+
+        Args:
+          verbose: if True, prints out the model information. Defaults to False
+          imgsz: the size of the image that the model will be trained on. Defaults to 640
+        """
         model_info(self, verbose, imgsz)
 
     def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        """
+        `_apply()` is a function that applies a function to all the tensors in the model that are not
+        parameters or registered buffers
+
+        Args:
+          fn: the function to apply to the model
+
+        Returns:
+          A model that is a Detect() object.
+        """
         self = super()._apply(fn)
         m = self.model[-1]  # Detect()
         if isinstance(m, (Detect, Segment)):
@@ -72,6 +128,12 @@ class BaseModel(nn.Module):
         return self
 
     def load(self, weights):
+        """
+        > This function loads the weights of the model from a file
+
+        Args:
+          weights: The weights to load into the model.
+        """
         # Force all tasks to implement this function
         raise NotImplementedError("This function needs to be implemented by derived classes!")