Tweak a few docstrings

Author: rwightman

timm/data/auto_augment.py

@@ -584,22 +584,22 @@ def __repr__(self):
 
 
 def auto_augment_transform(config_str: str, hparams: Optional[Dict] = None):
-    """
-    Create a AutoAugment transform
+    """ Create a AutoAugment transform
 
     Args:
         config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
             dashes ('-').
             The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
-
-            The remaining sections:
-                'mstd' -  float std deviation of magnitude noise applied
-            Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
-
+            While the remaining sections define other arguments
+                * 'mstd' -  float std deviation of magnitude noise applied
         hparams: Other hparams (kwargs) for the AutoAugmentation scheme
 
     Returns:
          A PyTorch compatible Transform
+
+    Examples::
+
+        'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
     """
     config = config_str.split('-')
     policy_name = config[0]
@@ -764,27 +764,30 @@ def rand_augment_transform(
         hparams: Optional[Dict] = None,
         transforms: Optional[Union[str, Dict, List]] = None,
 ):
-    """
-    Create a RandAugment transform
+    """ Create a RandAugment transform
 
     Args:
         config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated
             by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand').
-            The remaining sections, not order sepecific determine
-                'm' - integer magnitude of rand augment
-                'n' - integer num layers (number of transform ops selected per image)
-                'p' - float probability of applying each layer (default 0.5)
-                'mstd' -  float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
-                'mmax' - set upper bound for magnitude to something other than default of  _LEVEL_DENOM (10)
-                'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
-                't' - str name of transform set to use
-            Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
-            'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2
-
+            The remaining sections, not order specific determine
+                * 'm' - integer magnitude of rand augment
+                * 'n' - integer num layers (number of transform ops selected per image)
+                * 'p' - float probability of applying each layer (default 0.5)
+                * 'mstd' -  float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
+                * 'mmax' - set upper bound for magnitude to something other than default of  _LEVEL_DENOM (10)
+                * 'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
+                * 't' - str name of transform set to use
         hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme
 
     Returns:
          A PyTorch compatible Transform
+
+    Examples::
+
+        'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
+
+        'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2
+
     """
     magnitude = _LEVEL_DENOM  # default to _LEVEL_DENOM for magnitude (currently 10)
     num_layers = 2  # default to 2 ops per image

timm/data/transforms_factory.py

@@ -114,10 +114,10 @@ def transforms_imagenet_train(
 
     Returns:
         If separate==True, the transforms are returned as a tuple of 3 separate transforms
-        for use in a mixing dataset that passes
-         * all data through the first (primary) transform, called the 'clean' data
-         * a portion of the data through the secondary transform
-         * normalizes and converts the branches above with the third, final transform
+          for use in a mixing dataset that passes
+            * all data through the first (primary) transform, called the 'clean' data
+            * a portion of the data through the secondary transform
+            * normalizes and converts the branches above with the third, final transform
     """
     train_crop_mode = train_crop_mode or 'rrc'
     assert train_crop_mode in {'rrc', 'rkrc', 'rkrr'}

timm/utils/model.py

@@ -96,7 +96,7 @@ def extract_spp_stats(
         hook_fns,
         input_shape=[8, 3, 224, 224]):
     """Extract average square channel mean and variance of activations during 
-    forward pass to plot Signal Propogation Plots (SPP).
+        forward pass to plot Signal Propogation Plots (SPP).
     
     Paper: https://arxiv.org/abs/2101.08692
 
@@ -111,7 +111,8 @@ def extract_spp_stats(
 def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, mode='freeze'):
     """
     Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is
-    done in place.
+        done in place.
+
     Args:
         root_module (nn.Module, optional): Root module relative to which the `submodules` are referenced.
         submodules (list[str]): List of modules for which the parameters will be (un)frozen. They are to be provided as
@@ -180,6 +181,7 @@ def _add_submodule(module, name, submodule):
 def freeze(root_module, submodules=[], include_bn_running_stats=True):
     """
     Freeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+
     Args:
         root_module (nn.Module): Root module relative to which `submodules` are referenced.
         submodules (list[str]): List of modules for which the parameters will be frozen. They are to be provided as
@@ -214,6 +216,7 @@ def freeze(root_module, submodules=[], include_bn_running_stats=True):
 def unfreeze(root_module, submodules=[], include_bn_running_stats=True):
     """
     Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+
     Args:
         root_module (nn.Module): Root module relative to which `submodules` are referenced.
         submodules (list[str]): List of submodules for which the parameters will be (un)frozen. They are to be provided