diff --git a/timm/utils/model.py b/timm/utils/model.py
index cfd42806..856bb981 100644
--- a/timm/utils/model.py
+++ b/timm/utils/model.py
@@ -3,6 +3,7 @@
 Hacked together by / Copyright 2020 Ross Wightman
 """
 from .model_ema import ModelEma
+import torch 
 
 
 def unwrap_model(model):
@@ -14,3 +15,66 @@ def unwrap_model(model):
 
 def get_state_dict(model, unwrap_fn=unwrap_model):
     return unwrap_fn(model).state_dict()
+
+
+def avg_sq_ch_mean(model, input, output): 
+    "calculate average channel square mean of output activations"
+    return torch.mean(output.mean(axis=[0,2,3])**2).item()
+
+
+def avg_ch_var(model, input, output): 
+    "calculate average channel variance of output activations"
+    return torch.mean(output.var(axis=[0,2,3])).item()
+
+
+class ActivationStatsHook:
+    """Iterates through each of `model`'s modules and if module's class name 
+    is present in `layer_names` then registers `hook_fns` inside that module
+    and stores activation stats inside `self.stats`.
+
+    Arguments:
+        model (nn.Module): model from which we will extract the activation stats
+        layer_names (List[str]): The layer name to look for to register forward 
+            hook. Example, `BasicBlock`, `Bottleneck`
+        hook_fns (List[Callable]): List of hook functions to be registered at every
+            module in `layer_names`.
+    
+    Inspiration from https://docs.fast.ai/callback.hook.html.
+    """
+
+    def __init__(self, model, layer_names, hook_fns=[avg_sq_ch_mean, avg_ch_var]):
+        self.model = model
+        self.layer_names = layer_names 
+        self.hook_fns = hook_fns
+        self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns)
+        for hook_fn in hook_fns: 
+            self.register_hook(layer_names, hook_fn)
+
+    def _create_hook(self, hook_fn):
+        def append_activation_stats(module, input, output):
+            out = hook_fn(module, input, output)
+            self.stats[hook_fn.__name__].append(out)
+        return append_activation_stats
+        
+    def register_hook(self, layer_names, hook_fn):
+        for layer in self.model.modules():
+            layer_name = layer.__class__.__name__
+            if layer_name not in layer_names: 
+                continue
+            layer.register_forward_hook(self._create_hook(hook_fn))
+
+
+def extract_spp_stats(model, 
+                      layer_names, 
+                      hook_fns=[avg_sq_ch_mean, avg_ch_var], 
+                      input_shape=[8, 3, 224, 224]):
+    """Extract average square channel mean and variance of activations during 
+    forward pass to plot Signal Propogation Plots (SPP).
+    
+    Paper: https://arxiv.org/abs/2101.08692
+    """ 
+    x = torch.normal(0., 1., input_shape)
+    hook = ActivationStatsHook(model, layer_names, hook_fns)
+    _ = model(x)
+    return hook.stats
+    
\ No newline at end of file