Add to extract stats for SPP

pull/525/head
Aman Arora 4 years ago
parent a2727c1bf7
commit 20626e8387

@ -3,6 +3,7 @@
Hacked together by / Copyright 2020 Ross Wightman Hacked together by / Copyright 2020 Ross Wightman
""" """
from .model_ema import ModelEma from .model_ema import ModelEma
import torch
def unwrap_model(model): def unwrap_model(model):
@ -14,3 +15,66 @@ def unwrap_model(model):
def get_state_dict(model, unwrap_fn=unwrap_model): def get_state_dict(model, unwrap_fn=unwrap_model):
return unwrap_fn(model).state_dict() 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
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