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98 lines
3.4 KiB
98 lines
3.4 KiB
""" Model / state_dict utils
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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from .model_ema import ModelEma
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import torch
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import fnmatch
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_SUB_MODULE_ATTR = ('module', 'model')
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def unwrap_model(model, recursive=True):
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for attr in _SUB_MODULE_ATTR:
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sub_module = getattr(model, attr, None)
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if sub_module is not None:
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return unwrap_model(sub_module) if recursive else sub_module
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return model
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def get_state_dict(model, unwrap_fn=unwrap_model):
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return unwrap_fn(model).state_dict()
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def avg_sq_ch_mean(model, input, output):
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"""calculate average channel square mean of output activations
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"""
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return torch.mean(output.mean(axis=[0, 2, 3]) ** 2).item()
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def avg_ch_var(model, input, output):
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"""calculate average channel variance of output activations"""
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return torch.mean(output.var(axis=[0, 2, 3])).item()
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def avg_ch_var_residual(model, input, output):
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"""calculate average channel variance of output activations"""
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return torch.mean(output.var(axis=[0, 2, 3])).item()
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class ActivationStatsHook:
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"""Iterates through each of `model`'s modules and matches modules using unix pattern
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matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is
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a match.
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Arguments:
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model (nn.Module): model from which we will extract the activation stats
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hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string
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matching with the name of model's modules.
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hook_fns (List[Callable]): List of hook functions to be registered at every
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module in `layer_names`.
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Inspiration from https://docs.fast.ai/callback.hook.html.
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Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example
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on how to plot Signal Propogation Plots using `ActivationStatsHook`.
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"""
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def __init__(self, model, hook_fn_locs, hook_fns):
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self.model = model
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self.hook_fn_locs = hook_fn_locs
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self.hook_fns = hook_fns
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if len(hook_fn_locs) != len(hook_fns):
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raise ValueError("Please provide `hook_fns` for each `hook_fn_locs`, \
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their lengths are different.")
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self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns)
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for hook_fn_loc, hook_fn in zip(hook_fn_locs, hook_fns):
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self.register_hook(hook_fn_loc, hook_fn)
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def _create_hook(self, hook_fn):
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def append_activation_stats(module, input, output):
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out = hook_fn(module, input, output)
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self.stats[hook_fn.__name__].append(out)
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return append_activation_stats
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def register_hook(self, hook_fn_loc, hook_fn):
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for name, module in self.model.named_modules():
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if not fnmatch.fnmatch(name, hook_fn_loc):
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continue
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module.register_forward_hook(self._create_hook(hook_fn))
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def extract_spp_stats(model,
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hook_fn_locs,
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hook_fns,
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input_shape=[8, 3, 224, 224]):
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"""Extract average square channel mean and variance of activations during
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forward pass to plot Signal Propogation Plots (SPP).
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Paper: https://arxiv.org/abs/2101.08692
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Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950
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"""
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x = torch.normal(0., 1., input_shape)
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hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns)
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_ = model(x)
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return hook.stats
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