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| """ Model / state_dict utils | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| from .model_ema import ModelEma | |
| import torch | |
| import fnmatch | |
| def unwrap_model(model): | |
| if isinstance(model, ModelEma): | |
| return unwrap_model(model.ema) | |
| else: | |
| return model.module if hasattr(model, 'module') else 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()\ | |
| def avg_ch_var_residual(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 matches modules using unix pattern | |
| matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is | |
| a match. | |
| Arguments: | |
| model (nn.Module): model from which we will extract the activation stats | |
| hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string | |
| matching with the name of model's modules. | |
| 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. | |
| Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example | |
| on how to plot Signal Propogation Plots using `ActivationStatsHook`. | |
| """ | |
| def __init__(self, model, hook_fn_locs, hook_fns): | |
| self.model = model | |
| self.hook_fn_locs = hook_fn_locs | |
| self.hook_fns = hook_fns | |
| if len(hook_fn_locs) != len(hook_fns): | |
| raise ValueError("Please provide `hook_fns` for each `hook_fn_locs`, \ | |
| their lengths are different.") | |
| self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns) | |
| for hook_fn_loc, hook_fn in zip(hook_fn_locs, hook_fns): | |
| self.register_hook(hook_fn_loc, 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, hook_fn_loc, hook_fn): | |
| for name, module in self.model.named_modules(): | |
| if not fnmatch.fnmatch(name, hook_fn_loc): | |
| continue | |
| module.register_forward_hook(self._create_hook(hook_fn)) | |
| def extract_spp_stats(model, | |
| hook_fn_locs, | |
| 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). | |
| Paper: https://arxiv.org/abs/2101.08692 | |
| Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 | |
| """ | |
| x = torch.normal(0., 1., input_shape) | |
| hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns) | |
| _ = model(x) | |
| return hook.stats | |