Hooks

Hooks are very useful to inspect what is happening during the forward and backward passes such as computing stats of the activations and gradients.

The module contains the following classes:

• Hook: Registers forward or backward hook for a single module
• Hooks: Registers forward or backward hook for multiple modules
• HooksCallback: Use callbacks to register and manage hooks
• ActivationStats: Computes means/stds for either activation or gradients and plot the computed stats.

ActivationStats

Bases: HooksCallback

Plot the means, std, histogram, and dead activations of all modules' activations if is_forward else gradients.

__init__(modules=None, is_forward=True, bins=40, bins_range=(0, 10))

Parameters:

Name	Type	Description	Default
modules	Module | Iterable[Module] | None	Modules to register the hook on. Default to all modules.	None
is_forward	bool	Whether to register func as a forward or backward hook.	True
bins	int	Number of histogram bins.	40
bins_range	Iterable	Lower/Upper end of the histogram's bins range.	(0, 10)

dead_chart(bins_range, figsize=(11, 5))

Plot a line chart of the "dead" activations percentage from all activations.

Parameters:

Name	Type	Description	Default
bins_range	list | tuple	Bins range around zero. Bins that are considered dead activations/gradients.	required
figsize	tuple	Width, height of the figure.	(11, 5)

plot_hist(figsize=(11, 5))

Plot histogram of activations/gradients as a heatmap.

Parameters:

Name	Type	Description	Default
figsize	(tuple, de)	Width, height of the heatmap figure.	(11, 5)

plot_stats(figsize=(10, 4))

Plot means of standard deviation of activations/gradients for each layer that has a registered hook.

Parameters:

Name	Type	Description	Default
figsize	tuple	Width, height of the figure.	(10, 4)

Hook

Register either a forward or a backward hook on the module.

__init__(module, func, is_forward=True, **kwargs)

Parameters:

Name	Type	Description	Default
module	Module	The module to register the hook on.	required
func	Callable	The hook to be registered.	required
is_forward	bool	Whether to register func as a forward or backward hook.	True

Hooks

Register hooks on all modules.

__init__(modules, func, is_forward=True, **kwargs)

Parameters:

Name	Type	Description	Default
modules	Module or Iterable[Module]	The module to register the hook on.	required
func	Callable	The hook to be registered.	required
is_forward	bool	Whether to register func as a forward or backward hook.	True

HooksCallback

Bases: Callback

Base class to run hooks on modules as a callback.

__init__(hookfunc, on_train=True, on_valid=False, modules=None, is_forward=True)

Parameters:

Name	Type	Description	Default
hookfunc	Callable	The hook to be registered.	required
on_train	bool	Whether to run the hook on modules during training.	True
on_valid	bool	Whether to run the hook on modules during validation.	False
modules	Module | Iterable[Module] | None	Modules to register the hook on. Default to all modules.	None
is_forward	bool	Whether to register func as a forward or backward hook.	True

compute_stats(hook, module, inp, outp, bins=40, bins_range=(0, 10))

Compute the means, std, and histogram of module activations/gradients and the hook stats.

Parameters:

Name	Type	Description	Default
hook	Hook	Registered hook on the provided module.	required
module	Module	Module to compute the stats on.	required
inp	Tensor	Input of the module.	required
outp	Tensor	Output of the module.	required
bins	int	Number of histogram bins.	40
bins_range	Iterable	lower/upper end of the histogram's bins range.	(0, 10)

get_hist(hook)

Return matrix-ready for plotting heatmap of activations/gradients.

get_min(hook, bins_range)

Compute the percentage of activations/gradients around zero from hook's histogram matrix.

Parameters:

Name	Type	Description	Default
hook	Hook	Hook that has the stats of the activations	required
bins_range	list | tuple	Bins range around zero.	required

Returns:

Type	Description
Tensor	Percentage of the activations around zero.