API

Extractor

class torchextractor.Extractor(model: torch.nn.modules.module.Module, module_names: Optional[Iterable[str]] = None, module_filter_fn: Optional[Callable] = None, capture_fn: Optional[Callable] = None)

Bases: torch.nn.modules.module.Module

Capture the intermediate feature maps of of model.

Parameters

model: nn.Module,

The model to extract features from.

module_names: list of str, default None

The fully qualified names of the modules producing the relevant feature maps.

module_filter_fn: callable, default None

A filtering function. Takes a module and module name as input and returns True for modules producing the relevant features. Either module_names or module_filter_fn should be provided but not both at the same time.

Example:

def module_filter_fn(module, name):
    return isinstance(module, torch.nn.Conv2d)

capture_fn: callable, default None

Operation to carry at each forward pass. The function should comply to the following interface.

Example:

def capture_fn(
        module: nn.Module,
        input: Any,
        output: Any,
        module_name:str,
        feature_maps: Dict[str, Any]
    ):
    feature_maps[module_name] = output

Methods

T_destination
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
clear_placeholder()	Resets the structure holding captured feature maps.
collect()	Returns the structure holding the most recent feature maps.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(*args, **kwargs)	Performs model computations and collects feature maps
half()	Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_full_backward_hook(hook)	Registers a backward hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Moves all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.

__call__
share_memory

Utils

torchextractor.list_module_names(model: torch.nn.modules.module.Module) → List[str]

List names of modules and submodules.

Parameters

model: nn.Module

PyTorch model to examine.

Returns

list[str]:

List of names

torchextractor.find_modules_by_names(model: torch.nn.modules.module.Module, names: Iterable[str]) → Dict[str, torch.nn.modules.module.Module]

Find some modules given their fully qualifying names.

Parameters

model: nn.Module

PyTorch model to examine.

names: list of str

List of fully qualifying names.

Returns

dict: name -> module

If no match is found for a name, it is not added to the returned structure