nn_module#

class nn_module#

Bases: Module

This class serves as a wrapper for all the mltoolbox models.

The steps to extend the mltoolbox with an additional model: 1. Extend the nn_module class, implementing the forward method. 2. Register the new class to DNNFactory using the @DNNFactory.register decoration

Example

import torch.nn as nn from pyhelayers.mltoolbox.model.nn_module import nn_module from pyhelayers.mltoolbox.model.DNN_factory import DNNFactory

@DNNFactory.register(‘new_model’) class newModel(nn_module):

def __init__(self, **kwargs):
super().__init__()

#define your torch.nn model self.cnn = …

def forward(self, x):
super().forward(x) x = self.cnn(x) return x

Add an import to the new class in your main (so that the new class gets registered on start)

Example

import newModel

__init__()#: Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

`__init__`()	Initializes internal Module state, shared by both nn.Module and ScriptModule.
`addBatchNormAfterActivation`(bn_info)	Adds batch normalization after specific layers, specified by the user
`add_batch_norm_after_conv`()	Adds batch normalization after each convolutional layer
`add_batch_norm_before_conv`()	Adds batch normalization before each convolutional layer, except for the first one
`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.
`assertSize`(x)	Asserts the required image size for the model
`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_range_awareness_loss`()
`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`(x)	Defines the computation performed at every call.
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`()	Returns any extra state to include in the module's state_dict.
`get_input_size`()	Returns the input size expected by the model
`get_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_pooling_by_type`(type)
`get_submodule`(target)	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`init_actual_data_range`()
`ipu`([device])	Moves all model parameters and buffers to the IPU.
`load_state_dict`(state_dict[, strict])	Copies parameters and buffers from `state_dict` into this module and its descendants.
`make_fhe_friendly`(add_bn[, pooling_type, ...])	Applies changes to the given model towards making it FHE-Friendly.
`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, remove_duplicate])	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.
`post_process_activations`()
`post_process_weighted_relu_act`()	Replaces each WeightedRelu activation in the given model, by the WeightedRelu.activation:
`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_load_state_dict_post_hook`(hook)	Registers a post hook to be run after module's `load_state_dict` is called.
`register_module`(name, module)	Alias for `add_module()`.
`register_parameter`(name, param)	Adds a parameter to the module.
`remove_range_awareness`()
`requires_grad_`([requires_grad])	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	This function is called from `load_state_dict()` to handle any extra state found within the state_dict.
`set_max_pooling_to_avg`()	Replaces each max-pooling by an average-pooling
`share_memory`()	See `torch.Tensor.share_memory_()`
`state_dict`(*args[, destination, prefix, ...])	Returns a dictionary containing references to the whole state of the module.
`to`(args, *kwargs)	Moves and/or casts the parameters and buffers.
`to_empty`(*, device)	Moves the parameters and buffers to the specified device without copying storage.
`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.

Attributes

`T_destination`
`dump_patches`

addBatchNormAfterActivation(bn_info)#

Adds batch normalization after specific layers, specified by the user

Parameters:: bn_info (list[bn_info]) – list of layers after which batch normalization should be added

add_batch_norm_after_conv()#: Adds batch normalization after each convolutional layer

add_batch_norm_before_conv()#: Adds batch normalization before each convolutional layer, except for the first one

assertSize(x: tensor)#

Asserts the required image size for the model

Parameters:: x (tensor) – data tensor
Raises:: AssertionError – Please resize the images to the matching size

class bn_info(after_layer_name: str, channels: int)#

Bases: object

Defines a layer data element for adding batch normalization after a layer

get_input_size()#

Returns the input size expected by the model

Returns:: the input size expected by the model: (channels, height, width)
Return type:: tuple

make_fhe_friendly(add_bn: bool, pooling_type: str = 'max', bn_list=None)#

Applies changes to the given model towards making it FHE-Friendly. This is the first step. The model may still contain Relu activations after the call to this function.

Parameters:

add_bn (bool) – If True batch normalization will be added
pooling_type (str, optional) – Required pooling type: ‘avg’ or ‘max’. Average pooling is considered FHE-Friendly, Max is not. Defaults to ‘max’.
bn_list (list[bn_info], optional) – If not None - the batch normalization will be added after the specified layers, if None the batch normalization will be added after each convolutional layer . Defaults to None.

post_process_weighted_relu_act()#

Replaces each WeightedRelu activation in the given model, by the WeightedRelu.activation:

Example

WeightedRelu( ratio=1.0: (activation): TrainablePolyReLU(coefs=[0.09679805487394333, 0.13760216534137726]))

will be replaced by the plain

TrainablePolyReLU(coefs=[0.09679805487394333, 0.13760216534137726])

set_max_pooling_to_avg()#: Replaces each max-pooling by an average-pooling