pytorch_pfn_extras.nn.modules.extended_sequential.ExtendedSequential#

class pytorch_pfn_extras.nn.modules.extended_sequential.ExtendedSequential(*args: Module)#

class pytorch_pfn_extras.nn.modules.extended_sequential.ExtendedSequential(arg: OrderedDict[str, Module])

Bases: Sequential

Sequential module with extended features from chainer.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Methods

`__init__`(*args)	Initializes internal Module state, shared by both nn.Module and ScriptModule.
`add_module`(name, module)	Adds a child module to the current module.
`append`(module)	Appends a given module to the end.
`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.
`compile`(args, *kwargs)	Compile this Module's forward using `torch.compile()`.
`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.
`extend`(sequential)
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(input)	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_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`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.
`insert`(index, module)
`ipu`([device])	Moves all model parameters and buffers to the IPU.
`load_state_dict`(state_dict[, strict, assign])	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, 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.
`pop`(key)
`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, *[, prepend, ...])	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[, prepend])	Registers a backward hook on the module.
`register_full_backward_pre_hook`(hook[, prepend])	Registers a backward pre-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.
`register_state_dict_pre_hook`(hook)	These hooks will be called with arguments: `self`, `prefix`, and `keep_vars` before calling `state_dict` on `self`.
`repeat`(n_repeat[, mode])	Repeats this Sequential multiple times.
`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.
`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[, recurse])	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])	Resets gradients of all model parameters.

Attributes

`T_destination`	alias of TypeVar('T_destination', bound=`Dict`[`str`, `Any`])
`call_super_init`
`dump_patches`

repeat(n_repeat, mode='init')#

Repeats this Sequential multiple times.

This method returns a Sequential object which has original Sequential multiple times repeatedly. The mode argument means how to copy this sequential to repeat.

The functions is supposed to behave the same way as repeat in chainer.

When the mode is set to init, the default value, modules will be copied and reinitialized by calling reset_parameters (or _reset_parameters) method.

To repeat user-defined modules, which have parameters or buffers, with mode=``init`` in this Sequential, you need to implement the reset_parameters or _reset_parameters method to the module to reinitialize parameters and (if necessary) buffers; otherwise the initialization cannot be performed and a warning message will be shown.

Parameters:

n_repeat (int) – Number of times to repeat.
mode (str) – It should be either init, copy, or share. init means parameters of each repeated element in the returned Sequential will be re-initialized, so that all elements have different initial parameters. copy means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initial parameters but can be changed independently. share means all the elements which consist the resulting Sequential object are same object because they are shallow-copied, so that all parameters of elements are shared with each other.

Return type:

ExtendedSequential

training: bool#