meddlr.metrics.MetricCollection

class meddlr.metrics.MetricCollection(metrics: Union[Metric, Sequence[Metric], Dict[str, Metric]], *additional_metrics: Metric, prefix: Optional[str] = None, postfix: Optional[str] = None)

The class that manages multiple metrics.

__init__(metrics: Union[Metric, Sequence[Metric], Dict[str, Metric]], *additional_metrics: Metric, prefix: Optional[str] = None, postfix: Optional[str] = None) → None

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

Methods

__init__(metrics, *additional_metrics[, ...])	Initializes internal Module state, shared by both nn.Module and ScriptModule.
add_metrics(metrics, *additional_metrics)	Add new metrics to Metric Collection.
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()	Remove all items from the ModuleDict.
clone([prefix, postfix])	Make a copy of the metric collection :param prefix: a string to append in front of the metric keys :param postfix: a string to append after the keys of the output dict
compute()	Compute the result for each metric in the collection.
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)	Iteratively call forward for each metric.
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.
ids([sync_dist])
ipu([device])	Moves all model parameters and buffers to the IPU.
items([keep_base, copy_state])	Return an iterable of the ModuleDict key/value pairs.
keys([keep_base])	Return an iterable of the ModuleDict key.
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, 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.
persistent([mode])	Method for post-init to change if metric states should be saved to its state_dict.
pop(key)	Remove key from the ModuleDict and return its module.
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.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
reset()	Iteratively call reset for each metric.
scan_summary(scan_id[, delimiter])	Get summary of results for a scan.
scans()
set_dtype(dst_type)	Transfer all metric state to specific dtype.
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.
summary([sync_dist])	Get summary of results overall scans.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
to_dict([group_by, sync_dist])
to_empty(*, device)	Moves the parameters and buffers to the specified device without copying storage.
to_pandas([sync_dist])
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
update(*args, **kwargs)	Iteratively call update for each metric.
values([copy_state])	Return an iterable of the ModuleDict values.
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	alias of TypeVar('T_destination', bound=Dict[str, Any])
call_super_init
compute_groups	Return a dict with the current compute groups in the collection.
dump_patches