MaskRCNNLoss
- class hybrid_learning.concepts.train_eval.kpis.batch_kpis.MaskRCNNLoss[source]
Bases:
ModuleLoss and associated data for a standard Mask R-CNN model.
Public Data Attributes:
Public Methods:
forward(*args)Calculate loss from output of the standard pytorch Mask R-CNN model in train mode.
Inherited from : py: class:Module
forward(*args)Calculate loss from output of the standard pytorch Mask R-CNN model in train mode.
register_buffer(name, tensor[, persistent])Adds a buffer to the module.
register_parameter(name, param)Adds a parameter to the module.
add_module(name, module)Adds a child module to the current module.
get_submodule(target)Returns the submodule given by
targetif it exists, otherwise throws an error.get_parameter(target)Returns the parameter given by
targetif it exists, otherwise throws an error.get_buffer(target)Returns the buffer given by
targetif it exists, otherwise throws an error.apply(fn)Applies
fnrecursively to every submodule (as returned by.children()) as well as self.cuda([device])Moves all model parameters and buffers to the GPU.
xpu([device])Moves all model parameters and buffers to the XPU.
cpu()Moves all model parameters and buffers to the CPU.
type(dst_type)Casts all parameters and buffers to
dst_type.float()Casts all floating point parameters and buffers to
floatdatatype.double()Casts all floating point parameters and buffers to
doubledatatype.half()Casts all floating point parameters and buffers to
halfdatatype.bfloat16()Casts all floating point parameters and buffers to
bfloat16datatype.to_empty(*, device)Moves the parameters and buffers to the specified device without copying storage.
to(*args, **kwargs)Moves and/or casts the parameters and buffers.
register_backward_hook(hook)Registers a backward hook on the module.
register_full_backward_hook(hook)Registers a backward hook on the module.
register_forward_pre_hook(hook)Registers a forward pre-hook on the module.
register_forward_hook(hook)Registers a forward hook on the module.
state_dict([destination, prefix, keep_vars])Returns a dictionary containing a whole state of the module.
load_state_dict(state_dict[, strict])Copies parameters and buffers from
state_dictinto this module and its descendants.parameters([recurse])Returns an iterator over module parameters.
named_parameters([prefix, recurse])Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
buffers([recurse])Returns an iterator over module buffers.
named_buffers([prefix, recurse])Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
children()Returns an iterator over immediate children modules.
named_children()Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
modules()Returns an iterator over all modules in the network.
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.
train([mode])Sets the module in training mode.
eval()Sets the module in evaluation mode.
requires_grad_([requires_grad])Change if autograd should record operations on parameters in this module.
zero_grad([set_to_none])Sets gradients of all model parameters to zero.
share_memory()extra_repr()Set the extra representation of the module
Special Methods:
Inherited from : py: class:Module
__init__()Initializes internal Module state, shared by both nn.Module and ScriptModule.
__call__(*input, **kwargs)Call self as a function.
__setstate__(state)__getattr__(name)__setattr__(name, value)Implement setattr(self, name, value).
__delattr__(name)Implement delattr(self, name).
__repr__()Return repr(self).
__dir__()Default dir() implementation.
- forward(*args)[source]
Calculate loss from output of the standard pytorch Mask R-CNN model in train mode. The model is assumed to provide an output format as is expected in the original pytorch source code which is a dict of loss values for each optimization aspect.