Classification

ClassificationNetworkBasePyTorch

class ClassificationNetworkBasePyTorch(in_channels: int, n_outputs: int, **kwargs)[source]

Bases: delira.models.abstract_network.AbstractPyTorchNetwork

Implements basic classification with ResNet18

References

https://arxiv.org/abs/1512.03385

See also

AbstractPyTorchNetwork

_apply(fn)
static _build_model(in_channels: int, n_outputs: int, **kwargs)[source]

builds actual model (resnet 18)

Parameters:
  • in_channels (int) – number of input channels
  • n_outputs (int) – number of outputs (usually same as number of classes)
  • **kwargs (dict) – additional keyword arguments
Returns:

created model

Return type:

torch.nn.Module

_get_name()
_init_kwargs = {}
_load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

Copies parameters and buffers from state_dict into only this module, but not its descendants. This is called on every submodule in load_state_dict(). Metadata saved for this module in input state_dict is provided as :attr`local_metadata`. For state dicts without metadata, :attr`local_metadata` is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get(“version”, None).

Note

state_dict is not the same object as the input state_dict to load_state_dict(). So it can be modified.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • prefix (str) – the prefix for parameters and buffers used in this module
  • local_metadata (dict) – a dict containing the metadata for this moodule. See
  • strict (bool) – whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module
  • missing_keys (list of str) – if strict=False, add missing keys to this list
  • unexpected_keys (list of str) – if strict=False, add unexpected keys to this list
  • error_msgs (list of str) – error messages should be added to this list, and will be reported together in load_state_dict()
_named_members(get_members_fn, prefix='', recurse=True)

Helper method for yielding various names + members of modules.

_register_load_state_dict_pre_hook(hook)

These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self. These arguments are exactly the same as those of _load_from_state_dict.

_register_state_dict_hook(hook)

These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set. Note that only parameters and buffers of self or its children are guaranteed to exist in state_dict. The hooks may modify state_dict inplace or return a new one.

_slow_forward(*input, **kwargs)
_tracing_name(tracing_state)
_version = 1
add_module(name, module)

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:
  • name (string) – name of the child module. The child module can be accessed from this module using the given name
  • parameter (Module) – child module to be added to the module.
apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch-nn-init).

Parameters:fn (Module -> None) – function to be applied to each submodule
Returns:self
Return type:Module

Example:

>>> def init_weights(m):
        print(m)
        if type(m) == nn.Linear:
            m.weight.data.fill_(1.0)
            print(m.weight)

>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
buffers(recurse=True)

Returns an iterator over module buffers.

Parameters:recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:torch.Tensor – module buffer

Example:

>>> for buf in model.buffers():
>>>     print(type(buf.data), buf.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
children()

Returns an iterator over immediate children modules.

Yields:Module – a child module
static closure(model: delira.models.abstract_network.AbstractPyTorchNetwork, data_dict: dict, optimizers: dict, criterions={}, metrics={}, fold=0, **kwargs)[source]

closure method to do a single backpropagation step

Parameters:
  • model (ClassificationNetworkBasePyTorch) – trainable model
  • data_dict (dict) – dictionary containing the data
  • optimizers (dict) – dictionary of optimizers to optimize model’s parameters
  • criterions (dict) – dict holding the criterions to calculate errors (gradients from different criterions will be accumulated)
  • metrics (dict) – dict holding the metrics to calculate
  • fold (int) – Current Fold in Crossvalidation (default: 0)
  • **kwargs – additional keyword arguments
Returns:

  • dict – Metric values (with same keys as input dict metrics)
  • dict – Loss values (with same keys as input dict criterions)
  • list – Arbitrary number of predictions as torch.Tensor

Raises:

AssertionError – if optimizers or criterions are empty or the optimizers are not specified

cpu()

Moves all model parameters and buffers to the CPU.

Returns:self
Return type:Module
cuda(device=None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:self
Return type:Module
double()

Casts all floating point parameters and buffers to double datatype.

Returns:self
Return type:Module
dump_patches = False
eval()

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

extra_repr()

Set the extra representation of the module

To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.

float()

Casts all floating point parameters and buffers to float datatype.

Returns:self
Return type:Module
forward(input_batch: torch.Tensor)[source]

Forward input_batch through network

Parameters:input_batch (torch.Tensor) – batch to forward through network
Returns:Classification Result
Return type:torch.Tensor
half()

Casts all floating point parameters and buffers to half datatype.

Returns:self
Return type:Module
init_kwargs

Returns all arguments registered as init kwargs

Returns:init kwargs
Return type:dict
load_state_dict(state_dict, strict=True)

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
modules()

Returns an iterator over all modules in the network.

Yields:Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential (
  (0): Linear (2 -> 2)
  (1): Linear (2 -> 2)
)
1 -> Linear (2 -> 2)
named_buffers(prefix='', recurse=True)

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:
  • prefix (str) – prefix to prepend to all buffer names.
  • recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
named_modules(memo=None, prefix='')

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential (
  (0): Linear (2 -> 2)
  (1): Linear (2 -> 2)
))
1 -> ('0', Linear (2 -> 2))
named_parameters(prefix='', recurse=True)

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:
  • prefix (str) – prefix to prepend to all parameter names.
  • recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())
parameters(recurse=True)

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:Parameter – module parameter

Example:

>>> for param in model.parameters():
>>>     print(type(param.data), param.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
static prepare_batch(batch: dict, input_device, output_device)[source]

Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)

Parameters:
  • batch (dict) – dictionary containing all the data
  • input_device (torch.device) – device for network inputs
  • output_device (torch.device) – device for network outputs
Returns:

dictionary containing data in correct type and shape and on correct device

Return type:

dict

register_backward_hook(hook)

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> Tensor or None

The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle

Warning

The current implementation will not have the presented behavior for complex Module that perform many operations. In some failure cases, grad_input and grad_output will only contain the gradients for a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook() directly on a specific input or output to get the required gradients.

register_buffer(name, tensor)

Adds a persistent buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the persistent state.

Buffers can be accessed as attributes using given names.

Parameters:
  • name (string) – name of the buffer. The buffer can be accessed from this module using the given name
  • tensor (Tensor) – buffer to be registered.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))
register_forward_hook(hook)

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None

The hook should not modify the input or output.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle
register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None

The hook should not modify the input.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle
register_parameter(name, param)

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:
  • name (string) – name of the parameter. The parameter can be accessed from this module using the given name
  • parameter (Parameter) – parameter to be added to the module.
share_memory()
state_dict(destination=None, prefix='', keep_vars=False)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns:a dictionary containing a whole state of the module
Return type:dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']
to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)
to(dtype, non_blocking=False)
to(tensor, non_blocking=False)

Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:
  • device (torch.device) – the desired device of the parameters and buffers in this module
  • dtype (torch.dtype) – the desired floating point type of the floating point parameters and buffers in this module
  • tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
Returns:

self

Return type:

Module

Example:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)
train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Returns:self
Return type:Module
type(dst_type)

Casts all parameters and buffers to dst_type.

Parameters:dst_type (type or string) – the desired type
Returns:self
Return type:Module
zero_grad()

Sets gradients of all model parameters to zero.

VGG3DClassificationNetworkPyTorch

class VGG3DClassificationNetworkPyTorch(in_channels: int, n_outputs: int, **kwargs)[source]

Bases: delira.models.classification.classification_network.ClassificationNetworkBasePyTorch

Exemplaric VGG Network for 3D Classification

Notes

The original network has been adjusted to fit for 3D data

References

https://arxiv.org/abs/1409.1556

_apply(fn)
static _build_model(in_channels: int, n_outputs: int, **kwargs)[source]

Helper Function to build the actual model

Parameters:
  • in_channels (int) – number of input channels
  • n_outputs (int) – number of outputs
  • **kwargs – additional keyword arguments
Returns:

ensembeled model

Return type:

torch.nn.Module

_get_name()
_init_kwargs = {}
_load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

Copies parameters and buffers from state_dict into only this module, but not its descendants. This is called on every submodule in load_state_dict(). Metadata saved for this module in input state_dict is provided as :attr`local_metadata`. For state dicts without metadata, :attr`local_metadata` is empty. Subclasses can achieve class-specific backward compatible loading using the version number at local_metadata.get(“version”, None).

Note

state_dict is not the same object as the input state_dict to load_state_dict(). So it can be modified.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • prefix (str) – the prefix for parameters and buffers used in this module
  • local_metadata (dict) – a dict containing the metadata for this moodule. See
  • strict (bool) – whether to strictly enforce that the keys in state_dict with prefix match the names of parameters and buffers in this module
  • missing_keys (list of str) – if strict=False, add missing keys to this list
  • unexpected_keys (list of str) – if strict=False, add unexpected keys to this list
  • error_msgs (list of str) – error messages should be added to this list, and will be reported together in load_state_dict()
_named_members(get_members_fn, prefix='', recurse=True)

Helper method for yielding various names + members of modules.

_register_load_state_dict_pre_hook(hook)

These hooks will be called with arguments: state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, before loading state_dict into self. These arguments are exactly the same as those of _load_from_state_dict.

_register_state_dict_hook(hook)

These hooks will be called with arguments: self, state_dict, prefix, local_metadata, after the state_dict of self is set. Note that only parameters and buffers of self or its children are guaranteed to exist in state_dict. The hooks may modify state_dict inplace or return a new one.

_slow_forward(*input, **kwargs)
_tracing_name(tracing_state)
_version = 1
add_module(name, module)

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters:
  • name (string) – name of the child module. The child module can be accessed from this module using the given name
  • parameter (Module) – child module to be added to the module.
apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch-nn-init).

Parameters:fn (Module -> None) – function to be applied to each submodule
Returns:self
Return type:Module

Example:

>>> def init_weights(m):
        print(m)
        if type(m) == nn.Linear:
            m.weight.data.fill_(1.0)
            print(m.weight)

>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
buffers(recurse=True)

Returns an iterator over module buffers.

Parameters:recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:torch.Tensor – module buffer

Example:

>>> for buf in model.buffers():
>>>     print(type(buf.data), buf.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
children()

Returns an iterator over immediate children modules.

Yields:Module – a child module
static closure(model: delira.models.abstract_network.AbstractPyTorchNetwork, data_dict: dict, optimizers: dict, criterions={}, metrics={}, fold=0, **kwargs)

closure method to do a single backpropagation step

Parameters:
  • model (ClassificationNetworkBasePyTorch) – trainable model
  • data_dict (dict) – dictionary containing the data
  • optimizers (dict) – dictionary of optimizers to optimize model’s parameters
  • criterions (dict) – dict holding the criterions to calculate errors (gradients from different criterions will be accumulated)
  • metrics (dict) – dict holding the metrics to calculate
  • fold (int) – Current Fold in Crossvalidation (default: 0)
  • **kwargs – additional keyword arguments
Returns:

  • dict – Metric values (with same keys as input dict metrics)
  • dict – Loss values (with same keys as input dict criterions)
  • list – Arbitrary number of predictions as torch.Tensor

Raises:

AssertionError – if optimizers or criterions are empty or the optimizers are not specified

cpu()

Moves all model parameters and buffers to the CPU.

Returns:self
Return type:Module
cuda(device=None)

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Parameters:device (int, optional) – if specified, all parameters will be copied to that device
Returns:self
Return type:Module
double()

Casts all floating point parameters and buffers to double datatype.

Returns:self
Return type:Module
dump_patches = False
eval()

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

extra_repr()

Set the extra representation of the module

To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.

float()

Casts all floating point parameters and buffers to float datatype.

Returns:self
Return type:Module
forward(input_batch: torch.Tensor)

Forward input_batch through network

Parameters:input_batch (torch.Tensor) – batch to forward through network
Returns:Classification Result
Return type:torch.Tensor
half()

Casts all floating point parameters and buffers to half datatype.

Returns:self
Return type:Module
init_kwargs

Returns all arguments registered as init kwargs

Returns:init kwargs
Return type:dict
load_state_dict(state_dict, strict=True)

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters:
  • state_dict (dict) – a dict containing parameters and persistent buffers.
  • strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
modules()

Returns an iterator over all modules in the network.

Yields:Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential (
  (0): Linear (2 -> 2)
  (1): Linear (2 -> 2)
)
1 -> Linear (2 -> 2)
named_buffers(prefix='', recurse=True)

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters:
  • prefix (str) – prefix to prepend to all buffer names.
  • recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields:

(string, torch.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
named_modules(memo=None, prefix='')

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Yields:(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential (
  (0): Linear (2 -> 2)
  (1): Linear (2 -> 2)
))
1 -> ('0', Linear (2 -> 2))
named_parameters(prefix='', recurse=True)

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters:
  • prefix (str) – prefix to prepend to all parameter names.
  • recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())
parameters(recurse=True)

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters:recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields:Parameter – module parameter

Example:

>>> for param in model.parameters():
>>>     print(type(param.data), param.size())
<class 'torch.FloatTensor'> (20L,)
<class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)
static prepare_batch(batch: dict, input_device, output_device)

Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)

Parameters:
  • batch (dict) – dictionary containing all the data
  • input_device (torch.device) – device for network inputs
  • output_device (torch.device) – device for network outputs
Returns:

dictionary containing data in correct type and shape and on correct device

Return type:

dict

register_backward_hook(hook)

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:

hook(module, grad_input, grad_output) -> Tensor or None

The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle

Warning

The current implementation will not have the presented behavior for complex Module that perform many operations. In some failure cases, grad_input and grad_output will only contain the gradients for a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook() directly on a specific input or output to get the required gradients.

register_buffer(name, tensor)

Adds a persistent buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the persistent state.

Buffers can be accessed as attributes using given names.

Parameters:
  • name (string) – name of the buffer. The buffer can be accessed from this module using the given name
  • tensor (Tensor) – buffer to be registered.

Example:

>>> self.register_buffer('running_mean', torch.zeros(num_features))
register_forward_hook(hook)

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None

The hook should not modify the input or output.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle
register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None

The hook should not modify the input.

Returns:a handle that can be used to remove the added hook by calling handle.remove()
Return type:torch.utils.hooks.RemovableHandle
register_parameter(name, param)

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters:
  • name (string) – name of the parameter. The parameter can be accessed from this module using the given name
  • parameter (Parameter) – parameter to be added to the module.
share_memory()
state_dict(destination=None, prefix='', keep_vars=False)

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns:a dictionary containing a whole state of the module
Return type:dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']
to(*args, **kwargs)

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)
to(dtype, non_blocking=False)
to(tensor, non_blocking=False)

Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters:
  • device (torch.device) – the desired device of the parameters and buffers in this module
  • dtype (torch.dtype) – the desired floating point type of the floating point parameters and buffers in this module
  • tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
Returns:

self

Return type:

Module

Example:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)
train(mode=True)

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Returns:self
Return type:Module
type(dst_type)

Casts all parameters and buffers to dst_type.

Parameters:dst_type (type or string) – the desired type
Returns:self
Return type:Module
zero_grad()

Sets gradients of all model parameters to zero.