mnist_pytorch#

A cleanlab-compatible PyTorch ConvNet classifier that can be used to find label issues in image data. This is a good example to reference for making your own bespoke model compatible with cleanlab.

You must have PyTorch installed: https://pytorch.org/get-started/locally/

Classes:

`CNN`([batch_size, epochs, log_interval, lr, ...])	Wraps a PyTorch CNN for the MNIST dataset within an sklearn template
`SimpleNet`()	Basic Pytorch CNN for MNIST-like data.

Functions:

`get_mnist_dataset`(loader)	Downloads MNIST as PyTorch dataset.
`get_sklearn_digits_dataset`(loader)	Downloads Sklearn handwritten digits dataset.

class cleanlab.experimental.mnist_pytorch.CNN(batch_size=64, epochs=6, log_interval=50, lr=0.01, momentum=0.5, no_cuda=False, seed=1, test_batch_size=None, dataset='mnist', loader=None)[source]#

Bases: sklearn.base.BaseEstimator

Wraps a PyTorch CNN for the MNIST dataset within an sklearn template

Defines .fit(), .predict(), and .predict_proba() functions. This template enables the PyTorch CNN to flexibly be used within the sklearn architecture – meaning it can be passed into functions like cross_val_predict as if it were an sklearn model. The cleanlab library requires that all models adhere to this basic sklearn template and thus, this class allows a PyTorch CNN to be used in for learning with noisy labels among other things.

Parameters

batch_size (int) –
epochs (int) –
log_interval (int) –
lr (float) –
momentum (float) –
no_cuda (bool) –
seed (int) –
test_batch_size (int, default None) –
dataset ({'mnist', 'sklearn-digits'}) –
loader ({'train', 'test'}) – Set to ‘test’ to force fit() and predict_proba() on test_set

Note

Be careful setting the loader param, it will override every other loader If you set this to ‘test’, but call .predict(loader = ‘train’) then .predict() will still predict on test!

batch_size#

Type: int

epochs#

Type: int

log_interval#

Type: int

lr#

Type: float

momentum#

Type: float

no_cuda#

Type: bool

seed#

Type: int

test_batch_size#

Type: int, default None

dataset#

Type: {'mnist', 'sklearn-digits'}

loader#

Set to ‘test’ to force fit() and predict_proba() on test_set

Type: {'train', 'test'}

fit()[source]#: fits the model to data.

predict()[source]#: get the fitted model’s prediction on test data

predict_proba()[source]#: get the fitted model’s probability distribution over classes for test data

Methods:

`fit`(train_idx[, train_labels, ...])	This function adheres to sklearn's "fit(X, y)" format for compatibility with scikit-learn.
`get_params`([deep])	Get parameters for this estimator.
`predict`([idx, loader])	Get predicted labels from trained model.
`predict_proba`([idx, loader])
`set_params`(**parameters)	Set the parameters of this estimator.

fit(train_idx, train_labels=None, sample_weight=None, loader='train')[source]#: This function adheres to sklearn’s “fit(X, y)” format for compatibility with scikit-learn. ** All inputs should be numpy arrays, not pyTorch Tensors train_idx is not X, but instead a list of indices for X (and y if train_labels is None). This function is a member of the cnn class which will handle creation of X, y from the train_idx via the train_loader.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters: deep (bool, default True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

predict(idx=None, loader=None)[source]#: Get predicted labels from trained model.

predict_proba(idx=None, loader=None)[source]#

set_params(**parameters)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

class cleanlab.experimental.mnist_pytorch.SimpleNet[source]#

Bases: torch.nn.modules.module.Module

Basic Pytorch CNN for MNIST-like data.

Attributes:

`T_destination`	alias of TypeVar('T_destination', bound=`Mapping`[`str`, `torch.Tensor`])
`dump_patches`	This allows better BC support for `load_state_dict()`.

Methods:

`__call__`(input, *kwargs)	Call self as a function.
`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.
`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[, T])	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.
`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.
`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_module`(name, module)	Alias for `add_module()`.
`register_parameter`(name, param)	Adds a parameter to the module.
`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`([destination, prefix, keep_vars])	Returns a dictionary containing a 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.

T_destination#: alias of TypeVar(‘T_destination’, bound=Mapping[str, torch.Tensor])

__call__(*input, **kwargs)#: Call self as a function.

add_module(name: str, module: Optional[torch.nn.modules.module.Module]) → None#

Adds a child module to the current module.

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

Args:

name (string): name of the child module. The child module can be: accessed from this module using the given name

module (Module): child module to be added to the module.

apply(fn: Callable[[torch.nn.modules.module.Module], None]) → torch.nn.modules.module.T#

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 nn-init-doc).

Args:: fn (Module -> None): function to be applied to each submodule
Returns:: Module: self

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.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)
)

bfloat16() → torch.nn.modules.module.T#

Casts all floating point parameters and buffers to bfloat16 datatype.

Note

This method modifies the module in-place.

Returns:: Module: self

buffers(recurse: bool = True) → Iterator[torch.Tensor]#

Returns an iterator over module buffers.

Args:

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), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

children() → Iterator[torch.nn.modules.module.Module]#

Returns an iterator over immediate children modules.

Yields:: Module: a child module

cpu() → torch.nn.modules.module.T#

Moves all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns:: Module: self

cuda(device: Optional[Union[int, torch.device]] = None) → torch.nn.modules.module.T#

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.

Note

This method modifies the module in-place.

Args:

device (int, optional): if specified, all parameters will be: copied to that device

Returns:

Module: self

double() → torch.nn.modules.module.T#

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns:: Module: self

dump_patches: bool = False#

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

eval() → torch.nn.modules.module.T#

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.

This is equivalent with self.train(False).

See locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it.

Returns:: Module: self

extra_repr() → str#

Set the extra representation of the module

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

float() → torch.nn.modules.module.T#

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns:: Module: self

forward(x, T=1.0)[source]#

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_buffer(target: str) → torch.Tensor#

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Args:

target: The fully-qualified string name of the buffer: to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns:

torch.Tensor: The buffer referenced by target

Raises:

AttributeError: If the target string references an invalid: path or resolves to something that is not a buffer

get_extra_state() → Any#

Returns any extra state to include in the module’s state_dict. Implement this and a corresponding set_extra_state() for your module if you need to store extra state. This function is called when building the module’s state_dict().

Note that extra state should be pickleable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes.

Returns:: object: Any extra state to store in the module’s state_dict

get_parameter(target: str) → torch.nn.parameter.Parameter#

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method’s functionality as well as how to correctly specify target.

Args:

target: The fully-qualified string name of the Parameter: to look for. (See get_submodule for how to specify a fully-qualified string.)

Returns:

torch.nn.Parameter: The Parameter referenced by target

Raises:

AttributeError: If the target string references an invalid: path or resolves to something that is not an nn.Parameter

get_submodule(target: str) → torch.nn.modules.module.Module#

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let’s say you have an nn.Module A that looks like this:

(The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.)

To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv").

The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used.

Args:

target: The fully-qualified string name of the submodule: to look for. (See above example for how to specify a fully-qualified string.)

Returns:

torch.nn.Module: The submodule referenced by target

Raises:

AttributeError: If the target string references an invalid: path or resolves to something that is not an nn.Module

half() → torch.nn.modules.module.T#

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns:: Module: self

load_state_dict(state_dict: OrderedDict[str, Tensor], strict: bool = 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.

Args:

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

Returns:

NamedTuple with missing_keys and unexpected_keys fields:

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

Note:

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

modules() → Iterator[torch.nn.modules.module.Module]#

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(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, torch.Tensor]]#

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

Args:: 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() → Iterator[Tuple[str, torch.nn.modules.module.Module]]#

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: Optional[Set[torch.nn.modules.module.Module]] = None, prefix: str = '', remove_duplicate: bool = True)#

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

Args:: memo: a memo to store the set of modules already added to the result prefix: a prefix that will be added to the name of the module remove_duplicate: whether to remove the duplicated module instances in the result

or not
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(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, torch.nn.parameter.Parameter]]#

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

Args:: 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: bool = True) → Iterator[torch.nn.parameter.Parameter]#

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Args:

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), param.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)

register_backward_hook(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[None, torch.Tensor]]) → torch.utils.hooks.RemovableHandle#

Registers a backward hook on the module.

This function is deprecated in favor of register_full_backward_hook() and the behavior of this function will change in future versions.

Returns:

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

register_buffer(name: str, tensor: Optional[torch.Tensor], persistent: bool = True) → None#

Adds a 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 module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Args:

name (string): name of the buffer. The buffer can be accessed: from this module using the given name
tensor (Tensor or None): buffer to be registered. If None, then operations: that run on buffers, such as cuda, are ignored. If None, the buffer is not included in the module’s state_dict.
persistent (bool): whether the buffer is part of this module’s: state_dict.

Example:

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

register_forward_hook(hook: Callable[[...], None]) → torch.utils.hooks.RemovableHandle#

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 or modified output

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

Returns:

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

register_forward_pre_hook(hook: Callable[[...], None]) → torch.utils.hooks.RemovableHandle#

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 or modified input

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

Returns:

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

register_full_backward_hook(hook: Callable[[torch.nn.modules.module.Module, Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[None, torch.Tensor]]) → torch.utils.hooks.RemovableHandle#

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) -> tuple(Tensor) or None

The grad_input and grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of grad_input in subsequent computations. grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in grad_input and grad_output will be None for all non-Tensor arguments.

For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module’s forward function.

Warning

Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error.

Returns:

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

register_module(name: str, module: Optional[torch.nn.modules.module.Module]) → None#: Alias for add_module().

register_parameter(name: str, param: Optional[torch.nn.parameter.Parameter]) → None#

Adds a parameter to the module.

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

Args:

name (string): name of the parameter. The parameter can be accessed: from this module using the given name
param (Parameter or None): parameter to be added to the module. If: None, then operations that run on parameters, such as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

requires_grad_(requires_grad: bool = True) → torch.nn.modules.module.T#

Change if autograd should record operations on parameters in this module.

This method sets the parameters’ requires_grad attributes in-place.

This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).

See locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it.

Args:

requires_grad (bool): whether autograd should record operations on: parameters in this module. Default: True.

Returns:

Module: self

set_extra_state(state: Any)#

This function is called from load_state_dict() to handle any extra state found within the state_dict. Implement this function and a corresponding get_extra_state() for your module if you need to store extra state within its state_dict.

Args:: state (dict): Extra state from the state_dict

share_memory() → torch.nn.modules.module.T#: See torch.Tensor.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. Parameters and buffers set to None are not included.

Returns:

dict:: a dictionary containing a whole state of the module

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)

to(memory_format=torch.channels_last)

Its signature is similar to torch.Tensor.to(), but only accepts floating point or complex dtypes. In addition, this method will only cast the floating point or complex 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.

Args:

device (torch.device): the desired device of the parameters: and buffers in this module
dtype (torch.dtype): the desired floating point or complex dtype of: the 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
memory_format (torch.memory_format): the desired memory: format for 4D parameters and buffers in this module (keyword only argument)

Returns:

Module: self

Examples:

>>> 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)

>>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
>>> linear.weight
Parameter containing:
tensor([[ 0.3741+0.j,  0.2382+0.j],
        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))
tensor([[0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j],
        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

to_empty(*, device: Union[str, torch.device]) → torch.nn.modules.module.T#

Moves the parameters and buffers to the specified device without copying storage.

Args:

device (torch.device): The desired device of the parameters: and buffers in this module.

Returns:

Module: self

train(mode: bool = True) → torch.nn.modules.module.T#

Sets the module in training mode.

Args:

mode (bool): whether to set training mode (True) or evaluation: mode (False). Default: True.

Returns:

Module: self

training: bool#

type(dst_type: Union[torch.dtype, str]) → torch.nn.modules.module.T#

Casts all parameters and buffers to dst_type.

Note

This method modifies the module in-place.

Args:: dst_type (type or string): the desired type
Returns:: Module: self

xpu(device: Optional[Union[int, torch.device]] = None) → torch.nn.modules.module.T#

Moves all model parameters and buffers to the XPU.

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

Note

This method modifies the module in-place.

Arguments:

device (int, optional): if specified, all parameters will be: copied to that device

Returns:

Module: self

zero_grad(set_to_none: bool = False) → None#

Sets gradients of all model parameters to zero. See similar function under torch.optim.Optimizer for more context.

Args:

set_to_none (bool): instead of setting to zero, set the grads to None.: See torch.optim.Optimizer.zero_grad() for details.

cleanlab.experimental.mnist_pytorch.get_mnist_dataset(loader)[source]#

Downloads MNIST as PyTorch dataset.

Parameters: loader (str (values: 'train' or 'test').) –

cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset(loader)[source]#

Downloads Sklearn handwritten digits dataset. Uses the last SKLEARN_DIGITS_TEST_SIZE examples as the test This is (hard-coded) – do not change.

Parameters: loader (str (values: 'train' or 'test').) –