class torch.jit.ScriptModule [source]
ScriptModule``s wrap a C++ ``torch::jit::Module. ScriptModule``s
contain methods, attributes, parameters, and
constants. These can be accessed the same as on a normal ``nn.Module.
add_module(name: str, module: Optional[Module]) → None Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
apply(fn: Callable[Module, None]) → 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 torch.nn.init).
fn (Module -> None) – function to be applied to each submodule
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() → T Casts all floating point parameters and buffers to bfloat16 datatype.
self
buffers(recurse: bool = True) → Iterator[torch.Tensor] Returns an iterator over module buffers.
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
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.
Module – a child module
property code Returns a pretty-printed representation (as valid Python syntax) of the internal graph for the forward method. See Inspecting Code for details.
property code_with_constants Returns a tuple of:
[0] a pretty-printed representation (as valid Python syntax) of the internal graph for the forward method. See code. [1] a ConstMap following the CONSTANT.cN format of the output in [0]. The indices in the [0] output are keys to the underlying constant’s values.
See Inspecting Code for details.
cpu() → T Moves all model parameters and buffers to the CPU.
self
cuda(device: Union[int, torch.device, None] = None) → 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.
double() → T Casts all floating point parameters and buffers to double datatype.
self
eval() → 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).
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() → T Casts all floating point parameters and buffers to float datatype.
self
property graph Returns a string representation of the internal graph for the forward method. See Interpreting Graphs for details.
half() → T Casts all floating point parameters and buffers to half datatype.
self
property inlined_graph Returns a string representation of the internal graph for the forward method. This graph will be preprocessed to inline all function and method calls. See Interpreting Graphs for details.
load_state_dict(state_dict: Dict[str, torch.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.
state_dict match the keys returned by this module’s state_dict() function. Default: True
NamedTuple with missing_keys and unexpected_keys fields
modules() → Iterator[torch.nn.modules.module.Module] Returns an iterator over all modules in the network.
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.
(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.
(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[Module]] = None, prefix: str = '') Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
(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.Tensor]] Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
(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.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
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[[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.
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.
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. grad_input will only correspond to the inputs given as positional arguments.
a handle that can be used to remove the added hook by calling handle.remove()
torch.utils.hooks.RemovableHandle
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.
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.
a handle that can be used to remove the added hook by calling handle.remove()
torch.utils.hooks.RemovableHandle
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).
a handle that can be used to remove the added hook by calling handle.remove()
torch.utils.hooks.RemovableHandle
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.
requires_grad_(requires_grad: bool = True) → 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).
save(f, _extra_files={}) See torch.jit.save for details.
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.
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 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.
torch.device) – the desired device of the parameters and buffers in this moduletorch.dtype) – the desired floating point type of the floating point parameters and buffers in this moduletorch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)self
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: bool = True) → T 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.
type(dst_type: Union[torch.dtype, str]) → T Casts all parameters and buffers to dst_type.
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.
set_to_none (bool) – instead of setting to zero, set the grads to None. See torch.optim.Optimizer.zero_grad() for details.
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.jit.ScriptModule.html