Model¶

class flood_forecast.da_rnn.model.DARNN(input_size: int, hidden_size_encoder: int, T: int, decoder_hidden_size: int, out_feats=1)[source]¶

Bases: torch.nn.modules.module.Module

input size: number of underlying factors (81) T: number of time steps (10) hidden_size: dimension of the hidden state

forward(x: torch.Tensor, y_history: torch.Tensor)[source]¶: will implement

T_destination = ~T_destination¶

add_module(name: str, module: 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[[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 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() → T¶

Casts all floating point parameters and buffers to bfloat16 datatype.

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() → T¶

Moves all model parameters and buffers to the CPU.

Returns:: Module: 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.

Arguments:

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

Returns:

Module: self

double() → T¶

Casts all floating point parameters and buffers to double datatype.

Returns:: Module: self

dump_patches = False¶

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).

Returns:: Module: self

extra_repr() → str¶

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() → T¶

Casts all floating point parameters and buffers to float datatype.

Returns:: Module: self

half() → T¶

Casts all floating point parameters and buffers to half datatype.

Returns:: Module: self

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.

Arguments:

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

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[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.

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.Tensor]]¶

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[[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.

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: 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): buffer to be registered. 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_parameter(name: str, param: 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): parameter to be added to the module.

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).

Args:

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

Returns:

Module: self

share_memory() → T¶

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:

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 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.

Args:

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
memory_format (torch.memory_format): the desired memory: format for 4D parameters and buffers in this module (keyword only argument)

Returns:

Module: 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.

Args:

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

Returns:

Module: self

type(dst_type: Union[torch.dtype, str]) → T¶

Casts all parameters and buffers to dst_type.

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

zero_grad() → None¶: Sets gradients of all model parameters to zero.