timesead.models.reconstruction.anom_trans ========================================= .. py:module:: timesead.models.reconstruction.anom_trans Classes ------- .. autoapisummary:: timesead.models.reconstruction.anom_trans.EncoderLayer timesead.models.reconstruction.anom_trans.Encoder timesead.models.reconstruction.anom_trans.AnomalyTransformer timesead.models.reconstruction.anom_trans.AnomTransf_Loss timesead.models.reconstruction.anom_trans.AnomTransf_Trainer timesead.models.reconstruction.anom_trans.AnomTransf_AnomalyDetector Functions --------- .. autoapisummary:: timesead.models.reconstruction.anom_trans.symm_kl_loss Module Contents --------------- .. py:class:: EncoderLayer(attention: torch.nn.Module, d_model: int, d_ff: Optional[int] = None, dropout: float = 0.1, activation: str = 'relu') Bases: :py:obj:`torch.nn.Module` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: attention .. py:attribute:: conv1 .. py:attribute:: conv2 .. py:attribute:: norm1 .. py:attribute:: norm2 .. py:attribute:: dropout .. py:attribute:: activation .. py:method:: forward(x: torch.Tensor, attn_mask=None) .. py:class:: Encoder(attn_layers: List[torch.nn.Module], norm_layer: Optional[torch.nn.Module] = None) Bases: :py:obj:`torch.nn.Module` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: attn_layers .. py:attribute:: norm :value: None .. py:method:: forward(x: torch.Tensor, attn_mask=None) .. py:class:: AnomalyTransformer(win_size: int, input_dim: int, d_model: int = 512, n_heads: int = 8, e_layers: int = 3, d_ff: int = 512, dropout: float = 0.0, activation: str = 'gelu', output_attention: bool = True) Bases: :py:obj:`timesead.models.BaseModel` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: output_attention :value: True .. py:attribute:: embedding .. py:attribute:: encoder .. py:attribute:: projection .. py:method:: forward(x: torch.Tensor) -> Tuple[torch.Tensor, Ellipsis] .. py:function:: symm_kl_loss(p, q, eps=0.0001, reduce=True) .. py:class:: AnomTransf_Loss(lamb: float = 3.0) Bases: :py:obj:`timesead.optim.loss.Loss` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: lamb :value: 3.0 .. py:attribute:: mse_loss .. py:method:: calc_association_disc(series: List[torch.Tensor], priors: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor] .. py:method:: forward(predictions: Tuple[torch.Tensor, Ellipsis], targets: Tuple[torch.Tensor, Ellipsis] = None, *args, **kwargs) -> torch.Tensor .. py:class:: AnomTransf_Trainer(*args, **kwargs) Bases: :py:obj:`timesead.optim.trainer.Trainer` .. py:method:: validate_batch(network: torch.nn.Module, val_metrics: Dict[str, Callable], b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) -> Dict[str, float] .. py:method:: train_batch(network: AnomalyTransformer, losses: List[timesead.optim.loss.Loss], optimizers: List[torch.optim.Optimizer], epoch: int, num_epochs: int, b_inputs: Tuple[torch.Tensor, Ellipsis], b_targets: Tuple[torch.Tensor, Ellipsis]) -> List[float] .. py:class:: AnomTransf_AnomalyDetector(model: AnomalyTransformer) Bases: :py:obj:`timesead.models.common.AnomalyDetector` Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: model .. py:method:: compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor Compute the online anomaly score for a batch of inputs. The output tensor must have the same shape as the output of `format_targets` when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window. :param inputs: tuple of input tensors :return: Tensor of shape (B,) that contains the anomaly scores for this batch .. py:method:: compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor :abstractmethod: Compute the offline anomaly score for a batch of inputs. The output tensor must have the same shape as the output of `format_targets` when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window. :param inputs: tuple of input tensors :return: Tensor of shape (N,) that contains the anomaly scores for this batch .. py:method:: fit(dataset: torch.utils.data.DataLoader) -> None Fit this anomaly detector on a dataset. Note that we assume only normal data here. :param dataset: A dataset .. py:method:: format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor Format the labels for a batch of targets. The output tensor must have the same shape as the output of `compute_online_anomaly_score` when called with the corresponding inputs for this batch. :param targets: tuple of target tensors :return: Tensor of shape (B,) that contains the ground truth labels for this batch