luz.transforms module¶
- class Argmax(dim=None, keepdim=False)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Output tensor.
- Return type
torch.Tensor
- training: bool¶
- class Center(accumulate_along=None)¶
Bases:
luz.transforms.ScaleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- training: bool¶
- class Compose(*transforms)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- fit(dataset, key)¶
- forward(x)¶
Transform tensor.
- Parameters
x (
Any) – Input tensor.- Returns
Output tensor.
- Return type
torch.Tensor
- inverse()¶
- Return type
- training: bool¶
- class Identity¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Output tensor, same as the input tensor.
- Return type
torch.Tensor
- inverse()¶
- Return type
- training: bool¶
- class Lookup(lookup_dict)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x – Input.
- Returns
Output tensor.
- Return type
torch.Tensor
- training: bool¶
- class NanToNum(*args, **kwargs)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type
Tensor
- training: bool¶
- class Normalize(accumulate_along=None)¶
Bases:
luz.transforms.ScaleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- training: bool¶
- class NormalizePerTensor(p, *args, **kwargs)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Normalized output tensor.
- Return type
torch.Tensor
- training: bool¶
- class PowerSeries(degree, dim=- 1)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Output tensor.
- Return type
torch.Tensor
- training: bool¶
- class Reshape(out_shape)¶
Bases:
luz.transforms.TensorTransformReshape tensor.
- Parameters
out_shape (
Iterable[int]) – Desired output shape.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Reshaped output tensor.
- Return type
torch.Tensor
- training: bool¶
- class Squeeze(dim)¶
Bases:
luz.transforms.TensorTransformSqueeze tensor.
- Parameters
dim (
Optional[int]) – Dimension to be squeezed.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Squeezed output tensor.
- Return type
torch.Tensor
- inverse()¶
- Return type
- training: bool¶
- class Standardize(accumulate_along=None)¶
Bases:
luz.transforms.ScaleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- training: bool¶
- class TensorTransform¶
Bases:
torch.nn.modules.module.ModuleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- inverse()¶
- Return type
- training: bool¶
- class Transform(**transforms)¶
Bases:
torch.nn.modules.module.ModuleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- fit(dataset)¶
- forward(data)¶
Transform data.
- Parameters
data (
Data) – Input data.- Returns
Output data.
- Return type
luz.Data
- training: bool¶
- class Transpose(dim0, dim1)¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Transposed output tensor.
- Return type
torch.Tensor
- inverse()¶
- Return type
- training: bool¶
- class Unsqueeze(dim)¶
Bases:
luz.transforms.TensorTransformUnsqueeze tensor.
- Parameters
dim (
Optional[int]) – Dimension to be unsqueezed.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Unsqueezed output tensor.
- Return type
torch.Tensor
- inverse()¶
- Return type
- training: bool¶
- class YeoJohnson¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- fit(dataset, key, batch_size=20)¶
- forward(x)¶
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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type
Tensor
- training: bool¶
- class ZeroMeanPerTensor¶
Bases:
luz.transforms.TensorTransformInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)¶
Transform tensor.
- Parameters
x (
Tensor) – Input tensor.- Returns
Output tensor.
- Return type
torch.Tensor
- training: bool¶