torchwrench.nn.modules.tensor module¶

Module versions of tensor functions that do not already exists in PyTorch.

class torchwrench.nn.modules.tensor.Abs(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of abs().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Angle(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of angle().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Exp(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of exp().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Exp2(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of exp2().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.FFT(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of fft().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.IFFT(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of ifft().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Imag(*, return_zeros: bool = False)[source]¶

Bases: Module

Module version of imag().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Interpolate(size: int | tuple[int, ...] | None = None, scale_factor: float | tuple[float, ...] | None = None, mode: str = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool | None = None, antialias: bool = False)[source]¶

Bases: Module

Module version of interpolate().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Log(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of log().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Log10(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of log10().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Log2(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of log2().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Max(dim: int | None = None, keepdim: bool = False, *, return_values: bool = True, return_indices: bool | None = None)[source]¶

Bases: Module

Module version of max().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor | max[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Mean(dim: int | None = None, keepdim: bool = False, dtype: dtype | None | 'default' | str | DTypeEnum = None)[source]¶

Bases: Module

Module version of mean().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Min(dim: int | None = None, keepdim: bool = False, *, return_values: bool = True, return_indices: bool | None = None)[source]¶

Bases: Module

Module version of min().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor | min[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Normalize(p: float = 2.0, dim: int = 1, eps: float = 1e-12)[source]¶

Bases: Module

Module version of normalize().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Permute(*args: int)[source]¶

Bases: Module

Module version of permute().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Pow(exponent: int | float | bool | Tensor)[source]¶

Bases: Module

Module version of pow().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Real(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of real().

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Repeat(*repeats: int)[source]¶

Bases: Module

Module version of repeat().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.RepeatInterleave(repeats: int | Tensor, dim: int, output_size: int | None = None)[source]¶

Bases: Module

Module version of repeat_interleave().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Reshape(*shape: int)[source]¶

Bases: Module

Module version of reshape().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Sort(dim: int = -1, descending: bool = False, *, return_values: bool = True, return_indices: bool = True)[source]¶

Bases: Module

Module version of sort().

forward(x: Tensor) → sort | Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.TensorTo(**kwargs)[source]¶

Bases: Module

Module version of to().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.ToList(*args: Any, **kwargs: Any)[source]¶

Bases: Module

Module version of tolist().

forward(x: Tensor) → list[source]¶

Define 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.

class torchwrench.nn.modules.tensor.Transpose(dim0: int, dim1: int, copy: bool = False)[source]¶

Bases: Module

Module version of transpose().

extra_repr() → str[source]¶

Return 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.

forward(x: Tensor) → Tensor[source]¶

Define 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.

class torchwrench.nn.modules.tensor.View(dtype: dtype, /)[source]¶

class torchwrench.nn.modules.tensor.View(size: Sequence[int], /)

class torchwrench.nn.modules.tensor.View(*size: int)

Bases: Module

forward(x: Tensor) → Tensor[source]¶

Define 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.