TimeMixin

class deepinv.physics.TimeMixin

Bases: object

Base class for temporal capabilities for physics and models.

Implements various methods to add or remove the time dimension.

Also provides template methods for temporal physics to implement.

static average(x: Tensor, mask: Tensor | None = None, dim: int = 2) → Tensor

Flatten time dim of x by averaging across frames. If mask is non-overlapping in time dim, then this will simply be the sum across frames.

Parameters:

• x (Tensor) – input tensor of shape (B,C,T,H,W) (e.g. time-varying k-space)

• mask (Tensor) – mask showing where x is non-zero. If not provided, then calculated from x.

• dim (int) – time dimension, defaults to 2 (i.e. shape B,C,T,H,W)

Return Tensor:

flattened tensor with time dim removed of shape (B,C,H,W)

static flatten(x: Tensor) → Tensor

Flatten time dim into batch dim.

Lets non-dynamic algorithms process dynamic data by treating time frames as batches.

Parameters:

x (Tensor) – input tensor of shape (B, C, T, H, W)

Return Tensor:

output tensor of shape (B*T, C, H, W)

static flatten_C(x: Tensor) → Tensor

Flatten time dim into channel dim.

Use when channel dim doesn’t matter and you don’t want to deal with annoying batch dimension problems (e.g. for transforms).

Parameters:

x (Tensor) – input tensor of shape (B, C, T, H, W)

Return Tensor:

output tensor of shape (B, C*T, H, W)

static repeat(x: Tensor, target: Tensor, dim: int = 2) → Tensor

Repeat static image across new time dim T times. Opposite of average.

Parameters:

• x (Tensor) – input tensor of shape (B,C,H,W)

• target (Tensor) – any tensor of desired shape (B,C,T,H,W)

• dim (int) – time dimension, defaults to 2 (i.e. shape B,C,T,H,W)

Return Tensor:

tensor with new time dim of shape B,C,T,H,W

static unflatten(x: Tensor, batch_size=1) → Tensor

Creates new time dim from batch dim. Opposite of flatten.

Parameters:

• x (Tensor) – input tensor of shape (B*T, C, H, W)

• batch_size (int) – batch size, defaults to 1

Return Tensor:

output tensor of shape (B, C, T, H, W)

static wrap_flatten_C(f: Callable[[Tensor], Tensor]) → Tensor

Flatten time dim into channel dim, apply function, then unwrap.

The first argument is assumed to be the tensor to be flattened.

Parameters:

f (Callable) – function to be wrapped

Return Callable:

wrapped function

Examples using TimeMixin:

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.