direct.nn.resnet package

Submodules

direct.nn.resnet.config module

class direct.nn.resnet.config.ResNetConfig(model_name: str = '???', engine_name: str | None = None, in_channels: int = 2, out_channels: int | None = None, hidden_channels: int = 32, num_blocks: int = 15, batchnorm: bool = True, scale: float | None = 0.1, image_init: str = 'sense')

Bases: ModelConfig

batchnorm: bool = True

hidden_channels: int = 32

image_init: str = 'sense'

in_channels: int = 2

num_blocks: int = 15

out_channels: Optional[int] = None

scale: Optional[float] = 0.1

direct.nn.resnet.resnet module

class direct.nn.resnet.resnet.ResNet(hidden_channels, in_channels=2, out_channels=None, num_blocks=15, batchnorm=True, scale=0.1)

Bases: Module

Simple residual network.

Consisted of a sequence of ResNetBlocks followed optionally by batch normalization blocks, followed by an output convolution layer.

forward(input_image)

Computes forward pass of ResNet.

Parameters:

input_image: torch.Tensor

Masked k-space of shape (N, in_channels, height, width).

Returns:

output: torch.Tensor

Output image of shape (N, height, width, complex=2).

Return type:

Tensor

training: bool

class direct.nn.resnet.resnet.ResNetBlock(in_channels, hidden_channels, scale=0.1)

Bases: Module

Main block of ResNet.

Consisted of a convolutional layer followed by a relu activation, a second convolution, and finally a scaled skip connection with the input.

forward(x)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

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.

training: bool

Module contents