fusionlab.layers.factories module#

class fusionlab.layers.factories.AdaptiveAvgPool(spatial_dims: int, output_size: int | Sequence[int])[source]#

Bases: object

Factory class for creating adaptive average pooling layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • output_size – the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H. H and W can be either a int, or None which means the size will be the same as that of the input.

class fusionlab.layers.factories.AdaptiveMaxPool(spatial_dims: int, output_size: int | Sequence[int], return_indices: bool = False)[source]#

Bases: object

Factory class for creating adaptive max pooling layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • output_size – the target output size \(L_{out}\).

  • return_indices – if True, will return the indices along with the outputs. Useful to pass to nn.MaxUnpool1d. Default: False

class fusionlab.layers.factories.AvgPool(spatial_dims: int, kernel_size: int | Sequence[int], stride: int | Sequence[int] | None = None, padding: int | Sequence[int] = 0, ceil_mode: bool = False, count_include_pad: bool = True)[source]#

Bases: object

Factory class for creating average pooling layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • kernel_size – the size of the window

  • stride – the stride of the window. Default value is kernel_size

  • padding – implicit zero padding to be added on both sides

  • ceil_mode – when True, will use ceil instead of floor to compute the output shape

  • count_include_pad – when True, will include the zero-padding in the averaging calculation

(the original version for 2d and 3d has a ‘divisor_override’ parameter which is neglected here)

class fusionlab.layers.factories.BatchNorm(spatial_dims: int, num_features: int, eps: float = 1e-05, momentum: float = 0.1, affine: bool = True, track_running_stats: bool = True)[source]#

Bases: object

Factory class for creating batch normalization layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • num_features – number of features or channels \(C\) of the input

  • eps – a value added to the denominator for numerical stability. Default: 1e-5

  • momentum – the value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1

  • affine – a boolean value that when set to True, this module has learnable affine parameters. Default: True

  • track_running_stats – a boolean value that when set to True, this module tracks the running mean and variance, and when set to False, this module does not track such statistics, and initializes statistics buffers running_mean and running_var as None. When these buffers are None, this module always uses batch statistics. in both training and eval modes. Default: True

class fusionlab.layers.factories.ConstantPad(spatial_dims: int, padding: int | Sequence[int], value: float)[source]#

Bases: object

Factory class for creating adaptive average pooling layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 4-tuple, uses (:math:` ext{padding_left}`, :math:` ext{padding_right}`, :math:` ext{padding_top}`, :math:` ext{padding_bottom}`)

  • value (float) – the value of padding

class fusionlab.layers.factories.ConvND(spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Sequence[int] | int, stride: Sequence[int] | int = 1, padding: int | Sequence[int] | None = None, dilation: Sequence[int] | int = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros')[source]#

Bases: object

Factory class for creating convolutional layers. This class is used to create convolutional layers with the same configuration.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • in_channels (int) – number of channels in the input data.

  • out_channels (int) – number of channels produced by the convolution.

  • kernel_size (int or tuple) – size of the convolving kernel.

  • stride (int or tuple, optional) – stride of the convolution. Default: 1

  • padding (int or tuple, optional) – zero-padding added to both sides of the input, None for same padding. Default: None

  • dilation (int or tuple, optional) – spacing between kernel elements. Default: 1

  • groups (int, optional) – number of blocked connections from input channels to output channels. Default: 1

  • bias (bool, optional) – whether to add a bias to the convolution. Default: True

  • padding_mode (str, optional) – type of padding. Default: ‘zeros’

class fusionlab.layers.factories.ConvT(spatial_dims, in_channels: int, out_channels: int, kernel_size: Sequence[int] | int, stride: Sequence[int] | int = 1, padding: int | Sequence[int] | None = None, output_padding: Sequence[int] | str = 0, groups: int = 1, bias: bool = True, dilation: Sequence[int] | int = 1, padding_mode: str = 'zeros')[source]#

Bases: object

Factory class for creating transposed convolutional layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • in_channels (int) – number of channels in the input data.

  • out_channels (int) – number of channels produced by the convolution.

  • kernel_size (int or tuple) – size of the convolving kernel.

  • stride (int or tuple, optional) – stride of the convolution. Default: 1

  • padding (int or tuple, optional) – zero-padding added to both sides of the input, None for same padding. Default: None

  • output_padding (int or tuple, optional) – additional size added to one side of each dimension in the output shape. Default: 0

  • groups (int, optional) – number of blocked connections from input channels to output channels. Default: 1

  • bias (bool, optional) – whether to add a bias to the convolution. Default: True

  • dilation (int or tuple, optional) – spacing between kernel elements. Default: 1

  • padding_mode (str, optional) – type of padding. Default: zeros

class fusionlab.layers.factories.MaxPool(spatial_dims: int, kernel_size: int | Sequence[int], stride: int | Sequence[int] | None = None, padding: Sequence[int] | int = 0, dilation: Sequence[int] | int = 1, return_indices: bool = False, ceil_mode: bool = False)[source]#

Bases: object

Factory class for creating maximum pooling layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • kernel_size – The size of the sliding window, must be > 0.

  • stride – The stride of the sliding window, must be > 0. Default value is kernel_size.

  • padding – Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

  • dilation – The stride between elements within a sliding window, must be > 0.

  • return_indices – If True, will return the argmax along with the max values. Useful for torch.nn.MaxUnpool1d later

  • ceil_mode – If True, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

class fusionlab.layers.factories.ReplicationPad(spatial_dims: int, padding: int | Sequence[int])[source]#

Bases: object

Factory class for creating replication padding layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 4-tuple, uses (:math:` ext{padding_left}`, :math:` ext{padding_right}`, :math:` ext{padding_top}`, :math:` ext{padding_bottom}`)

class fusionlab.layers.factories.Upsample(spatial_dims: int, size: int | Sequence[int] | None = None, scale_factor: int | Sequence[int] | None = None, mode: str = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool | None = None)[source]#

Bases: object

Factory class for creating Upsample layers.

Parameters:

  • spatial_dims (int) – number of spatial dimensions of the input data.

  • size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], optional) – output spatial sizes

  • scale_factor (float or Tuple[float] or Tuple[float, float] or Tuple[float, float, float], optional) – multiplier for spatial size. Has to match input size if it is a tuple.

  • mode (str, optional) – the upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest'

  • align_corners (bool, optional) – if True, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect when mode is 'linear', 'bilinear', 'bicubic', or 'trilinear'. Default: False

  • recompute_scale_factor (bool, optional) – recompute the scale_factor for use in the interpolation calculation. If recompute_scale_factor is True, then scale_factor must be passed in and scale_factor is used to compute the output size. The computed output size will be used to infer new scales for the interpolation. Note that when scale_factor is floating-point, it may differ from the recomputed scale_factor due to rounding and precision issues. If recompute_scale_factor is False, then size or scale_factor will be used directly for interpolation.