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nir.ir.conv

nir.ir.conv#

Module Contents#

Classes#

Conv1d

Convolutional layer in 1d.

Conv2d

Convolutional layer in 2d.
class nir.ir.conv.Conv1d#

Bases: nir.ir.node.NIRNode

Convolutional layer in 1d.

Note that the input_shape argument is required to disambiguate the shape, and is used to infer the exact output shape along with the other parameters. If the input_shape is None, the output shape will also be None.

The NIRGraph.infer_all_shapes function may be used to automatically infer the input and output types on the graph level.

Parameters

  • input_shape (Optional[int]) – Shape of spatial input (N,)

  • weight (np.ndarray) – Weight, shape (C_out, C_in, N)

  • stride (int) – Stride

  • padding (int | str) – Padding, if string must be ‘same’ or ‘valid’

  • dilation (int) – Dilation

  • groups (int) – Groups

  • bias (np.ndarray) – Bias array of shape (C_out,)

input_shape: Optional[int]#
weight: numpy.ndarray#
stride: int#
padding: Union[int, str]#
dilation: int#
groups: int#
bias: numpy.ndarray#
input_type: Optional[Dict[str, numpy.ndarray]]#
output_type: Optional[Dict[str, numpy.ndarray]]#
metadata: Dict[str, Any]#
__post_init__()#
class nir.ir.conv.Conv2d#

Bases: nir.ir.node.NIRNode

Convolutional layer in 2d.

Note that the input_shape argument is required to disambiguate the shape, and is used to infer the exact output shape along with the other parameters. If the input_shape is None, the output shape will also be None.

The NIRGraph.infer_all_shapes function may be used to automatically infer the input and output types on the graph level.

Parameters

  • input_shape (Optional[tuple[int, int]]) – Shape of spatial input (N_x, N_y)

  • weight (np.ndarray) – Weight, shape (C_out, C_in, N_x, N_y)

  • stride (int | int, int) – Stride

  • padding (int | int, int | str) – Padding, if string must be ‘same’ or ‘valid’

  • dilation (int | int, int) – Dilation

  • groups (int) – Groups

  • bias (np.ndarray) – Bias array of shape (C_out,)

input_shape: Optional[Tuple[int, int]]#
weight: numpy.ndarray#
stride: Union[int, Tuple[int, int]]#
padding: Union[int, Tuple[int, int], str]#
dilation: Union[int, Tuple[int, int]]#
groups: int#
bias: numpy.ndarray#
__post_init__()#
Contents