flax.linen.ConvLocal#

class flax.linen.ConvLocal(features, kernel_size, strides=1, padding='SAME', input_dilation=1, kernel_dilation=1, feature_group_count=1, use_bias=True, mask=None, dtype=None, param_dtype=<class 'jax.numpy.float32'>, precision=None, kernel_init=<function variance_scaling.<locals>.init>, bias_init=<function zeros>, parent=<flax.linen.module._Sentinel object>, name=None)[source]#

Local convolution Module wrapping lax.conv_general_dilated_local.

features#

number of convolution filters.

Type

int

kernel_size#

shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers.

Type

Sequence[int]

strides#

an integer or a sequence of n integers, representing the inter-window strides (default: 1).

Type

Union[None, int, Sequence[int]]

padding#

either the string ‘SAME’, the string ‘VALID’, the string ‘CIRCULAR’ (periodic boundary conditions), or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension. A single int is interpeted as applying the same padding in all dims and passign a single int in a sequence causes the same padding to be used on both sides. ‘CAUSAL’ padding for a 1D convolution will left-pad the convolution axis, resulting in same-sized output.

Type

Union[str, int, Sequence[Union[int, Tuple[int, int]]]]

input_dilation#

an integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of inputs (default: 1). Convolution with input dilation d is equivalent to transposed convolution with stride d.

Type

Union[None, int, Sequence[int]]

kernel_dilation#

an integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1). Convolution with kernel dilation is also known as ‘atrous convolution’.

Type

Union[None, int, Sequence[int]]

feature_group_count#

integer, default 1. If specified divides the input features into groups.

Type

int

use_bias#

whether to add a bias to the output (default: True).

Type

bool

mask#

Optional mask for the weights during masked convolution. The mask must be the same shape as the convolution weight matrix.

Type

Optional[Any]

dtype#

the dtype of the computation (default: infer from input and params).

Type

Optional[Any]

param_dtype#

the dtype passed to parameter initializers (default: float32).

Type

Any

precision#

numerical precision of the computation see jax.lax.Precision for details.

Type

Union[None, str, jax._src.lax.lax.Precision, Tuple[str, str], Tuple[jax._src.lax.lax.Precision, jax._src.lax.lax.Precision]]

kernel_init#

initializer for the convolutional kernel.

Type

Callable[[Any, Tuple[int, …], Any], Any]

bias_init#

initializer for the bias.

Type

Callable[[Any, Tuple[int, …], Any], Any]

__call__(inputs)#

Applies a (potentially unshared) convolution to the inputs.

Parameters

inputs – input data with dimensions (*batch_dims, spatial_dims…, features). This is the channels-last convention, i.e. NHWC for a 2d convolution and NDHWC for a 3D convolution. Note: this is different from the input convention used by lax.conv_general_dilated, which puts the spatial dimensions last. Note: If the input has more than 1 batch dimension, all batch dimensions are flattened into a single dimension for the convolution and restored before returning. In some cases directly vmap’ing the layer may yield better performance than this default flattening approach. If the input lacks a batch dimension it will be added for the convolution and removed n return, an allowance made to enable writing single-example code.

Returns

The convolved data.

Methods