lava.proc.conv

lava.proc.conv.models

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class lava.proc.conv.models.AbstractPyConvModel(proc_params=None)

Bases: PyLoihiProcessModel

Abstract template implementation of PyConvModel.

a_buf = None

a_out = None

clamp_precision(x)

Return type:

ndarray

dilation: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=8)

groups: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=8)

kernel_size: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=8)

num_message_bits: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=5)

num_weight_bits: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=5)

padding: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=8)

run_spk()

Function that runs in Spiking Phase

Return type:

None

s_in = None

stride: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int8'>, precision=8)

weight = None

weight_exp: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=8)

class lava.proc.conv.models.PyConvModelFixed(proc_params=None)

Bases: AbstractPyConvModel

Conv with fixed point synapse implementation.

a_buf: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=24)

a_out: PyOutPort = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

clamp_precision(x)

Return type:

ndarray

implements_process

alias of Conv

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

s_in: PyInPort = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'numpy.int32'>, precision=24)

tags: ty.List[str] = ['fixed_pt']

weight: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'numpy.int32'>, precision=8)

class lava.proc.conv.models.PyConvModelFloat(proc_params=None)

Bases: AbstractPyConvModel

Conv with float synapse implementation.

a_buf: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

a_out: PyOutPort = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyOutPortVectorDense'>, d_type=<class 'float'>, precision=None)

implements_process

alias of Conv

implements_protocol

alias of LoihiProtocol

required_resources: ty.List[ty.Type[AbstractResource]] = [<class 'lava.magma.core.resources.CPU'>]

s_in: PyInPort = LavaPyType(cls=<class 'lava.magma.core.model.py.ports.PyInPortVectorDense'>, d_type=<class 'float'>, precision=24)

tags: ty.List[str] = ['floating_pt']

weight: ndarray = LavaPyType(cls=<class 'numpy.ndarray'>, d_type=<class 'float'>, precision=None)

lava.proc.conv.process

digraph inheritance932624acc4 { bgcolor=transparent; rankdir=TB; size=""; "AbstractProcess" [URL="../lava.magma.core.process.html#lava.magma.core.process.process.AbstractProcess",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top",tooltip="The notion of a Process is inspired by the Communicating Sequential"]; "Conv" [URL="../lava/lava.proc.conv.html#lava.proc.conv.process.Conv",fillcolor=white,fontname="Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans",fontsize=10,height=0.25,shape=box,style="setlinewidth(0.5),filled",target="_top"]; "AbstractProcess" -> "Conv" [arrowsize=0.5,style="setlinewidth(0.5)"]; }

class lava.proc.conv.process.Conv(*, weight, weight_exp=0, input_shape=(1, 1, 1), padding=0, stride=1, dilation=1, groups=1, num_weight_bits=8, num_message_bits=0, name=None, log_config=None)

Bases: AbstractProcess

lava.proc.conv.utils

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class lava.proc.conv.utils.TensorOrder(value)

Bases: IntEnum

Defines how images are represented by tensors.

Meaning:

N: number of images in a batch H: height of an image W: width of an image C: number of channels of an image

CHWN = 2

HWCN = 3

NCHW = 1

NWHC = 4

lava.proc.conv.utils.conv(input_, weight, kernel_size, stride, padding, dilation, groups)

Convolution implementation

Parameters:

• input (3 dimensional np array) – convolution input.

• weight (4 dimensional np array) – convolution kernel weight.

• kernel_size (2 element tuple, list, or array) – convolution kernel size in XY/WH format.

• stride (2 element tuple, list, or array) – convolution stride in XY/WH format.

• padding (2 element tuple, list, or array) – convolution padding in XY/WH format.

• dilation (2 element tuple, list, or array) – dilation of convolution kernel in XY/WH format.

• groups (int) – number of convolution groups.

Returns:

convolution output

Return type:

3 dimensional np array

lava.proc.conv.utils.conv_scipy(input_, weight, kernel_size, stride, padding, dilation, groups)

Scipy based implementation of convolution

Parameters:

• input (3 dimensional np array) – convolution input.

• weight (4 dimensional np array) – convolution kernel weight.

• kernel_size (2 element tuple, list, or array) – convolution kernel size in XY/WH format.

• stride (2 element tuple, list, or array) – convolution stride in XY/WH format.

• padding (2 element tuple, list, or array) – convolution padding in XY/WH format.

• dilation (2 element tuple, list, or array) – dilation of convolution kernel in XY/WH format.

• groups (int) – number of convolution groups.

Returns:

convolution output

Return type:

3 dimensional np array

lava.proc.conv.utils.conv_to_sparse(input_shape, output_shape, kernel, stride, padding, dilation, group, order=TensorOrder.NWHC)

Translate convolution kernel into sparse matrix.

Parameters:

• input_shape (tuple of 3 ints) – Shape of input to the convolution.

• output_shape (tuple of 3 ints) – Shape of output from the convolution.

• kernel (numpy array with 4 dimensions) – Convolution kernel. The kernel should have four dimensions. The order of kernel tensor is described by order argument. See Notes for the supported orders.

• stride (tuple of 2 ints) – Convolution stride.

• padding (tuple of 2 ints) – Convolution padding.

• dilation (tuple of 2 ints) – Convolution dilation.

• group (int) – Convolution groups.

• order (TensorOrder, optional) – The order of convolution kernel tensor. The default is lava convolution order i.e. TensorOrder.NWHC

Return type:

Tuple[ndarray, ndarray, ndarray]

Returns:

• np.ndarray – Destination indices of sparse matrix. It is a linear array of ints.

• np.ndarray – Source indices of sparse matrix. It is a linear array of ints.

• np.ndarray – Weight value at non-zero location.

Raises:

• ValueError – If tensor order is not supported.

• AssertionError – if output channels is not divisible by group.

• AssertionError – if input channels is not divisible by group.

Notes

Supported tensor dimensions

Input/Output order	Kernel order	Operation type
WHC	NWHC	Default Lava order. The operation is convolution.
CHW	NCHW	Default PyTorch order. The operation in correlation.
HWC	HWCN	Default Tensorflow order. The operation in correlation.

lava.proc.conv.utils.make_tuple(value)

Create a tuple of two integers from the given input.

Parameters:

value (int or tuple(int) or tuple(int, int)) –

Returns:

tuple value of input

Return type:

tuple(int, int)

Raises:

Exception – if argument value is not 1/2 dimensional.

lava.proc.conv.utils.output_shape(input_shape, out_channels, kernel_size, stride, padding, dilation)

Calculates the output shape of convolution operation.

Parameters:

• input_shape (3 element tuple, list, or array) – shape of input to convolution in XYZ/WHC format.

• out_channels (int) – number of output channels.

• kernel_size (2 element tuple, list, or array) – convolution kernel size in XY/WH format.

• stride (2 element tuple, list, or array) – convolution stride in XY/WH format.

• padding (2 element tuple, list, or array) – convolution padding in XY/WH format.

• dilation (2 element tuple, list, or array) – dilation of convolution kernel in XY/WH format.

Returns:

shape of convolution output in XYZ/WHC format.

Return type:

tuple of 3 ints

Raises:

• Exception – for invalid x convolution dimension.

• Exception – for invalid y convolution dimension.

lava.proc.conv.utils.signed_clamp(x, bits)

clamps as if input is a signed value within the precision of bits.

Parameters:

• x (int, float, np array) – input

• bits (int) – number of bits for the variable

Returns:

clamped value

Return type:

same type as x