module `onnxrt.ops_cpu.op_lrn`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_lrn

Runtime operator.

Classes#

class	truncated documentation
`LRN`	LRN === Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). …

Properties#

property	truncated documentation
`args_default`	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
`args_default_modified`	Returns the list of modified parameters.
`args_mandatory`	Returns the list of optional arguments.
`args_optional`	Returns the list of optional arguments.
`atts_value`	Returns all parameters in a dictionary.

Methods#

method	truncated documentation
`__init__`
`_run`

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_lrn.LRN(onnx_node, desc=None, **options)#

Bases: OpRun

===

Local Response Normalization proposed in the [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, …, dk] in a tensor of shape (N x C x D1 x D2, …, Dk), its region is {X[n, i, d1, …, dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.

square_sum[n, c, d1, …, dk] = sum(X[n, i, d1, …, dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).

Y[n, c, d1, …, dk] = X[n, c, d1, …, dk] / (bias + alpha / size * square_sum[n, c, d1, …, dk] ) ^ beta

Attributes

alpha: Scaling parameter. Default value is namealphaf9.999999747378752e-05typeFLOAT (FLOAT)
beta: The exponent. Default value is namebetaf0.75typeFLOAT (FLOAT)
bias: Default value is namebiasf1.0typeFLOAT (FLOAT)
size (required): The number of channels to sum over default value cannot be automatically retrieved (INT)

Inputs

X (heterogeneous)T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE …].

Outputs

Y (heterogeneous)T: Output tensor, which has the shape and type as input tensor

Type Constraints

T tensor(float16), tensor(float), tensor(double), tensor(bfloat16): Constrain input and output types to float tensors.

Version

Onnx name: LRN

This version of the operator has been available since version 13.

Runtime implementation: LRN

__init__(onnx_node, desc=None, **options)#

_run(x, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.