Coverage for mlprodict/onnxrt/ops_cpu/op_lrn.py: 100%

1# -*- encoding: utf-8 -*- 

2# pylint: disable=E0203,E1101,C0111 

3""" 

4@file 

5@brief Runtime operator. 

6""" 

7import math 

8import numpy 

9from ._op import OpRun 

10 

11 

12class LRN(OpRun): 

13 

14 atts = { 

15 'alpha': 9.999999747378752e-05, 

16 'beta': 0.75, 

17 'bias': 1., 

18 'size': 3, 

19 } 

20 

21 def __init__(self, onnx_node, desc=None, **options): 

22 OpRun.__init__(self, onnx_node, desc=desc, 

23 expected_attributes=LRN.atts, 

24 **options) 

25 

26 def _run(self, x, attributes=None, verbose=0, fLOG=None): # pylint: disable=W0221 

27 if len(x.shape) != 4: 

28 raise RuntimeError( # pragma: no cover 

29 f"LRN only applies on 4D tensors but shape is {x.shape!r}.") 

30 square_sum = numpy.zeros(x.shape).astype(x.dtype) 

31 for ind in numpy.ndindex(x.shape): 

32 n, c, h, w = ind 

33 begin = max(0, c - int(math.floor((self.size - 1) / 2))) 

34 end = min(5, c + int(math.ceil((self.size - 1) / 2)) + 1) 

35 square_sum[n, c, h, w] = numpy.sum(x[n, begin:end, h, w] ** 2) 

36 y = x / ((self.bias + (self.alpha / self.size) * square_sum) ** self.beta) 

37 return (y.astype(x.dtype), )