Coverage for mlprodict/onnxrt/ops_cpu/op_qlinear_conv.py: 96%

1# -*- encoding: utf-8 -*- 

2# pylint: disable=E0203,E1101,C0111 

3""" 

4@file 

5@brief Runtime operator. 

6""" 

7import numpy 

8from ._op import OpRun 

9from .op_qlinear_conv_ import QLinearConvInt8, QLinearConvUInt8 # pylint: disable=E0611,E0401 

10 

11 

12class QLinearConv(OpRun): 

13 

14 atts = {'auto_pad': 'NOTSET', 

15 'group': 1, 

16 'dilations': [], 

17 'kernel_shape': [], 

18 'pads': [], 

19 'strides': []} 

20 

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

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

23 expected_attributes=QLinearConv.atts, 

24 **options) 

25 self._init() 

26 self._cstu8 = numpy.array([], dtype=numpy.uint8) 

27 self._csti8 = numpy.array([], dtype=numpy.int8) 

28 

29 def _init(self): 

30 self.rtu8_ = QLinearConvUInt8() 

31 self.rti8_ = QLinearConvInt8() 

32 for rt in [self.rtu8_, self.rti8_]: 

33 rt.init(self.auto_pad, 

34 numpy.array(self.dilations, dtype=numpy.int64), 

35 self.group, 

36 numpy.array(self.kernel_shape, dtype=numpy.int64), 

37 numpy.array(self.pads, dtype=numpy.int64), 

38 numpy.array(self.strides, dtype=numpy.int64)) 

39 

40 def _run(self, X, x_scale, x_zero_point, w, w_scale, w_zero_point, # pylint: disable=W0221 

41 y_scale, y_zero_point, B=None, attributes=None, verbose=0, fLOG=None): 

42 if X is None: 

43 raise ValueError( # pragma: no cover 

44 "X cannot be None for operator %r, ONNX=%r" % ( 

45 type(self), self.onnx_node)) 

46 if X.dtype == numpy.uint8: 

47 if B is None: 

48 b = self._cstu8 

49 else: 

50 b = B 

51 return (self.rtu8_.compute( 

52 X, x_scale, x_zero_point, w, w_scale, w_zero_point, # pylint: disable=W0221 

53 y_scale, y_zero_point, b), ) 

54 return (self.rti8_.compute( 

55 X, x_scale, x_zero_point, w, w_scale, w_zero_point, # pylint: disable=W0221 

56 y_scale, y_zero_point, B or self._csti8), )