def extract(cls, node: Node):
onnx_opset_version = get_onnx_opset_version(node)
if onnx_opset_version is not None and onnx_opset_version >= 11:
mode = onnx_attr(node, 'mode', 's', default=b'nearest').decode()
transformation_mode = onnx_attr(node,
'coordinate_transformation_mode',
's',
default=b'half_pixel').decode()
nearest_mode = onnx_attr(node,
'nearest_mode',
's',
default=b'round_prefer_floor').decode()
cubic_coeff_a = onnx_attr(node,
'cubic_coeff_a',
'f',
default=-0.75)
attrs = {
'mode': mode,
'coordinate_transformation_mode': transformation_mode,
'nearest_mode': nearest_mode,
'cube_coeff': cubic_coeff_a
}
ONNXResize11Op.update_node_stat(node, attrs)
else:
mode = onnx_attr(node, 'mode', 's', default=b'nearest').decode()
UpsampleOp.update_node_stat(node, {'mode': mode})
return cls.enabled
def extract(cls, node):
onnx_opset_version = get_onnx_opset_version(node)
if onnx_opset_version is not None and onnx_opset_version >= 9:
mode = onnx_attr(node,
'mode',
's',
default='nearest',
dst_type=lambda x: x.decode())
ONNXResize10.update_node_stat(node, {'mode': mode})
else:
mode = onnx_attr(node,
'mode',
's',
default='nearest',
dst_type=lambda x: x.decode())
scales = onnx_attr(
node,
'scales',
'floats',
dst_type=lambda x: np.array(x, dtype=np.float32))
width_scale = onnx_attr(node, 'width_scale', 'f')
height_scale = onnx_attr(node, 'height_scale', 'f')
supported_modes = ['nearest', 'linear']
if mode not in supported_modes:
raise Error(
'Error decoding Upsample node {}, mode = {} is not in the list of supported modes {}.',
node.name, mode, supported_modes)
if scales is not None:
if scales.shape != (4, ):
raise Error(
'Upsample scales attribute is wrong for node {}. Only 4D scales are supported.',
node.name)
if math.fabs(scales[0] - 1) > 1e-5 or math.fabs(scales[1] -
1) > 1e-5:
raise Error(
'Upsampling of batch and feature dimensions is not supported for node {}.',
node.name)
height_scale = scales[2]
width_scale = scales[3]
if (width_scale is None
or height_scale is None) and len(node.in_nodes()) != 2:
raise Error(
'One/both of widths_scale = {} and height_scale = {} is not defined for Upsample node {}.',
width_scale, height_scale, node.name)
UpsampleOp.update_node_stat(
node, {
'mode': mode,
'height_scale': height_scale,
'width_scale': width_scale
})
return cls.enabled
def extract(node: Node):
mode = onnx_attr(node, 'mode', 's', default=b'nearest').decode()
UpsampleOp.update_node_stat(node, {'mode': mode})
return __class__.enabled
