OPS#

Module of otx.core.ov.ops.

class otx.core.ov.ops.AddV1(name: str, **kwargs)#

AddV1 class.

ATTRIBUTE_FACTORY#: alias of AddV1Attribute

TYPE = 'Add'#

VERSION = 1#

forward(input_0, input_1)#: AddV1’s forward function.

class otx.core.ov.ops.Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.AvgPoolV1(*args, **kwargs)#

AvgPoolV1 class.

ATTRIBUTE_FACTORY#: alias of AvgPoolV1Attribute

TYPE = 'AvgPool'#

VERSION = 1#

forward(inputs)#: AvgPoolV1’s forward function.

class otx.core.ov.ops.BatchNormalizationV0(*args, **kwargs)#

BatchNormalizationV0 class.

ATTRIBUTE_FACTORY#: alias of BatchNormalizationV0Attribute

TYPE = 'BatchNormInference'#

VERSION = 0#

forward(inputs, gamma, beta, mean, variance)#: BatchNormalizationV0’s forward function.

class otx.core.ov.ops.BroadcastV3(name: str, **kwargs)#

BroadcastV3 class.

ATTRIBUTE_FACTORY#: alias of BroadcastV3Attribute

TYPE = 'Broadcast'#

VERSION = 3#

forward(inputs, target_shape, axes_mapping=None)#: BroadcastV3’s forward function.

class otx.core.ov.ops.ClampV0(name: str, **kwargs)#

ClampV0 class.

ATTRIBUTE_FACTORY#: alias of ClampV0Attribute

TYPE = 'Clamp'#

VERSION = 0#

forward(inputs)#: ClampV0’s forward function.

class otx.core.ov.ops.ConcatV0(name: str, **kwargs)#

ConcatV0 class.

ATTRIBUTE_FACTORY#: alias of ConcatV0Attribute

TYPE = 'Concat'#

VERSION = 0#

forward(*inputs)#: ConcatV0’s forward function.

class otx.core.ov.ops.ConstantV0(*args, **kwargs)#

ConstantV0 class.

ATTRIBUTE_FACTORY#: alias of ConstantV0Attribute

TYPE = 'Constant'#

VERSION = 0#

forward()#: ConstantV0’s forward function.

classmethod from_ov(ov_op)#: ConstantV0’s from_ov function.

class otx.core.ov.ops.ConvertV0(name: str, **kwargs)#

ConvertV0 class.

ATTRIBUTE_FACTORY#: alias of ConvertV0Attribute

TYPE = 'Convert'#

VERSION = 0#

static convert_ov_type(ov_type)#: ConvertV0’s convert_ov_type function.

static convert_torch_type(torch_type)#: ConvertV0’s convert_torch_type function.

forward(inputs)#: ConvertV0’s forward function.

class otx.core.ov.ops.ConvolutionV1(name: str, **kwargs)#

ConvolutionV1 class.

ATTRIBUTE_FACTORY#: alias of ConvolutionV1Attribute

TYPE = 'Convolution'#

VERSION = 1#

forward(inputs, weight)#: ConvolutionV1’s forward function.

class otx.core.ov.ops.DetectionOutputV0(name: str, **kwargs)#

DetectionOutputV0 class.

ATTRIBUTE_FACTORY#: alias of DetectionOutputV0Attribute

TYPE = 'DetectionOutput'#

VERSION = 0#

forward(loc_data, conf_data, prior_data, arm_conf_data=None, arm_loc_data=None)#: DetectionOutputV0’s forward.

class otx.core.ov.ops.DivideV1(name: str, **kwargs)#

DivideV1 class.

ATTRIBUTE_FACTORY#: alias of DivideV1Attribute

TYPE = 'Divide'#

VERSION = 1#

forward(input_0, input_1)#: DivideV1’s forward function.

class otx.core.ov.ops.EinsumV7(name: str, **kwargs)#

EinsumV7 class.

ATTRIBUTE_FACTORY#: alias of EinsumV7Attribute

TYPE = 'Einsum'#

VERSION = 7#

forward(*inputs)#: EinsumV7’s forward function.

class otx.core.ov.ops.EluV0(name: str, **kwargs)#

EluV0 class.

ATTRIBUTE_FACTORY#: alias of EluV0Attribute

TYPE = 'Elu'#

VERSION = 0#

forward(inputs)#: EluV0’s forward function.

class otx.core.ov.ops.ExpV0(name: str, **kwargs)#

ExpV0 class.

ATTRIBUTE_FACTORY#: alias of ExpV0Attribute

TYPE = 'Exp'#

VERSION = 0#

forward(inputs)#: ExpV0’s forward function.

class otx.core.ov.ops.GatherV0(name: str, **kwargs)#

GatherV0 class.

ATTRIBUTE_FACTORY#: alias of GatherV0Attribute

TYPE = 'Gather'#

VERSION = 0#

forward(inputs, indices, axis)#: GatherV0’s forward function.

class otx.core.ov.ops.GatherV1(name: str, **kwargs)#

GatherV1 class.

ATTRIBUTE_FACTORY#: alias of GatherV1Attribute

TYPE = 'Gather'#

VERSION = 1#

forward(inputs, indices, axis)#: GatherV1’s forward function.

class otx.core.ov.ops.GeluV7(name: str, **kwargs)#

GeluV7 class.

ATTRIBUTE_FACTORY#: alias of GeluV7Attribute

TYPE = 'Gelu'#

VERSION = 7#

forward(inputs)#: GeluV7’s forward function.

class otx.core.ov.ops.GroupConvolutionV1(name: str, **kwargs)#

GroupConvolutionV1 class.

ATTRIBUTE_FACTORY#: alias of GroupConvolutionV1Attribute

TYPE = 'GroupConvolution'#

VERSION = 1#

forward(inputs, weight)#: GroupConvolutionV1’s forward function.

class otx.core.ov.ops.HSigmoidV5(name: str, **kwargs)#

HSigmoidV5 class.

ATTRIBUTE_FACTORY#: alias of HSigmoidV5Attribute

TYPE = 'HSigmoid'#

VERSION = 5#

forward(inputs)#: HSigmoidV5’s forward function.

class otx.core.ov.ops.HSwishV4(name: str, **kwargs)#

HSwishV4 class.

ATTRIBUTE_FACTORY#: alias of HSwishV4Attribute

TYPE = 'HSwish'#

VERSION = 4#

forward(inputs)#: HSwishV4’s forward function.

class otx.core.ov.ops.HardSigmoidV0(name: str, **kwargs)#

HardSigmoidV0 class.

ATTRIBUTE_FACTORY#: alias of HardSigmoidV0Attribute

TYPE = 'HardSigmoid'#

VERSION = 0#

forward(inputs, alpha, beta)#: HardSigmoidV0’s forward function.

class otx.core.ov.ops.InterpolateV4(*args, **kwargs)#

InterpolateV4 class.

ATTRIBUTE_FACTORY#: alias of InterpolateV4Attribute

TYPE = 'Interpolate'#

VERSION = 4#

forward(inputs, sizes, scales, axes=None)#: InterpolateV4’s forward function.

class otx.core.ov.ops.LocalResponseNormalizationV0(name: str, **kwargs)#

LocalResponseNormalizationV0 class.

ATTRIBUTE_FACTORY#: alias of LocalResponseNormalizationV0Attribute

TYPE = 'LRN'#

VERSION = 0#

forward(inputs, axes)#: LocalResponseNormalizationV0’s forward function.

class otx.core.ov.ops.MVNV6(name: str, **kwargs)#

MVNV6 class.

ATTRIBUTE_FACTORY#: alias of MVNV6Attribute

TYPE = 'MVN'#

VERSION = 6#

forward(inputs, axes)#: MVNV6’s forward function.

class otx.core.ov.ops.MatMulV0(name: str, **kwargs)#

MatMulV0 class.

ATTRIBUTE_FACTORY#: alias of MatMulV0Attribute

TYPE = 'MatMul'#

VERSION = 0#

forward(input_a, input_b)#: MatMulV0’s forward function.

class otx.core.ov.ops.MaxPoolV0(name: str, **kwargs)#

MaxPoolV0 class.

ATTRIBUTE_FACTORY#: alias of MaxPoolV0Attribute

TYPE = 'MaxPool'#

VERSION = 0#

forward(inputs)#: MaxPoolV0’s forward function.

class otx.core.ov.ops.MishV4(name: str, **kwargs)#

MishV4 class.

ATTRIBUTE_FACTORY#: alias of MishV4Attribute

TYPE = 'Mish'#

VERSION = 4#

forward(inputs)#: MishV4’s forward function.

class otx.core.ov.ops.MultiplyV1(name: str, **kwargs)#

MultiplyV1 class.

ATTRIBUTE_FACTORY#: alias of MultiplyV1Attribute

TYPE = 'Multiply'#

VERSION = 1#

forward(input_0, input_1)#: MultiplyV1’s forward function.

class otx.core.ov.ops.NonMaxSuppressionV5(name: str, **kwargs)#

NonMaxSuppressionV5 class.

ATTRIBUTE_FACTORY#: alias of NonMaxSuppressionV5Attribute

TYPE = 'NonMaxSuppression'#

VERSION = 5#

forward(boxes, scores, max_output_boxes_per_class, iou_threshold=0, score_threshold=0, soft_nms_sigma=0)#: NonMaxSuppressionV5’s forward function.

class otx.core.ov.ops.NonMaxSuppressionV9(name: str, **kwargs)#

NonMaxSuppressionV9 class.

ATTRIBUTE_FACTORY#: alias of NonMaxSuppressionV9Attribute

TYPE = 'NonMaxSuppression'#

VERSION = 9#

forward(boxes, scores, max_output_boxes_per_class, iou_threshold=0, score_threshold=0, soft_nms_sigma=0)#: NonMaxSuppressionV9’s forward function.

class otx.core.ov.ops.NormalizeL2V0(name: str, **kwargs)#

NormalizeL2V0 class.

ATTRIBUTE_FACTORY#: alias of NormalizeL2V0Attribute

TYPE = 'NormalizeL2'#

VERSION = 0#

forward(inputs, axes)#: NormalizeL2V0’s forward function.

class otx.core.ov.ops.Operation(name: str, **kwargs)#

Operation class.

ATTRIBUTE_FACTORY#: alias of Attribute

TYPE = ''#

VERSION = -1#

property attrs#: Operation’s attrs property.

classmethod from_ov(ov_op)#: Operation’s from_ov function.

property name: str#: Operation’s name property.

property shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#: Operation’s shape property.

property type: str#: Operation’s type property.

property version: int#: Operation’s version property.

class otx.core.ov.ops.OperationRegistry(name, add_name_as_attr=False)#

OperationRegistry class.

get_by_name(name)#: Get obj from name.

get_by_type_version(types, version)#: Get obj from type and version.

register(name: Optional[Any] = None)#: Register function from name.

class otx.core.ov.ops.PReluV0(name: str, **kwargs)#

PReluV0 class.

ATTRIBUTE_FACTORY#: alias of PReluV0Attribute

TYPE = 'PRelu'#

VERSION = 0#

forward(inputs, slope)#: PReluV0’s forward function.

training: bool#

class otx.core.ov.ops.PadV1(*args, **kwargs)#

PadV1 class.

ATTRIBUTE_FACTORY#: alias of PadV1Attribute

TYPE = 'Pad'#

VERSION = 1#

forward(inputs, pads_begin, pads_end, pad_value=0)#: PadV1’s forward function.

static get_torch_pad_dim(pads_begin, pads_end)#: PadV1’s get_torch_pad_dim function.

static get_torch_pad_mode(pad_mode)#: PadV1’s get_torch_pad_mode function.

training: bool#

class otx.core.ov.ops.ParameterV0(name: str, **kwargs)#

ParameterV0 class.

ATTRIBUTE_FACTORY#: alias of ParameterV0Attribute

TYPE = 'Parameter'#

VERSION = 0#

forward(inputs)#: ParameterV0’s forward function.

classmethod from_ov(ov_op)#: ParameterV0’s from_ov function.

training: bool#

class otx.core.ov.ops.PriorBoxClusteredV0(name: str, **kwargs)#

PriorBoxClusteredV0 class.

ATTRIBUTE_FACTORY#: alias of PriorBoxClusteredV0Attribute

TYPE = 'PriorBoxClustered'#

VERSION = 0#

forward(output_size, image_size)#: PriorBoxClusteredV0’s forward function.

training: bool#

class otx.core.ov.ops.PriorBoxV0(name: str, **kwargs)#

PriorBoxV0 class.

ATTRIBUTE_FACTORY#: alias of PriorBoxV0Attribute

TYPE = 'PriorBox'#

VERSION = 0#

forward(output_size, image_size)#: PriorBoxV0’s forward function.

training: bool#

class otx.core.ov.ops.ProposalV4(name: str, **kwargs)#

ProposalV4 class.

ATTRIBUTE_FACTORY#: alias of ProposalV4Attribute

TYPE = 'Proposal'#

VERSION = 4#

forward(class_probs, bbox_deltas, image_shape)#: ProposalV4’s forward function.

training: bool#

class otx.core.ov.ops.ROIPoolingV0(name: str, **kwargs)#

ROIPoolingV0 class.

ATTRIBUTE_FACTORY#: alias of ROIPoolingV0Attribute

TYPE = 'ROIPooling'#

VERSION = 0#

forward(inputs, boxes)#: ROIPoolingV0’s forward function.

training: bool#

class otx.core.ov.ops.RangeV4(name: str, **kwargs)#

RangeV4 class.

ATTRIBUTE_FACTORY#: alias of RangeV4Attribute

TYPE = 'Range'#

VERSION = 4#

forward(start, stop, step)#: RangeV4’s forward function.

training: bool#

class otx.core.ov.ops.ReduceMeanV1(name: str, **kwargs)#

ReduceMeanV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceMeanV1Attribute

TYPE = 'ReduceMean'#

VERSION = 1#

forward(inputs, axes)#: ReduceMeanV1’s forward function.

training: bool#

class otx.core.ov.ops.ReduceMinV1(name: str, **kwargs)#

ReduceMinV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceMinV1Attribute

TYPE = 'ReduceMin'#

VERSION = 1#

forward(inputs, axes)#: ReduceMinV1’s forward function.

training: bool#

class otx.core.ov.ops.ReduceProdV1(name: str, **kwargs)#

ReduceMeanV1Attribute class.

ATTRIBUTE_FACTORY#: alias of ReduceProdV1Attribute

TYPE = 'ReduceProd'#

VERSION = 1#

forward(inputs, axes)#: ReduceMeanV1Attribute’s forward function.

training: bool#

class otx.core.ov.ops.ReduceSumV1(name: str, **kwargs)#

ReduceSumV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceSumV1Attribute

TYPE = 'ReduceSum'#

VERSION = 1#

forward(inputs, axes)#: ReduceSumV1’s forward function.

training: bool#

class otx.core.ov.ops.RegionYoloV0(name: str, **kwargs)#

RegionYoloV0 class.

ATTRIBUTE_FACTORY#: alias of RegionYoloV0Attribute

TYPE = 'RegionYolo'#

VERSION = 0#

forward(inputs)#: RegionYoloV0’s forward function.

training: bool#

class otx.core.ov.ops.ReluV0(name: str, **kwargs)#

ReluV0 class.

ATTRIBUTE_FACTORY#: alias of ReluV0Attribute

TYPE = 'Relu'#

VERSION = 0#

forward(inputs)#: ReluV0’s forward function.

training: bool#

class otx.core.ov.ops.ReshapeV1(name: str, **kwargs)#

ReshapeV1 class.

ATTRIBUTE_FACTORY#: alias of ReshapeV1Attribute

TYPE = 'Reshape'#

VERSION = 1#

forward(inputs, shape)#: ReshapeV1’s forward function.

training: bool#

class otx.core.ov.ops.ResultV0(name: str, **kwargs)#

ResultV0 class.

ATTRIBUTE_FACTORY#: alias of ResultV0Attribute

TYPE = 'Result'#

VERSION = 0#

forward(inputs)#: ResultV0’s forward function.

training: bool#

class otx.core.ov.ops.ScatterNDUpdateV3(name: str, **kwargs)#

ScatterNDUpdateV3 class.

ATTRIBUTE_FACTORY#: alias of ScatterNDUpdateV3Attribute

TYPE = 'ScatterNDUpdate'#

VERSION = 3#

forward(inputs, indicies, updates)#: ScatterNDUpdateV3’s forward function.

training: bool#

class otx.core.ov.ops.ScatterUpdateV3(name: str, **kwargs)#

ScatterUpdateV3 class.

ATTRIBUTE_FACTORY#: alias of ScatterUpdateV3Attribute

TYPE = 'ScatterUpdate'#

VERSION = 3#

forward(inputs, indicies, updates, axis)#: ScatterUpdateV3’s forward function.

training: bool#

class otx.core.ov.ops.SeluV0(name: str, **kwargs)#

SeluV0 class.

ATTRIBUTE_FACTORY#: alias of SeluV0Attribute

TYPE = 'Selu'#

VERSION = 0#

forward(inputs, alpha, lambda_)#: SeluV0’s forward function.

training: bool#

class otx.core.ov.ops.ShapeOfV0(name: str, **kwargs)#

ShapeOfV0 class.

ATTRIBUTE_FACTORY#: alias of ShapeOfV0Attribute

TYPE = 'ShapeOf'#

VERSION = 0#

forward(inputs)#: ShapeOfV0’s forward function.

training: bool#

class otx.core.ov.ops.ShapeOfV3(name: str, **kwargs)#

ShapeOfV3 class.

ATTRIBUTE_FACTORY#: alias of ShapeOfV3Attribute

TYPE = 'ShapeOf'#

VERSION = 3#

forward(inputs)#: ShapeOfV3’s forward function.

training: bool#

class otx.core.ov.ops.ShuffleChannelsV0(name: str, **kwargs)#

ShuffleChannelsV0 class.

ATTRIBUTE_FACTORY#: alias of ShuffleChannelsV0Attribute

TYPE = 'ShuffleChannels'#

VERSION = 0#

forward(inputs)#: ShuffleChannelsV0’s forward function.

training: bool#

class otx.core.ov.ops.SigmoidV0(name: str, **kwargs)#

SigmoidV0 class.

ATTRIBUTE_FACTORY#: alias of SigmoidV0Attribute

TYPE = 'Sigmoid'#

VERSION = 0#

forward(inputs)#: SigmoidV0’s forward function.

training: bool#

class otx.core.ov.ops.SoftMaxV0(name: str, **kwargs)#

SoftMaxV0 class.

ATTRIBUTE_FACTORY#: alias of SoftMaxV0Attribute

TYPE = 'Softmax'#

VERSION = 0#

forward(inputs)#: SoftMaxV0’s forward function.

training: bool#

class otx.core.ov.ops.SoftMaxV1(name: str, **kwargs)#

SoftMaxV1 class.

ATTRIBUTE_FACTORY#: alias of SoftMaxV1Attribute

TYPE = 'Softmax'#

VERSION = 1#

forward(inputs)#: SoftMaxV1’s forward function.

training: bool#

class otx.core.ov.ops.SplitV1(name: str, **kwargs)#

SplitV1 class.

ATTRIBUTE_FACTORY#: alias of SplitV1Attribute

TYPE = 'Split'#

VERSION = 1#

forward(inputs, axis)#: SplitV1’s forward function.

training: bool#

class otx.core.ov.ops.SqueezeV0(name: str, **kwargs)#

SqueezeV0 class.

ATTRIBUTE_FACTORY#: alias of SqueezeV0Attribute

TYPE = 'Squeeze'#

VERSION = 0#

forward(inputs, dims=None)#: SqueezeV0’s forward function.

training: bool#

class otx.core.ov.ops.StridedSliceV1(name: str, **kwargs)#

StridedSliceV1 class.

ATTRIBUTE_FACTORY#: alias of StridedSliceV1Attribute

TYPE = 'StridedSlice'#

VERSION = 1#

forward(inputs, begin, end, stride=None)#: StridedSliceV1’s forward function.

training: bool#

class otx.core.ov.ops.SubtractV1(name: str, **kwargs)#

SubtractV1 class.

ATTRIBUTE_FACTORY#: alias of SubtractV1Attribute

TYPE = 'Subtract'#

VERSION = 1#

forward(input_0, input_1)#: SubtractV1’s forward function.

training: bool#

class otx.core.ov.ops.SwishV4(name: str, **kwargs)#

SwishV4 class.

ATTRIBUTE_FACTORY#: alias of SwishV4Attribute

TYPE = 'Swish'#

VERSION = 4#

forward(inputs, beta=1.0)#: SwishV4’s forward function.

training: bool#

class otx.core.ov.ops.TanV0(name: str, **kwargs)#

TanV0 class.

ATTRIBUTE_FACTORY#: alias of TanV0Attribute

TYPE = 'Tan'#

VERSION = 0#

forward(inputs)#: TanV0’s forward function.

training: bool#

class otx.core.ov.ops.TanhV0(name: str, **kwargs)#

TanhV0 class.

ATTRIBUTE_FACTORY#: alias of TanhV0Attribute

TYPE = 'Tanh'#

VERSION = 0#

forward(inputs)#: TanhV0’s forward function.

training: bool#

class otx.core.ov.ops.TileV0(name: str, **kwargs)#

TileV0 class.

ATTRIBUTE_FACTORY#: alias of TileV0Attribute

TYPE = 'Tile'#

VERSION = 0#

forward(inputs, repeats)#: TileV0’s forward function.

training: bool#

class otx.core.ov.ops.TopKV3(name: str, **kwargs)#

TopKV3 class.

ATTRIBUTE_FACTORY#: alias of TopKV3Attribute

TYPE = 'TopK'#

VERSION = 3#

forward(inputs, k)#: TopKV3’s forward function.

training: bool#

class otx.core.ov.ops.TransposeV1(name: str, **kwargs)#

TransposeV1 class.

ATTRIBUTE_FACTORY#: alias of TransposeV1Attribute

TYPE = 'Transpose'#

VERSION = 1#

forward(inputs, order)#: TransposeV1’s forward function.

training: bool#

class otx.core.ov.ops.UnsqueezeV0(name: str, **kwargs)#

UnsqueezeV0 class.

ATTRIBUTE_FACTORY#: alias of UnsqueezeV0Attribute

TYPE = 'Unsqueeze'#

VERSION = 0#

forward(inputs, dims)#: UnsqueezeV0’s forward function.

training: bool#

class otx.core.ov.ops.VariadicSplitV1(name: str, **kwargs)#

VariadicSplitV1 class.

ATTRIBUTE_FACTORY#: alias of VariadicSplitV1Attribute

TYPE = 'VariadicSplit'#

VERSION = 1#

forward(inputs, axis, split_lengths)#: VariadicSplitV1’s forward function.

training: bool#

Activation-related modules for otx.core.ov.ops.activations.

class otx.core.ov.ops.activations.ClampV0(name: str, **kwargs)#

ClampV0 class.

ATTRIBUTE_FACTORY#: alias of ClampV0Attribute

TYPE = 'Clamp'#

VERSION = 0#

forward(inputs)#: ClampV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.ClampV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], min: float, max: float)#

ClampV0Attribute class.

max: float#

min: float#

class otx.core.ov.ops.activations.EluV0(name: str, **kwargs)#

EluV0 class.

ATTRIBUTE_FACTORY#: alias of EluV0Attribute

TYPE = 'Elu'#

VERSION = 0#

forward(inputs)#: EluV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.EluV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], alpha: float)#

EluV0Attribute class.

alpha: float#

class otx.core.ov.ops.activations.ExpV0(name: str, **kwargs)#

ExpV0 class.

ATTRIBUTE_FACTORY#: alias of ExpV0Attribute

TYPE = 'Exp'#

VERSION = 0#

forward(inputs)#: ExpV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.ExpV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ExpV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.GeluV7(name: str, **kwargs)#

GeluV7 class.

ATTRIBUTE_FACTORY#: alias of GeluV7Attribute

TYPE = 'Gelu'#

VERSION = 7#

forward(inputs)#: GeluV7’s forward function.

training: bool#

class otx.core.ov.ops.activations.GeluV7Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], approximation_mode: str = 'ERF')#

GeluV7Attribute class.

approximation_mode: str = 'ERF'#

class otx.core.ov.ops.activations.HSigmoidV5(name: str, **kwargs)#

HSigmoidV5 class.

ATTRIBUTE_FACTORY#: alias of HSigmoidV5Attribute

TYPE = 'HSigmoid'#

VERSION = 5#

forward(inputs)#: HSigmoidV5’s forward function.

training: bool#

class otx.core.ov.ops.activations.HSigmoidV5Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

HSigmoidV5Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.HSwishV4(name: str, **kwargs)#

HSwishV4 class.

ATTRIBUTE_FACTORY#: alias of HSwishV4Attribute

TYPE = 'HSwish'#

VERSION = 4#

forward(inputs)#: HSwishV4’s forward function.

training: bool#

class otx.core.ov.ops.activations.HSwishV4Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

HSwishV4Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.HardSigmoidV0(name: str, **kwargs)#

HardSigmoidV0 class.

ATTRIBUTE_FACTORY#: alias of HardSigmoidV0Attribute

TYPE = 'HardSigmoid'#

VERSION = 0#

forward(inputs, alpha, beta)#: HardSigmoidV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.HardSigmoidV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

HardSigmoidV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.MishV4(name: str, **kwargs)#

MishV4 class.

ATTRIBUTE_FACTORY#: alias of MishV4Attribute

TYPE = 'Mish'#

VERSION = 4#

forward(inputs)#: MishV4’s forward function.

training: bool#

class otx.core.ov.ops.activations.MishV4Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

MishV4Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.PReluV0(name: str, **kwargs)#

PReluV0 class.

ATTRIBUTE_FACTORY#: alias of PReluV0Attribute

TYPE = 'PRelu'#

VERSION = 0#

forward(inputs, slope)#: PReluV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.PReluV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

PReluV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.ReluV0(name: str, **kwargs)#

ReluV0 class.

ATTRIBUTE_FACTORY#: alias of ReluV0Attribute

TYPE = 'Relu'#

VERSION = 0#

forward(inputs)#: ReluV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.ReluV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ReluV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.SeluV0(name: str, **kwargs)#

SeluV0 class.

ATTRIBUTE_FACTORY#: alias of SeluV0Attribute

TYPE = 'Selu'#

VERSION = 0#

forward(inputs, alpha, lambda_)#: SeluV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.SeluV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

SeluV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.SigmoidV0(name: str, **kwargs)#

SigmoidV0 class.

ATTRIBUTE_FACTORY#: alias of SigmoidV0Attribute

TYPE = 'Sigmoid'#

VERSION = 0#

forward(inputs)#: SigmoidV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.SigmoidV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

SigmoidV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.SoftMaxV0(name: str, **kwargs)#

SoftMaxV0 class.

ATTRIBUTE_FACTORY#: alias of SoftMaxV0Attribute

TYPE = 'Softmax'#

VERSION = 0#

forward(inputs)#: SoftMaxV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.SoftMaxV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], axis: int = 1)#

SoftMaxV0Attribute class.

axis: int = 1#

class otx.core.ov.ops.activations.SoftMaxV1(name: str, **kwargs)#

SoftMaxV1 class.

ATTRIBUTE_FACTORY#: alias of SoftMaxV1Attribute

TYPE = 'Softmax'#

VERSION = 1#

forward(inputs)#: SoftMaxV1’s forward function.

training: bool#

class otx.core.ov.ops.activations.SoftMaxV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], axis: int = 1)#

SoftMaxV1Attribute class.

axis: int = 1#

class otx.core.ov.ops.activations.SwishV4(name: str, **kwargs)#

SwishV4 class.

ATTRIBUTE_FACTORY#: alias of SwishV4Attribute

TYPE = 'Swish'#

VERSION = 4#

forward(inputs, beta=1.0)#: SwishV4’s forward function.

training: bool#

class otx.core.ov.ops.activations.SwishV4Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

SwishV4Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.activations.TanhV0(name: str, **kwargs)#

TanhV0 class.

ATTRIBUTE_FACTORY#: alias of TanhV0Attribute

TYPE = 'Tanh'#

VERSION = 0#

forward(inputs)#: TanhV0’s forward function.

training: bool#

class otx.core.ov.ops.activations.TanhV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

TanhV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

Arithmetics-related codes for otx.core.ov.ops.arithmetics.

class otx.core.ov.ops.arithmetics.AddV1(name: str, **kwargs)#

AddV1 class.

ATTRIBUTE_FACTORY#: alias of AddV1Attribute

TYPE = 'Add'#

VERSION = 1#

forward(input_0, input_1)#: AddV1’s forward function.

training: bool#

class otx.core.ov.ops.arithmetics.AddV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], auto_broadcast: str = 'numpy')#

AddV1Attribute class.

auto_broadcast: str = 'numpy'#

class otx.core.ov.ops.arithmetics.DivideV1(name: str, **kwargs)#

DivideV1 class.

ATTRIBUTE_FACTORY#: alias of DivideV1Attribute

TYPE = 'Divide'#

VERSION = 1#

forward(input_0, input_1)#: DivideV1’s forward function.

training: bool#

class otx.core.ov.ops.arithmetics.DivideV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], m_pythondiv: bool = True, auto_broadcast: str = 'numpy')#

DivideV1Attribute class.

auto_broadcast: str = 'numpy'#

m_pythondiv: bool = True#

class otx.core.ov.ops.arithmetics.MultiplyV1(name: str, **kwargs)#

MultiplyV1 class.

ATTRIBUTE_FACTORY#: alias of MultiplyV1Attribute

TYPE = 'Multiply'#

VERSION = 1#

forward(input_0, input_1)#: MultiplyV1’s forward function.

training: bool#

class otx.core.ov.ops.arithmetics.MultiplyV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], auto_broadcast: str = 'numpy')#

MultiplyV1Attribute class.

auto_broadcast: str = 'numpy'#

class otx.core.ov.ops.arithmetics.SubtractV1(name: str, **kwargs)#

SubtractV1 class.

ATTRIBUTE_FACTORY#: alias of SubtractV1Attribute

TYPE = 'Subtract'#

VERSION = 1#

forward(input_0, input_1)#: SubtractV1’s forward function.

training: bool#

class otx.core.ov.ops.arithmetics.SubtractV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], auto_broadcast: str = 'numpy')#

SubtractV1Attribute class.

auto_broadcast: str = 'numpy'#

class otx.core.ov.ops.arithmetics.TanV0(name: str, **kwargs)#

TanV0 class.

ATTRIBUTE_FACTORY#: alias of TanV0Attribute

TYPE = 'Tan'#

VERSION = 0#

forward(inputs)#: TanV0’s forward function.

training: bool#

class otx.core.ov.ops.arithmetics.TanV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

TanV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

OPS (OperationRegistry) module for otx.core.ov.ops.builder.

class otx.core.ov.ops.builder.OperationRegistry(name, add_name_as_attr=False)#

OperationRegistry class.

get_by_name(name)#: Get obj from name.

get_by_type_version(types, version)#: Get obj from type and version.

register(name: Optional[Any] = None)#: Register function from name.

Convolutions-related module for otx.core.ov.ops.

class otx.core.ov.ops.convolutions.ConvolutionV1(name: str, **kwargs)#

ConvolutionV1 class.

ATTRIBUTE_FACTORY#: alias of ConvolutionV1Attribute

TYPE = 'Convolution'#

VERSION = 1#

forward(inputs, weight)#: ConvolutionV1’s forward function.

training: bool#

class otx.core.ov.ops.convolutions.ConvolutionV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], strides: List[int], pads_begin: List[int], pads_end: List[int], dilations: List[int], auto_pad: str = 'explicit')#

ConvolutionV1Attribute class.

auto_pad: str = 'explicit'#

dilations: List[int]#

pads_begin: List[int]#

pads_end: List[int]#

strides: List[int]#

class otx.core.ov.ops.convolutions.GroupConvolutionV1(name: str, **kwargs)#

GroupConvolutionV1 class.

ATTRIBUTE_FACTORY#: alias of GroupConvolutionV1Attribute

TYPE = 'GroupConvolution'#

VERSION = 1#

forward(inputs, weight)#: GroupConvolutionV1’s forward function.

training: bool#

class otx.core.ov.ops.convolutions.GroupConvolutionV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], strides: List[int], pads_begin: List[int], pads_end: List[int], dilations: List[int], auto_pad: str = 'explicit')#

GroupConvolutionV1Attribute class.

dilations: List[int]#

pads_begin: List[int]#

pads_end: List[int]#

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

strides: List[int]#

Generation-related module for otx.core.ov.ops.

class otx.core.ov.ops.generation.RangeV4(name: str, **kwargs)#

RangeV4 class.

ATTRIBUTE_FACTORY#: alias of RangeV4Attribute

TYPE = 'Range'#

VERSION = 4#

forward(start, stop, step)#: RangeV4’s forward function.

training: bool#

class otx.core.ov.ops.generation.RangeV4Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], output_type: str)#

RangeV4Attribute class.

output_type: str#

Image Processings-related code for otx.core.ov.ops.

class otx.core.ov.ops.image_processings.InterpolateV4(*args, **kwargs)#

InterpolateV4 class.

ATTRIBUTE_FACTORY#: alias of InterpolateV4Attribute

TYPE = 'Interpolate'#

VERSION = 4#

forward(inputs, sizes, scales, axes=None)#: InterpolateV4’s forward function.

training: bool#

class otx.core.ov.ops.image_processings.InterpolateV4Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], mode: str, shape_calculation_mode: str, coordinate_transformation_mode: str = 'half_pixel', nearest_mode: str = 'round_prefer_floor', antialias: bool = False, pads_begin: ~typing.List[int] = <factory>, pads_end: ~typing.List[int] = <factory>, cube_coeff: float = -0.75)#

InterpolateV4Attribute class.

antialias: bool = False#

coordinate_transformation_mode: str = 'half_pixel'#

cube_coeff: float = -0.75#

mode: str#

nearest_mode: str = 'round_prefer_floor'#

pads_begin: List[int]#

pads_end: List[int]#

shape_calculation_mode: str#

Infrastructure-related modules for otx.core.ov.ops.

class otx.core.ov.ops.infrastructures.ConstantV0(*args, **kwargs)#

ConstantV0 class.

ATTRIBUTE_FACTORY#: alias of ConstantV0Attribute

TYPE = 'Constant'#

VERSION = 0#

forward()#: ConstantV0’s forward function.

classmethod from_ov(ov_op)#: ConstantV0’s from_ov function.

training: bool#

class otx.core.ov.ops.infrastructures.ConstantV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], element_type: str, offset: int = 0, size: int = 0, is_parameter: bool = False)#

ConstantV0Attribute class.

element_type: str#

is_parameter: bool = False#

offset: int = 0#

size: int = 0#

class otx.core.ov.ops.infrastructures.ParameterV0(name: str, **kwargs)#

ParameterV0 class.

ATTRIBUTE_FACTORY#: alias of ParameterV0Attribute

TYPE = 'Parameter'#

VERSION = 0#

forward(inputs)#: ParameterV0’s forward function.

classmethod from_ov(ov_op)#: ParameterV0’s from_ov function.

training: bool#

class otx.core.ov.ops.infrastructures.ParameterV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], element_type: Optional[str] = None, layout: Optional[Tuple[str]] = None, permute: Optional[Tuple[int]] = None, verify_shape: bool = True)#

ParameterV0Attribute class.

element_type: Optional[str] = None#

layout: Optional[Tuple[str]] = None#

permute: Optional[Tuple[int]] = None#

verify_shape: bool = True#

class otx.core.ov.ops.infrastructures.ResultV0(name: str, **kwargs)#

ResultV0 class.

ATTRIBUTE_FACTORY#: alias of ResultV0Attribute

TYPE = 'Result'#

VERSION = 0#

forward(inputs)#: ResultV0’s forward function.

training: bool#

class otx.core.ov.ops.infrastructures.ResultV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ResultV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

MatMul-related modules for otx.core.ov.ops.

class otx.core.ov.ops.matmuls.EinsumV7(name: str, **kwargs)#

EinsumV7 class.

ATTRIBUTE_FACTORY#: alias of EinsumV7Attribute

TYPE = 'Einsum'#

VERSION = 7#

forward(*inputs)#: EinsumV7’s forward function.

training: bool#

class otx.core.ov.ops.matmuls.EinsumV7Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], equation: str)#

EinsumV7Attribute class.

equation: str#

class otx.core.ov.ops.matmuls.MatMulV0(name: str, **kwargs)#

MatMulV0 class.

ATTRIBUTE_FACTORY#: alias of MatMulV0Attribute

TYPE = 'MatMul'#

VERSION = 0#

forward(input_a, input_b)#: MatMulV0’s forward function.

training: bool#

class otx.core.ov.ops.matmuls.MatMulV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], transpose_a: bool = False, transpose_b: bool = False)#

MatMulV0Attribute class.

transpose_a: bool = False#

transpose_b: bool = False#

Movement-related modules for otx.core.ov.ops.

class otx.core.ov.ops.movements.BroadcastV3(name: str, **kwargs)#

BroadcastV3 class.

ATTRIBUTE_FACTORY#: alias of BroadcastV3Attribute

TYPE = 'Broadcast'#

VERSION = 3#

forward(inputs, target_shape, axes_mapping=None)#: BroadcastV3’s forward function.

training: bool#

class otx.core.ov.ops.movements.BroadcastV3Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], mode: str = 'numpy')#

BroadcastV3Attribute class.

mode: str = 'numpy'#

class otx.core.ov.ops.movements.ConcatV0(name: str, **kwargs)#

ConcatV0 class.

ATTRIBUTE_FACTORY#: alias of ConcatV0Attribute

TYPE = 'Concat'#

VERSION = 0#

forward(*inputs)#: ConcatV0’s forward function.

training: bool#

class otx.core.ov.ops.movements.ConcatV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], axis: int)#

ConcatV0Attribute class.

axis: int#

class otx.core.ov.ops.movements.GatherV0(name: str, **kwargs)#

GatherV0 class.

ATTRIBUTE_FACTORY#: alias of GatherV0Attribute

TYPE = 'Gather'#

VERSION = 0#

forward(inputs, indices, axis)#: GatherV0’s forward function.

training: bool#

class otx.core.ov.ops.movements.GatherV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], batch_dims: int = 0)#

GatherV0Attribute class.

batch_dims: int = 0#

class otx.core.ov.ops.movements.GatherV1(name: str, **kwargs)#

GatherV1 class.

ATTRIBUTE_FACTORY#: alias of GatherV1Attribute

TYPE = 'Gather'#

VERSION = 1#

forward(inputs, indices, axis)#: GatherV1’s forward function.

training: bool#

class otx.core.ov.ops.movements.GatherV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

GatherV1Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.movements.PadV1(*args, **kwargs)#

PadV1 class.

ATTRIBUTE_FACTORY#: alias of PadV1Attribute

TYPE = 'Pad'#

VERSION = 1#

forward(inputs, pads_begin, pads_end, pad_value=0)#: PadV1’s forward function.

static get_torch_pad_dim(pads_begin, pads_end)#: PadV1’s get_torch_pad_dim function.

static get_torch_pad_mode(pad_mode)#: PadV1’s get_torch_pad_mode function.

training: bool#

class otx.core.ov.ops.movements.PadV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], pad_mode: str)#

PadV1Attribute class.

pad_mode: str#

class otx.core.ov.ops.movements.ScatterNDUpdateV3(name: str, **kwargs)#

ScatterNDUpdateV3 class.

ATTRIBUTE_FACTORY#: alias of ScatterNDUpdateV3Attribute

TYPE = 'ScatterNDUpdate'#

VERSION = 3#

forward(inputs, indicies, updates)#: ScatterNDUpdateV3’s forward function.

training: bool#

class otx.core.ov.ops.movements.ScatterNDUpdateV3Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ScatterNDUpdateV3Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.movements.ScatterUpdateV3(name: str, **kwargs)#

ScatterUpdateV3 class.

ATTRIBUTE_FACTORY#: alias of ScatterUpdateV3Attribute

TYPE = 'ScatterUpdate'#

VERSION = 3#

forward(inputs, indicies, updates, axis)#: ScatterUpdateV3’s forward function.

training: bool#

class otx.core.ov.ops.movements.ScatterUpdateV3Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ScatterUpdateV3Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.movements.ShuffleChannelsV0(name: str, **kwargs)#

ShuffleChannelsV0 class.

ATTRIBUTE_FACTORY#: alias of ShuffleChannelsV0Attribute

TYPE = 'ShuffleChannels'#

VERSION = 0#

forward(inputs)#: ShuffleChannelsV0’s forward function.

training: bool#

class otx.core.ov.ops.movements.ShuffleChannelsV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], axis: int = 1, group: int = 1)#

ShuffleChannelsV0Attribute class.

axis: int = 1#

group: int = 1#

class otx.core.ov.ops.movements.SplitV1(name: str, **kwargs)#

SplitV1 class.

ATTRIBUTE_FACTORY#: alias of SplitV1Attribute

TYPE = 'Split'#

VERSION = 1#

forward(inputs, axis)#: SplitV1’s forward function.

training: bool#

class otx.core.ov.ops.movements.SplitV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], num_splits: int)#

SplitV1Attribute class.

num_splits: int#

class otx.core.ov.ops.movements.StridedSliceV1(name: str, **kwargs)#

StridedSliceV1 class.

ATTRIBUTE_FACTORY#: alias of StridedSliceV1Attribute

TYPE = 'StridedSlice'#

VERSION = 1#

forward(inputs, begin, end, stride=None)#: StridedSliceV1’s forward function.

training: bool#

class otx.core.ov.ops.movements.StridedSliceV1Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], begin_mask: ~typing.List[int], end_mask: ~typing.List[int], new_axis_mask: ~typing.List[int] = <factory>, shrink_axis_mask: ~typing.List[int] = <factory>, ellipsis_mask: ~typing.List[int] = <factory>)#

StridedSliceV1Attribute class.

begin_mask: List[int]#

ellipsis_mask: List[int]#

end_mask: List[int]#

new_axis_mask: List[int]#

shrink_axis_mask: List[int]#

class otx.core.ov.ops.movements.TileV0(name: str, **kwargs)#

TileV0 class.

ATTRIBUTE_FACTORY#: alias of TileV0Attribute

TYPE = 'Tile'#

VERSION = 0#

forward(inputs, repeats)#: TileV0’s forward function.

training: bool#

class otx.core.ov.ops.movements.TileV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

TileV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.movements.TransposeV1(name: str, **kwargs)#

TransposeV1 class.

ATTRIBUTE_FACTORY#: alias of TransposeV1Attribute

TYPE = 'Transpose'#

VERSION = 1#

forward(inputs, order)#: TransposeV1’s forward function.

training: bool#

class otx.core.ov.ops.movements.TransposeV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

TransposeV1Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.movements.VariadicSplitV1(name: str, **kwargs)#

VariadicSplitV1 class.

ATTRIBUTE_FACTORY#: alias of VariadicSplitV1Attribute

TYPE = 'VariadicSplit'#

VERSION = 1#

forward(inputs, axis, split_lengths)#: VariadicSplitV1’s forward function.

training: bool#

class otx.core.ov.ops.movements.VariadicSplitV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

VariadicSplitV1Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

otx.core.ov.ops.movements.get_torch_padding(pads_begin, pads_end, auto_pad, input_size, weight_size, stride, dilation=None)#: Getter function for torch padding.

Normalization-related modules for otx.core.ov.ops.

class otx.core.ov.ops.normalizations.BatchNormalizationV0(*args, **kwargs)#

BatchNormalizationV0 class.

ATTRIBUTE_FACTORY#: alias of BatchNormalizationV0Attribute

TYPE = 'BatchNormInference'#

VERSION = 0#

forward(inputs, gamma, beta, mean, variance)#: BatchNormalizationV0’s forward function.

training: bool#

class otx.core.ov.ops.normalizations.BatchNormalizationV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], epsilon: float, max_init_iter: int = 2)#

BatchNormalizationV0Attribute class.

epsilon: float#

max_init_iter: int = 2#

class otx.core.ov.ops.normalizations.LocalResponseNormalizationV0(name: str, **kwargs)#

LocalResponseNormalizationV0 class.

ATTRIBUTE_FACTORY#: alias of LocalResponseNormalizationV0Attribute

TYPE = 'LRN'#

VERSION = 0#

forward(inputs, axes)#: LocalResponseNormalizationV0’s forward function.

training: bool#

class otx.core.ov.ops.normalizations.LocalResponseNormalizationV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], alpha: float, beta: float, bias: float, size: int)#

LocalResponseNormalizationV0Attribute class.

alpha: float#

beta: float#

bias: float#

size: int#

class otx.core.ov.ops.normalizations.MVNV6(name: str, **kwargs)#

MVNV6 class.

ATTRIBUTE_FACTORY#: alias of MVNV6Attribute

TYPE = 'MVN'#

VERSION = 6#

forward(inputs, axes)#: MVNV6’s forward function.

training: bool#

class otx.core.ov.ops.normalizations.MVNV6Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], normalize_variance: bool, eps: float, eps_mode: str)#

MVNV6Attribute class.

eps: float#

eps_mode: str#

normalize_variance: bool#

class otx.core.ov.ops.normalizations.NormalizeL2V0(name: str, **kwargs)#

NormalizeL2V0 class.

ATTRIBUTE_FACTORY#: alias of NormalizeL2V0Attribute

TYPE = 'NormalizeL2'#

VERSION = 0#

forward(inputs, axes)#: NormalizeL2V0’s forward function.

training: bool#

class otx.core.ov.ops.normalizations.NormalizeL2V0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], eps: float, eps_mode: str)#

NormalizeL2V0Attribute class.

eps: float#

eps_mode: str#

Object-detection-related modules for otx.core.ov.ops.

class otx.core.ov.ops.object_detections.DetectionOutputV0(name: str, **kwargs)#

DetectionOutputV0 class.

ATTRIBUTE_FACTORY#: alias of DetectionOutputV0Attribute

TYPE = 'DetectionOutput'#

VERSION = 0#

forward(loc_data, conf_data, prior_data, arm_conf_data=None, arm_loc_data=None)#: DetectionOutputV0’s forward.

training: bool#

class otx.core.ov.ops.object_detections.DetectionOutputV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], keep_top_k: List[int], nms_threshold: float, background_label_id: int = 0, top_k: int = -1, variance_encoded_in_target: bool = False, code_type: str = 'caffe.PriorBoxParameter.CORNER', share_location: bool = True, confidence_threshold: float = 0, clip_after_nms: bool = False, clip_before_nms: bool = False, decrease_label_id: bool = False, normalized: bool = False, input_height: int = 1, input_width: int = 1, objectness_score: float = 0)#

DetectionOutputV0Attribute class.

background_label_id: int = 0#

clip_after_nms: bool = False#

clip_before_nms: bool = False#

code_type: str = 'caffe.PriorBoxParameter.CORNER'#

confidence_threshold: float = 0#

decrease_label_id: bool = False#

input_height: int = 1#

input_width: int = 1#

keep_top_k: List[int]#

nms_threshold: float#

normalized: bool = False#

objectness_score: float = 0#

share_location: bool = True#

top_k: int = -1#

variance_encoded_in_target: bool = False#

class otx.core.ov.ops.object_detections.PriorBoxClusteredV0(name: str, **kwargs)#

PriorBoxClusteredV0 class.

ATTRIBUTE_FACTORY#: alias of PriorBoxClusteredV0Attribute

TYPE = 'PriorBoxClustered'#

VERSION = 0#

forward(output_size, image_size)#: PriorBoxClusteredV0’s forward function.

training: bool#

class otx.core.ov.ops.object_detections.PriorBoxClusteredV0Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], offset: float, width: ~typing.List[float] = <factory>, height: ~typing.List[float] = <factory>, clip: bool = False, step: float = 0.0, step_w: float = 0.0, step_h: float = 0.0, variance: ~typing.List[float] = <factory>)#

PriorBoxClusteredV0Attribute class.

clip: bool = False#

height: List[float]#

offset: float#

step: float = 0.0#

step_h: float = 0.0#

step_w: float = 0.0#

variance: List[float]#

width: List[float]#

class otx.core.ov.ops.object_detections.PriorBoxV0(name: str, **kwargs)#

PriorBoxV0 class.

ATTRIBUTE_FACTORY#: alias of PriorBoxV0Attribute

TYPE = 'PriorBox'#

VERSION = 0#

forward(output_size, image_size)#: PriorBoxV0’s forward function.

training: bool#

class otx.core.ov.ops.object_detections.PriorBoxV0Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], offset: float, min_size: ~typing.List[float] = <factory>, max_size: ~typing.List[float] = <factory>, aspect_ratio: ~typing.List[float] = <factory>, flip: bool = False, clip: bool = False, step: float = 0, variance: ~typing.List[float] = <factory>, scale_all_sizes: bool = True, fixed_ratio: ~typing.List[float] = <factory>, fixed_size: ~typing.List[float] = <factory>, density: ~typing.List[float] = <factory>)#

PriorBoxV0Attribute class.

aspect_ratio: List[float]#

clip: bool = False#

density: List[float]#

fixed_ratio: List[float]#

fixed_size: List[float]#

flip: bool = False#

max_size: List[float]#

min_size: List[float]#

offset: float#

scale_all_sizes: bool = True#

step: float = 0#

variance: List[float]#

class otx.core.ov.ops.object_detections.ProposalV4(name: str, **kwargs)#

ProposalV4 class.

ATTRIBUTE_FACTORY#: alias of ProposalV4Attribute

TYPE = 'Proposal'#

VERSION = 4#

forward(class_probs, bbox_deltas, image_shape)#: ProposalV4’s forward function.

training: bool#

class otx.core.ov.ops.object_detections.ProposalV4Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], base_size: int, pre_nms_topn: int, post_nms_topn: int, nms_thresh: float, feat_stride: int, min_size: int, ratio: List[float], scale: List[float], clip_before_nms: bool = True, clip_after_nms: bool = False, normalize: bool = False, box_size_scale: float = 1.0, box_coordinate_scale: float = 1.0, framework: str = '')#

ProposalV4Attribute class.

base_size: int#

box_coordinate_scale: float = 1.0#

box_size_scale: float = 1.0#

clip_after_nms: bool = False#

clip_before_nms: bool = True#

feat_stride: int#

framework: str = ''#

min_size: int#

nms_thresh: float#

normalize: bool = False#

post_nms_topn: int#

pre_nms_topn: int#

ratio: List[float]#

scale: List[float]#

class otx.core.ov.ops.object_detections.ROIPoolingV0(name: str, **kwargs)#

ROIPoolingV0 class.

ATTRIBUTE_FACTORY#: alias of ROIPoolingV0Attribute

TYPE = 'ROIPooling'#

VERSION = 0#

forward(inputs, boxes)#: ROIPoolingV0’s forward function.

training: bool#

class otx.core.ov.ops.object_detections.ROIPoolingV0Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], pooled_h: int, pooled_w: int, spatial_scale: float, method: str = 'max', output_size: ~typing.List[int] = <factory>)#

ROIPoolingV0Attribute class.

method: str = 'max'#

output_size: List[int]#

pooled_h: int#

pooled_w: int#

spatial_scale: float#

class otx.core.ov.ops.object_detections.RegionYoloV0(name: str, **kwargs)#

RegionYoloV0 class.

ATTRIBUTE_FACTORY#: alias of RegionYoloV0Attribute

TYPE = 'RegionYolo'#

VERSION = 0#

forward(inputs)#: RegionYoloV0’s forward function.

training: bool#

class otx.core.ov.ops.object_detections.RegionYoloV0Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], axis: int, coords: int, classes: int, end_axis: int, num: int, anchors: ~typing.Optional[~typing.List[float]] = None, do_softmax: bool = True, mask: ~typing.List[int] = <factory>)#

RegionYoloV0Attribute class.

anchors: Optional[List[float]] = None#

axis: int#

classes: int#

coords: int#

do_softmax: bool = True#

end_axis: int#

mask: List[int]#

num: int#

Operation-related modules for otx.core.ov.ops.

class otx.core.ov.ops.op.Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.op.Operation(name: str, **kwargs)#

Operation class.

ATTRIBUTE_FACTORY#: alias of Attribute

TYPE = ''#

VERSION = -1#

property attrs#: Operation’s attrs property.

classmethod from_ov(ov_op)#: Operation’s from_ov function.

property name: str#: Operation’s name property.

property shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#: Operation’s shape property.

training: bool#

property type: str#: Operation’s type property.

property version: int#: Operation’s version property.

Pooling-related modules for otx.core.ov.ops.

class otx.core.ov.ops.poolings.AvgPoolV1(*args, **kwargs)#

AvgPoolV1 class.

ATTRIBUTE_FACTORY#: alias of AvgPoolV1Attribute

TYPE = 'AvgPool'#

VERSION = 1#

forward(inputs)#: AvgPoolV1’s forward function.

training: bool#

class otx.core.ov.ops.poolings.AvgPoolV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], exclude_pad: bool, strides: List[int], pads_begin: List[int], pads_end: List[int], kernel: List[int], rounding_type: str = 'floor', auto_pad: str = 'explicit')#

AvgPoolV1Attribute class.

auto_pad: str = 'explicit'#

exclude_pad: bool#

kernel: List[int]#

pads_begin: List[int]#

pads_end: List[int]#

rounding_type: str = 'floor'#

strides: List[int]#

class otx.core.ov.ops.poolings.MaxPoolV0(name: str, **kwargs)#

MaxPoolV0 class.

ATTRIBUTE_FACTORY#: alias of MaxPoolV0Attribute

TYPE = 'MaxPool'#

VERSION = 0#

forward(inputs)#: MaxPoolV0’s forward function.

training: bool#

class otx.core.ov.ops.poolings.MaxPoolV0Attribute(shape: ~typing.Optional[~typing.Union[~typing.Tuple[~typing.Tuple[int]], ~typing.Tuple[int]]], strides: ~typing.List[int], pads_begin: ~typing.List[int], pads_end: ~typing.List[int], kernel: ~typing.List[int], rounding_type: str = 'floor', auto_pad: str = 'explicit', dilations: ~typing.List[int] = <factory>, index_element_type: str = 'i64', axis: int = 0)#

MaxPoolV0Attribute class.

auto_pad: str = 'explicit'#

axis: int = 0#

dilations: List[int]#

index_element_type: str = 'i64'#

kernel: List[int]#

pads_begin: List[int]#

pads_end: List[int]#

rounding_type: str = 'floor'#

strides: List[int]#

Redunction-related modules for otx.core.ov.ops.

class otx.core.ov.ops.reductions.ReduceMeanV1(name: str, **kwargs)#

ReduceMeanV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceMeanV1Attribute

TYPE = 'ReduceMean'#

VERSION = 1#

forward(inputs, axes)#: ReduceMeanV1’s forward function.

training: bool#

class otx.core.ov.ops.reductions.ReduceMeanV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], keep_dims: bool = False)#

ReduceMeanV1Attribute class.

keep_dims: bool = False#

class otx.core.ov.ops.reductions.ReduceMinV1(name: str, **kwargs)#

ReduceMinV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceMinV1Attribute

TYPE = 'ReduceMin'#

VERSION = 1#

forward(inputs, axes)#: ReduceMinV1’s forward function.

training: bool#

class otx.core.ov.ops.reductions.ReduceMinV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], keep_dims: bool = False)#

ReduceMinV1Attribute class.

keep_dims: bool = False#

class otx.core.ov.ops.reductions.ReduceProdV1(name: str, **kwargs)#

ReduceMeanV1Attribute class.

ATTRIBUTE_FACTORY#: alias of ReduceProdV1Attribute

TYPE = 'ReduceProd'#

VERSION = 1#

forward(inputs, axes)#: ReduceMeanV1Attribute’s forward function.

training: bool#

class otx.core.ov.ops.reductions.ReduceProdV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], keep_dims: bool = False)#

ReduceMeanV1Attribute class.

keep_dims: bool = False#

class otx.core.ov.ops.reductions.ReduceSumV1(name: str, **kwargs)#

ReduceSumV1 class.

ATTRIBUTE_FACTORY#: alias of ReduceSumV1Attribute

TYPE = 'ReduceSum'#

VERSION = 1#

forward(inputs, axes)#: ReduceSumV1’s forward function.

training: bool#

class otx.core.ov.ops.reductions.ReduceSumV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], keep_dims: bool = False)#

ReduceSumV1Attribute class.

keep_dims: bool = False#

Shape-mainpulation-related modules for otx.core.ov.ops.

class otx.core.ov.ops.shape_manipulations.ReshapeV1(name: str, **kwargs)#

ReshapeV1 class.

ATTRIBUTE_FACTORY#: alias of ReshapeV1Attribute

TYPE = 'Reshape'#

VERSION = 1#

forward(inputs, shape)#: ReshapeV1’s forward function.

training: bool#

class otx.core.ov.ops.shape_manipulations.ReshapeV1Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], special_zero: bool)#

ReshapeV1Attribute class.

special_zero: bool#

class otx.core.ov.ops.shape_manipulations.ShapeOfV0(name: str, **kwargs)#

ShapeOfV0 class.

ATTRIBUTE_FACTORY#: alias of ShapeOfV0Attribute

TYPE = 'ShapeOf'#

VERSION = 0#

forward(inputs)#: ShapeOfV0’s forward function.

training: bool#

class otx.core.ov.ops.shape_manipulations.ShapeOfV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

ShapeOfV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.shape_manipulations.ShapeOfV3(name: str, **kwargs)#

ShapeOfV3 class.

ATTRIBUTE_FACTORY#: alias of ShapeOfV3Attribute

TYPE = 'ShapeOf'#

VERSION = 3#

forward(inputs)#: ShapeOfV3’s forward function.

training: bool#

class otx.core.ov.ops.shape_manipulations.ShapeOfV3Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], output_type: str = 'i64')#

ShapeOfV3Attribute class.

output_type: str = 'i64'#

class otx.core.ov.ops.shape_manipulations.SqueezeV0(name: str, **kwargs)#

SqueezeV0 class.

ATTRIBUTE_FACTORY#: alias of SqueezeV0Attribute

TYPE = 'Squeeze'#

VERSION = 0#

forward(inputs, dims=None)#: SqueezeV0’s forward function.

training: bool#

class otx.core.ov.ops.shape_manipulations.SqueezeV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

SqueezeV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

class otx.core.ov.ops.shape_manipulations.UnsqueezeV0(name: str, **kwargs)#

UnsqueezeV0 class.

ATTRIBUTE_FACTORY#: alias of UnsqueezeV0Attribute

TYPE = 'Unsqueeze'#

VERSION = 0#

forward(inputs, dims)#: UnsqueezeV0’s forward function.

training: bool#

class otx.core.ov.ops.shape_manipulations.UnsqueezeV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]])#

UnsqueezeV0Attribute class.

shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]]#

Sorting-maximization-related modules for otx.core.ov.ops.

class otx.core.ov.ops.sorting_maximization.NonMaxSuppressionV5(name: str, **kwargs)#

NonMaxSuppressionV5 class.

ATTRIBUTE_FACTORY#: alias of NonMaxSuppressionV5Attribute

TYPE = 'NonMaxSuppression'#

VERSION = 5#

forward(boxes, scores, max_output_boxes_per_class, iou_threshold=0, score_threshold=0, soft_nms_sigma=0)#: NonMaxSuppressionV5’s forward function.

training: bool#

class otx.core.ov.ops.sorting_maximization.NonMaxSuppressionV5Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], box_encoding: str = 'corner', sort_result_descending: bool = True, output_type: str = 'i64')#

NonMaxSuppressionV5Attribute class.

box_encoding: str = 'corner'#

output_type: str = 'i64'#

sort_result_descending: bool = True#

class otx.core.ov.ops.sorting_maximization.NonMaxSuppressionV9(name: str, **kwargs)#

NonMaxSuppressionV9 class.

ATTRIBUTE_FACTORY#: alias of NonMaxSuppressionV9Attribute

TYPE = 'NonMaxSuppression'#

VERSION = 9#

forward(boxes, scores, max_output_boxes_per_class, iou_threshold=0, score_threshold=0, soft_nms_sigma=0)#: NonMaxSuppressionV9’s forward function.

training: bool#

class otx.core.ov.ops.sorting_maximization.NonMaxSuppressionV9Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], box_encoding: str = 'corner', sort_result_descending: bool = True, output_type: str = 'i64')#

NonMaxSuppressionV9Attribute class.

box_encoding: str = 'corner'#

output_type: str = 'i64'#

sort_result_descending: bool = True#

class otx.core.ov.ops.sorting_maximization.TopKV3(name: str, **kwargs)#

TopKV3 class.

ATTRIBUTE_FACTORY#: alias of TopKV3Attribute

TYPE = 'TopK'#

VERSION = 3#

forward(inputs, k)#: TopKV3’s forward function.

training: bool#

class otx.core.ov.ops.sorting_maximization.TopKV3Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], axis: int, mode: str, sort: str, index_element_type: str = 'i32')#

TopKV3Attribute class.

axis: int#

index_element_type: str = 'i32'#

mode: str#

sort: str#

Type-conversion-related modules for otx.core.ov.ops.

class otx.core.ov.ops.type_conversions.ConvertV0(name: str, **kwargs)#

ConvertV0 class.

ATTRIBUTE_FACTORY#: alias of ConvertV0Attribute

TYPE = 'Convert'#

VERSION = 0#

static convert_ov_type(ov_type)#: ConvertV0’s convert_ov_type function.

static convert_torch_type(torch_type)#: ConvertV0’s convert_torch_type function.

forward(inputs)#: ConvertV0’s forward function.

training: bool#

class otx.core.ov.ops.type_conversions.ConvertV0Attribute(shape: Optional[Union[Tuple[Tuple[int]], Tuple[int]]], destination_type: str)#

ConvertV0Attribute class.

destination_type: str#

Utils function for otx.core.ov.ops.

otx.core.ov.ops.utils.convert_op_to_torch(op_node: Node)#: Convert op Node to torch.

otx.core.ov.ops.utils.get_dynamic_shape(output)#: Getter function for dynamic shape.