def Sum(data_0, **kwargs): _inputs = [] for i in (data_0, ): _add_input(i, _inputs) idx = omm.op_counter["Sum"] omm.op_counter["Sum"] += 1 node = onnx.helper.make_node("Sum", _inputs, [f'_t_Sum_{idx}_sum'], name=f"Sum_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Equal(A, B, **kwargs): _inputs = [] for i in (A, B): _add_input(i, _inputs) idx = omm.op_counter["Equal"] omm.op_counter["Equal"] += 1 node = onnx.helper.make_node("Equal", _inputs, [f'_t_Equal_{idx}_C'], name=f"Equal_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Det(X, **kwargs): _inputs = [] for i in (X, ): _add_input(i, _inputs) idx = omm.op_counter["Det"] omm.op_counter["Det"] += 1 node = onnx.helper.make_node("Det", _inputs, [f'_t_Det_{idx}_Y'], name=f"Det_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Clip(input, min=None, max=None, **kwargs): _inputs = [] for i in (input, min, max): _add_input(i, _inputs) idx = omm.op_counter["Clip"] omm.op_counter["Clip"] += 1 node = onnx.helper.make_node("Clip", _inputs, [f'_t_Clip_{idx}_output'], name=f"Clip_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Resize(X, roi, scales, sizes=None, **kwargs): _inputs = [] for i in (X, roi, scales, sizes): _add_input(i, _inputs) idx = omm.op_counter["Resize"] omm.op_counter["Resize"] += 1 node = onnx.helper.make_node("Resize", _inputs, [f'_t_Resize_{idx}_Y'], name=f"Resize_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Conv(X, W, B=None, **kwargs): _inputs = [] for i in (X, W, B): _add_input(i, _inputs) idx = omm.op_counter["Conv"] omm.op_counter["Conv"] += 1 node = onnx.helper.make_node("Conv", _inputs, [f'_t_Conv_{idx}_Y'], name=f"Conv_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def SequenceEmpty(**kwargs): _inputs = [] for i in (): _add_input(i, _inputs) idx = omm.op_counter["SequenceEmpty"] omm.op_counter["SequenceEmpty"] += 1 node = onnx.helper.make_node("SequenceEmpty", _inputs, [f'_t_SequenceEmpty_{idx}_output'], name=f"SequenceEmpty_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Gemm(A, B, C=None, **kwargs): _inputs = [] for i in (A, B, C): _add_input(i, _inputs) idx = omm.op_counter["Gemm"] omm.op_counter["Gemm"] += 1 node = onnx.helper.make_node("Gemm", _inputs, [f'_t_Gemm_{idx}_Y'], name=f"Gemm_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def ScatterElements(data, indices, updates, **kwargs): _inputs = [] for i in (data, indices, updates): _add_input(i, _inputs) idx = omm.op_counter["ScatterElements"] omm.op_counter["ScatterElements"] += 1 node = onnx.helper.make_node("ScatterElements", _inputs, [f'_t_ScatterElements_{idx}_output'], name=f"ScatterElements_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def SoftmaxCrossEntropyLoss(scores, labels, weights=None, **kwargs): _inputs = [] for i in (scores, labels, weights): _add_input(i, _inputs) idx = omm.op_counter["SoftmaxCrossEntropyLoss"] omm.op_counter["SoftmaxCrossEntropyLoss"] += 1 node = onnx.helper.make_node("SoftmaxCrossEntropyLoss", _inputs, [f'_t_SoftmaxCrossEntropyLoss_{idx}_output', f'_t_SoftmaxCrossEntropyLoss_{idx}_log_prob'], name=f"SoftmaxCrossEntropyLoss_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def MeanVarianceNormalization(X, **kwargs): _inputs = [] for i in (X, ): _add_input(i, _inputs) idx = omm.op_counter["MeanVarianceNormalization"] omm.op_counter["MeanVarianceNormalization"] += 1 node = onnx.helper.make_node("MeanVarianceNormalization", _inputs, [f'_t_MeanVarianceNormalization_{idx}_Y'], name=f"MeanVarianceNormalization_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Loop(M, cond, v_initial=None, **kwargs): _inputs = [] for i in (M, cond, v_initial): _add_input(i, _inputs) idx = omm.op_counter["Loop"] omm.op_counter["Loop"] += 1 node = onnx.helper.make_node("Loop", _inputs, [f'_t_Loop_{idx}_v_final_and_scan_outputs'], name=f"Loop_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def QuantizeLinear(x, y_scale, y_zero_point=None, **kwargs): _inputs = [] for i in (x, y_scale, y_zero_point): _add_input(i, _inputs) idx = omm.op_counter["QuantizeLinear"] omm.op_counter["QuantizeLinear"] += 1 node = onnx.helper.make_node("QuantizeLinear", _inputs, [f'_t_QuantizeLinear_{idx}_y'], name=f"QuantizeLinear_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def NegativeLogLikelihoodLoss(input, target, weight=None, **kwargs): _inputs = [] for i in (input, target, weight): _add_input(i, _inputs) idx = omm.op_counter["NegativeLogLikelihoodLoss"] omm.op_counter["NegativeLogLikelihoodLoss"] += 1 node = onnx.helper.make_node("NegativeLogLikelihoodLoss", _inputs, [f'_t_NegativeLogLikelihoodLoss_{idx}_loss'], name=f"NegativeLogLikelihoodLoss_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Pad(data, pads, constant_value=None, **kwargs): _inputs = [] for i in (data, pads, constant_value): _add_input(i, _inputs) idx = omm.op_counter["Pad"] omm.op_counter["Pad"] += 1 node = onnx.helper.make_node("Pad", _inputs, [f'_t_Pad_{idx}_output'], name=f"Pad_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def CumSum(x, axis, **kwargs): _inputs = [] for i in (x, axis): _add_input(i, _inputs) idx = omm.op_counter["CumSum"] omm.op_counter["CumSum"] += 1 node = onnx.helper.make_node("CumSum", _inputs, [f'_t_CumSum_{idx}_y'], name=f"CumSum_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Slice(data, starts, ends, axes=None, steps=None, **kwargs): _inputs = [] for i in (data, starts, ends, axes, steps): _add_input(i, _inputs) idx = omm.op_counter["Slice"] omm.op_counter["Slice"] += 1 node = onnx.helper.make_node("Slice", _inputs, [f'_t_Slice_{idx}_output'], name=f"Slice_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Range(start, limit, delta, **kwargs): _inputs = [] for i in (start, limit, delta): _add_input(i, _inputs) idx = omm.op_counter["Range"] omm.op_counter["Range"] += 1 node = onnx.helper.make_node("Range", _inputs, [f'_t_Range_{idx}_output'], name=f"Range_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Unsqueeze(data, **kwargs): _inputs = [] for i in (data, ): _add_input(i, _inputs) idx = omm.op_counter["Unsqueeze"] omm.op_counter["Unsqueeze"] += 1 node = onnx.helper.make_node("Unsqueeze", _inputs, [f'_t_Unsqueeze_{idx}_expanded'], name=f"Unsqueeze_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def SequenceAt(input_sequence, position, **kwargs): _inputs = [] for i in (input_sequence, position): _add_input(i, _inputs) idx = omm.op_counter["SequenceAt"] omm.op_counter["SequenceAt"] += 1 node = onnx.helper.make_node("SequenceAt", _inputs, [f'_t_SequenceAt_{idx}_tensor'], name=f"SequenceAt_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def If(cond, **kwargs): _inputs = [] for i in (cond, ): _add_input(i, _inputs) idx = omm.op_counter["If"] omm.op_counter["If"] += 1 node = onnx.helper.make_node("If", _inputs, [f'_t_If_{idx}_outputs'], name=f"If_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def OneHot(indices, depth, values, **kwargs): _inputs = [] for i in (indices, depth, values): _add_input(i, _inputs) idx = omm.op_counter["OneHot"] omm.op_counter["OneHot"] += 1 node = onnx.helper.make_node("OneHot", _inputs, [f'_t_OneHot_{idx}_output'], name=f"OneHot_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Gather(data, indices, **kwargs): _inputs = [] for i in (data, indices): _add_input(i, _inputs) idx = omm.op_counter["Gather"] omm.op_counter["Gather"] += 1 node = onnx.helper.make_node("Gather", _inputs, [f'_t_Gather_{idx}_output'], name=f"Gather_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def MaxUnpool(X, I, output_shape=None, **kwargs): _inputs = [] for i in (X, I, output_shape): _add_input(i, _inputs) idx = omm.op_counter["MaxUnpool"] omm.op_counter["MaxUnpool"] += 1 node = onnx.helper.make_node("MaxUnpool", _inputs, [f'_t_MaxUnpool_{idx}_output'], name=f"MaxUnpool_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Constant(**kwargs): _inputs = [] for i in (): _add_input(i, _inputs) idx = omm.op_counter["Constant"] omm.op_counter["Constant"] += 1 node = onnx.helper.make_node("Constant", _inputs, [f'_t_Constant_{idx}_output'], name=f"Constant_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Softmax(input, **kwargs): _inputs = [] for i in (input, ): _add_input(i, _inputs) idx = omm.op_counter["Softmax"] omm.op_counter["Softmax"] += 1 node = onnx.helper.make_node("Softmax", _inputs, [f'_t_Softmax_{idx}_output'], name=f"Softmax_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def ReduceMean(data, **kwargs): _inputs = [] for i in (data, ): _add_input(i, _inputs) idx = omm.op_counter["ReduceMean"] omm.op_counter["ReduceMean"] += 1 node = onnx.helper.make_node("ReduceMean", _inputs, [f'_t_ReduceMean_{idx}_reduced'], name=f"ReduceMean_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def SequenceLength(input_sequence, **kwargs): _inputs = [] for i in (input_sequence, ): _add_input(i, _inputs) idx = omm.op_counter["SequenceLength"] omm.op_counter["SequenceLength"] += 1 node = onnx.helper.make_node("SequenceLength", _inputs, [f'_t_SequenceLength_{idx}_length'], name=f"SequenceLength_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def DepthToSpace(input, **kwargs): _inputs = [] for i in (input, ): _add_input(i, _inputs) idx = omm.op_counter["DepthToSpace"] omm.op_counter["DepthToSpace"] += 1 node = onnx.helper.make_node("DepthToSpace", _inputs, [f'_t_DepthToSpace_{idx}_output'], name=f"DepthToSpace_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node
def Transpose(data, **kwargs): _inputs = [] for i in (data, ): _add_input(i, _inputs) idx = omm.op_counter["Transpose"] omm.op_counter["Transpose"] += 1 node = onnx.helper.make_node("Transpose", _inputs, [f'_t_Transpose_{idx}_transposed'], name=f"Transpose_{idx}", **kwargs) onnx.checker.check_node(node, omm.ctx) omm.model.graph.node.append(node) return node