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OperatorKernels.md

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Supported Operators Data Types

This file is automatically generated from the def files via this script. Do not modify directly and instead edit operator definitions.

Operators implemented by CPUExecutionProvider

Op Name Parameters OpSet Version Types Supported
Operator Domain: ai.onnx.ml
Abs (in X:T, out Y:T) 6+ T = tensor(int32), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(int64), tensor(double)
Acos (in input:T, out output:T) 7+ T = tensor(float)
Acosh (in input:T, out output:T) 9+ T = tensor(float)
Add (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Affine (in X:T, out Y:T) 1+ T = tensor(float)
And (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
ArgMax (in data:T, out reduced:tensor(int64)) 1+ T = tensor(int32), tensor(float)
ArgMin (in data:T, out reduced:tensor(int64)) 1+ T = tensor(int32), tensor(float)
ArrayFeatureExtractor (in X:T, in Y:tensor(int64), out Z:T) 1+ T = tensor(string), tensor(int32), tensor(float), tensor(int64), tensor(double)
Asin (in input:T, out output:T) 7+ T = tensor(float)
Asinh (in input:T, out output:T) 9+ T = tensor(float)
Atan (in input:T, out output:T) 7+ T = tensor(float)
Atanh (in input:T, out output:T) 9+ T = tensor(float)
AveragePool (in X:T, out Y:T) 10+ T = tensor(float)
[7, 9] T = tensor(float)
BatchNormalization (in X:T, in scale:T, in B:T, in mean:T, in var:T, out Y:T, out mean:T, out var:T, out saved_mean:T, out saved_var:T) [7, 9] B = tensor(float)
X = tensor(float)
mean = tensor(float)
scale = tensor(float)
var = tensor(float)
Binarizer (in X:T, out Y:T) 1+ T = tensor(float)
Cast (in input:T1, out output:T2) 9+ T1 = tensor(string)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[6, 9] T1 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
CastMap (in X:T1, out Y:T2) 1+ T1 = unknown
T2 = tensor(string), tensor(float), tensor(int64)
CategoryMapper (in X:T1, out Y:T2) 1+ T1 = tensor(string), tensor(int64)
T2 = tensor(string), tensor(int64)
Ceil (in X:T, out Y:T) 6+ T = tensor(float)
Clip (in input:T, out output:T) 6+ T = tensor(float)
Compress (in input:T, in condition:T1, out output:T) 9+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T1 = tensor(bool)
Concat (in inputs:T, out concat_result:T) 4+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
ConstantOfShape (in input:T1, out output:T2) 9+ T1 = tensor(int64)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Conv (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float)
ConvInteger (in x:T1, in w:T2, in x_zero_point:T1, in w_zero_point:T2, out y:T3) 10+ T1 = tensor(uint8)
T2 = tensor(uint8)
T3 = tensor(int32)
ConvTranspose (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float)
Cos (in input:T, out output:T) 7+ T = tensor(float)
Cosh (in input:T, out output:T) 9+ T = tensor(float)
Crop (in input:T, out output:T) 1+ T = tensor(float)
DepthToSpace (in input:T, out output:T) [1, 4] T = tensor(float)
DequantizeLinear (in x:T, in x_scale:tensor(float), in x_zero_point:T, out y:tensor(float)) 10+ x = tensor(uint8), unknown
x_scale = tensor(float)
x_zero_point = tensor(uint8), unknown
y = tensor(float)
DictVectorizer (in X:T1, out Y:T2) 1+ T1 = unknown
T2 = tensor(string), tensor(float), tensor(int64), tensor(double)
Div (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Dropout (in data:T, out output:T, out mask:T) or (in data:T, out output:T, out mask:T1) 10+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(bool)
[7, 9] T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(bool)
DynamicSlice (in data:T, in starts:Tind, in ends:Tind, in axes:Tind, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Elu (in X:T, out Y:T) 6+ T = tensor(float)
Equal (in A:T, in B:T, out C:T1) 11+ T = tensor(float)
T1 = tensor(bool)
7+ T = tensor(int32), tensor(bool), tensor(int64)
T1 = tensor(bool)
Erf (in input:T, out output:T) 9+ T = tensor(float)
Exp (in input:T, out output:T) 6+ T = tensor(float), tensor(double)
Expand (in input:T, in shape:tensor(int64), out output:T) 8+ T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
EyeLike (in input:T1, out output:T2) 9+ T1 = tensor(uint64), tensor(int32), tensor(float), tensor(int64), tensor(double)
T2 = tensor(uint64), tensor(int32), tensor(float), tensor(int64), tensor(double)
FeatureVectorizer (in X:T1, out Y:tensor(float)) 1+ T1 = tensor(int32), tensor(float), tensor(int64), tensor(double)
Flatten (in input:T, out output:T) 9+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[1, 8] T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Floor (in X:T, out Y:T) 6+ T = tensor(float)
GRU (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, out Y:T, out Y_h:T) 7+ T = tensor(float), tensor(double)
T1 = tensor(int32)
Gather (in data:T, in indices:Tind, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Gemm (in A:T, in B:T, in C:T, out Y:T) [7, 9] T = tensor(float)
GlobalAveragePool (in X:T, out Y:T) 1+ T = tensor(float)
GlobalLpPool (in X:T, out Y:T) 2+ T = tensor(float)
GlobalMaxPool (in X:T, out Y:T) 1+ T = tensor(float)
Greater (in A:T, in B:T, out C:T1) 9+ T = tensor(int32), tensor(int64)
T1 = tensor(bool)
[7, 9] T = tensor(float)
T1 = tensor(bool)
HardSigmoid (in X:T, out Y:T) 6+ T = tensor(float)
Hardmax (in input:T, out output:T) 1+ T = tensor(float)
Identity (in input:T, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
If (in cond:B, out outputs:V) 1+ B = tensor(bool)
V = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
ImageScaler (in input:T, out output:T) 1+ T = tensor(float)
Imputer (in X:T, out Y:T) 1+ T = tensor(float), tensor(int64)
InstanceNormalization (in input:T, in scale:T, in B:T, out output:T) 6+ T = tensor(float)
IsInf (in X:T1, out Y:T2) 10+ T1 = tensor(float), tensor(double)
T2 = tensor(bool)
IsNaN (in X:T1, out Y:T2) 9+ T1 = tensor(float), tensor(MLFloat16)
T2 = tensor(bool)
LRN (in X:T, out Y:T) 1+ T = tensor(float)
LSTM (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, in initial_c:T, in P:T, out Y:T, out Y_h:T, out Y_c:T) 7+ T = tensor(float), tensor(double)
T1 = tensor(int32)
LabelEncoder (in X:T1, out Y:T2) 2+ T1 = tensor(string), tensor(float), tensor(int64)
T2 = tensor(string), tensor(float), tensor(int64)
[1, 1] T1 = tensor(string), tensor(int64)
T2 = tensor(string), tensor(int64)
LeakyRelu (in X:T, out Y:T) 6+ T = tensor(float)
Less (in A:T, in B:T, out C:T1) 9+ T = tensor(int32), tensor(int64)
T1 = tensor(bool)
[7, 9] T = tensor(float)
T1 = tensor(bool)
LinearClassifier (in X:T1, out Y:T2, out Z:tensor(float)) 1+ T1 = tensor(int32), tensor(float), tensor(int64), tensor(double)
T2 = tensor(string), tensor(int64)
LinearRegressor (in X:T, out Y:tensor(float)) 1+ T = tensor(float)
Log (in input:T, out output:T) 6+ T = tensor(float)
LogSoftmax (in input:T, out output:T) 1+ T = tensor(float)
Loop (in M:I, in cond:B, in v_initial:V, out v_final_and_scan_outputs:V) 1+ B = tensor(bool)
I = tensor(int64)
V = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
LpNormalization (in input:T, out output:T) 1+ T = tensor(float)
LpPool (in X:T, out Y:T) 2+ T = tensor(float)
MatMul (in A:T, in B:T, out Y:T) [1, 9] T = tensor(float), tensor(double)
[9, 9] T = tensor(uint64), tensor(int32), tensor(int64), tensor(uint32)
MatMulInteger (in A:T1, in B:T2, in a_zero_point:T1, in b_zero_point:T2, out Y:T3) 10+ T1 = tensor(uint8)
T2 = tensor(uint8)
T3 = tensor(int32)
Max (in data_0:T, out max:T) 8+ T = tensor(float), tensor(double)
[6, 7] T = tensor(float)
MaxPool (in X:T, out Y:T) or (in X:T, out Y:T, out Indices:I) 10+ I = tensor(int64)
T = tensor(float)
[1, 7] T = tensor(float)
[8, 9] I = tensor(int64)
T = tensor(float)
MaxRoiPool (in X:T, in rois:T, out Y:T) 1+ T = tensor(float)
MaxUnpool (in X:T1, in I:T2, in output_shape:T2, out output:T1) 9+ T1 = tensor(float)
T2 = tensor(int64)
Mean (in data_0:T, out mean:T) 8+ T = tensor(float)
[6, 7] T = tensor(float)
MeanVarianceNormalization (in X:T, out Y:T) or (in input:T, out output:T) 9+ T = tensor(float)
[1, 8] T = tensor(float)
Min (in data_0:T, out min:T) 8+ T = tensor(float)
[6, 7] T = tensor(float)
Mod (in A:T, in B:T, out C:T) 10+ T = tensor(int32), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Mul (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Multinomial (in input:T1, out output:T2) 7+ T1 = tensor(float)
T2 = tensor(int32), tensor(int64)
Neg (in X:T, out Y:T) 6+ T = tensor(int32), tensor(float), unknown
NonZero (in X:T, out Y:tensor(int64)) 9+ T = tensor(int32), tensor(float), tensor(bool), tensor(int64)
Normalizer (in X:T, out Y:tensor(float)) 1+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Not (in X:T, out Y:T) 1+ T = tensor(bool)
T1 = tensor(bool)
OneHot (in indices:T1, in depth:T2, in values:T3, out output:T3) 9+ T1 = tensor(int32), tensor(float), tensor(int64)
T2 = tensor(int32), tensor(float), tensor(int64)
T3 = tensor(string), tensor(int32), tensor(float), tensor(int64)
OneHotEncoder (in X:T, out Y:tensor(float)) 1+ T = tensor(string), tensor(float), tensor(int64), tensor(double)
Or (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
PRelu (in X:T, in slope:T, out Y:T) [7, 9] T = tensor(float)
Pad (in data:T, out output:T) 2+ T = tensor(float)
ParametricSoftplus (in X:T, out Y:T) 1+ T = tensor(float)
Pow (in X:T, in Y:T, out Z:T) 7+ T = tensor(float), tensor(double)
QLinearConv (in x:T1, in x_scale:tensor(float), in x_zero_point:T1, in w:T2, in w_scale:tensor(float), in w_zero_point:T2, in y_scale:tensor(float), in y_zero_point:T3, in B:T4, out y:T3) 10+ T1 = tensor(uint8)
T2 = tensor(uint8)
T3 = tensor(uint8)
T4 = tensor(int32)
QLinearMatMul (in a:T1, in a_scale:tensor(float), in a_zero_point:T1, in b:T2, in b_scale:tensor(float), in b_zero_point:T2, in y_scale:tensor(float), in y_zero_point:T3, out y:T3) 10+ T1 = tensor(uint8)
T2 = tensor(uint8)
T3 = tensor(uint8)
QuantizeLinear (in x:T1, in y_scale:tensor(float), in y_zero_point:T2, out y:T2) 10+ x = tensor(float)
y = tensor(uint8), unknown
y_zero_point = tensor(uint8), unknown
RNN (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, out Y:T, out Y_h:T) 7+ T = tensor(float)
T1 = tensor(int32)
RandomNormal (out output:T) 1+ T = tensor(float), tensor(double)
RandomNormalLike (in input:T1, out output:T2) 1+ T1 = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T2 = tensor(float), tensor(double)
RandomUniform (out output:T) 1+ T = tensor(float), tensor(double)
RandomUniformLike (in input:T1, out output:T2) 1+ T1 = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T2 = tensor(float), tensor(double)
Reciprocal (in X:T, out Y:T) 6+ T = tensor(float)
ReduceL1 (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceL2 (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceLogSum (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceLogSumExp (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceMax (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceMean (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceMin (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceProd (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float)
ReduceSum (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float), tensor(double)
ReduceSumSquare (in data:T, out reduced:T) 1+ T = tensor(int32), tensor(float), tensor(double)
Relu (in X:T, out Y:T) 6+ T = tensor(float)
Reshape (in data:T, in shape:tensor(int64), out reshaped:T) or (in data:T, out reshaped:T) 5+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
shape = tensor(int64)
Reshape_1 [1, 4] T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Resize (in X:T, in scales:tensor(float), out Y:T) 10+ T = tensor(int32), tensor(float), tensor(uint8)
ReverseSequence (in input:T, in sequence_lens:tensor(int64), out Y:T) 10+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
RoiAlign (in X:T1, in rois:T1, in batch_indices:T2, out Y:T1) 10+ T = tensor(float), tensor(double)
T2 = tensor(int64)
SVMClassifier (in X:T1, out Y:T2, out Z:tensor(float)) 1+ T1 = tensor(int32), tensor(float), tensor(int64), tensor(double)
T2 = tensor(string), tensor(int64)
SVMRegressor (in X:T, out Y:tensor(float)) 1+ T = tensor(float)
Scale (in input:T, out output:T) 1+ T = tensor(float)
ScaledTanh (in input:T, out output:T) 1+ T = tensor(float)
Scaler (in X:T, out Y:tensor(float)) 1+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Scan (in sequence_lens:I, in initial_state_and_scan_inputs:V, out final_state_and_scan_outputs:V) or (in initial_state_and_scan_inputs:V, out final_state_and_scan_outputs:V) 9+ I = tensor(int64)
V = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[8, 8] I = tensor(int64)
V = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Scatter (in data:T, in indices:Tind, in updates:T, out output:T) 9+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Selu (in X:T, out Y:T) 6+ T = tensor(float)
Shape (in data:T, out shape:T1) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T1 = tensor(int64)
Shrink (in input:T, out output:T) 9+ T = tensor(int32), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sigmoid (in X:T, out Y:T) 6+ T = tensor(float)
Sign (in input:T, out output:T) 9+ T = tensor(int32), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sin (in input:T, out output:T) 7+ T = tensor(float), tensor(double)
Sinh (in input:T, out output:T) 9+ T = tensor(float)
Size (in data:T, out size:T1) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(int64), tensor(double)
T1 = tensor(int64)
Slice (in data:T, out output:T) or (in data:T, in starts:Tind, in ends:Tind, in axes:Tind, in steps:Tind, out output:T) 10+ T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
[1, 9] T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Softmax (in input:T, out output:T) 1+ T = tensor(float)
Softplus (in X:T, out Y:T) 1+ T = tensor(float)
Softsign (in input:T, out output:T) 1+ T = tensor(float)
SpaceToDepth (in input:T, out output:T) 1+ T = tensor(float)
Split (in input:T, out outputs:T) or (in input:T, in split:T, out outputs...:T) 2+ T = tensor(string), tensor(int32), tensor(float)
Sqrt (in X:T, out Y:T) 6+ T = tensor(float), tensor(double)
Squeeze (in data:T, out squeezed:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
StringNormalizer (in X:tensor(string), out Y:tensor(string)) 10+ T = tensor(string)
Sub (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(float), tensor(int64), tensor(double)
Sum (in data_0:T, out sum:T) 8+ T = tensor(float)
[6, 7] T = tensor(float)
Tan (in input:T, out output:T) 7+ T = tensor(float)
Tanh (in input:T, out output:T) 6+ T = tensor(float)
TfIdfVectorizer (in X:T, out Y:T1) 9+ T = tensor(string), tensor(int32), tensor(int64)
T1 = tensor(float)
ThresholdedRelu (in X:T, out Y:T) 1+ T = tensor(float)
10+ T = tensor(float)
Tile (in input:T, in tiles:T, in axis:T, out output:T) or (in input:T, in repeats:T1, out output:T) 6+ T = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(int64), tensor(double)
T1 = tensor(int64)
TopK (in X:T, in K:tensor(int64), out Values:T, out Indices:I) or (in X:T, out Values:T, out Indices:I) 10+ I = tensor(int64)
T = tensor(float)
[1, 9] I = tensor(int64)
T = tensor(float)
Transpose (in data:T, out transposed:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
TreeEnsembleClassifier (in X:T1, out Y:T2, out Z:tensor(float)) 1+ T1 = tensor(int32), tensor(float), tensor(int64), tensor(double)
T2 = tensor(string), tensor(int64)
TreeEnsembleRegressor (in X:T, out Y:tensor(float)) 1+ T = tensor(float)
Unsqueeze (in data:T, out expanded:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Upsample (in X:T, out Y:T) or (in X:T, in scales:tensor(float), out Y:T) [7, 9] T = tensor(int32), tensor(float), tensor(uint8)
Where (in condition:B, in X:T, in Y:T, out output:T) 9+ T = tensor(string), tensor(int32), tensor(float)
Xor (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
ZipMap (in X:tensor(float), out Z:T) 1+ T = unknown
Operator Domain: com.microsoft
AttnLSTM (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, in initial_c:T, in P:T, in QW:T, in MW:T, in V:T, in M:T, in memory_seq_lens:T1, in AW:T, out Y:T, out Y_h:T, out Y_c:T) 1+ T = tensor(float), tensor(double)
T1 = tensor(int32)
ConvTransposeWithDynamicPads (in X:T, in W:T, in Pads:tensor(int64), in B:T, out Y:T) 1+ T = tensor(float)
CropAndResize (in X:T1, in rois:T1, in batch_indices:T2, in crop_size:T2, out Y:T1) 1+ T = tensor(float)
T2 = tensor(int32)
ExpandDims (in X:T, in axis:tensor(int32), out Y:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
axis = tensor(int32)
FusedConv (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float)
FusedGemm (in A:T, in B:T, in C:T, out Y:T) 1+ T = tensor(float)
GatherND (in data:T, in indices:Tind, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(string), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
MaxpoolWithMask (in X:T, in M:tensor(int32), out Y:T) 1+ X = tensor(float)
MurmurHash3 (in X:T1, out Y:T2) 1+ T1 = tensor(string), tensor(int32), tensor(uint32)
T2 = tensor(int32), tensor(uint32)
Pad (in data:T, in pads:tensor(int64), in value:T, out output:T) 1+ T = tensor(float)
Range (in start:T, in limit:T, in delta:T, out Y:T) 1+ T = tensor(int32), tensor(float), tensor(int64), tensor(int16), tensor(double)
SampleOp (in X:T, out Y:T) 1+ T = tensor(float)
Tokenizer (in X:T, out Y:T) 1+ T = tensor(string)
Unique (in x:T, out y:T, out idx:tensor(int64), out counts:tensor(int64)) 1+ T = tensor(float)
WordConvEmbedding (in Sequence:T, in W:T1, in B:T1, in C:T1, out Y:T1) 1+ T = tensor(int32)
T1 = tensor(float)
Operator Domain: com.microsoft.nchwc
AveragePool (in X:T, out Y:T) 1+ T = tensor(float)
Conv (in X:T, in W:T, in B:T, in Sum:T, out Y:T) 1+ T = tensor(float)
GlobalAveragePool (in X:T, out Y:T) 1+ T = tensor(float)
GlobalMaxPool (in X:T, out Y:T) 1+ T = tensor(float)
MaxPool (in X:T, out Y:T) 1+ T = tensor(float)
ReorderInput (in X:T, out Y:T) 1+ T = tensor(float)
ReorderOutput (in X:T, out Y:T) 1+ T = tensor(float)

Operators implemented by CUDAExecutionProvider

Op Name Parameters OpSet Version Types Supported
Operator Domain: ai.onnx.ml
Abs (in X:T, out Y:T) 6+ T = tensor(int32), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Add (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Affine (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
And (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
ArgMax (in data:T, out reduced:tensor(int64)) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ArgMin (in data:T, out reduced:tensor(int64)) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
AveragePool (in X:T, out Y:T) 10+ T = tensor(float), tensor(MLFloat16), tensor(double)
[7, 9] I = tensor(int64)
T = tensor(float), tensor(MLFloat16), tensor(double)
BatchNormalization (in X:T, in scale:T, in B:T, in mean:T, in var:T, out Y:T, out mean:T, out var:T, out saved_mean:T, out saved_var:T) 9+ B = tensor(float), tensor(MLFloat16), tensor(double)
X = tensor(float), tensor(MLFloat16), tensor(double)
mean = tensor(float), tensor(MLFloat16), tensor(double)
scale = tensor(float), tensor(MLFloat16), tensor(double)
var = tensor(float), tensor(MLFloat16), tensor(double)
[7, 8] B = tensor(float), tensor(MLFloat16), tensor(double)
X = tensor(float), tensor(MLFloat16), tensor(double)
mean = tensor(float), tensor(MLFloat16), tensor(double)
scale = tensor(float), tensor(MLFloat16), tensor(double)
var = tensor(float), tensor(MLFloat16), tensor(double)
Cast (in input:T1, out output:T2) 9+ T1 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[6, 8] T1 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Ceil (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Compress (in input:T, in condition:T1, out output:T) 9+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T1 = tensor(bool)
Concat (in inputs:T, out concat_result:T) 4+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
ConstantOfShape (in input:T1, out output:T2) 9+ T1 = tensor(int64)
T2 = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Conv (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ConvTranspose (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Crop (in input:T, out output:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Div (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Dropout (in data:T, out output:T, out mask:T) or (in data:T, out output:T, out mask:T1) 10+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(bool)
[7, 9] T = tensor(float), tensor(MLFloat16), tensor(double)
DynamicSlice (in data:T, in starts:Tind, in ends:Tind, in axes:Tind, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Elu (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Equal (in A:T, in B:T, out C:T1) 7+ T = tensor(int32), tensor(bool), tensor(int64)
Erf (in input:T, out output:T) 9+ T = tensor(float), tensor(MLFloat16), tensor(double)
Exp (in input:T, out output:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Expand (in input:T, in shape:tensor(int64), out output:T) 8+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Flatten (in input:T, out output:T) 9+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[1, 8] T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Floor (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
GRU (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, out Y:T, out Y_h:T) 7+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(int32)
Gather (in data:T, in indices:Tind, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Gemm (in A:T, in B:T, in C:T, out Y:T) 9+ T = tensor(float), tensor(MLFloat16), tensor(double)
[7, 8] T = tensor(float), tensor(MLFloat16), tensor(double)
GlobalAveragePool (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
GlobalMaxPool (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Greater (in A:T, in B:T, out C:T1) 9+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T1 = tensor(bool)
[7, 8] T = tensor(float), tensor(MLFloat16), tensor(double)
HardSigmoid (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Identity (in input:T, out output:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
ImageScaler (in input:T, out output:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
InstanceNormalization (in input:T, in scale:T, in B:T, out output:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
LRN (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
LSTM (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, in initial_c:T, in P:T, out Y:T, out Y_h:T, out Y_c:T) 7+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(int32)
LeakyRelu (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Log (in input:T, out output:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
MatMul (in A:T, in B:T, out Y:T) 9+ T = tensor(float), tensor(MLFloat16), tensor(double)
[1, 8] T = tensor(float), tensor(MLFloat16), tensor(double)
Max (in data_0:T, out max:T) 8+ T = tensor(float), tensor(MLFloat16), tensor(double)
[6, 7] T = tensor(float), tensor(MLFloat16), tensor(double)
MaxPool (in X:T, out Y:T) or (in X:T, out Y:T, out Indices:I) 10+ T = tensor(float), tensor(MLFloat16), tensor(double)
[1, 7] I = tensor(int64)
T = tensor(float), tensor(MLFloat16), tensor(double)
[8, 9] I = tensor(int64)
T = tensor(float), tensor(MLFloat16), tensor(double)
MemcpyFromHost (in X:T, out Y:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
MemcpyToHost (in X:T, out Y:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Min (in data_0:T, out min:T) 8+ T = tensor(float), tensor(MLFloat16), tensor(double)
[6, 7] T = tensor(float), tensor(MLFloat16), tensor(double)
Mul (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Neg (in X:T, out Y:T) 6+ T = tensor(int32), tensor(int16), unknown, tensor(float), tensor(MLFloat16), tensor(int64), tensor(double)
Or (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
PRelu (in X:T, in slope:T, out Y:T) 7+ T = tensor(float), tensor(MLFloat16), tensor(double)
Pad (in data:T, out output:T) 2+ T = tensor(float), tensor(MLFloat16), tensor(double)
ParametricSoftplus (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Pow (in X:T, in Y:T, out Z:T) 7+ T = tensor(float), tensor(MLFloat16), tensor(double)
RNN (in X:T, in W:T, in R:T, in B:T, in sequence_lens:T1, in initial_h:T, out Y:T, out Y_h:T) 7+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(int32)
Reciprocal (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceL1 (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceL2 (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceLogSum (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceLogSumExp (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceMax (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceMean (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceMin (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceProd (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceSum (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
ReduceSumSquare (in data:T, out reduced:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Relu (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Reshape (in data:T, in shape:tensor(int64), out reshaped:T) or (in data:T, out reshaped:T) 5+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
shape = tensor(int64)
Reshape_1 [1, 4] T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Resize (in X:T, in scales:tensor(float), out Y:T) 10+ T = tensor(int32), tensor(float), tensor(MLFloat16), tensor(uint8), tensor(double)
ScaledTanh (in input:T, out output:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Selu (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Shape (in data:T, out shape:T1) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
T1 = tensor(int64)
Shrink (in input:T, out output:T) 9+ T = tensor(int32), tensor(int16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sigmoid (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Slice (in data:T, out output:T) or (in data:T, in starts:Tind, in ends:Tind, in axes:Tind, in steps:Tind, out output:T) 10+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
[1, 9] T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tind = tensor(int32), tensor(int64)
Softmax (in input:T, out output:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Softplus (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Softsign (in input:T, out output:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Split (in input:T, out outputs:T) or (in input:T, in split:T, out outputs...:T) 2+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sqrt (in X:T, out Y:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
Squeeze (in data:T, out squeezed:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sub (in A:T, in B:T, out C:T) 7+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Sum (in data_0:T, out sum:T) 8+ T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
[6, 7] T = tensor(int32), tensor(uint32), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Tanh (in input:T, out output:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
ThresholdedRelu (in X:T, out Y:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
10+ T = tensor(float), tensor(MLFloat16), tensor(double)
Tile (in input:T, in tiles:T, in axis:T, out output:T) or (in input:T, in repeats:T1, out output:T) 6+ T = tensor(float), tensor(MLFloat16), tensor(double)
T1 = tensor(int64)
Transpose (in data:T, out transposed:T) 1+ T = tensor(float), tensor(MLFloat16), tensor(double)
Unsqueeze (in data:T, out expanded:T) 1+ T = tensor(int32), tensor(bool), tensor(int16), tensor(bfloat16), tensor(uint8), unknown, tensor(uint32), tensor(uint16), tensor(float), tensor(uint64), tensor(MLFloat16), tensor(int64), tensor(double)
Upsample (in X:T, out Y:T) or (in X:T, in scales:tensor(float), out Y:T) [7, 9] T = tensor(int32), tensor(float), tensor(MLFloat16), tensor(uint8), tensor(double)
Xor (in A:T, in B:T, out C:T1) 7+ T = tensor(bool)
T1 = tensor(bool)
Operator Domain: com.microsoft
ConvTransposeWithDynamicPads (in X:T, in W:T, in Pads:tensor(int64), in B:T, out Y:T) 1+ T = tensor(float)

Operators implemented by MKLDNNExecutionProvider

Op Name Parameters OpSet Version Types Supported
Operator Domain: ai.onnx.ml
AveragePool (in X:T, out Y:T) [7, 8] T = tensor(float)
BatchNormalization (in X:T, in scale:T, in B:T, in mean:T, in var:T, out Y:T, out mean:T, out var:T, out saved_mean:T, out saved_var:T) 7+ T = tensor(float)
Conv (in X:T, in W:T, in B:T, out Y:T) 1+ T = tensor(float)
Gemm (in A:T, in B:T, in C:T, out Y:T) 7+ T = tensor(float)
GlobalAveragePool (in X:T, out Y:T) [1, 8] T = tensor(float)
GlobalMaxPool (in X:T, out Y:T) [1, 8] T = tensor(float)
LRN (in X:T, out Y:T) 1+ T = tensor(float)
MaxPool (in X:T, out Y:T) or (in X:T, out Y:T, out Indices:I) [1, 7] T = tensor(float)
[8, 8] T = tensor(float)
Relu (in X:T, out Y:T) 6+ T = tensor(float)
Sum (in data_0:T, out sum:T) 6+ T = tensor(float)