InstanceNormalization#
InstanceNormalization  6#
Version
domain: main
since_version: 6
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 6.
Summary
Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022.
y = scale * (x  mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel.
Attributes
epsilon: The epsilon value to use to avoid division by zero. Default value is
9.999999747378752e06
.
Inputs
input (heterogeneous)  T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.
scale (heterogeneous)  T: The input 1dimensional scale tensor of size C.
B (heterogeneous)  T: The input 1dimensional bias tensor of size C.
Outputs
output (heterogeneous)  T: The output tensor of the same shape as input.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Examples
Differences
0  0  Carries out instance normalization as described in the paper  Carries out instance normalization as described in the paper 
1  1  https://arxiv.org/abs/1607.08022.  https://arxiv.org/abs/1607.08022. 
2  2 


3  3  y = scale * (x  mean) / sqrt(variance + epsilon) + B,  y = scale * (x  mean) / sqrt(variance + epsilon) + B, 
4  4  where mean and variance are computed per instance per channel.  where mean and variance are computed per instance per channel. 
5  5 


6  6  **Attributes**  **Attributes** 
7  7 


8  * **consumed_inputs**:  
9  legacy optimization attribute.  
10  8  * **epsilon**:  * **epsilon**: 
11  9  The epsilon value to use to avoid division by zero, default is 

12  1e5f. Default value is 9.999999747378752e06.  
13  10 


14  11  **Inputs**  **Inputs** 
15  12 


16  13  * **input** (heterogeneous)  **T**:  * **input** (heterogeneous)  **T**: 
17  The input 4dimensional tensor of shape NCHW.  
14  Input data tensor from the previous operator; dimensions for image  
15  case are (N x C x H x W), where N is the batch size, C is the number  
16  of channels, and H and W are the height and the width of the data.  
17  For non image case, the dimensions are in the form of (N x C x D1 x  
18  D2 ... Dn), where N is the batch size.  
18  19  * **scale** (heterogeneous)  **T**:  * **scale** (heterogeneous)  **T**: 
19  20  The input 1dimensional scale tensor of size C.  The input 1dimensional scale tensor of size C. 
20  21  * **B** (heterogeneous)  **T**:  * **B** (heterogeneous)  **T**: 
21  22  The input 1dimensional bias tensor of size C.  The input 1dimensional bias tensor of size C. 
22  23 


23  24  **Outputs**  **Outputs** 
24  25 


25  26  * **output** (heterogeneous)  **T**:  * **output** (heterogeneous)  **T**: 
26  27  The output 4dimensional tensor of the same shape as input. 

27  28 


28  29  **Type Constraints**  **Type Constraints** 
29  30 


30  31  * **T** in (  * **T** in ( 
31  32  tensor(double),  tensor(double), 
32  33  tensor(float),  tensor(float), 
33  34  tensor(float16)  tensor(float16) 
34  35  ):  ): 
35  36  Constrain input and output types to float tensors.  Constrain input and output types to float tensors. 
InstanceNormalization  1#
Version
domain: main
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: False
This version of the operator has been available since version 1.
Summary
Carries out instance normalization as described in the paper https://arxiv.org/abs/1607.08022.
y = scale * (x  mean) / sqrt(variance + epsilon) + B, where mean and variance are computed per instance per channel.
Attributes
consumed_inputs: legacy optimization attribute.
epsilon: The epsilon value to use to avoid division by zero, default is 1e5f. Default value is
9.999999747378752e06
.
Inputs
input (heterogeneous)  T: The input 4dimensional tensor of shape NCHW.
scale (heterogeneous)  T: The input 1dimensional scale tensor of size C.
B (heterogeneous)  T: The input 1dimensional bias tensor of size C.
Outputs
output (heterogeneous)  T: The output 4dimensional tensor of the same shape as input.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.