Operator Changelog¶

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

For an operator input/output’s differentiability, it can be differentiable, non-differentiable, or undefined. If a variable’s differentiability is not specified, that variable has undefined differentiability.

ai.onnx (default)¶

Version 1 of the default ONNX operator set¶

Abs-1¶

Absolute takes one input data (Tensor) and produces one output data (Tensor) where the absolute is, y = abs(x), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Add-1¶

Performs element-wise binary addition (with limited broadcast support).

If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor’s shape. The starting of the mutually equal shape is specified by the argument “axis”, and if it is not set, suffix matching is assumed. 1-dim expansion doesn’t work yet.

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0): Pass 1 to enable broadcasting
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

A : T: First operand, should share the type with the second operand.
B : T: Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs¶

C : T: Result, has same dimensions and type as A

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

And-1¶

Returns the tensor resulted from performing the and logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(bool): Constrains input to boolean tensor.
T1 : tensor(bool): Constrains output to boolean tensor.

ArgMax-1¶

Computes the indices of the max elements of the input tensor’s element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 0): The axis in which to compute the arg indices.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : tensor(int64): Reduced output tensor with integer data type.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to all numeric tensors.

ArgMin-1¶

Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 0): The axis in which to compute the arg indices.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : tensor(int64): Reduced output tensor with integer data type.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to all numeric tensors.

AveragePool-1¶

AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following:

output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)

* pad_shape[i] is sum of pads along axis i

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:

VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

And pad shape will be following if SAME_UPPER or SAME_LOWER:

pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]

The output of each pooling window is divided by the number of elements exclude pad.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required): The size of the kernel along each axis.
pads : list of ints: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints: Stride along each spatial axis.

Inputs¶

X : 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. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].

Outputs¶

Y : T: Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

BatchNormalization-1¶

Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below:

Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode)

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints (required): legacy optimization attribute.
epsilon : float (default is 1e-05): The epsilon value to use to avoid division by zero, default is 1e-5f.
is_test : int (default is 0): If set to nonzero, run spatial batch normalization in test mode, default is 0.
momentum : float (default is 0.9): Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.
spatial : int (default is 1): If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.

Inputs¶

X : T: The input 4-dimensional tensor of shape NCHW.
scale : T: The scale as a 1-dimensional tensor of size C to be applied to the output.
B : T: The bias as a 1-dimensional tensor of size C to be applied to the output.
mean : T: The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.
var : T: The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.

Outputs (1 - 5)¶

Y : T: The output 4-dimensional tensor of the same shape as X.
mean (optional) : T: The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.
var (optional) : T: The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.
saved_mean (optional) : T: Saved mean used during training to speed up gradient computation. Should not be used for testing.
saved_var (optional) : T: Saved variance used during training to speed up gradient computation. Should not be used for testing.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Cast-1¶

The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns an output tensor of the same size in the converted type. The ‘to’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message. NOTE: Casting to and from strings is not supported yet.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

to : string (required): The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto

Inputs¶

input : T1: Input tensor to be cast.

Outputs¶

output : T2: Output tensor with the same shape as input with type specified by the 'to' argument

Type Constraints¶

T1 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool): Constrain input types. Casting from strings and complex are not supported.
T2 : tensor(float16), tensor(float), tensor(double), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(bool): Constrain output types. Casting to strings and complex are not supported.

Ceil-1¶

Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Clip-1¶

Clip operator limits the given input within an interval. The interval is specified with arguments ‘min’ and ‘max’. They default to numeric_limits::lowest() and numeric_limits::max() respectively.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.
max : float: Maximum value, above which element is replaced by max
min : float: Minimum value, under which element is replaced by min

Inputs¶

input : T: Input tensor whose elements to be clipped

Outputs¶

output : T: Output tensor with clipped input elements

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Concat-1¶

Concatenate a list of tensors into a single tensor

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: Which axis to concat on. Default value is 1.

Inputs (1 - ∞)¶

inputs (variadic) : T: List of tensors for concatenation

Outputs¶

concat_result : T: Concatenated tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain output types to float tensors.

Constant-1¶

A constant tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

value : tensor (required): The value for the elements of the output tensor.

Inputs¶

Outputs¶

output : T: Output tensor containing the same value of the provided tensor.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Conv-1¶

The convolution operator consumes an input tensor and a filter, and computes the output.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
dilations : list of ints: dilation value along each spatial axis of the filter.
group : int (default is 1): number of groups input channels and output channels are divided into.
kernel_shape : list of ints: The shape of the convolution kernel. If not present, should be inferred from input W.
pads : list of ints: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints: Stride along each spatial axis.

Inputs (2 - 3)¶

X : T: Input data tensor from previous layer; has size (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 width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
W : T: The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
B (optional) : T: Optional 1D bias to be added to the convolution, has size of M.

Outputs¶

Y : T: Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

ConvTranspose-1¶

The convolution transpose operator consumes an input tensor and a filter, and computes the output.

If the pads parameter is provided the shape of the output is calculated via the following equation:

output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]

output_shape can also be explicitly specified in which case pads values are auto generated using these equations:

total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]
If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)
Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
dilations : list of ints: dilation value along each spatial axis of the filter.
group : int (default is 1): number of groups input channels and output channels are divided into.
kernel_shape : list of ints: The shape of the convolution kernel. If not present, should be inferred from input W.
output_padding : list of ints: The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.
output_shape : list of ints: The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads
pads : list of ints: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints: Stride along each spatial axis.

Inputs (2 - 3)¶

X : T: Input data tensor from previous layer; has size (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 width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)
W : T: The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)
B (optional) : T: Optional 1D bias to be added to the convolution, has size of M.

Outputs¶

Y : T: Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

DepthToSpace-1¶

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

blocksize : int (required): Blocks of [blocksize, blocksize] are moved.

Inputs¶

input : T: Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.

Outputs¶

output : T: Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Div-1¶

Performs element-wise binary division (with limited broadcast support).

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0): Pass 1 to enable broadcasting
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

A : T: First operand, should share the type with the second operand.
B : T: Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs¶

C : T: Result, has same dimensions and type as A

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Dropout-1¶

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.
is_test : int (default is 0): (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.
ratio : float (default is 0.5): (float, default 0.5) the ratio of random dropout

Inputs¶

data : T: The input data as Tensor.

Outputs (1 - 2)¶

output : T: The output.
mask (optional) : T: The output mask. If is_test is nonzero, this output is not filled.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Elu-1¶

Elu takes one input data (Tensor) and produces one output data (Tensor) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 1.0): Coefficient of ELU default to 1.0.
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: 1D input tensor

Outputs¶

Y : T: 1D input tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Equal-1¶

Returns the tensor resulted from performing the equal logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(bool), tensor(int32), tensor(int64): Constrains input to integral tensors.
T1 : tensor(bool): Constrains output to boolean tensor.

Exp-1¶

Calculates the exponential of the given input tensor, element-wise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

input : T: Input tensor

Outputs¶

output : T: The exponential of the input tensor computed element-wise

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Flatten-1¶

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 1): Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n).

Inputs¶

input : T: A tensor of rank >= axis.

Outputs¶

output : T: A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Floor-1¶

Floor takes one input data (Tensor) and produces one output data (Tensor) where the floor is, y = floor(x), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

GRU-1¶

Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN.

Notations:

X - input tensor

z - update gate

r - reset gate

h - hidden gate

t - time step (t-1 means previous time step)

W[zrh] - W parameter weight matrix for update, reset, and hidden gates

R[zrh] - R recurrence weight matrix for update, reset, and hidden gates

Wb[zrh] - W bias vectors for update, reset, and hidden gates

Rb[zrh] - R bias vectors for update, reset, and hidden gates

WB[zrh] - W parameter weight matrix for backward update, reset, and hidden gates

RB[zrh] - R recurrence weight matrix for backward update, reset, and hidden gates

WBb[zrh] - W bias vectors for backward update, reset, and hidden gates

RBb[zrh] - R bias vectors for backward update, reset, and hidden gates

H - Hidden state

num_directions - 2 if direction == bidirectional else 1

Activation functions:

Relu(x)                - max(0, x)

Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})

Sigmoid(x)             - 1/(1 + e^{-x})

(NOTE: Below are optional)

Affine(x)              - alpha*x + beta

LeakyRelu(x)           - x if x >= 0 else alpha * x

ThresholdedRelu(x)     - x if x >= alpha else 0

ScaledTanh(x)          - alpha*Tanh(beta*x)

HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)

Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)

Softsign(x)            - x/(1 + |x|)

Softplus(x)            - log(1 + e^x)

Equations (Default: f=Sigmoid, g=Tanh):

- zt = f(Xt*(Wz^T) + Ht-1*Rz + Wbz + Rbz)

- rt = f(Xt*(Wr^T) + Ht-1*Rr + Wbr + Rbr)

- ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*Rh + Rbh + Wbh) # default, when linear_before_reset = 0

- ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*Rh + Rbh) + Wbh) # when linear_before_reset != 0

- Ht = (1 - zt) (.) ht + zt (.) Ht-1

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

activation_alpha : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.
activation_beta : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.
activations : list of strings: A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float: Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is foward): Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int: Number of neurons in the hidden layer
output_sequence : int (default is 0): The sequence output for the hidden is optional if 0. Default 0.

Inputs (3 - 6)¶

X : T: The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T: The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.
R : T: The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.
B (optional) : T: The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0
sequence_lens (optional) : T1: Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T: Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.

Outputs¶

Y (optional) : T: A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h : T: The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.
T1 : tensor(int32): Constrain seq_lens to integer tensor.

Gather-1¶

Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates them in an output tensor of rank q + (r - 1). Example 1:

  data = [
      [1.0, 1.2],
      [2.3, 3.4],
      [4.5, 5.7],
  ]
  indices = [
      [0, 1],
      [1, 2],
  ]
  output = [
      [
          [1.0, 1.2],
          [2.3, 3.4],
      ],
      [
          [2.3, 3.4],
          [4.5, 5.7],
      ],
  ]

Example 2:

  data = [
      [1.0, 1.2, 1.9],
      [2.3, 3.4, 3.9],
      [4.5, 5.7, 5.9],
  ]
  indices = [
      [0, 2],
  ]
  axis = 1,
  output = [
      [
          [1.0, 1.9],
          [2.3, 3.9],
          [4.5, 5.9],
      ],
  ]

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 0): Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]

Inputs¶

data : T: Tensor of rank r >= 1.
indices : Tind: Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.

Outputs¶

output : T: Tensor of rank q + (r - 1).

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to any tensor type.
Tind : tensor(int32), tensor(int64): Constrain indices to integer types

Gemm-1¶

General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 1.0): Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
beta : float (default is 1.0): Scalar multiplier for input tensor C, the default value is 1.0.
broadcast : int (default is 0): Whether C should be broadcasted
transA : int (default is 0): Whether A should be transposed
transB : int (default is 0): Whether B should be transposed

Inputs¶

A : T: Input tensor A
B : T: Input tensor B
C : T: Input tensor C, can be inplace.

Outputs¶

Y : T: Output tensor.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

GlobalAveragePool-1¶

GlobalAveragePool consumes an input tensor X and applies average pooling across the values in the same channel. This is equivalent to AveragePool with kernel size equal to the spatial dimension of input tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

X (differentiable) : 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.

Outputs¶

Y (differentiable) : T: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

GlobalLpPool-1¶

GlobalLpPool consumes an input tensor X and applies lp pool pooling across the the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

p : float (default is 2.0): p value of the Lp norm used to pool over the input data, default is 2.0.

Inputs¶

X : 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 dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.

Outputs¶

Y : T: Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

GlobalMaxPool-1¶

GlobalMaxPool consumes an input tensor X and applies max pooling across the values in the same channel. This is equivalent to MaxPool with kernel size equal to the spatial dimension of input tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

X (differentiable) : 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.

Outputs¶

Y (differentiable) : T: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Greater-1¶

Returns the tensor resulted from performing the greater logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrains input to float tensors.
T1 : tensor(bool): Constrains output to boolean tensor.

HardSigmoid-1¶

HardSigmoid takes one input data (Tensor) and produces one output data (Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta)), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 0.2): Value of alpha default to 0.2
beta : float (default is 0.5): Value of beta default to 0.5
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Hardmax-1¶

The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the hardmax values of the corresponding input.

Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, …, a_{k-1}, a_k, …, a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * … * a_{k-1}, a_k * … * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * … * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * … * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 1): Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size

Inputs¶

input : T: The input tensor that's coerced into a 2D matrix of size (NxD) as described above.

Outputs¶

output : T: The output values with the same shape as input tensor (the original size without coercion).

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Identity-1¶

Identity operator

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

input : T: Input tensor

Outputs¶

output : T: Tensor to copy input into.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

If-1¶

If conditional

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

else_branch : graph (required): Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.
then_branch : graph (required): Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.

Inputs¶

cond : B: Condition for the if

Outputs (1 - ∞)¶

outputs (variadic, heterogeneous) : V: Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.

Type Constraints¶

V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): All Tensor types
B : tensor(bool): Only bool

InstanceNormalization-1¶

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.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.
epsilon : float (default is 1e-05): The epsilon value to use to avoid division by zero, default is 1e-5f.

Inputs¶

input : T: The input 4-dimensional tensor of shape NCHW.
scale : T: The input 1-dimensional scale tensor of size C.
B : T: The input 1-dimensional bias tensor of size C.

Outputs¶

output : T: The output 4-dimensional tensor of the same shape as input.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

LRN-1¶

Local Response Normalization proposed in the AlexNet paper. It normalizes over local input regions. The local region is defined across the channels. For an element X[n, c, d1, …, dk] in a tensor of shape (N x C x D1 x D2, …, Dk), its region is {X[n, i, d1, …, dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}.

square_sum[n, c, d1, …, dk] = sum(X[n, i, d1, …, dk] ^ 2), where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2)).

Y[n, c, d1, …, dk] = X[n, c, d1, …, dk] / (bias + alpha / size * square_sum[n, c, d1, …, dk] ) ^ beta

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 0.0001): Scaling parameter.
beta : float (default is 0.75): The exponent.
bias : float (default is 1.0)
size : int (required): The number of channels to sum over

Inputs¶

X : 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. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].

Outputs¶

Y : T: Output tensor, which has the shape and type as input tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

LSTM-1¶

Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN.

Notations:

X - input tensor

i - input gate

o - output gate

f - forget gate

c - cell gate

t - time step (t-1 means previous time step)

W[iofc] - W parameter weight matrix for input, output, forget, and cell gates

R[iofc] - R recurrence weight matrix for input, output, forget, and cell gates

Wb[iofc] - W bias vectors for input, output, forget, and cell gates

Rb[iofc] - R bias vectors for input, output, forget, and cell gates

P[iof] - P peephole weight vector for input, output, and forget gates

WB[iofc] - W parameter weight matrix for backward input, output, forget, and cell gates

RB[iofc] - R recurrence weight matrix for backward input, output, forget, and cell gates

WBb[iofc] - W bias vectors for backward input, output, forget, and cell gates

RBb[iofc] - R bias vectors for backward input, output, forget, and cell gates

PB[iof] - P peephole weight vector for backward input, output, and forget gates

H - Hidden state

num_directions - 2 if direction == bidirectional else 1

Activation functions:

Relu(x)                - max(0, x)

Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})

Sigmoid(x)             - 1/(1 + e^{-x})

(NOTE: Below are optional)

Affine(x)              - alpha*x + beta

LeakyRelu(x)           - x if x >= 0 else alpha * x

ThresholdedRelu(x)     - x if x >= alpha else 0

ScaledTanh(x)          - alpha*Tanh(beta*x)

HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)

Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)

Softsign(x)            - x/(1 + |x|)

Softplus(x)            - log(1 + e^x)

Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):

- it = f(Xt*(Wi^T) + Ht-1*Ri + Pi (.) Ct-1 + Wbi + Rbi)

- ft = f(Xt*(Wf^T) + Ht-1*Rf + Pf (.) Ct-1 + Wbf + Rbf)

- ct = g(Xt*(Wc^T) + Ht-1*Rc + Wbc + Rbc)

- Ct = ft (.) Ct-1 + it (.) ct

- ot = f(Xt*(Wo^T) + Ht-1*Ro + Po (.) Ct + Wbo + Rbo)

- Ht = ot (.) h(Ct)

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

activation_alpha : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings: A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.
clip : float: Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward): Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int: Number of neurons in the hidden layer
input_forget : int (default is 0): Couple the input and forget gates if 1, default 0.
output_sequence : int (default is 0): The sequence output for the hidden is optional if 0. Default 0.

Inputs (3 - 8)¶

X : T: The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T: The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.
R : T: The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.
B (optional) : T: The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1: Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T: Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
initial_c (optional) : T: Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.
P (optional) : T: The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.

Outputs (0 - 3)¶

Y (optional) : T: A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h (optional) : T: The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.
Y_c (optional) : T: The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.
T1 : tensor(int32): Constrain seq_lens to integer tensor.

LeakyRelu-1¶

LeakyRelu takes input data (Tensor) and an argument alpha, and produces one output data (Tensor) where the function f(x) = alpha * x for x < 0, f(x) = x for x >= 0, is applied to the data tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 0.01): Coefficient of leakage default to 0.01.
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Less-1¶

Returns the tensor resulted from performing the less logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrains input to float tensors.
T1 : tensor(bool): Constrains output to boolean tensor.

Log-1¶

Calculates the natural log of the given input tensor, element-wise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

input : T: Input tensor

Outputs¶

output : T: The natural log of the input tensor computed element-wise

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

LogSoftmax-1¶

The operator computes the logsoftmax (log of softmax) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the logsoftmax values of the corresponding input.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 1): Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size

Inputs¶

input : T: The input tensor that's coerced into a 2D matrix of size (NxD) as described above.

Outputs¶

output : T: The output values with the same shape as input tensor (the original size without coercion).

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Loop-1¶

Generic Looping construct. This loop has multiple termination conditions:

Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M.
Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not.

This table summarizes the operating modes of this operator with equivalent C-style code:

  Operator inputs defined as (max_trip_count, condition_var).

  input ("", ""):
      for (int i=0; ; ++i) {
        cond = ... // Note this value is ignored, but is required in the body
      }

  input ("", cond) // Note this is analogous to a while loop
      bool cond = ...;
      for (int i=0; cond; ++i) {
        cond = ...;
      }

  input ("", 1) // Note this is analogous to a do-while loop
      bool cond = true
      for (int i=0; cond; ++i) {
        cond = ...;
      }

  input (trip_count, "") // Note this is analogous to a for loop
      int trip_count = ...
      for (int i=0; i < trip_count; ++i) {
        cond = ...; // ignored
      }

  input (trip_count, cond)
      int trip_count = ...;
      bool cond = ...;
      for (int i=0; i < trip_count && cond; ++i) {
        cond = ...;
      }

Sample usage - cond as well as trip count

  graph predict-net {
    %a = Constant[value = <Scalar Tensor [3]>]()
    %b = Constant[value = <Scalar Tensor [6]>]()
    %keepgoing = Constant[value = <Scalar Tensor [1]>]()
    %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
    %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
    return
  }

  graph body-net (
    %i[INT32, scalar]
    %keepgoing[BOOL, scalar]
    %b[INT32, scalar]
  ) {
    %my_local = Add(%a, %b)
    %b_out = Sub(%a, %b)
    %keepgoing_out = Greater(%my_local, %b_out)
    %user_defined_vals = Add(%b, %b)
    return %keepgoing_out, %b_out, %user_defined_vals
  }

Sample equivalent C code

  {
    /* User-defined code (enclosing scope) */
    int a = 3, b = 6;
    bool keepgoing = true; // Analogous to input cond
    /* End user-defined code */

    /* Implicitly-defined code */
    const int max_trip_count = 10; // Analogous to input M
    int user_defined_vals[]; // Imagine this is resizable
    /* End implicitly-defined code */
    for (int i=0; i < max_trip_count && keepgoing; ++i) {
      /* User-defined code (loop body) */
      int my_local = a + b; // Reading values in the enclosing scope is fine
      b = a - b; // writes fine if we specify b as a loop-carried dependency
      keepgoing = my_local > b; // keepgoing is a loop-carried dependency
      user_defined_vals[i] = b + b;
      /* End user-defined code */
    }
    // my_local = 123; // Can't do this. my_local was defined in the the body

    // These below values are live-out from the loop and therefore accessible
    b_out; user_defined_vals; keepgoing_out;
  }

There are several things of note in this code snippet:

Values from the enclosing scope (i.e. variable a here) are in scope and can be referenced in the inputs of the loop.
Any variables which you wish to make available in the enclosing scope (i.e. the variables b and keepgoing) must be declared as either loop-carried dependencies (both at the op inputs and output and at the body net input and output) or scan_outputs.
Values created in the body cannot be accessed in the enclosing scope.

Note that the semantics of this op support “diagonal” or “wavefront” execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

body : graph (required): The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.

Inputs (3 - ∞)¶

M (optional) : I: A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
cond (optional) : B: A boolean termination condition. Optional. Pass empty string to skip.
v_initial (variadic, heterogeneous) : V: The initial values of any loop-carried dependencies (values that change across loop iterations)

Outputs (1 - ∞)¶

v_final_and_scan_outputs (variadic, heterogeneous) : V: Final N loop carried dependency values then K scan_outputs

Type Constraints¶

V : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): All Tensor types
I : tensor(int64): tensor of int64, which should be a scalar.
B : tensor(bool): tensor of bool, which should be a scalar.

LpNormalization-1¶

Given a matrix, apply Lp-normalization along the provided axis.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is -1): The axis on which to apply normalization, -1 mean last axis.
p : int (default is 2): The order of the normalization, only 1 or 2 are supported.

Inputs¶

input (differentiable) : T: Input matrix

Outputs¶

output (differentiable) : T: Matrix after normalization

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

LpPool-1¶

LpPool consumes an input tensor X and applies Lp pooling across the the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.
kernel_shape : list of ints: The size of the kernel along each axis.
p : float (default is 2.0): p value of the Lp norm used to pool over the input data, default is 2.0.
pads : list of ints: Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.
strides : list of ints: Stride along each axis.

Inputs¶

X : 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 dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.

Outputs¶

Y : T: Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

MatMul-1¶

Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

A : T: N-dimensional matrix A
B : T: N-dimensional matrix B

Outputs¶

Y : T: Matrix multiply results from A * B

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Max-1¶

Element-wise max of each of the input tensors. All inputs and outputs must have the same shape and data type.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs (1 - ∞)¶

data_0 (variadic) : T: List of tensors for Max.

Outputs¶

max : T: Output tensor. Same dimension as inputs.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

MaxPool-1¶

MaxPool consumes an input tensor X and applies max pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. max pooling consisting of computing the max on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following:

output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)

* pad_shape[i] is sum of pads along axis i

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:

VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

And pad shape will be following if SAME_UPPER or SAME_LOWER:

pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]

The output of each pooling window is maximum number of elements exclude pad.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required): The size of the kernel along each axis.
pads : list of ints: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints: Stride along each spatial axis.

Inputs¶

X : 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. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].

Outputs¶

Y : T: Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

MaxRoiPool-1¶

ROI max pool consumes an input tensor X and region of interests (RoIs) to apply max pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

pooled_shape : list of ints (required): ROI pool output shape (height, width).
spatial_scale : float (default is 1.0): Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.

Inputs¶

X (differentiable) : 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.
rois (non-differentiable) : T: RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].

Outputs¶

Y (differentiable) : T: RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Mean-1¶

Element-wise mean of each of the input tensors. All inputs and outputs must have the same shape and data type.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs (1 - ∞)¶

data_0 (variadic) : T: List of tensors for Mean.

Outputs¶

mean : T: Output tensor. Same dimension as inputs.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Min-1¶

Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs (1 - ∞)¶

data_0 (variadic) : T: List of tensors for Min

Outputs¶

min : T: Output tensor. Same dimension as inputs.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Mul-1¶

Performs element-wise binary multiplication (with limited broadcast support).

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0): Pass 1 to enable broadcasting
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

A : T: First operand, should share the type with the second operand.
B : T: Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs¶

C : T: Result, has same dimensions and type as A

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Neg-1¶

Neg takes one input data (Tensor) and produces one output data (Tensor) where each element flipped sign, y = -x, is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Not-1¶

Returns the negation of the input tensor element-wise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

X (non-differentiable) : T: Input tensor

Outputs¶

Y (non-differentiable) : T: Output tensor

Type Constraints¶

T : tensor(bool): Constrains input/output to boolean tensors.

Or-1¶

Returns the tensor resulted from performing the or logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(bool): Constrains input to boolean tensor.
T1 : tensor(bool): Constrains output to boolean tensor.

PRelu-1¶

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor
slope : T: Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Pad-1¶

Given data tensor, paddings, mode, and value. Example: Insert 0 paddings to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] paddings = [0, 0, 2, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ]

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

mode : string (default is constant): Three modes: constant(default), reflect, edge
paddings : list of ints (required): List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.
value : float (default is 0.0): One float, indicates the value to be filled, default is 0

Inputs¶

data : T: Input tensor.

Outputs¶

output : T: Tensor after padding.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Pow-1¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise.

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0): Pass 1 to enable broadcasting

Inputs¶

X : T: Input tensor of any shape, base of the exponent.
Y : T: Input tensor of any shape broadcastable to X shape, the exponent component.

Outputs¶

Z : T: Output tensor (same size as X)

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

RNN-1¶

Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN.

Notations:

X - input tensor

i - input gate

t - time step (t-1 means previous time step)

Wi - W parameter weight matrix for input gate

Ri - R recurrence weight matrix for input gate

Wbi - W parameter bias vector for input gate

Rbi - R parameter bias vector for input gate

WBi - W parameter weight matrix for backward input gate

RBi - R recurrence weight matrix for backward input gate

WBbi - WR bias vectors for backward input gate

RBbi - RR bias vectors for backward input gate

H - Hidden state

num_directions - 2 if direction == bidirectional else 1

Activation functions:

Relu(x)                - max(0, x)

Tanh(x)                - (1 - e^{-2x})/(1 + e^{-2x})

Sigmoid(x)             - 1/(1 + e^{-x})

(NOTE: Below are optional)

Affine(x)              - alpha*x + beta

LeakyRelu(x)           - x if x >= 0 else alpha * x

ThresholdedRelu(x)     - x if x >= alpha else 0

ScaledTanh(x)          - alpha*Tanh(beta*x)

HardSigmoid(x)         - min(max(alpha*x + beta, 0), 1)

Elu(x)                 - x if x >= 0 else alpha*(e^x - 1)

Softsign(x)            - x/(1 + |x|)

Softplus(x)            - log(1 + e^x)

Equations (Default: f=Tanh):

- Ht = f(Xt*(Wi^T) + Ht-1*Ri + Wbi + Rbi)

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

activation_alpha : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.
activation_beta : list of floats: Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.
activations : list of strings (default is ['Tanh', 'Tanh']): One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.
clip : float: Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.
direction : string (default is forward): Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.
hidden_size : int: Number of neurons in the hidden layer
output_sequence : int (default is 0): The sequence output for the hidden is optional if 0. Default 0.

Inputs (3 - 6)¶

X : T: The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.
W : T: The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.
R : T: The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.
B (optional) : T: The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.
sequence_lens (optional) : T1: Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.
initial_h (optional) : T: Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.

Outputs (0 - 2)¶

Y (optional) : T: A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.
Y_h (optional) : T: The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.
T1 : tensor(int32): Constrain seq_lens to integer tensor.

RandomNormal-1¶

Generate a tensor with random values drawn from a normal distribution. The shape of the tensor is specified by the shape argument and the parameter of the normal distribution specified by mean and scale.

The data type is specified by the ‘dtype’ argument. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

dtype : int (default is 1): The data type for the elements of the output tensor. Default is TensorProto::FLOAT.
mean : float (default is 0.0): The mean of the normal distribution.
scale : float (default is 1.0): The standard deviation of the normal distribution.
seed : float: (Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required): The shape of the output tensor.

Inputs¶

Outputs¶

output : T: Output tensor of random values drawn from normal distribution

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain output types to float tensors.

RandomNormalLike-1¶

Generate a tensor with random values drawn from a normal distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the normal distribution are specified by mean and scale.

The data type is specified by the ‘dtype’ argument, or copied from the input tensor if not provided. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message, and be valid as an output type.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

dtype : int: (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
mean : float (default is 0.0): The mean of the normal distribution.
scale : float (default is 1.0): The standard deviation of the normal distribution.
seed : float: (Optional) Seed to the random generator, if not specified we will auto generate one.

Inputs¶

input : T1: Input tensor to copy shape and optionally type information from.

Outputs¶

output : T2: Output tensor of random values drawn from normal distribution

Type Constraints¶

T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(float16), tensor(float), tensor(double): Constrain output types to float tensors.

RandomUniform-1¶

Generate a tensor with random values drawn from a uniform distribution. The shape of the tensor is specified by the shape argument and the range by low and high.

The data type is specified by the ‘dtype’ argument. The ‘dtype’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

dtype : int (default is 1): The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.
high : float (default is 1.0): Upper boundary of the output values.
low : float (default is 0.0): Lower boundary of the output values.
seed : float: (Optional) Seed to the random generator, if not specified we will auto generate one.
shape : list of ints (required): The shape of the output tensor.

Inputs¶

Outputs¶

output : T: Output tensor of random values drawn from uniform distribution

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain output types to float tensors.

RandomUniformLike-1¶

Generate a tensor with random values drawn from a uniform distribution. The shape of the output tensor is copied from the shape of the input tensor, and the parameters of the uniform distribution are specified by low and high.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

dtype : int: (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.
high : float (default is 1.0): Upper boundary of the output values.
low : float (default is 0.0): Lower boundary of the output values.
seed : float: (Optional) Seed to the random generator, if not specified we will auto generate one.

Inputs¶

input : T1: Input tensor to copy shape and optionally type information from.

Outputs¶

output : T2: Output tensor of random values drawn from uniform distribution

Type Constraints¶

T1 : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.
T2 : tensor(float16), tensor(float), tensor(double): Constrain output types to float tensors.

Reciprocal-1¶

Reciprocal takes one input data (Tensor) and produces one output data (Tensor) where the reciprocal is, y = 1/x, is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

ReduceL1-1¶

Computes the L1 norm of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceL2-1¶

Computes the L2 norm of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceLogSum-1¶

Computes the log sum of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceLogSumExp-1¶

Computes the log sum exponent of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceMax-1¶

Computes the max of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceMean-1¶

Computes the mean of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceMin-1¶

Computes the min of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceProd-1¶

Computes the product of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceSum-1¶

Computes the sum of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

ReduceSumSquare-1¶

Computes the sum square of the input tensor’s element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
keepdims : int (default is 1): Keep the reduced dimension or not, default 1 mean keep reduced dimension.

Inputs¶

data : T: An input tensor.

Outputs¶

reduced : T: Reduced output tensor.

Type Constraints¶

T : tensor(uint32), tensor(uint64), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain input and output types to high-precision numeric tensors.

Relu-1¶

Relu takes one input data (Tensor) and produces one output data (Tensor) where the rectified linear function, y = max(0, x), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Reshape-1¶

Reshape the input tensor similar to numpy.reshape. It takes a tensor as input and an argument shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.
shape : list of ints: New shape

Inputs¶

data : T: An input tensor.

Outputs¶

reshaped : T: Reshaped data.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Selu-1¶

Selu takes one input data (Tensor) and produces one output data (Tensor) where the scaled exponential linear unit function, y = gamma * (alpha * e^x - alpha) for x <= 0, y = gamma * x for x > 0, is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

alpha : float (default is 1.6732): Coefficient of SELU default to 1.6732.
consumed_inputs : list of ints: legacy optimization attribute.
gamma : float (default is 1.0507): Coefficient of SELU default to 1.0507.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Shape-1¶

Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

data : T: An input tensor.

Outputs¶

shape : T1: Shape of the input tensor

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Input tensor can be of arbitrary type.
T1 : tensor(int64): Constrain output to int64 tensor.

Sigmoid-1¶

Sigmoid takes one input data (Tensor) and produces one output data (Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Size-1¶

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

data : T: An input tensor.

Outputs¶

size : T1: Total number of elements of the input tensor

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Input tensor can be of arbitrary type.
T1 : tensor(int64): Constrain output to int64 tensor, which should be a scalar though.

Slice-1¶

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses axes, starts and ends attributes to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input data tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1]. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ]

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].
ends : list of ints (required): Ending indices (exclusive) of corresponding axis in axes`
starts : list of ints (required): Starting indices of corresponding axis in `axes`

Inputs¶

data : T: Tensor of data to extract slices from.

Outputs¶

output : T: Sliced data tensor.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Softmax-1¶

The operator computes the softmax (normalized exponential) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the softmax values of the corresponding input.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is 1): Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size

Inputs¶

input : T: The input tensor that's coerced into a 2D matrix of size (NxD) as described above.

Outputs¶

output : T: The output values with the same shape as input tensor (the original size without coercion).

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Softplus-1¶

Softplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to the tensor elementwise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

X (differentiable) : T: 1D input tensor

Outputs¶

Y (differentiable) : T: 1D input tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Softsign-1¶

Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

input (differentiable) : T: Input tensor

Outputs¶

output (differentiable) : T: The softsign (x/(1+|x|)) values of the input tensor computed element-wise

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

SpaceToDepth-1¶

SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

blocksize : int (required): Blocks of [blocksize, blocksize] are moved.

Inputs¶

input : T: Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.

Outputs¶

output : T: Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Split-1¶

Split a tensor into a list of tensors, along the specified ‘axis’. The lengths of the split can be specified using argument ‘axis’ or optional second input blob to the operator. Otherwise, the tensor is split to equal sized parts.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: Which axis to split on
split : list of ints: length of each output

Inputs (1 - 2)¶

input : T: The tensor to split
split (optional) : T: Optional list of output lengths (see also arg 'split')

Outputs (1 - ∞)¶

outputs... (variadic) : T: One or more outputs forming list of tensors after splitting

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input types to float tensors.

Sqrt-1¶

Square root takes one input data (Tensor) and produces one output data (Tensor) where the square root is, y = x^0.5, is applied to the tensor elementwise. If x is negative, then it will return NaN.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

X : T: Input tensor

Outputs¶

Y : T: Output tensor

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Squeeze-1¶

Remove single-dimensional entries from the shape of a tensor. Takes a parameter axes with a list of axes to squeeze. If axes is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints: List of non-negative integers, indicate the dimensions to squeeze.

Inputs¶

data : T: Tensors with at least max(dims) dimensions.

Outputs¶

squeezed : T: Reshaped tensor with same data as input.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Sub-1¶

Performs element-wise binary subtraction (with limited broadcast support).

For example, the following tensor shapes are supported (with broadcast=1):

shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
shape(A) = (2, 3, 4, 5), shape(B) = (5,)
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute broadcast=1 needs to be passed to enable broadcasting.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions. See doc for details.
broadcast : int (default is 0): Pass 1 to enable broadcasting
consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

A : T: First operand, should share the type with the second operand.
B : T: Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

Outputs¶

C : T: Result, has same dimensions and type as A

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Sum-1¶

Element-wise sum of each of the input tensors. All inputs and outputs must have the same shape and data type.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs (1 - ∞)¶

data_0 (variadic) : T: List of tensors for Sum.

Outputs¶

sum : T: Output tensor. Same dimension as inputs.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Tanh-1¶

Calculates the hyperbolic tangent of the given input tensor element-wise.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

consumed_inputs : list of ints: legacy optimization attribute.

Inputs¶

input : T: 1-D input tensor

Outputs¶

output : T: The hyperbolic tangent values of the input tensor computed element-wise

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Tile-1¶

Repeat the elements of a tensor along an axis.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Inputs¶

input : T: Input tensor of any shape.
tiles : T: Number of repeated copies to make of the input tensor.
axis : T: Axis along which to repeat.

Outputs¶

output : T: Output tensor of same shape and type as input.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input types to float tensors.
T1 : tensor(int64): Constrain tiles and axis's type to int64 tensors.

TopK-1¶

Retrieve the top-K elements along a specified axis. Given an input tensor of shape [a_1, a_2, …, a_n, r] and integer argument k, return two outputs: -Value tensor of shape [a_1, a_2, …, a_{axis-1}, k, a_{axis+1}, … a_n] which contains the values of the top k elements along the specified axis -Index tensor of shape [a_1, a_2, …, a_{axis-1}, k, a_{axis+1}, … a_n] which contains the indices of the top k elements (original indices from the input tensor). Given two equivalent values, this operator uses the indices along the axis as a tiebreaker. That is, the element with the lower index will appear first.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int (default is -1): Dimension on which to do the sort.
k : int (required): Number of top elements to retrieve

Inputs¶

X : T: Tensor of shape [a_1, a_2, ..., a_n, r]

Outputs¶

Values : T: Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor
Indices : I: Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.
I : tensor(int64): Constrain index tensor to int64

Transpose-1¶

Transpose the input tensor similar to numpy.transpose. For example, when perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape will be (2, 1, 3).

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

perm : list of ints: A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.

Inputs¶

data : T: An input tensor.

Outputs¶

transposed : T: Transposed output.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Unsqueeze-1¶

Insert single-dimensional entries to the shape of a tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor. For example: Given a tensor such that tensor with shape [3, 4, 5], then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axes : list of ints (required): List of non-negative integers, indicate the dimensions to be inserted

Inputs¶

data : T: Original tensor

Outputs¶

expanded : T: Reshaped tensor with same data as input.

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Upsample-1¶

Upsample the input tensor. The width and height of the output tensor are: output_width = floor(input_width * width_scale), output_height = floor(input_height * height_scale). Example: Given data tensor, width_scale, height_scale, mode, Upsample the input 4-D tensor in nearest mode: data = [[[ [1, 2], [3, 4] ]]] width_scale = 2 height_scale = 2 mode = “nearest” output = [[[ [1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4] ]]]

Version¶

No versioning maintained for experimental ops.

Attributes¶

height_scale : float (required): The scale along height dimension. It takes value greater than or equal to 1.
mode : string (default is nearest): Two interpolation modes: nearest(default), bilinear
width_scale : float (required): The scale along width dimension. It takes value greater than or equal to 1.

Inputs¶

X : T: 4-D tensor, [N,C,H,W]

Outputs¶

Y : T: 4-D tensor after resizing, [N,C,H,W]

Type Constraints¶

T : tensor(bool), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double): Constrain output types to bool, int32, int64, float16, float, double tensors.

Xor-1¶

Returns the tensor resulted from performing the xor logical operation elementwise on the input tensors A and B.

If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of Add for a detailed description of the broadcasting rules.

Version¶

This version of the operator has been available since version 1 of the default ONNX operator set.

Attributes¶

axis : int: If set, defines the broadcast dimensions.
broadcast : int (default is 0): Enable broadcasting

Inputs¶

A : T: Left input tensor for the logical operator.
B : T: Right input tensor for the logical operator.

Outputs¶

C : T1: Result tensor.

Type Constraints¶

T : tensor(bool): Constrains input to boolean tensor.
T1 : tensor(bool): Constrains output to boolean tensor.

Version 2 of the default ONNX operator set¶

GlobalLpPool-2¶

GlobalLpPool consumes an input tensor X and applies lp pool pooling across the values in the same channel. This is equivalent to LpPool with kernel size equal to the spatial dimension of input tensor.

Version¶

This version of the operator has been available since version 2 of the default ONNX operator set.

Attributes¶

p : int (default is 2): p value of the Lp norm used to pool over the input data.

Inputs¶

X (differentiable) : 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.

Outputs¶

Y (differentiable) : T: Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

LpPool-2¶

LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Version¶

This version of the operator has been available since version 2 of the default ONNX operator set.

Attributes¶

auto_pad : string (default is NOTSET): auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
kernel_shape : list of ints (required): The size of the kernel along each axis.
p : int (default is 2): p value of the Lp norm used to pool over the input data.
pads : list of ints: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides : list of ints: Stride along each spatial axis.

Inputs¶

X : 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.

Outputs¶

Y : T: Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Pad-2¶

Given data tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ]

Version¶

This version of the operator has been available since version 2 of the default ONNX operator set.

Attributes¶

mode : string (default is constant): Three modes: constant(default), reflect, edge
pads : list of ints (required): List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.
value : float (default is 0.0): One float, indicates the value to be filled.

Inputs¶

data : T: Input tensor.

Outputs¶

output : T: Tensor after padding.

Type Constraints¶

T : tensor(float16), tensor(float), tensor(double): Constrain input and output types to float tensors.

Split-2¶

Split a tensor into a list of tensors, along the specified ‘axis’. Lengths of the parts can be specified using argument ‘split’. Otherwise, the tensor is split to equal sized parts.

Version¶

This version of the operator has been available since version 2 of the default ONNX operator set.

Attributes¶

axis : int (default is 0): Which axis to split on.
split : list of ints: length of each output

Inputs¶

input : T: The tensor to split

Outputs (1 - ∞)¶

outputs (variadic) : T: One or more outputs forming list of tensors after splitting

Type Constraints¶

T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128): Constrain input and output types to all tensor types.

Version 3 of the default ONNX operator set¶

GRU-3¶