# UFF Operators¶

All shapes include batch dimension, unless otherwise specified.

## Input¶

An input to the network. Expects a CHW shape.

### Supported Datatypes¶

float32, float16, int32, int8

## Identity¶

Identity layer.

### Inputs¶

- Input0 [Tensor or Constant]
Input0 to the identity. Must be float32.

### Supported Datatypes¶

float32

## Const¶

A constant in the network. Should not include batch dimension.

### Supported Datatypes¶

float32, float16, int32, int8

## Conv¶

A convolution operation.

### Inputs¶

- Input0 [Tensor]
The input to the convolution. Must be 4 dimensional. Automatically transposed to NCHW.

- Kernel [Constant]
The kernel weights for the convolution. Must be 4 dimensional. Automatically transposed to NCHW.

### Attributes¶

- dilation [List[int]]
The HW dilations.

- strides [List[int]]
The HW strides.

- padding [List[int]]
The HW padding. Asymmetric padding is unsupported.

### Supported Datatypes¶

float32, float16, int32, int8

## ConvTranspose¶

A transposed convolution, also known as deconvolution.

### Inputs¶

- Input0 [Tensor]
The input to the transposed convolution. Must be 4 dimensional. Automatically transposed to NCHW.

- Kernel [Constant]
The kernel weights for the transposed convolution. Must be 4 dimensional. Automatically transposed to NCHW.

- Shape [Constant]
The HW dimensions of the output.

### Attributes¶

- strides [List[int]]
The HW strides.

- padding [List[int]]
The HW padding. Asymmetric padding is unsupported.

### Supported Datatypes¶

float32, float16, int32, int8

## Pool¶

A pooling layer.

### Inputs¶

- Input0 [Tensor]
The input to the pooling layer. Must be 4 dimensional.

### Attributes¶

- func [Enum[max, avg]]
The type of pooling to apply.

- kernel [List[int]]
The HW shape of the kernel.

- strides [List[int]]
The HW strides.

- padding [List[int]]
The HW padding.

### Supported Datatypes¶

float32, float16, int32, int8

## FullyConnected¶

A fully connected layer.

### Inputs¶

- Input0 [Tensor]
The input to the fully connected layer. Must be at least 4 dimensional. Automatically transposed to -NC-.

- Weights [Constant]
The weights for the fully connected layer. Must be 3 dimensional. Automatically transposed to CHW, where C is the number of output channels.

### Supported Datatypes¶

float32, float16, int32, int8

## LRN¶

An LRN layer.

### Inputs¶

- Input0 [Tensor]
The input to the LRN. Must be at least 4 dimensional.

### Attributes¶

- window_size [int]
The window size.

- alpha [double]
The LRN alpha value.

- beta [double]
The LRN beta value.

- k [double]
The LRN k value.

### Supported Datatypes¶

float32, float16, int32, int8

## Binary¶

A binary layer.

### Inputs¶

- Input0 [Tensor or Constant]
The first input to the binary layer.

- Input1 [Tensor or Constant]
The second input to the binary layer.

If either input is a constant, then at least one of the inputs must be 4 dimensional.

### Attributes¶

- func [Enum[min, max, mul, sub, div, add, pow]]
The type of operation to perform.

### Supported Datatypes¶

float32, float16, int32, int8

## Unary¶

A unary layer.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the unary layer.

The output of a unary layer with a Constant input is treated as a Constant, and therefore will not work with layers expecting a Tensor input.

### Attributes¶

- func [Enum[neg, exp, log, abs, sqrt, rsqrt, square, sin, cos, tan, sinh, cosh, asin, acos, atan, asinh, acosh, atanh, ceil, floor]]
The type of operation to perform.

### Supported Datatypes¶

float32, float16, int32, int8

## Reshape¶

A reshape layer. NOTE: this layer destroys order information. Therefore, subsequent layers will cease to automatically transpose their inputs to the correct format.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the reshape layer.

- Shape [Constant]
The desired shape. If the shape has fewer than 3 non-batch dimensions, 1s are inserted in the least significant dimensions. For example, if the shape specified is [1, 300, 5], it will be treated as [1, 300, 5, 1] instead. - -1 specifies that the dimension should be automatically deduced - this can only be used at most once in any given shape. - 0 specifies that the dimension should be copied from the input.

The output of a reshape layer with a Constant input is treated as a Constant, and therefore will not work with layers expecting a Tensor input.

### Supported Datatypes¶

float32, float16, int32, int8

## ExpandDims¶

An unsqueeze layer. NOTE: this layer destroys order information. Therefore, subsequent layers will cease to automatically transpose their inputs to the correct format.

### Inputs¶

- Input0 [Tensor]
The input to unsqueeze.

If the resulting output has fewer than 3 non-batch dimensions, it is unsqueezed further with additional 1s inserted in the least significant dimensions. For example, with an input of shape [1, 300], and axis of 1, the shape of the output will be [1, 300, 1, 1] rather than [1, 300, 1].

### Attributes¶

- axis [int]
The axis on which to unsqueeze, excluding batch dimension. For example, unsqueezing an NCHW tensor with an axis of 0 will result in a N1CHW tensor.

### Supported Datatypes¶

float32, float16, int32, int8

## ArgMax¶

An argmax layer.

### Inputs¶

- Input0 [Tensor]
The input to the argmax layer.

### Attributes¶

- axis [int]
The axis on which to perform the argmax, with 0 corresponding to the batch dimension. The specified dimension is removed. For example, performing argmax on an input of shape [1, 300, 150], with an axis of 1 would result in an output of shape [1, 150]. NOTE: argmax on the batch dimension is not supported.

### Supported Datatypes¶

float32, float16, int32, int8

## ArgMin¶

An argmin layer.

### Inputs¶

- Input0 [Tensor]
The input to the argmin layer.

### Attributes¶

- axis [int]
The axis on which to perform the argmin, with 0 corresponding to the batch dimension. The specified dimension is removed. For example, performing argmin on an input of shape [1, 300, 150], with an axis of 1 would result in an output of shape [1, 150]. NOTE: argmin on the batch dimension is not supported.

### Supported Datatypes¶

float32, float16, int32, int8

## Transpose¶

A transpose layer. A no-op in the network, this layer only modifies the UFF parser’s internal order information. Therefore, when followed by any layer that destroys order information, the transpose will not be performed.

### Inputs¶

- Input0 [Tensor]
The input to the transpose layer.

### Attributes¶

- permutation [int]
The permutation to perform. Must be 4 dimensional.

### Supported Datatypes¶

float32, float16, int32, int8

## Reduce¶

A reduce layer. NOTE: this layer destroys order information. Therefore, subsequent layers will cease to automatically transpose their inputs to the correct format.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the reduce layer.

The output of a reduce layer with a Constant input is treated as a Constant, and therefore will not work with layers expecting a Tensor input.

### Attributes¶

- func [Enum[sum, prod, max, min, mean]]
The reduction operation to perform.

- axes [List[int]]
The axes on which to reduce, with 0 corresponding to the batch dimension. Reduction on the batch dimension is unsupported.

- keepdims [bool]
Whether to keep the dimensions which were reduced. NOTE: The UFF parser ignored this value, and always keeps dimensions.

### Supported Datatypes¶

float32, float16, int32, int8

## Concat¶

A concatenation layer.

### Inputs¶

- Inputs (variadic) [List[Tensor]]
The tensors to concatenate. All inputs are transposed to the same format as the first input. Inputs must be at least 4 dimensional.

### Attributes¶

- axis [int]
The axis on which to perform the concatenation, with 0 corresponding to the batch dimension. Concatenating on the batch dimension is unsupported.

### Supported Datatypes¶

float32, float16, int32, int8

## MarkOutput¶

The output of the network.

### Inputs¶

- Inputs (variadic) [List[Tensor]]
The inputs to this layer. Automatically transposed to the same order as the outputs of the original TensorFlow network.

### Supported Datatypes¶

float32, float16, int32, int8

## Activation¶

An activation layer.

### Inputs¶

- Input0 [Tensor]
The input to the activation.

### Attributes¶

- func [Enum[relu, relu6, sigmoid, tanh, elu, selu, softsign, softplus]]
The operation to perform.

### Supported Datatypes¶

float32, float16, int32, int8

## Softmax¶

A softmax layer.

### Inputs¶

- Input0 [Tensor]
The input to the softmax.

### Attributes¶

- axis [int]
The axis on which to perform the reduction. NOTE: This value is ignored by the UFF parser.

### Supported Datatypes¶

float32, float16, int32, int8

## BatchNorm¶

A batchnorm layer.

### Inputs¶

- Input0 [Tensor]
The input to the batchnorm. Must be 4 dimensional.

- Gamma [Constant]
The gamma values.

- Beta [Constant]
The beta values.

- Mean [Constant]
The mean values.

- Variance [Constant]
The variance values.

### Attributes¶

- epsilon [double]
The epsilon value.

### Supported Datatypes¶

float32, float16, int32, int8

## Shape¶

A shape layer. Returns the shape of its input. NOTE: the output of this layer is a Constant, and therefore will not work with layers expecting a Tensor input.

### Inputs¶

- Input0 [Tensor]
The input to the shape layer.

### Supported Datatypes¶

float32, float16, int32, int8

## StridedSlice¶

A strided slice layer.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the strided slice.

- Begin [Constant]
The indices at which to begin slicing.

- End [Constant]
The indices at which to end slicing.

- Strides [Constant]
Strides to use when slicing.

### Attributes¶

- begin_mask [int]
See TensorFlow stridedSlice documentation.

- end_mask [int]
See TensorFlow stridedSlice documentation.

- shrink_axis_mask [int]
See TensorFlow stridedSlice documentation. This value is ignored unless the input is a constant.

### Supported Datatypes¶

float32, float16, int32, int8

## Stack¶

A stack layer. NOTE: the output of this layer is a Constant, and therefore will not work with layers expecting a Tensor input.

### Inputs¶

- Inputs (variadic) [List[Constant]]
The inputs to the stack layer.

### Attributes¶

- axis [int]
The axis on which to stack. NOTE: this value is ignored by the UFF parser.

### Supported Datatypes¶

float32, float16, int32, int8

## Squeeze¶

Not implemented

## Flatten¶

A flatten layer. A no-op in the UFF parser.

### Inputs¶

- Input0 [Tensor]
The tensor to flatten.

### Supported Datatypes¶

float32, float16, int32, int8

## Pad¶

A padding layer.

### Inputs¶

- Input0 [Tensor or Constant]
The input to pad. The input is automatically transposed if padding is applied to non-HW dimensions.

- Padding [Constant]
The padding to apply. Padding is supported on 2 dimensions at most.

The output of a padding layer with a Constant input is treated as a Constant, and therefore will not work with layers expecting a Tensor input.

### Supported Datatypes¶

float32, float16, int32, int8

## Gather¶

A gather layer.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the gather layer. If the input is constant, it is assumed to be of shape NC11.

- Indices [Tensor or Constant]
The indices to gather along. These are assumed to be on the first non-batch dimension.

### Supported Datatypes¶

float32, float16, int32, int8

## GatherV2¶

A gatherV2 layer.

### Inputs¶

- Input0 [Tensor or Constant]
The input to the gather layer. If the input is constant, it is assumed to be of shape NC11.

- Indices [Tensor or Constant]
The indices to gather along.

### Attributes¶

- axis [int]
The axis along which to gather, excluding batch dimension.

### Supported Datatypes¶

float32, float16, int32, int8