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<a class="mk-toclify" id="table-of-contents"></a>
# Table of Contents
- [Operators](#operators)
- [Activation](#activation)
- [AddN](#addn)
- [ArgMin/ArgMax](#argminargmax)
- [Batch2Space](#batch2space)
- [BatchNorm](#batchnorm)
- [Clip](#clip)
- [Concat](#concat)
- [Conv2d](#conv2d)
- [DeConv2d](#deconv2d)
- [DeConv1d](#deconv1d)
- [DepthToSpace](#depthtospace)
- [Dropout](#dropout)
- [Add](#add)
- [Sub](#sub)
- [Multiply](#multiply)
- [Div](#div)
- [Pow](#pow)
- [Minimum](#minimum)
- [Maximum](#maximum)
- [FloorDiv](#floordiv)
- [FullyConnected](#fullyconnected)
- [Gather](#gather)
- [GatherNd](#gathernd)
- [GroupedConv2d](#groupedconv2d)
- [L2Normalization](#l2normalization)
- [LocalResponseNormalization](#localresponsenormalization)
- [And](#and)
- [Or](#or)
- [LogSoftmax](#logsoftmax)
- [Matmul](#matmul)
- [MaxpoolWithArgmax](#maxpoolwithargmax)
- [MaxUnpool2d](#maxunpool2d)
- [Moments](#moments)
- [NBG](#nbg)
- [Pad](#pad)
- [Pool2d](#pool2d)
- [ReduceMin](#reducemin)
- [ReduceMax](#reducemax)
- [ReduceAny](#reduceany)
- [ReduceAll](#reduceall)
- [ReduceProd](#reduceprod)
- [ReduceMean](#reducemean)
- [ReduceSum](#reducesum)
- [Greater](#greater)
- [GreaterOrEqual](#greaterorequal)
- [Less](#less)
- [LessOrEqual](#lessorequal)
- [NotEqual](#notequal)
- [Equal](#equal)
- [Reorg](#reorg)
- [Reshape](#reshape)
- [Resize](#resize)
- [Resize1d](#resize1d)
- [Reverse](#reverse)
- [ScatterND](#scatternd)
- [Select](#select)
- [DataConvert](#dataconvert)
- [Neg](#neg)
- [Abs](#abs)
- [Sin](#sin)
- [Exp](#exp)
- [Log](#log)
- [Sqrt](#sqrt)
- [Rsqrt](#rsqrt)
- [Square](#square)
- [LogicalNot](#logicalnot)
- [Floor](#floor)
- [Cast](#cast)
- [Slice](#slice)
- [Softmax](#softmax)
- [Space2Batch](#space2batch)
- [SpaceToDepth](#spacetodepth)
- [Split](#split)
- [Squeeze](#squeeze)
- [Stack](#stack)
- [StridedSlice](#stridedslice)
- [Tile](#tile)
- [Transpose](#transpose)
- [Unidirectional sequence lstm](#unidirectional-sequence-lstm)
- [Unstack](#unstack)
<a class="mk-toclify" id="operators"></a>
# Operators
<a class="mk-toclify" id="activation"></a>
## Activation
Activation functions:
```
Relu(x) : max(0, x)
Relu1(x) : -1 if x <= -1; x if -1 < x < 1; 1 if x >= 1
Relu6(x) : 0 if x <= 0; x if 0 < x < 6; 6 if x >= 6
Elu(x) : x if x >= 0 else alpha*(e^x - 1)
Tanh(x) : (1 - e^{-2x})/(1 + e^{-2x})
Sigmoid(x) : 1/(1 + e^{-x})
Swish(x) : x * sigmoid(x)
HardSwish(x) : 0 if x <= -3; x(x + 3)/6 if -3 < x < 3; x if x >= 3
Mish(x) : x if x >= 0 else alpha * x
HardSigmoid(x) : min(max(alpha*x + beta, 0), 1)
SoftRelu(x) : log(1 + e^x). Also known as SoftPlus.
LeakyRelu(x) : alpha * x if x <= 0; x if x > 0. alpha is a scalar.
Prelu(x) : alpha * x if x <= 0; x if x > 0. alpha is a tensor.
- axis : describes the axis of the inputs when coerced to 2D.
Linear(x, a, b) : a*x + b.
Gelu(x) : x * P(X <= x), where P(x) ~ N(0, 1). https://tensorflow.google.cn/api_docs/python/tf/nn/gelu
```
<a class="mk-toclify" id="addn"></a>
## AddN
```
AddN(x) : Input0 + Input1 + ... + InputN
```
<a class="mk-toclify" id="argminargmax"></a>
## ArgMin/ArgMax
Computes the indices of the **min/max** elements of the input tensor's element
along the provided **axis**. The type of the output tensor is integer.
<a class="mk-toclify" id="batch2space"></a>
## Batch2Space
This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions
of shape **block_size** + [batch], interleaves these blocks back into the grid
defined by the spatial dimensions [1, ..., M], to obtain a result with the same
rank as the input. This is the reverse transformation of Space2Batch.
- crop : corp the output tensor for ROI usage.
<a class="mk-toclify" id="batchnorm"></a>
## BatchNorm
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167.
$$\hat x_i\leftarrow \frac{x_i-\mu_\mathcal{B}}{\sqrt{\sigma_\mathcal{B}^2+\epsilon}}$$
$$y_i=\gamma\hat x_i+\beta\equiv BN_{\gamma,\beta}(x_i)$$
<a class="mk-toclify" id="clip"></a>
## Clip
Clip(x) : min if x <= min; x if min < x < max; max if x >= max
<a class="mk-toclify" id="concat"></a>
## Concat
Concatenate a list of tensors into a single tensor.
- axis : Which axis to concat on.
<a class="mk-toclify" id="conv2d"></a>
## Conv2d
Performs a 2-D convolution operation, include classic Conv2D /
Depthwise Conv2D / Group Conv2D / Dilation Conv2D.
Input:
- input [WHCN or CWHN].
- kernel [ WHIcOc ] (Ic: Input Channels. Oc: Output Channels).
- bias [ O ]. Optional.
Attribute:
- weights : the output channel number for weight tensor.
- ksize : the height and width for weight tensor.
- padding : AUTO, VALID or SAME.
- pad : pad value for each spatial axis.
- stride : stride along each spatial axis.
- dilation : dilation value along each spatial axis of the filter.
- multiplier: function similar to group attribute on other framework,
but the value is different. multiplier = weights / group.
- layout : WHCN or CWHN.
<a class="mk-toclify" id="deconv2d"></a>
## DeConv2d
Performs the transpose of 2-D convolution operation.
This operation is sometimes called "deconvolution" after Deconvolutional Networks,
but is actually the transpose (gradient) of Conv2D rather than an actual deconvolution.
- oc_count_ : the out channel count for weight tensor.
- pad_type : SAME, VALID or AUTO.
- ksize : the height and width for weight tensor.
- padding : AUTO, VALID or SAME.
- pad : pad value for each spatial axis.
- stride : stride along each spatial axis.
- output_padding : specifying the amount of padding along the height and width of
the output tensor.
- group : the feature count of each group.
- input_layout : Layout for input, WHCN by default.
- kernel_layout: Layout for kernel, WHIO by default.
<a class="mk-toclify" id="deconv1d"></a>
## DeConv1d
Performs the transpose of 1-D convolution operation.
This operation is sometimes called "deconvolution1d" after Deconvolutional Networks,
but is actually the transpose (gradient) of Conv2D rather than an actual deconvolution.
- weights : the channel number for weight tensor.
- ksize : the length for weight tensor.
- padding : AUTO, VALID or SAME.
- pad : pad value for each spatial axis.
- stride : stride along each spatial axis.
- output_padding : specifying the amount of padding along the height and width of
the output tensor.
<a class="mk-toclify" id="depthtospace"></a>
## DepthToSpace
DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth.
Chunks of data of size block_size * block_size from depth are rearranged into
non-overlapping blocks of size block_size x block_size.
The width of the output tensor is input_depth * block_size, whereas the height
is input_height * block_size. The depth of the input tensor must be divisible
by block_size * block_size
- crop : corp the output tensor for ROI usage.
<a class="mk-toclify" id="dropout"></a>
## Dropout
The Dropout layer randomly sets input units to 0 with a frequency of rate at
each step during training time, which helps prevent overfitting.
TIM-VX only focus on inference time, and just scaling input tensor by **ratio**
for Dropout operator.
<a class="mk-toclify" id="add"></a>
## Add
Add(x, y) : x + y. This operation supports broadcasting.
<a class="mk-toclify" id="sub"></a>
## Sub
Sub(x, y) : x - y. This operation supports broadcasting.
<a class="mk-toclify" id="multiply"></a>
## Multiply
Multiply(x, y) : Multiplies two tensors, element-wise, also known as Hadamard
product. This operation supports broadcasting.
- scale: scaling the product.
<a class="mk-toclify" id="div"></a>
## Div
Div(x, y) : x / y. This operation supports broadcasting.
<a class="mk-toclify" id="pow"></a>
## Pow
Pow(x, y) : x ^ y. This operation supports broadcasting.
<a class="mk-toclify" id="minimum"></a>
## Minimum
Minimum(x, y) : min(x, y). This operation supports broadcasting.
<a class="mk-toclify" id="maximum"></a>
## Maximum
Maximum(x, y) : max(x, y). This operation supports broadcasting.
<a class="mk-toclify" id="floordiv"></a>
## FloorDiv
FloorDiv(x, y): floor( x / y ). This operation supports broadcasting.
<a class="mk-toclify" id="fullyconnected"></a>
## FullyConnected
Denotes a fully (densely) connected layer, which connects all elements in the
input tensor with each element in the output tensor.
- axis: Describes the axis of the inputs when coerced to 2D.
- weights: the output channel number for weight tensor.
<a class="mk-toclify" id="gather"></a>
## Gather
Gather slices from input, **axis** according to **indices**.
<a class="mk-toclify" id="gathernd"></a>
## GatherNd
An operation similar to Gather but gathers across multiple axis at once.
<a class="mk-toclify" id="groupedconv2d"></a>
## GroupedConv2d
Performs a grouped 2-D convolution operation.
Input:
- input [WHCN or CWHN].
- kernel [ WHIcOc ] (Ic: Input Channels. Oc: Output Channels).
- bias [ O ]. Optional.
Attribute:
- weights : the output channel number for weight tensor.
- ksize : the height and width for weight tensor.
- padding : AUTO, VALID or SAME.
- pad : pad value for each spatial axis.
- stride : stride along each spatial axis.
- dilation : dilation value along each spatial axis of the filter.
- group_number: Split conv to n group.
- layout : WHCN or CWHN.
<a class="mk-toclify" id="l2normalization"></a>
## L2Normalization
Applies L2 normalization along the axis dimension:
```
output[batch, row, col, channel] =
input[batch, row, col, channel] /
sqrt(sum_{c} pow(input[batch, row, col, c], 2))
```
<a class="mk-toclify" id="localresponsenormalization"></a>
## LocalResponseNormalization
Applies Local Response Normalization along the depth dimension:
```
sqr_sum[a, b, c, d] = sum(
pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
output = input / pow((bias + alpha * sqr_sum), beta)
```
<a class="mk-toclify" id="and"></a>
## And
Returns the truth value of x AND y element-wise. This operation supports broadcasting.
<a class="mk-toclify" id="or"></a>
## Or
Returns the truth value of x OR y element-wise. This operation supports broadcasting.
<a class="mk-toclify" id="logsoftmax"></a>
## LogSoftmax
Computes the log softmax activation on the input tensor element-wise, per batch.
```
logsoftmax = logits - log(reduce_sum(exp(logits), axis))
```
<a class="mk-toclify" id="matmul"></a>
## Matmul
Multiplies matrix a by matrix b, producing a * b.
- transpose_a: If True, a is transposed before multiplication.
- transpose_b: If True, b is transposed before multiplication.
- adjoint_a: If True, a is conjugated and transposed before multiplication.
- adjoint_b: If True, b is conjugated and transposed before multiplication.
<a class="mk-toclify" id="maxpoolwithargmax"></a>
## MaxpoolWithArgmax
Performs an 2-D Max pooling operation and return indices
- padding : AUTO, VALID or SAME.
- ksize : filter size.
- stride : stride along each spatial axis.
- round_type : CEILING or FLOOR.
<a class="mk-toclify" id="maxunpool2d"></a>
## MaxUnpool2d
Performs an 2-D Max pooling operation upsample
- stride : stride along each spatial axis.
- ksize : filter size.
<a class="mk-toclify" id="moments"></a>
## Moments
The mean and variance are calculated by aggregating the contents of x across axes.
If x is 1-D and axes = [0] this is just the mean and variance of a vector.
- axes : Axes along which to compute mean and variance.
- keep_dims : Produce moments with the same dimensionality as input.
<a class="mk-toclify" id="nbg"></a>
## NBG
Network Binary Graph is a precompile technology, which can compile a fuse graph into
a bianry file.
<a class="mk-toclify" id="pad"></a>
## Pad
Pads a tensor.
- const_val : the value to pad.
<a class="mk-toclify" id="pool2d"></a>
## Pool2d
Performs an 2-D pooling operation.
- type : MAX, AVG, L2 or AVG_ANDROID.
- padding : AUTO, VALID or SAME.
- ksize : filter size.
- stride : stride along each spatial axis.
- round_type : CEILING or FLOOR.
<a class="mk-toclify" id="reducemin"></a>
## ReduceMin
Reduces a tensor by computing the minimum of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reducemax"></a>
## ReduceMax
Reduces a tensor by computing the maximum of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reduceany"></a>
## ReduceAny
Reduces a tensor by computing the "logical or" of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reduceall"></a>
## ReduceAll
Reduces a tensor by computing the "logical and" of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reduceprod"></a>
## ReduceProd
Reduces a tensor by computing the multiplying of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reducemean"></a>
## ReduceMean
Reduces a tensor by computing the mean of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="reducesum"></a>
## ReduceSum
Reduces a tensor by computing the summing of elements along given dimensions.
- axis : the dimensions to reduce.
- keep_dims : If keep_dims is true, the reduced dimensions are retained with
length 1. Otherwise, the rank of the tensor is reduced by 1 for each entry
in dimensions
<a class="mk-toclify" id="greater"></a>
## Greater
For input tensors x and y, computes x > y elementwise.
<a class="mk-toclify" id="greaterorequal"></a>
## GreaterOrEqual
For input tensors x and y, computes x >= y elementwise.
<a class="mk-toclify" id="less"></a>
## Less
For input tensors x and y, computes x < y elementwise.
<a class="mk-toclify" id="lessorequal"></a>
## LessOrEqual
For input tensors x and y, computes x <= y elementwise.
<a class="mk-toclify" id="notequal"></a>
## NotEqual
For input tensors x and y, computes x != y elementwise.
<a class="mk-toclify" id="equal"></a>
## Equal
For input tensors x and y, computes x == y elementwise.
<a class="mk-toclify" id="reorg"></a>
## Reorg
The layer used in YOLOv2. See also https://github.com/pjreddie/darknet/blob/master/src/reorg_layer.c
<a class="mk-toclify" id="reshape"></a>
## Reshape
Given tensor, this operation returns a tensor that has the same values as tensor, but with a newly specified shape.
- size : defining the shape of the output tensor.
<a class="mk-toclify" id="resize"></a>
## Resize
Resizes images to given size.
- type : NEAREST_NEIGHBOR, BILINEAR or AREA.
- factor : scale the input size. DO NOT use it with target_height / target_width together.
- align_corners : If True, the centers of the 4 corner pixels of the input and output
tensors are aligned, preserving the values at the corner pixels.
- half_pixel_centers : If True, the pixel centers are assumed to be at (0.5, 0.5).
This is the default behavior of image.resize in TF 2.0. If this parameter is True,
then align_corners parameter must be False.
- target_height / target_width : output height / width. DO NOT use it with factor together.
<a class="mk-toclify" id="resize1d"></a>
## Resize1d
Resize1ds 1D tensors to given size.
- type : NEAREST_NEIGHBOR, BILINEAR or AREA.
- factor : scale the input size. DO NOT use it with target_height / target_width together.
- align_corners : If True, the centers of the 4 corner pixels of the input and output
tensors are aligned, preserving the values at the corner pixels.
- half_pixel_centers : If True, the pixel centers are assumed to be at (0.5, 0.5).
This is the default behavior of image.resize in TF 2.0. If this parameter is True,
then align_corners parameter must be False.
- target_height / target_width : output height / width. DO NOT use it with factor together.
<a class="mk-toclify" id="reverse"></a>
## Reverse
Reverses specific dimensions of a tensor.
- axis : The indices of the dimensions to reverse.
<a class="mk-toclify" id="scatternd"></a>
## ScatterND
Scatter updates into a new tensor according to indices.
- shape : The shape of the resulting tensor.
<a class="mk-toclify" id="select"></a>
## Select
Using a tensor of booleans c and input tensors x and y select values elementwise
from both input tensors: O[i] = C[i] ? x[i] : y[i].
<a class="mk-toclify" id="dataconvert"></a>
## DataConvert
Change the format from input tensor to output tensor.
<a class="mk-toclify" id="neg"></a>
## Neg
Neg(x) : -x
<a class="mk-toclify" id="abs"></a>
## Abs
Abs(x) : x if x >= 0; -x if x < 0.
<a class="mk-toclify" id="sin"></a>
## Sin
Sin(x) : sin(x)
<a class="mk-toclify" id="exp"></a>
## Exp
Exp(x) : e^x
<a class="mk-toclify" id="log"></a>
## Log
Log(x) : ln(x)
<a class="mk-toclify" id="sqrt"></a>
## Sqrt
Sqrt(x) : $$\sqrt{x}$$
<a class="mk-toclify" id="rsqrt"></a>
## Rsqrt
Rsqrt(x) : $$\frac{1}{\sqrt{x}}$$
<a class="mk-toclify" id="square"></a>
## Square
Square : x^2
<a class="mk-toclify" id="logicalnot"></a>
## LogicalNot
LogicalNot(x) : NOT x
<a class="mk-toclify" id="floor"></a>
## Floor
returns the largest integer less than or equal to a given number.
<a class="mk-toclify" id="cast"></a>
## Cast
Change the format from input tensor to output tensor. This operation ignores
the scale and zeroPoint of quanized tensors.
<a class="mk-toclify" id="slice"></a>
## Slice
Extracts a slice of specified size from the input tensor starting at a specified location.
- start : the beginning indices of the slice in each dimension.
- length : the size of the slice in each dimension.
<a class="mk-toclify" id="softmax"></a>
## Softmax
Computes the softmax activation on the input tensor element-wise, per batch,
by normalizing the input vector so the maximum coefficient is zero:
```
output[batch, i] =
exp((input[batch, i] - max(input[batch, :])) * beta) /
sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
```
<a class="mk-toclify" id="space2batch"></a>
## Space2Batch
This operation divides "spatial" dimensions [1, ..., M] of the input into a grid
of blocks of shape **block_size**, and interleaves these blocks with the "batch"
dimension (0) such that in the output, the spatial dimensions [1, ..., M] correspond
to the position within the grid, and the batch dimension combines both the position
within a spatial block and the original batch position. Prior to division into blocks,
the spatial dimensions of the input are optionally zero padded according to paddings.
This is the reverse transformation of Batch2Space.
- pad : the paddings for each spatial dimension of the input tensor.
<a class="mk-toclify" id="spacetodepth"></a>
## SpaceToDepth
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. This is the reverse
transformation of DepthToSpace.
<a class="mk-toclify" id="split"></a>
## Split
Splits a tensor along a given axis into num_splits subtensors.
- axis : the axis along which to split.
- slices : indicating the number of splits along given axis.
<a class="mk-toclify" id="squeeze"></a>
## Squeeze
Removes dimensions of size 1 from the shape of a tensor.
- axis : the dimensions to squeeze.
<a class="mk-toclify" id="stack"></a>
## Stack
Packs the list of tensors in inputs into a tensor with rank one higher than
each tensor in values, by packing them along the **axis** dimension.
<a class="mk-toclify" id="stridedslice"></a>
## StridedSlice
Extracts a strided slice of a tensor.
Roughly speaking, this op extracts a slice of size (end - begin) / stride from
the given input tensor. Starting at the location specified by begin the slice
continues by adding stride to the index until all dimensions are not less than end.
Note that a stride can be negative, which causes a reverse slice.
- begin_dims : the starts of the dimensions of the input tensor to be sliced.
- end_dims : the ends of the dimensions of the input tensor to be sliced.
- stride_dims : the strides of the dimensions of the input tensor to be sliced.
- begin_mask : if the ith bit of begin_mask is set, begin[i] is ignored and
the fullest possible range in that dimension is used instead.
- end_mask : if the ith bit of end_mask is set, end[i] is ignored and the fullest
possible range in that dimension is used instead.
- shrink_axis_mask : if the ith bit of shrink_axis_mask is set, the ith dimension
specification shrinks the dimensionality by 1, taking on the value at index begin[i].
In this case, the ith specification must define a slice of size 1,
e.g. begin[i] = x, end[i] = x + 1.
<a class="mk-toclify" id="tile"></a>
## Tile
Constructs a tensor by tiling a given tensor.
- multiples : Must be one of the following types: int32, int64.
Length must be the same as the number of dimensions in input.
<a class="mk-toclify" id="transpose"></a>
## Transpose
Transposes the input tensor, permuting the dimensions according to the
**perm** tensor.
The returned tensor's dimension i corresponds to the input dimension perm[i].
If perm is not given, it is set to (n-1...0), where n is the rank of the input
tensor. Hence by default, this operation performs a regular matrix transpose on
2-D input Tensors.
<a class="mk-toclify" id="unidirectional-sequence-lstm"></a>
## Unidirectional sequence lstm
how to bind input/output: take unidirectional_sequence_lstm_test.cc
<a class="mk-toclify" id="unstack"></a>
## Unstack
Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
- axis : An int. The axis to unstack along. Defaults to the first dimension.
Negative values wrap around, so the valid range is [-R, R).
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