972 lines
29 KiB
Markdown
972 lines
29 KiB
Markdown
<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)
|
||
- [Broadcast](#broadcast)
|
||
- [Clip](#clip)
|
||
- [Concat](#concat)
|
||
- [Conv2d](#conv2d)
|
||
- [Conv3d](#conv3d)
|
||
- [DeConv2d](#deconv2d)
|
||
- [DeConv1d](#deconv1d)
|
||
- [DepthToSpace](#depthtospace)
|
||
- [Dropout](#dropout)
|
||
- [Add](#add)
|
||
- [Sub](#sub)
|
||
- [Multiply](#multiply)
|
||
- [Div](#div)
|
||
- [Pow](#pow)
|
||
- [Minimum](#minimum)
|
||
- [Maximum](#maximum)
|
||
- [FloorDiv](#floordiv)
|
||
- [Erf](#erf)
|
||
- [FullyConnected](#fullyconnected)
|
||
- [Gather](#gather)
|
||
- [GatherElements](#gatherelements)
|
||
- [GatherNd](#gathernd)
|
||
- [GroupedConv1d](#groupedconv1d)
|
||
- [GroupedConv2d](#groupedconv2d)
|
||
- [L2Normalization](#l2normalization)
|
||
- [LocalResponseNormalization](#localresponsenormalization)
|
||
- [And](#and)
|
||
- [Or](#or)
|
||
- [LogSoftmax](#logsoftmax)
|
||
- [Matmul](#matmul)
|
||
- [MaxpooGrad](#maxpoograd)
|
||
- [MaxpoolWithArgmax](#maxpoolwithargmax)
|
||
- [MaxpoolWithArgmax2](#maxpoolwithargmax2)
|
||
- [MaxUnpool2d](#maxunpool2d)
|
||
- [Moments](#moments)
|
||
- [NBG](#nbg)
|
||
- [OneHot](#onehot)
|
||
- [Pad](#pad)
|
||
- [Pool2d](#pool2d)
|
||
- [Classic Pool2d](#classic-pool2d)
|
||
- [Global Pool2d](#global-pool2d)
|
||
- [Adaptive Pool2d](#adaptive-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)
|
||
- [RoiAlign](#roialign)
|
||
- [RoiPool](#roipool)
|
||
- [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)
|
||
- [Ceil](#ceil)
|
||
- [Cast](#cast)
|
||
- [Slice](#slice)
|
||
- [Softmax](#softmax)
|
||
- [Space2Batch](#space2batch)
|
||
- [SpaceToDepth](#spacetodepth)
|
||
- [Split](#split)
|
||
- [Squeeze](#squeeze)
|
||
- [Stack](#stack)
|
||
- [StridedSlice](#stridedslice)
|
||
- [Svdf](#svdf)
|
||
- [Tile](#tile)
|
||
- [Topk](#topk)
|
||
- [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
|
||
|
||
HardSigmoid(x) : min(max(alpha*x + beta, 0), 1)
|
||
|
||
SoftRelu(x) : log(1 + e^x). Also known as SoftPlus.
|
||
|
||
Mish(x) : x * tanh(softrelu(x))
|
||
|
||
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
|
||
|
||
Selu(x, alpha, gamma) : gamma * x if(x>=0), gamma * alpha * (exp(x)-1) x<0
|
||
|
||
Celu(x, alpha) : x if x >= 0; alpha * (exp(x/alpha) - 1)
|
||
```
|
||
|
||
<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="broadcast"></a>
|
||
## Broadcast
|
||
|
||
Broadcast an array for a compatible shape. See also numpy.broadcast_to().
|
||
|
||
Input:
|
||
- input.
|
||
|
||
Attribute:
|
||
- shape: the shape which broadcast to.
|
||
- dimensions(optional): Which dimension in the target shape each dimension
|
||
of the operand shape corresponds to. For BroadcastInDim.
|
||
|
||
<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="conv3d"></a>
|
||
## Conv3d
|
||
|
||
Performs a 3-D convolution operation
|
||
|
||
Input:
|
||
- input [WHDCN].
|
||
- kernel [ WHDIcOc ] (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. (left, right, top, bottom, front, rear).
|
||
- 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.
|
||
- input_layout : WHDCN or WHCDN.
|
||
- kernel_layout : WHDIcOc
|
||
|
||
<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="erf"></a>
|
||
## Erf
|
||
|
||
Computes the Gauss error function of x element-wise.
|
||
|
||
- no parameters
|
||
|
||
<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="gatherelements"></a>
|
||
## GatherElements
|
||
|
||
GatherElements slices from input, **axis** according to **indices**.
|
||
out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0,
|
||
out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1,
|
||
out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2,
|
||
https://github.com/onnx/onnx/blob/main/docs/Operators.md#GatherElements
|
||
|
||
<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="groupedconv1d"></a>
|
||
## GroupedConv1d
|
||
|
||
Performs a grouped 1-D convolution operation.
|
||
|
||
Input:
|
||
- input [WCN].
|
||
- kernel [ WIcOc ] (Ic: Input Channels. Oc: Output Channels).Ic*group=C.
|
||
- 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: Split conv to n group.
|
||
- layout : WCN or CWN.
|
||
|
||
<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="maxpoograd"></a>
|
||
## MaxpooGrad
|
||
|
||
Acquire the gradient of 2-D Max pooling operation's input tensor. \
|
||
Like the tensorflow_XLA op SelectAndScatter, see https://tensorflow.google.cn/xla/operation_semantics?hl=en#selectandscatter.
|
||
|
||
- padding : AUTO, VALID or SAME.
|
||
- ksize : filter size.
|
||
- stride : stride along each spatial axis.
|
||
- round_type : CEILING or FLOOR.
|
||
|
||
* Inputs:
|
||
|
||
- 0 : input tensor of 2-D Max pooling.
|
||
- 1 : gradient of 2-D Max pooling output tensor.
|
||
|
||
<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="maxpoolwithargmax2"></a>
|
||
## MaxpoolWithArgmax2
|
||
|
||
Performs an 2-D Max pooling operation and return indices(which start at the beginning of the input tensor).
|
||
|
||
- 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="onehot"></a>
|
||
## OneHot
|
||
|
||
Create a one-hot tensor.
|
||
|
||
- depth : A scalar defining the depth of the one hot dimension.
|
||
- on_value : A scalar defining the value to fill in output.
|
||
- off_value : A scalar defining the value to fill in output.
|
||
- axis : The axis to fill.
|
||
|
||
<a class="mk-toclify" id="pad"></a>
|
||
## Pad
|
||
|
||
Pads a tensor.
|
||
|
||
- const_val : the value to pad.
|
||
- pad_mode : the mode of pad.
|
||
- front_size : Add pad values to the left and top.
|
||
- back_size : Add pad values to the right and bottom.
|
||
|
||
<a class="mk-toclify" id="pool2d"></a>
|
||
## Pool2d
|
||
|
||
<a class="mk-toclify" id="classic-pool2d"></a>
|
||
### Classic Pool2d
|
||
|
||
Performs an 2-D pooling operation.
|
||
|
||
- type : MAX, AVG, L2 or AVG_ANDROID.
|
||
- padding : AUTO, VALID or SAME.
|
||
- pad : Specify the number of pad values for left, right, top, and bottom.
|
||
- ksize : filter size.
|
||
- stride : stride along each spatial axis.
|
||
- round_type : CEILING or FLOOR.
|
||
|
||
<a class="mk-toclify" id="global-pool2d"></a>
|
||
### Global Pool2d
|
||
|
||
- type : MAX, AVG, L2 or AVG_ANDROID.
|
||
- input_size : input size(only [W， H])
|
||
- round_type : CEILING or FLOOR.
|
||
|
||
<a class="mk-toclify" id="adaptive-pool2d"></a>
|
||
### Adaptive Pool2d
|
||
|
||
Same as torch.nn.AdaptiveXXXPool2d.
|
||
|
||
- type : MAX, AVG, L2 or AVG_ANDROID.
|
||
- input_size : input size(only [W， H])
|
||
- output_size : output size(only [W， H])
|
||
- 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="roialign"></a>
|
||
## RoiAlign
|
||
|
||
Select and scale the feature map of each region of interest to a unified output
|
||
size by average pooling sampling points from bilinear interpolation.
|
||
|
||
- output_height : specifying the output height of the output tensor.
|
||
- output_width : specifying the output width of the output tensor.
|
||
- height_ratio : specifying the ratio from the height of original image to the
|
||
height of feature map.
|
||
- width_ratio : specifying the ratio from the width of original image to the
|
||
width of feature map.
|
||
- height_sample_num : specifying the number of sampling points in height dimension
|
||
used to compute the output.
|
||
- width_sample_num :specifying the number of sampling points in width dimension
|
||
used to compute the output.
|
||
|
||
<a class="mk-toclify" id="roipool"></a>
|
||
## RoiPool
|
||
|
||
Select and scale the feature map of each region of interest to a unified output
|
||
size by max-pooling.
|
||
|
||
pool_type : only support max-pooling (MAX)
|
||
scale : The ratio of image to feature map (Range: 0 < scale <= 1)
|
||
size : The size of roi pooling (height/width)
|
||
|
||
|
||
<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="ceil"></a>
|
||
## Ceil
|
||
|
||
returns the largest integer more 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.
|
||
Dimensions below the dimension specified by axis will be packed together with other inputs.
|
||
|
||
<a class="mk-toclify" id="stridedslice"></a>
|
||
## StridedSlice
|
||
|
||
Extracts a strided slice of a tensor.Same as tensorflow.
|
||
|
||
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="svdf"></a>
|
||
## Svdf
|
||
|
||
Performs an 2-D pooling operation.
|
||
|
||
- rank : The rank of the SVD approximation.
|
||
- num_units : corresponds to the number of units.
|
||
- spectrogram_length : corresponds to the fixed-size of the memory.
|
||
|
||
<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="topk"></a>
|
||
## Topk
|
||
|
||
Finds values and indices of the k largest entries for the last dimension.
|
||
|
||
- k : Number of top elements to look for along the last dimension.
|
||
|
||
<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). |