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SOTA
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Accelerator Toolkit
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Deep Learning Toolkit
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- Resume
- Add
- AlphaDropout
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- Output Predict
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- SeparableConv1D
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- Exp
- Identity
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- AveragePool
- Bernouilli
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- DynamicTimeWarping
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- GlobalAveragePool
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- HammingWindow
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- lrfft
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- Log
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- LpNormalization
- LpPool
- LRN
- MeanVarianceNormalization
- MicrosoftGelu
- Mish
- Multinomial
- MurmurHash3
- Neg
- NhwcMaxPool
- NonZero
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- OptionalGetElement
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- ReduceSumInteger
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- Add
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- BiasAdd
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- ConvInteger
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- DFT
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- PackedAttention
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- Pad
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- QuantizeLinear
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- Range
- ReduceL1
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- ReduceMax
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- RelativePositionBias
- Reshape
- Resize
- RestorePadding
- ReverseSequence
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- ScatterElements
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- SequenceAt
- SequenceErase
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- Sinh
- Slice
- SparseToDenseMatMul
- SplitToSequence
- Squeeze
- STFT
- StringConcat
- Sub
- Tile
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- Where
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- Attention
- AttnLSTM
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- Dropout
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- EmbedLayerNormalization
- GemmaRotaryEmbedding
- GroupQueryAttention
- GRU
- LayerNormalization
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- QAttention
- RemovePadding
- RNN
- Sampling
- SkipGroupNorm
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- SoftmaxCrossEntropyLoss
- SparseAttention
- TopK
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- AdditiveAttention
- Attention
- BatchNormalization
- Bidirectional
- Conv1D
- Conv2D
- Conv1DTranspose
- Conv2DTranspose
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- Conv3D
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- Dense
- DepthwiseConv2D
- Embedding
- LayerNormalization
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- MultiHeadAttention
- RNN (GRU)
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- SimpleRNN
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- AdditiveAttention
- Attention
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- Conv3D
- ConvLSTM1D
- ConvLSTM2D
- ConvLSTM3D
- Conv3DTranspose
- DepthwiseConv2D
- Dense
- Embedding
- LayerNormalization
- GRU
- PReLU 2D
- PReLU 3D
- PReLU 4D
- MultiHeadAttention
- LSTM
- PReLU 5D
- SeparableConv1D
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- SimpleRNN
- RNN (GRU)
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- RNN (SimpleRNN)
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- AdditiveAttention
- Attention
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- Bidirectional
- Conv1D
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- Conv3DTranspose
- ConvLSTM1D
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- ConvLSTM3D
- Dense
- DepthwiseConv2D
- Embedding
- GRU
- LayerNormalization
- LSTM
- MultiHeadAttention
- PReLU 2D
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- PReLU 4D
- PReLU 5D
- Resume
- SeparableConv1D
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- Dense
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- Conv1D
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- DepthwiseConv2D
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- BatchNormalization
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- PReLU 2D
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- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
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- RNN (GRU)
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- Conv1D
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- ConvLSTM3D
- Conv1DTranspose
- Conv2DTranspose
- Conv3DTranspose
- DepthwiseConv2D
- SeparableConv1D
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- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
- PReLU 5D
- Bidirectional
- GRU
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- SimpleRNN
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- Accuracy
- BinaryAccuracy
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- MeanTensor
- OneHotIoU
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- Poisson
- Precision
- PrecisionAtRecall
- Recall
- RecallAtPrecision
- RootMeanSquaredError
- SensitivityAtSpecificity
- SparseCategoricalAccuracy
- SparseCategoricalCrossentropy
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- Specificity
- SpecificityAtSensitivity
- SquaredHinge
- Sum
- TopKCategoricalAccuracy
- TrueNegatives
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- Resume
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- Dense
- Embedding
- AdditiveAttention
- Attention
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- DepthwiseConv2D
- SeparableConv1D
- SeparableConv2D
- BatchNormalization
- LayerNormalization
- PReLU 2D
- PReLU 3D
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- PReLU 5D
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- Dense
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- LayerNormalization
- PReLU 2D
- PReLU 3D
- PReLU 4D
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- GRU
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Computer Vision Toolkit
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CUDA Toolkit
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- Resume
- Array size
- Index Array
- Replace Subset
- Insert Into Array
- Delete From Array
- Initialize Array
- Build Array
- Concatenate Array
- Array Subset
- Min & Max
- Reshape Array
- Short Array
- Reverse 1D array
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- Split 2D Array
- Rotate 1D Array
- Increment Array Element
- Decrement Array Element
- Interpolate 1D Array
- Threshold 1D Array
- Interleave 1D Array
- Decimate 1D Array
- Transpose Array
- Remove Duplicate From 1D Array
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- Resume
- Add
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- Divide
- Quotient & Remainder
- Increment
- Decrement
- Add Array Element
- Multiply Array Element
- Absolute
- Round To Nearest
- Round Toward -Infinity
- Round Toward +Infinity
- Scale By Power Of Two
- Square Root
- Square
- Negate
- Reciprocal
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Resample
Description
Resamples an image to a user-defined size. Type : polymorphic.

Input parameters
Image Src : class,Β type accepted U8, I16, RGB and HSL.
Optional Rectangle : cluster, defines a four-element cluster that contains the left, top, right, and bottom coordinates of the region to process. The VI applies the operation to the entire image if the four-element are equal to 0.
Left : integer, left coordinate.
Top : integer, top coordinate.
Right : integer, right coordinate.
Bottom : integer, bottom coordinate.
Resample Parameters : cluster,
Width (X) : integer, output image width.
Height (Y) : integer, output image height.
Interpolation : integer, specifies the interpolation method used to resample the image.
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- INTER_NEAREST : nearest neighbor interpolation
- INTER_LINEAR : bilinear interpolation
- INTER_CUBIC : bicubic interpolation
- INTER_AREA : resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method
- INTER_LANCZOS4 : Lanczos interpolation over 8×8 neighborhood
- INTER_LINEAR_EXACT : bit exact bilinear interpolation
- INTER_NEAREST_EXACT : bit exact nearest neighbor interpolation
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Output parameters
Image Dst : class
Examples
All these examples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Computer Vision βlibrary to run it).
