Dedicated accelerator for Machine Learning workloads.

The device runs NNAPI models on single or multi-core CPU.

The device can run NNAPI models and also accelerate graphics APIs such as OpenGL ES and Vulkan.

The device does not fall into any category below.

The device type cannot be provided.

NO fused activation function.

Fused ReLU activation function.

Fused ReLU1 activation function.

Fused ReLU6 activation function.

An 8 bit boolean scalar value.

Values of this operand type are either true or false. A zero value represents false; any other value represents true.

Available since API level 29.

An IEEE 754 16 bit floating point scalar value.

Available since API level 29.

A 32 bit floating point scalar value.

A signed 32 bit integer scalar value.

A tensor of 8 bit boolean values.

Values of this operand type are either true or false. A zero value represents false; any other value represents true.

Available since API level 29.

A tensor of IEEE 754 16 bit floating point values.

Available since API level 29.

A tensor of 32 bit floating point values.

A tensor of 32 bit integer values.

A tensor of 16 bit unsigned integers that represent real numbers.

Attached to this tensor are two numbers that can be used to convert the 16 bit integer to the real value and vice versa. These two numbers are:

• scale: a 32 bit floating point value greater than zero.
• zeroPoint: a 32 bit integer, in range [0, 65535].

The formula is: real_value = (integer_value - zeroPoint) * scale.

Available since API level 29.

A tensor of 16 bit signed integers that represent real numbers.

Attached to this tensor is a number representing real value scale that is used to convert the 16 bit number to a real value in the following way: realValue = integerValue * scale.

scale is a 32 bit floating point with value greater then zero.

Available since API level 29.

A tensor of 8 bit unsigned integers that represent real numbers.

Attached to this tensor are two numbers that can be used to convert the 8 bit integer to the real value and vice versa. These two numbers are:

• scale: a 32 bit floating point value greater than zero.
• zeroPoint: a 32 bit integer, in range [0, 255].

The formula is: real_value = (integer_value - zeroPoint) * scale.

A tensor of 8 bit signed integers that represent real numbers.

Attached to this tensor is a number representing real value scale that is used to convert the 8 bit number to a real value in the following way: realValue = integerValue * scale.

scale is a 32 bit floating point with value greater then zero.

Available since API level 29.

A tensor of 8 bit signed integers that represent real numbers.

This tensor is associated with additional fields that can be used to convert the 8 bit signed integer to the real value and vice versa. These fields are:

• channelDim: a 32 bit unsigned integer indicating channel dimension.
• scales: an array of positive 32 bit floating point values. The size of the scales array must be equal to dimensions[channelDim].

ANeuralNetworksModel_setOperandSymmPerChannelQuantParams must be used to set the parameters for an Operand of this type.

The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).

The formula is: realValue[..., C, ...] = integerValue[..., C, ...] * scales[C] where C is an index in the Channel dimension.

Available since API level 29.

An unsigned 32 bit integer scalar value.

Computes the absolute value of a tensor, element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Adds two tensors, element-wise.

Takes two input tensors of identical OperandCode and compatible dimensions. The output is the sum of both input tensors, optionally modified by an activation function.

Two dimensions are compatible when:

1. they are equal, or
2. one of them is 1

The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

Example:

input1.dimension = {4, 1, 2}
input2.dimension = {5, 4, 3, 1}
output.dimension = {5, 4, 3, 2}

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0.
• 2: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.

Outputs:

• 0: The sum, a tensor of the same OperandCode as input0.

Available since API level 27.

Returns the index of the largest element along an axis.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor specifying the input. Must be non-empty.
• 1: An ANEURALNETWORKS_INT32 scalar specifying the axis to reduce across. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n).

Outputs:

• 0: An (n - 1)-D ANEURALNETWORKS_TENSOR_INT32 tensor.

Available since API level 29.

Returns the index of the smallest element along an axis.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor specifying the input. Must be non-empty.
• 1: An ANEURALNETWORKS_INT32 scalar specifying the axis to reduce across. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n).

Outputs:

• 0: An (n - 1)-D ANEURALNETWORKS_TENSOR_INT32 tensor.

Available since API level 29.

Performs a 2-D average pooling operation.

The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, channel] =
    sum_{di, dj}(
        input[b, strides[1] * i + di, strides[2] * j + dj, channel]
    ) / sum(1)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 9: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 10: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 6: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 7: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].

Available since API level 27.

Transform axis-aligned bounding box proposals using bounding box deltas.

Given the positions of bounding box proposals and the corresponding bounding box deltas for each class, return the refined bounding box regions. The resulting bounding boxes are cliped against the edges of the image.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT16_ASYMM

Inputs:

• 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the bounding box proposals, each line with format [x1, y1, x2, y2]. For tensor of type ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, the zeroPoint must be 0 and the scale must be 0.125.
• 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the bounding box delta for each region of interest and each class. The bounding box deltas are organized in the following order [dx, dy, dw, dh], where dx and dy is the relative correction factor for the center position of the bounding box with respect to the width and height, dw and dh is the log-scale relative correction factor for the width and height. For input0 of type ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM.
• 2: An 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [num_rois], specifying the batch index of each box. Boxes with the same batch index are grouped together.
• 3: A 2-D Tensor of shape [batches, 2], specifying the information of each image in the batch, each line with format [image_height, image_width].

Outputs:

• 0: A tensor of the same OperandCode as input0, with shape [num_rois, num_classes * 4], specifying the coordinates of each output bounding box for each class, with format [x1, y1, x2, y2].

Available since API level 29.

BatchToSpace for N-dimensional tensors.

This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions of shape block_shape + [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 of SpaceToBatch.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: An n-D tensor, specifying the tensor to be reshaped
• 1: A 1-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the block sizes for each spatial dimension of the input tensor. All values must be >= 1.
• 2: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

Performs a forward LSTM on the input followed by a backward LSTM.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: 3, either time-major or batch-major.

All input and output tensors must be of the same type.

Inputs:

• 0: The input. A 3-D tensor of shape: If time-major: [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] where "max_time" is the number of timesteps (sequence length), "batch_size" corresponds to the batching dimension, and "input_size" is the size of the input.
• 1: The forward input-to-input weights. Optional. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of cell units.
• 2: The forward input-to-forget weights. A 2-D tensor of shape [num_units, input_size].
• 3: The forward input-to-cell weights. A 2-D tensor of shape [num_units, input_size].
• 4: The forward input-to-output weights. A 2-D tensor of shape [num_units, input_size].
• 5: The forward recurrent-to-input weights. Optional. A 2-D tensor of shape [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., “num_units”), or the second dimension of the “projection_weights”, if defined.
• 6: The forward recurrent-to-forget weights. A 2-D tensor of shape [num_units, output_size].
• 7: The forward recurrent-to-cell weights. A 2-D tensor of shape [num_units, output_size].
• 8: The forward recurrent-to-output weights. A 2-D tensor of shape [num_units, output_size].
• 9: The forward cell-to-input weights. Optional. A 1-D tensor of shape [num_units].
• 10: The forward cell-to-forget weights. Optional. A 1-D tensor of shape [num_units].
• 11: The forward cell-to-output weights. Optional. A 1-D tensor of shape [num_units].
• 12: The forward input gate bias. Optional. A 1-D tensor of shape [num_units].
• 13: The forward forget gate bias. A 1-D tensor of shape [num_units].
• 14: The forward cell gate bias. A 1-D tensor of shape [num_units].
• 15: The forward output gate bias. A 1-D tensor of shape [num_units].
• 16: The forward projection weights. Optional. A 2-D tensor of shape [output_size, num_units].
• 17: The forward projection bias. Optional. A 1-D tensor of shape [output_size].
• 18: The backward input-to-input weights. Optional. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of cell units.
• 19: The backward input-to-forget weights. A 2-D tensor of shape [num_units, input_size].
• 20: The backward input-to-cell weights. A 2-D tensor of shape [num_units, input_size].
• 21: The backward input-to-output weights. A 2-D tensor of shape [num_units, input_size].
• 22: The backward recurrent-to-input weights. Optional. A 2-D tensor of shape [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., “num_units”), or the second dimension of the “projection_weights”, if defined.
• 23: The backward recurrent-to-forget weights. A 2-D tensor of shape [num_units, output_size].
• 24: The backward recurrent-to-cell weights. A 2-D tensor of shape [num_units, output_size].
• 25: The backward recurrent-to-output weights. A 2-D tensor of shape [num_units, output_size].
• 26: The backward cell-to-input weights. Optional. A 1-D tensor of shape [num_units].
• 27: The backward cell-to-forget weights. Optional. A 1-D tensor of shape [num_units].
• 28: The backward cell-to-output weights. Optional. A 1-D tensor of shape [num_units].
• 29: The backward input gate bias. Optional. A 1-D tensor of shape [num_units].
• 30: The backward forget gate bias. A 1-D tensor of shape [num_units].
• 31: The backward cell gate bias. A 1-D tensor of shape [num_units].
• 32: The backward output gate bias. A 1-D tensor of shape [num_units].
• 33: The backward projection weights. Optional. A 2-D tensor of shape [output_size, num_units].
• 34: The backward projection bias. Optional. A 1-D tensor of shape [output_size].
• 35: The forward input activation state. A 2-D tensor of shape [batch_size, output_size].
• 36: The forward input cell state. A 2-D tensor of shape [batch_size, num_units].
• 37: The backward input activation state. A 2-D tensor of shape [batch_size, output_size].
• 38: The backward input cell state. A 2-D tensor of shape [batch_size, num_units].
• 39: The auxiliary input. Optional. A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
• 40: The forward auxiliary input-to-input weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 41: The forward auxiliary input-to-forget weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 42: The forward auxiliary input-to-cell weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 43: The forward auxiliary input-to-output weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 44: The backward auxiliary input-to-input weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 45: The backward auxiliary input-to-forget weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 46: The backward auxiliary input-to-cell weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 47: The backward auxiliary input-to-output weights. Optional. A 2-D tensor of shape [num_units, input_size].
• 48: The activation function. A value indicating the activation function:
  • 0: None;
  • 1: Relu;
  • 3: Relu6;
  • 4: Tanh;
  • 6: Sigmoid.
• 49: The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled. If all the input tensors have type ANEURALNETWORKS_TENSOR_FLOAT32, this scalar must be of the type ANEURALNETOWORKS_FLOAT32, otherwise if all the input tensors have the type ANEURALNETWORKS_TENSOR_FLOAT16, this scalar must be of type ANEURALNETWORKS_FLOAT16.
• 50: The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. If all the input tensors have type ANEURALNETWORKS_TENSOR_FLOAT32, this scalar must be of the type ANEURALNETOWORKS_FLOAT32, otherwise if all the input tensors have the type ANEURALNETWORKS_TENSOR_FLOAT16, this scalar must be of type ANEURALNETWORKS_FLOAT16.
• 51: merge_outputs An ANEURALNETWORKS_BOOL scalar specifying if the outputs from forward and backward cells should be merged.
• 52: time_major An ANEURALNETWORKS_BOOL scalar specifying the shape format of input and output tensors.

Outputs:

• 0: The forward output. A 3-D tensor of shape: If time-major: [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size]
• 1: The backward output. Unused if merge_outputs is true. A 3-D tensor of shape: If time-major: [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size]

Available since API level 29.

A recurrent neural network layer that applies a basic RNN cell to a sequence of inputs in forward and backward directions.

This Op unrolls the input along the sequence dimension, and implements the following operation for each element in the sequence s = 1...sequence_length: fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ + fw_state * fw_recurrent_weights’ + fw_bias)

And for each element in sequence t = sequence_length : 1 bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ + bw_state * bw_recurrent_weights’ + bw_bias)

Where:

• “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
• “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the current “state” which itself is the output from the previous time step computation;
• “{fw,bw}_bias” is a bias vector (added to each output vector in the batch);
• “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).

The op also supports an auxiliary input. Regular cell feeds one input into the two RNN cells in the following way:

  INPUT  (INPUT_REVERSED)
    |         |

| FW_RNN BW_RNN |

| | FW_OUT BW_OUT

An op with an auxiliary input takes two inputs and feeds them into the RNN cells in the following way:

  AUX_INPUT   (AUX_INPUT_REVERSED)
      |             |
INPUT | (INPUT_R'D.)|
  |   |       |     |

| FW_RNN BW_RNN |

| | FW_OUT BW_OUT

While stacking this op on top of itself, this allows to connect both forward and backward outputs from previous cell to the next cell's inputs.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

The input tensors must all be the same type.

Inputs:

• 0: input. A 3-D tensor. The shape is defined by the input 6 (timeMajor). If it is set to true, then the input has a shape [maxTime, batchSize, inputSize], otherwise the input has a shape [batchSize, maxTime, inputSize].
• 1: fwWeights. A 2-D tensor of shape [fwNumUnits, inputSize].
• 2: fwRecurrentWeights. A 2-D tensor of shape [fwNumUnits, fwNumUnits].
• 3: fwBias. A 1-D tensor of shape [fwNumUnits].
• 4: fwHiddenState. A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden state input for the first time step of the computation.
• 5: bwWeights. A 2-D tensor of shape [bwNumUnits, inputSize].
• 6: bwRecurrentWeights. A 2-D tensor of shape [bwNumUnits, bwNumUnits].
• 7: bwBias. A 1-D tensor of shape [bwNumUnits].
• 8: bwHiddenState A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden state input for the first time step of the computation.
• 9: auxInput. A 3-D tensor. The shape is the same as of the input 0.
• 10:fwAuxWeights. A 2-D tensor of shape [fwNumUnits, inputSize].
• 11:bwAuxWeights. A 2-D tensor of shape [bwNumUnits, inputSize].
• 12:fusedActivationFunction. A FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation.
• 13:timeMajor An ANEURALNETWORKS_BOOL scalar specifying the shape format of input and output tensors.
• 14:mergeOutputs An ANEURALNETWORKS_BOOL scalar specifying if the outputs from forward and backward cells are separate (if set to false) or concatenated (if set to true). Outputs:
• 0: fwOutput. A 3-D tensor. The first two dimensions of the shape are defined by the input 6 (timeMajor) and the third dimension is defined by the input 14 (mergeOutputs). If timeMajor is set to true, then the first two dimensions are [maxTime, batchSize], otherwise they are set to [batchSize, maxTime]. If mergeOutputs is set to true, then the third dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set to fwNumUnits.
• 1: bwOutput. A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then this tensor is not produced. The shape is defined by the input 6 (timeMajor). If it is set to true, then the shape is set to [maxTime, batchSize, bwNumUnits], otherwise the shape is set to [batchSize, maxTime, bwNumUnits].

Available since API level 29.

Greedily selects a subset of bounding boxes in descending order of score.

This op applies hard NMS algorithm to each class. In each loop of execution, the box with maximum score gets selected, and any boxes with the intersection-over-union (IOU) greater than a threshold are removed from the pending set.

Axis-aligned bounding boxes are represented by its upper-left corner coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid bounding box should satisfy x1 <= x2 and y1 <= y2.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Inputs:

• 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score of each bounding box proposal. The boxes are grouped by batches in the first dimension.
• 1: A 2-D Tensor specifying the bounding boxes of shape [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2]. The boxes are grouped by batches in the first dimension. The sequential order of the boxes corresponds with input0. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of 0 and scale of 0.125.
• 2: A 1-D Tensor of shape [batches], specifying the number of boxes for each image in the batch.
• 3: An ANEURALNETWORKS_FLOAT32 scalar, score_threshold. Boxes with scores lower than the threshold are filtered before sending to the NMS algorithm.
• 4: An ANEURALNETWORKS_FLOAT32 scalar, specifying the IoU threshold.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the maximum number of selected bounding boxes for each image. Set to a negative value for unlimited number of output bounding boxes.

Outputs:

• 0: A 1-D Tensor of the same OperandCode as input0, with shape [num_output_rois], specifying the score of each output box. The boxes are grouped by batches, but the sequential order in each batch is not guaranteed. For type of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale and zero point must be the same as input0.
• 1: A 2-D Tensor of the same OperandCode as input1, with shape [num_output_rois, 4], specifying the coordinates of each output bounding box with the same format as input1. The sequential order of the boxes corresponds with output0. For type of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, the scale must be 0.125 and the zero point must be 0.
• 2: A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [num_output_rois], specifying the class of each output box. The sequential order of the boxes corresponds with output0.
• 3: A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [batches], specifying the number of output boxes for each image.

Available since API level 29.

Casts a tensor to a new type.

This operation ignores the scale and zeroPoint of quanized tensors, e.g. it treats a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM input as a tensor of uint8 values.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: A tensor.

Outputs:

• 0: A tensor with the same shape as input0.

Available since API level 29.

Shuffle the channels of the input tensor.

Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE divide the channel dimension into num_groups groups, and reorganize the channels by grouping channels with the same index in each group.

Along the channel dimension, the output is calculated using this formula:

output_channel[k * num_groups + g] = input_channel[g * group_size + k]

where group_size = num_channels / num_groups

The number of channels must be divisible by num_groups.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be shuffled.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the number of groups.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the dimension channel shuffle would be performed on. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n).

Outputs:

• 0: A tensor of the same OperandCode and same shape as input0.

Available since API level 29.

Concatenates the input tensors along the given dimension.

The input tensors must have identical OperandCode and the same dimensions except the dimension along the concatenation axis.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (full support since API level 29, see the input section)

Supported tensor rank: up to 4

Inputs:

• 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. Before API level 29, all input tensors of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM must have the same scale and zeroPoint as the output tensor.
• n: An ANEURALNETWORKS_INT32 scalar, specifying the concatenation axis.

Outputs:

• 0: The output, a tensor of the same OperandCode as the input tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].

Available since API level 27.

Performs an 2-D convolution operation.

The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of images, applying the filter to each window of each image of the appropriate size.

The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, channel] =
    sum_{di, dj, k} (
        input[b, strides[1] * i + di, strides[2] * j + dj, k] *
        filter[channel, di, dj, k]
    ) + bias[channel]

Supported tensor OperandCode configurations:

• 32 bit Floating point :
• * ANEURALNETWORKS_TENSOR_FLOAT32 for input, filter, output, and bias.
• Quantized:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).

Available since API level 29:

• Quantized with symmetric per channel quantization for the filter:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, and output.
• * ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL for filter.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (scale set to 0.0,
• * each value scaling is separate and equal to input.scale * filter.scales[channel]).
• 16 bit Floating point:
• ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (extraParams.channelQuant.channelDim) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 9: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 10: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.
• 11: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on width dimension. If this input is set, input 12 (dilation factor for height) must be specified as well. Available since API level 29.
• 12: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on height dimension. If this input is set, input 11 (dilation factor for width) must be specified as well. Available since API level 29.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (extraParams.channelQuant.channelDim) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 7: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.
• 8: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on width dimension. If this input is set, input 9 (dilation factor for height) must be specified as well. Available since API level 29.
• 9: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on height dimension. If this input is set, input 8 (dilation factor for width) must be specified as well. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale (for filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL this condition must be true for all filter scales).

Available since API level 27.

Performs a depthwise 2-D convolution operation.

Given an input tensor of shape [batches, height, width, depth_in] and a filter tensor of shape [1, filter_height, filter_width, depth_out] containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different filter to each input channel (expanding from 1 channel to channel_multiplier channels for each), then concatenates the results together.

The output has depth_out = depth_in * depth_multiplier channels. The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, k * channel_multiplier + q] =
    sum_{di, dj} (
        input[b, strides[1] * i + di, strides[2] * j + dj, k] *
        filter[1, di, dj, k * channel_multiplier + q]
    ) + bias[k * channel_multiplier + q]

Supported tensor OperandCode configurations:

• 32 bit Floating point :
• * ANEURALNETWORKS_TENSOR_FLOAT32 for input, filter, output, and bias.
• Quantized:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).

Available since API level 29:

• Quantized with symmetric per channel quantization for the filter:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, and output.
• * ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL for filter.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (scale set to 0.0,
• * each value scaling is separate and equal to input.scale * filter.scales[channel]).

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], specifying the filter. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (extraParams.channelQuant.channelDim) must be set to 3.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 9: An ANEURALNETWORKS_INT32 scalar, specifying the depthwise multiplier.
• 10: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 11: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.
• 12: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on width dimension. If this input is set, input 13 (dilation factor for height) must be specified as well. Available since API level 29.
• 13: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on height dimension. If this input is set, input 12 (dilation factor for width) must be specified as well. Available since API level 29.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], specifying the filter.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the depthwise multiplier.
• 7: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 8: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.
• 9: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on width dimension. If this input is set, input 10 (dilation factor for height) must be specified as well. Available since API level 29.
• 10: An optional ANEURALNETWORKS_INT32 scalar, specifying the dilation factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on height dimension. If this input is set, input 9 (dilation factor for width) must be specified as well. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale (for filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL this condition must be true for all filter scales).

Available since API level 27.

Rearranges data from depth into blocks of spatial data.

More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. The value block_size indicates the input block size and how the data is moved.

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

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the block_size. block_size must be >=1 and block_size * block_size must be a divisor of the input depth.
• 2: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size, depth/(block_size*block_size)].

Available since API level 27.

Dequantizes the input tensor.

The formula is:

output = (input - zeroPoint) * scale.

Supported input tensor OperandCode:

• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM (since API level 29)
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL (since API level 29)

Supported output tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32.

Supported tensor rank: up to 4

Inputs:

• 0: A tensor.

Outputs:

• 0: A tensor with the same shape as input0.

Available since API level 27.

Apply postprocessing steps to bounding box detections.

Bounding box detections are generated by applying transformation on a set of predefined anchors with the bounding box deltas from bounding box regression. A final step of hard NMS is applied to limit the number of returned boxes.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Inputs:

• 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying the score of each anchor with each class. Class 0 for each [batches, num_anchors, 0] is background and will be ignored.
• 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with the first four values in length_box_encoding specifying the bounding box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw], where dy and dx is the linear-scale relative correction factor for the center position of the bounding box with respect to the width and height, dh and dw is the log-scale relative correction factor for the width and height. All the entries in length_box_encoding beyond the first four values are ignored in this operation.
• 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and ctr_x are the center position of the box, and h and w are the height and the width.
• 3: An ANEURALNETWORKS_FLOAT32 scalar, specifying the scaling factor for dy in bounding box deltas.
• 4: An ANEURALNETWORKS_FLOAT32 scalar, specifying the scaling factor for dx in bounding box deltas.
• 5: An ANEURALNETWORKS_FLOAT32 scalar, specifying the scaling factor for dh in bounding box deltas.
• 6: An ANEURALNETWORKS_FLOAT32 scalar, specifying the scaling factor for dw in bounding box deltas.
• 7: An ANEURALNETWORKS_BOOL scalar, set to true to use regular multi-class NMS algorithm that do NMS separately for each class, set to false for a faster algorithm that only do one single NMS using the highest class score..
• 8: An ANEURALNETWORKS_INT32 scalar, max_num_detections, specifying the maximum number of boxes for the output. Boxes with the lowest scores are discarded to meet the limit.
• 9: An ANEURALNETWORKS_INT32 scalar, only used when input7 is set to false, specifying the maximum number of classes per detection.
• 10: An ANEURALNETWORKS_INT32 scalar, only used when input7 is set to true, specifying the maximum number of detections when applying NMS algorithm for each single class.
• 11: An ANEURALNETWORKS_FLOAT32 scalar, score_threshold. Boxes with scores lower than the threshold are filtered before sending to the NMS algorithm.
• 12: An ANEURALNETWORKS_FLOAT32 scalar, specifying the IoU threshold for hard NMS.
• 13: An ANEURALNETWORKS_BOOL scalar, set to true to include background class in the list of label map for the output, set to false to not include the background. When the background class is included, it has label 0 and the output classes start at 1 in the label map, otherwise, the output classes start at 0.

Outputs:

• 0: A 2-D tensor of the same OperandCode as input0, with shape [batches, max_num_detections], specifying the score of each output detections.
• 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the coordinates of each output bounding box, with format [y1, x1, y2, x2].
• 2: A 2-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [batches, max_num_detections], specifying the class label for each output detection.
• 3: An 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [batches], specifying the number of valid output detections for each batch.

Available since API level 29.

Element-wise division of two tensors.

Takes two input tensors of identical OperandCode and compatible dimensions. The output is the result of dividing the first input tensor by the second, optionally modified by an activation function.

Two dimensions are compatible when:

1. they are equal, or
2. one of them is 1

The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

Example: input1.dimension = {4, 1, 2} input2.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2}

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the first input.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0.
• 2: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

Looks up sub-tensors in the input tensor.

This operator takes for input a tensor of values (Values) and a one-dimensional tensor of selection indices (Lookups). The output tensor is the concatenation of sub-tensors of Values as selected by Lookups.

Think of Values as being sliced along its first dimension: The entries in Lookups select which slices are concatenated together to create the output tensor.

For example, if Values has shape of [40, 200, 300] and Lookups has shape of [3], all three values found in Lookups are expected to be between 0 and 39. The resulting tensor must have shape of [3, 200, 300].

If a value in Lookups is out of bounds, the operation must fail and an error must be reported.

Supported value tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported value tensor rank: from 2

Inputs:

• 0: Lookups. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The values are indices into the first dimension of Values.
• 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are extracted.

Output:

• 0: A n-D tensor with the same rank and shape as the Values tensor, except for the first dimension which has the same size as Lookups' only dimension.

Available since API level 27.

For input tensors x and y, computes x == y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Computes exponential of x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Inserts a dimension of 1 into a tensor's shape.

Given a tensor input, this operation inserts a dimension of 1 at the given dimension index of input's shape. The dimension index starts at zero; if you specify a negative dimension index, it is counted backward from the end.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor.
• 1: An ANEURALNETWORKS_INT32 scalar specifying the dimension index to expand. Must be in the range [-(n + 1), (n + 1)).

Outputs:

• 0: An (n + 1)-D tensor with the same OperandCode and data as input0.

Available since API level 29.

Computes element-wise floor() on the input tensor.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor, of the same OperandCode and dimensions as the input tensor.

Available since API level 27.

Denotes a fully (densely) connected layer, which connects all elements in the input tensor with each element in the output tensor.

This layer implements the operation:

outputs = activation(inputs * weights’ + bias)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor of at least rank 2, specifying the input. If rank is greater than 2, then it gets flattened to a 2-D Tensor. The (flattened) 2-D Tensor is reshaped (if necessary) to [batch_size, input_size], where "input_size" corresponds to the number of inputs to the layer, matching the second dimension of weights, and "batch_size" is calculated by dividing the number of elements by "input_size".
• 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where "num_units" corresponds to the number of output nodes.
• 2: A 1-D tensor, of shape [num_units], specifying the bias. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the bias should also be of ANEURALNETWORKS_TENSOR_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale.
• 3: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.

Outputs:

• 0: The output tensor, of shape [batch_size, num_units]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale.

Available since API level 27.

Gathers values along an axis.

Produces an output tensor with shape input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] where:

Vector indices (output is rank(input0)).

output[a_0, ..., a_n, i, b_0, ..., b_n] = input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]

Higher rank indices (output is rank(input0) + rank(indices) - 1).

output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor from which to gather values.
• 1: An ANEURALNETWORKS_INT32 scalar specifying the axis. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n).
• 2: A k-D tensor ANEURALNETWORKS_TENSOR_INT32 of indices. The values must be in the bounds of the corresponding dimensions of input0.

Outputs:

• 0: An (n + k - 1)-D tensor with the same OperandCode as input0.

Available since API level 29.

Generate aixs-aligned bounding box proposals.

Bounding box proposals are generated by applying transformation on a set of predefined anchors with the bounding box deltas from bounding box regression. A final step of hard NMS is applied to limit the number of returned boxes.

Axis-aligned bounding boxes are represented by its upper-left corner coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid bounding box should satisfy x1 <= x2 and y1 <= y2.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Inputs:

• 0: A 4-D Tensor specifying the score of each anchor at each location. With "NHWC" data layout, the tensor shape is [batches, height, width, num_anchors]. With "NCHW" data layout, the tensor shape is [batches, num_anchors, height, width].
• 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data layout, the tensor shape is [batches, height, width, num_anchors * 4]. With "NCHW" data layout, the tensor shape is [batches, num_anchors * 4, height, width]. The box deltas are encoded in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale relative correction factor for the center position of the bounding box with respect to the width and height, dw and dh is the log-scale relative correction factor for the width and height. The last dimensions is the channel dimension.
• 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each predefined anchor, with format [x1, y1, x2, y2]. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, with scale of 0.125.
• 3: A 2-D Tensor of shape [batches, 2], specifying the size of each image in the batch, with format [image_height, image_width]. For input0 of type OperandType::TENSOR_QUANT8_ASYMM, this tensor should be of OperandType::TENSOR_QUANT16_SYMM, with scale of 0.125.
• 4: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the height of original image to the height of feature map.
• 5: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the width of original image to the width of feature map.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the maximum number of boxes before going into the hard NMS algorithm. Boxes with the lowest scores are discarded to meet the limit. Set to a non-positive value for unlimited number.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the maximum number of boxes returning from the hard NMS algorithm. Boxes with the lowest scores are discarded to meet the limit. Set to a non-positive value for unlimited number.
• 8: An ANEURALNETWORKS_FLOAT32 scalar, specifying the IoU threshold for hard NMS.
• 9: An ANEURALNETWORKS_FLOAT32 scalar, min_size. Boxes with height or width lower than the absolute threshold are filtered out.
• 10: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and input1. Set to false for NHWC.

Outputs:

• 0: A tensor of the same OperandCode as input0, of shape [num_output_rois], specifying the score of each output box. The boxes are grouped by batches, but the sequential order in each batch is not guaranteed. For type of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale and zero point must be the same as input0.
• 1: A tensor of the same OperandCode as input3, of shape [num_output_rois, 4], specifying the coordinates of each output bounding box for each class, with format [x1, y1, x2, y2]. The sequential order of the boxes corresponds with output0. For type of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, the scale must be 0.125 and the zero point must be 0.
• 2: A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [batches], specifying the number of output boxes for each image.

Available since API level 29.

For input tensors x and y, computes x > y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

For input tensors x and y, computes x >= y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Performs a grouped 2-D convolution operation.

Given an input tensor of shape [batches, height, width, depth_in] and a filter tensor of shape [depth_out, filter_height, filter_width, depth_group] containing depth_out convolutional filters of depth depth_group, GROUPED_CONV applies a group of different filters to each input channel group, then concatenates the results together.

Specifically, the input channels are divided into num_groups groups, each with depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional filters are also divided into num_groups groups, i.e. depth_out is divisible by num_groups. GROUPED_CONV applies each group of filters to the corresponding input channel group, and the result are concatenated together.

The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, g * channel_multiplier + q] =
    sum_{di, dj, dk} (
        input[b, strides[1] * i + di, strides[2] * j + dj,
              g * depth_group + dk] *
        filter[g * channel_multiplier + q, di, dj, dk]
    ) + bias[channel]

where channel_multiplier = depth_out / num_groups

Supported tensor OperandCode configurations:

• 32 bit Floating point :
• * ANEURALNETWORKS_TENSOR_FLOAT32 for input, filter, output, and bias.
• 16 bit Floating point:
• ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.
• Quantized:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).
• Quantized with symmetric per channel quantization for the filter:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, and output.
• * ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL for filter.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (scale set to 0.0,
• * each value scaling is separate and equal to input.scale * filter.scales[channel]).

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input, where depth_in = num_groups * depth_group.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_group], specifying the filter, where depth_out must be divisible by num_groups. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (channelDim at ANeuralNetworksSymmPerChannelQuantParams) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 9: An ANEURALNETWORKS_INT32 scalar, specifying the number of groups.
• 10: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 11: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input, where depth_in = num_groups * depth_group.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_group], specifying the filter, where depth_out must be divisible by num_groups. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (channelDim at ANeuralNetworksSymmPerChannelQuantParams) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the number of groups.
• 7: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 8: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale (for filter tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL this condition must be true for all filter scales).

Available since API level 29.

Looks up sub-tensors in the input tensor using a key-value map.

This operator takes for input a tensor of values (Values), a one-dimensional tensor of selection values (Lookups) and a one-dimensional tensor that maps these values to Values indexes. The output tensor is the concatenation of sub-tensors of Values as selected by Lookups via Keys.

Think of Values as being sliced along its outer-most dimension. The output is a concatenation of selected slices, with one slice for each entry of Lookups. The slice selected is the one at the same index as the Maps entry that matches the value in Lookups.

For a hit, the corresponding sub-tensor of Values is included in the Output tensor. For a miss, the corresponding sub-tensor in Output must have zero values.

For example, if Values has shape of [40, 200, 300], Keys should have a shape of [40]. If Lookups tensor has shape of [3], three slices are being concatenated, so the resulting tensor must have the shape of [3, 200, 300]. If the first entry in Lookups has the value 123456, that value must be located in Keys tensor. If the sixth entry of Keys contains 123456, the sixth slice of Values must be selected. If no entry in Keys has 123456, a slice of zeroes must be concatenated.

Supported value tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported value tensor rank: from 2

Inputs:

• 0: Lookups. A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [ k ].
• 1: Keys. A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [ n ]; Keys and Values pair represent a map, i.e., the ith element in Keys (Keys[i]) is the key to select the ith sub-tensor in Values (Values[i]), where 0 <= i <= n-1. Keys tensor MUST be sorted in ascending order.
• 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.

Outputs:

• 0: Output. A tensor with shape [ k …].
• 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup hits (True) or not (False). Stored as ANEURALNETWORKS_TENSOR_QUANT8_ASYMM with offset 0 and scale 1.0f. A non-zero byte represents True, a hit. A zero indicates otherwise.

Available since API level 27.

Localize the maximum keypoints from heatmaps.

This operation approximates the accurate maximum keypoint scores and indices after bicubic upscaling by using Taylor expansion up to the quadratic term.

The bounding box is represented by its upper-left corner coordinate (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. A valid bounding box should satisfy x1 <= x2 and y1 <= y2.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D Tensor of shape [num_boxes, heatmap_size, heatmap_size, num_keypoints], specifying the heatmaps, the height and width of heatmaps should be the same, and must be greater than or equal to 2.
• 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes, each with format [x1, y1, x2, y2]. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of 0 and scale of 0.125.
• 2: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0. Set to false for NHWC.

Outputs:

• 0: A tensor of the same OperandCode as input0, with shape [num_boxes, num_keypoints], specifying score of the keypoints.
• 1: A tensor of the same OperandCode as input1, with shape [num_boxes, num_keypoints, 2], specifying the location of the keypoints, the second dimension is organized as [keypoint_x, keypoint_y].

Available since API level 29.

Applies instance normalization to the input tensor.

The values in the output tensor are computed as:

output[b, h, w, c] =
    (input[b, h, w, c] - mean[b, c]) * gamma /
    sqrt(var[b, c] + epsilon) + beta

Where the mean and variance are computed across the spatial dimensions:

mean[b, c] =
    sum_{h, w}(input[b, h, w, c]) / sum(1)

var[b, c] =
    sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: An n-D tensor, specifying the tensor to be normalized.
• 1: An ANEURALNETWORKS_FLOAT32 scalar, specifying gamma, the scale applied to the normalized tensor.
• 2: An ANEURALNETWORKS_FLOAT32 scalar, specifying beta, the offset applied to the normalized tensor.
• 3: An ANEURALNETWORKS_FLOAT32 scalar, specifying epsilon, the small value added to variance to avoid dividing by zero.
• 4: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Outputs:

• 0: A tensor of the same OperandCode and same shape as input0.

Available since API level 29.

Applies L2 normalization along the depth dimension.

The values in the output tensor are computed as:

output[batch, row, col, channel] =
    input[batch, row, col, channel] /
    sqrt(sum_{c} pow(input[batch, row, col, c], 2))

For input tensor with rank less than 4, independently normalizes each 1-D slice along dimension dim.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4 Tensors with rank less than 4 are only supported since API level 29.

Inputs:

• 0: An n-D tensor, specifying the tensor to be normalized.
• 1: An optional ANEURALNETWORKS_INT32 scalar, default to -1, specifying the dimension normalization would be performed on. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n). Available since API level 29.

Outputs:

• 0: A tensor of the same OperandCode and same shape as input0.

Available since API level 27.

Performs an 2-D L2 pooling operation.

The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, c] =
    sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
         sum(1))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 9: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 10: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 6: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 7: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].

Available since API level 27.

For input tensors x and y, computes x < y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

For input tensors x and y, computes x <= y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Applies Local Response Normalization along the depth dimension.

The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius.

The output is calculated using this formula:

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)

For input tensor with rank less than 4, independently normalizes each 1-D slice along specified dimension.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4 Tensors with rank less than 4 are only supported since API level 29.

Inputs:

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the radius of the normalization window.
• 2: An ANEURALNETWORKS_FLOAT32 scalar, specifying the bias, must not be zero.
• 3: An ANEURALNETWORKS_FLOAT32 scalar, specifying the scale factor, alpha.
• 4: An ANEURALNETWORKS_FLOAT32 scalar, specifying the exponent, beta.
• 5: An optional ANEURALNETWORKS_INT32 scalar, default to -1, specifying the dimension normalization would be performed on. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n). Available since API level 29.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 27.

Computes natural logarithm of x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Returns the truth value of x AND y element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.
• 1: A tensor of ANEURALNETWORKS_TENSOR_BOOL8 and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Computes the truth value of NOT x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Returns the truth value of x OR y element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.
• 1: A tensor of ANEURALNETWORKS_TENSOR_BOOL8 and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Computes sigmoid activation on the input tensor element-wise.

The output is calculated using this formula:

output = 1 / (1 + exp(-input))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input.

Outputs:

• 0: The output tensor of same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale must be 1.f / 256 and the zeroPoint must be 0.

Available since API level 27.

Computes the log softmax activations given logits.

The output is calculated using this formula:

output = logits * beta - log(reduce_sum(exp(logits * beta), axis))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor specifying the input logits.
• 1: An ANEURALNETWORKS_FLOAT32 scalar, specifying the positive scaling factor for the exponent, beta.
• 2: An ANEURALNETWORKS_INT32 scalar specifying the axis to reduce across. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n).

Outputs:

• 0: The output tensor of the same OperandCode and shape as input0.

Available since API level 29.

Projects an input to a bit vector via locality senstive hashing.

Supported input tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported input tensor rank: from 1

Inputs:

• 0: Hash functions. Dim.size == 2, DataType: Float. Tensor[0].Dim[0]: Number of hash functions. Tensor[0].Dim[1]: Number of projected output bits generated by each hash function. If the projection type is Sparse: Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
• 1: Input. Dim.size >= 1, no restriction on DataType.
• 2: Weight. Optional. Dim.size == 1, DataType: Float. If not set, each input element is considered to have the same weight of 1.0. Tensor[1].Dim[0] == Tensor[2].Dim[0]
• 3: Type: Sparse: Value LSHProjectionType_SPARSE(=3) (since API level 29). Computed bit vector is considered to be sparse. Each output element is an int32 made up of multiple bits computed from hash functions.NOTE: To avoid collisions across hash functions, an offset value of k * (1 << Tensor[0].Dim[1]) will be added to each signature, where k is the index of the hash function.Value LSHProjectionType_SPARSE_DEPRECATED(=1). Legacy behavior that does not include the offset value.Dense: Value LSHProjectionType_DENSE(=2). Computed bit vector is considered to be dense. Each output element represents a bit and can take the value of either 0 or 1.

Outputs:

• 0: If the projection type is Sparse: Output.Dim == { Tensor[0].Dim[0] } A tensor of int32 that represents hash signatures,If the projection type is Dense: Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } A flattened tensor that represents projected bit vectors.

Available since API level 27. The offset value for sparse projections was added in API level 29.

Performs a single time step in a Long Short-Term Memory (LSTM) layer.

The LSTM operation is described by the following equations.

\begin{eqnarray*} i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\ f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\ C_t =& clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\ o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\ & & \\ & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) & if\ there\ is\ a\ projection; \\ h_t =& & \\ & o_t \odot g(C_t) & otherwise. \\ \end{eqnarray*} Where:

• $x_t$ is the input,
• $i_t$ is the input gate,
• $f_t$ is the forget gate,
• $C_t$ is the cell state,
• $o_t$ is the output,
• $h_t$ is the output state,
• $\sigma$ is the logistic sigmoid function,
• $g$ is the cell input and cell output activation function, usually $tahn$,
• $W_{xi}$ is the input-to-input weight matrix,
• $W_{hi}$ is the recurrent to input weight matrix,
• $W_{ci}$ is the cell-to-input weight matrix,
• $b_i$ is the input gate bias,
• $W_{xf}$ is the input-to-forget weight matrix,
• $W_{hf}$ is the recurrent-to-forget weight matrix,
• $W_{cf}$ is the cell-to-forget weight matrix,
• $b_f$ is the forget gate bias,
• $W_{xc}$ is the input-to-cell weight matrix,
• $W_{hc}$ is the recurrent-to-cell weight matrix,
• $b_c$ is the cell bias,
• $W_{xo}$ is the input-to-output weight matrix,
• $W_{ho}$ is the recurrent-to-output weight matrix,
• $W_{co}$ is the cell-to-output weight matrix,
• $b_o$ is the output gate bias,
• $W_{proj}$ is the projection weight matrix,
• $b_{proj}$ is the projection bias,
• $t_{cell}$ is the threshold for clipping the cell state, and
• $t_{proj}$ is the threshold for clipping the projected output.
• $\odot$ is the Hadamard product that takes two matrices and produces another matrix, each element of which is the product of the corresponding elements of the input matrices.

Since API level 29 LSTM supports layer normalization. In case layer normalization is used, the inputs to internal activation functions (sigmoid and $g$) are normalized, rescaled and recentered following an approach from section 3.1 from https://arxiv.org/pdf/1607.06450.pdf

The operation has the following independently optional inputs:

• The input-to-input weights ( $W_{xi}$), recurrent-to-input weights ( $W_{hi}$), cell-to-input ( $W_{ci}$) weights, and input gate bias ( $b_i$) either all have values, or none of them have values (i.e., all set to null). If they have no values, coupling of input and forget gates (CIFG) is used, in which case the input gate ( $i_t$) is calculated using the following equation instead. \begin{eqnarray*} i_t = 1 - f_t \end{eqnarray*}
• The cell-to-forget weights ( $W_{cf}$) and cell-to-output weights ( $W_{co}$) either both have values or neither of them have values. If they have values, the peephole optimization is used. Additionally, if CIFG is not used, cell-to-input weights ( $W_{ci}$) is also required to have values for peephole optimization.
• The projection weights ( $W_{proj}$) is required only for the recurrent projection layer, and should otherwise have no value.
• The projection bias ( $b_{proj}$) may (but not required to) have a value if the recurrent projection layer exists, and should otherwise have no value.
• (API level >= 29) The four layer normalization weights either all have values or none of them have values. Layer normalization is used when values are present.

References:

The default non-peephole non-CIFG implementation is based on: http://www.bioinf.jku.at/publications/older/2604.pdf S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.

The peephole implementation and projection layer is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014. (However, the concept of peephole optimization was introduced in work prior to this paper.)

The coupling of input and forget gate (CIFG) is based on: http://arxiv.org/pdf/1503.04069.pdf Greff et al. "LSTM: A Search Space Odyssey"

The layer normalization is based on: https://arxiv.org/pdf/1607.06450.pdf Jimmy Ba et al. "Layer Normalization"

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

All input and output tensors must be of the same type.

Inputs:

• 0: The input ( $x_t$). A 2-D tensor of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
• 1: The input-to-input weights ( $W_{xi}$). Optional. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of cell units.
• 2: The input-to-forget weights ( $W_{xf}$). A 2-D tensor of shape [num_units, input_size].
• 3: The input-to-cell weights ( $W_{xc}$). A 2-D tensor of shape [num_units, input_size].
• 4: The input-to-output weights ( $W_{xo}$). A 2-D tensor of shape [num_units, input_size].
• 5: The recurrent-to-input weights ( $W_{hi}$). Optional. A 2-D tensor of shape [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., “num_units”), or the second dimension of the “projection_weights”, if defined.
• 6: The recurrent-to-forget weights ( $W_{hf}$). A 2-D tensor of shape [num_units, output_size].
• 7: The recurrent-to-cell weights ( $W_{hc}$). A 2-D tensor of shape [num_units, output_size].
• 8: The recurrent-to-output weights ( $W_{ho}$). A 2-D tensor of shape [num_units, output_size].
• 9: The cell-to-input weights ( $W_{ci}$). Optional. A 1-D tensor of shape [num_units].
• 10:The cell-to-forget weights ( $W_{cf}$). Optional. A 1-D tensor of shape [num_units].
• 11:The cell-to-output weights ( $W_{co}$). Optional. A 1-D tensor of shape [num_units].
• 12:The input gate bias ( $b_i$). Optional. A 1-D tensor of shape [num_units].
• 13:The forget gate bias ( $b_f$). A 1-D tensor of shape [num_units].
• 14:The cell bias ( $b_c$). A 1-D tensor of shape [num_units].
• 15:The output gate bias ( $b_o$). A 1-D tensor of shape [num_units].
• 16:The projection weights ( $W_{proj}$). Optional. A 2-D tensor of shape [output_size, num_units].
• 17:The projection bias ( $b_{proj}$). Optional. A 1-D tensor of shape [output_size].
• 18:The output state (in) ( $h_{t-1}$). A 2-D tensor of shape [batch_size, output_size].
• 19:The cell state (in) ( $C_{t-1}$). A 2-D tensor of shape [batch_size, num_units].
• 20:The activation function ( $g$). A value indicating the activation function:
  • 0: None;
  • 1: Relu;
  • 3: Relu6;
  • 4: Tanh;
  • 6: Sigmoid.
• 21:The clipping threshold ( $t_{cell}$) for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled. Until API level 29 this scalar must be of type ANEURALNETWORKS_FLOAT32. Since API level 29, if all the input tensors have type ANEURALNETWORKS_TENSOR_FLOAT32, this scalar must be of the type ANEURALNETWORKS_FLOAT32, otherwise if all the input tensors have the type ANEURALNETWORKS_TENSOR_FLOAT16, this scalar must be of type ANEURALNETWORKS_FLOAT16.
• 22:The clipping threshold ( $t_{proj}$) for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. Until API level 29 this scalar must be of type ANEURALNETWORKS_FLOAT32. Since API level 29, if all the input tensors have type ANEURALNETWORKS_TENSOR_FLOAT32, this scalar must be of the type ANEURALNETWORKS_FLOAT32, otherwise if all the input tensors have the type ANEURALNETWORKS_TENSOR_FLOAT16, this scalar must be of type ANEURALNETWORKS_FLOAT16. Since API level 29 there are additional inputs to this op:
• 23:The input layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at input gate.
• 24:The forget layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at forget gate.
• 25:The cell layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at cell gate.
• 26:The output layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at output gate.

Outputs:

• 0: The scratch buffer. A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or [batch_size, num_units * 4] without CIFG.
• 1: The output state (out) ( $h_t$). A 2-D tensor of shape [batch_size, output_size].
• 2: The cell state (out) ( $C_t$). A 2-D tensor of shape [batch_size, num_units].
• 3: The output ( $o_t$). A 2-D tensor of shape [batch_size, output_size]. This is effectively the same as the current “output state (out)” value.

Available since API level 27.

Returns the element-wise maximum of two tensors.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and compatible dimensions with input0.

Outputs:

• 0: The sum, a tensor of the same OperandCode as input0.

Available since API level 29.

Performs an 2-D max pooling operation.

The output dimensions are functions of the filter dimensions, stride, and padding.

The values in the output tensor are computed as:

output[b, i, j, channel] =
    max_{di, dj} (
        input[b, strides[1] * i + di, strides[2] * j + dj, channel]
    )

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 9: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 10: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the filter width.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the filter height.
• 6: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 7: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].

Available since API level 27.

Computes the mean of elements across dimensions of a tensor.

Reduces the input tensor along the given dimensions to reduce. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with a single element is returned.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: A tensor, specifying the input.
• 1: A 1-D Tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
• 2: An ANEURALNETWORKS_INT32 scalar, keep_dims. If positive, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

Returns the element-wise minimum of two tensors.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and compatible dimensions with input0.

Outputs:

• 0: The sum, a tensor of the same OperandCode as input0.

Available since API level 29.

Multiplies two tensors, element-wise.

Takes two input tensors of identical OperandCode and compatible dimensions. The output is the product of both input tensors, optionally modified by an activation function.

Two dimensions are compatible when:

1. they are equal, or
2. one of them is 1

The size of the resulting output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0.
• 2: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.

Outputs:

• 0: The product, a tensor of the same OperandCode as input0. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input1_scale * input2_scale.

Available since API level 27.

Computes numerical negative value element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

For input tensors x and y, computes x != y elementwise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

This operation supports broadcasting.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and dimensions compatible with input0.

Outputs:

• 0: A tensor of ANEURALNETWORKS_TENSOR_BOOL8.

Available since API level 29.

Pads a tensor with zeros.

This operation pads a tensor according to the specified paddings.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be padded.
• 1: A 2-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the paddings for each spatial dimension of the input tensor. The shape of the tensor must be {rank(input0), 2}. padding[i, 0] specifies the number of elements to be padded in the front of dimension i. padding[i, 1] specifies the number of elements to be padded after the end of dimension i.

Outputs:

• 0: A tensor of the same OperandCode as input0. The output tensor has the same rank as input0, and each dimension of the output tensor has the same size as the corresponding dimension of the input tensor plus the size of the padding: output0.dimension[i] = padding[i, 0] + input0.dimension[i] + padding[i, 1]

Available since API level 28.

Pads a tensor with the given constant value according to the specified paddings.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be padded.
• 1: A 2-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the paddings for each spatial dimension of the input tensor. The shape of the tensor must be {rank(input0), 2}. padding[i, 0] specifies the number of elements to be padded in the front of dimension i. padding[i, 1] specifies the number of elements to be padded after the end of dimension i.
• 2: An scalar specifying the value to use for padding input0. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the pad value should be of ANEURALNETWORKS_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the pad value should be of ANEURALNETWORKS_INT32. The scale and zeroPoint are assumed to be the same as in input0.

Outputs:

• 0: A tensor of the same OperandCode as input0. The output tensor has the same rank as input0, and each dimension of the output tensor has the same size as the corresponding dimension of the input tensor plus the size of the padding: output0.dimension[i] = padding[i, 0] + input0.dimension[i] + padding[i, 1]

Available since API level 29.

Computes the power of one value to another.

Given a tensor base and a tensor exponent, this operation computes base^exponent elementwise.

This operations supports broadcasting. The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

For example: base.dimension = {4, 1, 2} exponent.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2}

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1

Inputs:

• 0: A tensor specifying the base.
• 1: A tensor specifying the exponent.

Outputs:

• 0: An output tensor.

Available since API level 29.

Parametric Rectified Linear Unit.

It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha is a learned array with the same OperandCode and compatible dimensions as input x.

Two dimensions are compatible when:

1. they are equal, or
2. one of them is 1

The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

Example: input.dimension = {4, 1, 2} alpha.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2}

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: A tensor, specifying the input.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0, specifying the alpha.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Quantizes the input tensor.

The formula is:

output = max(0, min(255, round(input / scale) + zeroPoint)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0, but with ANEURALNETWORKS_TENSOR_QUANT8_ASYMM.

Available since API level 29.

A version of quantized LSTM, using 16 bit quantization for internal state.

There is no projection layer, so cell state size is equal to the output size.

Inputs:

• 0: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize] specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128.
• 1: The input-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying input-to-input part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 2: The input-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying input-to-forget part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 3: The input-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying input-to-cell part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 4: The input-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying input-to-output part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 5: The recurrent-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying recurrent-to-input part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 6: The recurrent-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying recurrent-to-forget part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 7: The recurrent-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying recurrent-to-cell part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 8: The recurrent-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [outputSize, inputSize] specifying recurrent-to-output part of weights for fully-connected layer inside the LSTM cell. Quantization zero point and scale must be the same across all the weights.
• 9: The input gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input and weights scales and zeroPoint equal to 0.
• 10:The forget gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input and weights scales and zeroPoint equal to 0.
• 11:The cell bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input and weights scales and zeroPoint equal to 0.
• 12:The output gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input and weights scales and zeroPoint equal to 0.
• 13: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize] specifying the cell state from the previous time step of the LSTM cell. It is quantized using a quantization range of -2^4, 2^4 * 32767/32768.
• 14: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] specifying the output of the LSTM cell from previous time-step. Tensor is quantized with a fixed quantization range of -1, 127/128.

Outputs:

• 0: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize] which contains a cell state from the current time step. Tensor is quantized using a quantization range of -2^4, 2^4 * 32767/32768.
• 1: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] which contains the output value. Tensor is quantized with a fixed quantization range of -1, 127/128.

Draws samples from a multinomial distribution.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Inputs:

• 0: A 2-D tensor with shape [batches, classes], specifying the unnormalized log-probabilities for all classes.
• 1: A scalar ANEURALNETWORKS_INT32, specifying the number of independent samples to draw for each row slice.
• 2: A 1-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [2], specifying seeds used to initialize the random distribution. Outputs:
• 0: A 2-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [batches, samples], containing the drawn samples.

Available since API level 29.

Reduces a tensor by computing the "logical and" of elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Reduces a tensor by computing the "logical or" of elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_BOOL8

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Reduces a tensor by computing the maximum of elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Reduces a tensor by computing the minimum of elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Reduces a tensor by multiplying elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Reduces a tensor by summing elements along given dimensions.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to reduce. Dimension values must be in the range [-n, n).
• 2: An ANEURALNETWORKS_BOOL scalar, keep_dims. If true, retains reduced dimensions with length 1.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 29.

Computes rectified linear activation on the input tensor element-wise.

The output is calculated using this formula:

output = max(0, input)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 27.

Computes rectified linear 1 activation on the input tensor element-wise.

The output is calculated using this formula:

output = min(1.f, max(-1.f, input))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 27.

Computes rectified linear 6 activation on the input tensor element-wise.

The output is calculated using this formula:

output = min(6, max(0, input))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 27.

Reshapes a tensor.

Given tensor, this operation returns a tensor that has the same values as tensor, but with a newly specified shape.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the tensor to be reshaped.
• 1: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output tensor. The number of elements implied by shape must be the same as the number of elements in the input tensor.

Outputs:

• 0: The output tensor, of shape specified by the input shape.

Available since API level 27.

Resizes images to given size using the bilinear interpretation.

Resized images must be distorted if their output aspect ratio is not the same as input aspect ratio. The corner pixels of output may not be the same as corner pixels of input.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the output height of the output tensor.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 3: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].

Available since API level 27.

Resizes images to given size using the nearest neighbor interpretation.

Resized images must be distorted if their output aspect ratio is not the same as input aspect ratio. The corner pixels of output may not be the same as corner pixels of input.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the output height of the output tensor.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 3: An ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0.

Outputs:

• 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].

Available since API level 29.

A basic recurrent neural network layer.

This layer implements the operation: outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)

Where:

• “input_weights” is a weight matrix that multiplies the inputs;
• “recurrent_weights” is a weight matrix that multiplies the current “state” which itself is the output from the previous time step computation;
• “bias” is a bias vector (added to each output vector in the batch);
• “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

The input tensors must all be the same type.

Inputs:

• 0: input. A 2-D tensor of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
• 1: weights. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of units.
• 2: recurrent_weights. A 2-D tensor of shape [num_units, num_units], with columns corresponding to the weights from each unit.
• 3: bias. A 1-D tensor of shape [num_units].
• 4: hidden state (in). A 2-D tensor of shape [batch_size, num_units].
• 5: fused_activation_function. An optional FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation.

Outputs:

• 0: hidden state (out). A 2-D tensor of shape [batch_size, num_units].
• 1: output. A 2-D tensor of shape [batch_size, num_units]. This is effectively the same as the current state value.

Available since API level 27.

Select and scale the feature map of each region of interest to a unified output size by average pooling sampling points from bilinear interpolation.

The region of interest is represented by its upper-left corner coordinate (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. A spatial scaling factor is applied to map into feature map coordinate. A valid region of interest should satisfy x1 <= x2 and y1 <= y2.

No rounding is applied in this operation. The sampling points are unified distributed in the pooling bin and their values are calculated by bilinear interpolation.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, specifying the feature map.
• 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the regions of interest, each line with format [x1, y1, x2, y2]. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of 0 and scale of 0.125.
• 2: An 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [num_rois], specifying the batch index of each box. Boxes with the same batch index are grouped together.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the output height of the output tensor.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 5: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the height of original image to the height of feature map.
• 6: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the width of original image to the width of feature map.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the number of sampling points in height dimension used to compute the output. Set to 0 for adaptive value of ceil(roi_height/out_height).
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the number of sampling points in width dimension used to compute the output. Set to 0 for adaptive value of ceil(roi_width/out_width).
• 9: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Outputs:

• 0: A tensor of the same OperandCode as input0. The output shape is [num_rois, out_height, out_width, depth].

Available since API level 29.

Select and scale the feature map of each region of interest to a unified output size by max-pooling.

The region of interest is represented by its upper-left corner coordinate (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. A spatial scaling factor is applied to map into feature map coordinate. A valid region of interest should satisfy x1 <= x2 and y1 <= y2.

Rounding is applied in this operation to ensure integer boundary for regions of interest and pooling bins.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, specifying the feature map.
• 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the regions of interest, each line with format [x1, y1, x2, y2]. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of 0 and scale of 0.125.
• 2: An 1-D ANEURALNETWORKS_TENSOR_INT32 tensor, of shape [num_rois], specifying the batch index of each box. Boxes with the same batch index are grouped together.
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the output height of the output tensor.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 5: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the height of original image to the height of feature map.
• 6: An ANEURALNETWORKS_FLOAT32 scalar, specifying the ratio from the width of original image to the width of feature map.
• 7: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Outputs:

• 0: A tensor of the same OperandCode as input0. The output shape is [num_rois, out_height, out_width, depth].

Available since API level 29.

Computes reciprocal of square root of x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

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].

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: A tensor of type ANEURALNETWORKS_TENSOR_BOOL8 acting as a mask that chooses, based on the value at each element, whether the corresponding element in the output should be taken from input1 (if true) or input2 (if false).
• 1: An input tensor of the same shape as input0.
• 2: An input tensor of the same shape and type as input1.

Outputs:

• 0: A tensor of the same type and shape as input1 and input2.

Computes sin of x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Extracts a slice of specified size from the input tensor starting at a specified location.

The starting location is specified as a 1-D tensor containing offsets for each dimension. The size is specified as a 1-D tensor containing either size of a slice along corresponding dimension or -1. In the latter case, all the remaining elements in dimension are included in the slice. Slice size in each dimension cannot be zero.

A sum of begin offset and a size of a slice must not exceed size of a corresponding dimension.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor to take slice from.
• 1: A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 specifying the beginning indices of the slice in each dimension.
• 2: A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 specifying the size of the slice in each dimension.

Outputs:

• 0: An n-D tensor of the same type as the input containing the slice.

Available since API level 29.

Computes the softmax activation on the input tensor element-wise, per batch, by normalizing the input vector so the maximum coefficient is zero.

The output is calculated using this formula:

output[batch, i] =
    exp((input[batch, i] - max(input[batch, :])) * beta) /
    sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}

For input tensor with rank other than 2, the activation will be applied independently on each 1-D slice along specified dimension.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4. Tensors with rank other than 2 or 4 are only supported since API level 29.

Inputs:

• 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
• 1: An ANEURALNETWORKS_FLOAT32 scalar, specifying the positive scaling factor for the exponent, beta.
• 2: An optional ANEURALNETWORKS_INT32 scalar, default to -1, specifying the dimension the activation would be performed on. Negative index is used to specify axis from the end (e.g. -1 for the last axis). Must be in the range [-n, n). Available since API level 29.

Outputs:

• 0: The output tensor of same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale must be 1.f / 256 and the zeroPoint must be 0.

Available since API level 27.

SpaceToBatch for N-Dimensional tensors.

This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape block_shape, 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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: An n-D tensor, specifying the input.
• 1: A 1-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the block sizes for each spatial dimension of the input tensor. All values must be >= 1.
• 2: A 2-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the paddings for each spatial dimension of the input tensor. All values must be >= 0. The shape of the tensor must be {M, 2}, where M is the number of spatial dimensions. padding[i, 0] specifies the number of element to be padded in the front of dimension i. padding[i, 1] specifies the number of element to be padded after the end of dimension i.
• 3: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

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. The value block_size indicates the input block size and how the data is moved.

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 depth of the output tensor is input_depth * block_size * block_size. The input tensor's height and width must be divisible by block_size.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Inputs:

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the block_size. block_size must be >=1 and block_size must be a divisor of both the input height and width.
• 2: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0. Available since API level 29.

Outputs:

• 0: The output 4-D tensor, of shape [batches, height/block_size, width/block_size, depth_in*block_size*block_size].

Available since API level 27.

Splits a tensor along a given axis into num_splits subtensors.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor to split.
• 1: An ANEURALNETWORKS_INT32 scalar specifying the axis along which to split.
• 2: An ANEURALNETWORKS_INT32 scalar indicating the number of splits along given axis. Must evenly divide axis size.

Outputs:

• 0 ~ (num_splits - 1): Resulting subtensors.

Available since API level 29.

Computes square root of x element-wise.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

• 0: The output tensor of same shape as input0.

Available since API level 29.

Removes dimensions of size 1 from the shape of a tensor.

Given a tensor input, this operation returns a tensor of the same OperandCode with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying the axes (input1).

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, the tensor to be squeezed.
• 1: An optional 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to squeeze. If specified only squeezes the dimensions listed. Otherwise, squeezes all dimensions. The dimension index starts at 0. An error must be reported if squeezing a dimension that is not 1.

Outputs:

• 0: A tensor of the same OperandCode as input0. Contains the same data as input, but has one or more dimensions of size 1 removed.

Available since API level 28.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be sliced.
• 1: begin, a 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input0).
• 2: end, a 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input0).
• 3: strides, a 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The strides of the dimensions of the input tensor to be sliced. The length must be of rank(input0). The entries must be non-zero.
• 4: begin_mask, an ANEURALNETWORKS_INT32 scalar. If the ith bit of begin_mask is set, begin[i] is ignored and the fullest possible range in that dimension is used instead.
• 5: end_mask, an ANEURALNETWORKS_INT32 scalar. If the ith bit of end_mask is set, end[i] is ignored and the fullest possible range in that dimension is used instead.
• 6: shrink_axis_mask, an ANEURALNETWORKS_INT32 scalar. 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.

Outputs:

• 0: A tensor of the same OperandCode as input0 and rank (n - k), where k is the number of bits set in shrink_axis_mask.

Available since API level 28.

Element-wise subtraction of two tensors.

Takes two input tensors of identical OperandCode and compatible dimensions. The output is the result of subtracting the second input tensor from the first one, optionally modified by an activation function.

Two dimensions are compatible when:

1. they are equal, or
2. one of them is 1

The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward.

Example: input1.dimension = {4, 1, 2} input2.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2}

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since API level 29)

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the first input.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0.
• 2: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

SVDF op is a kind of stateful layer derived from the notion that a densely connected layer that's processing a sequence of input frames can be approximated by using a singular value decomposition of each of its nodes.

The implementation is based on:

https://research.google.com/pubs/archive/43813.pdf

P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. “Compressing Deep Neural Networks using a Rank-Constrained Topology”. INTERSPEECH, 2015.

It processes the incoming input using a 2-stage filtering mechanism:

• stage 1 performs filtering on the "features" dimension, whose outputs get pushed into a memory of fixed-size memory_size.
• stage 2 performs filtering on the "time" dimension of the memory_size memoized outputs of stage 1.

Specifically, for rank 1, this layer implements the operation:

memory = push(conv1d(inputs, weights_feature, feature_dim,
                     "ANEURALNETWORKS_PADDING_VALID"));
outputs = activation(memory * weights_time + bias);

Where:

• “weights_feature” is a weights matrix that processes the inputs (by convolving the input with every “feature filter”), and whose outputs get pushed, stacked in order, into the fixed-size “memory” (the oldest entry gets dropped);
• “weights_time” is a weights matrix that processes the “memory” (by a batched matrix multiplication on the num_units);
• “bias” is an optional bias vector (added to each output vector in the batch); and
• “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).

Each rank adds a dimension to the weights matrices by means of stacking the filters.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32

All input tensors must be the same type.

Inputs:

• 0: input. A 2-D tensor of shape [batch_size, input_size], where “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
• 1: weights_feature. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of units.
• 2: weights_time. A 2-D tensor of shape [num_units, memory_size], where “memory_size” corresponds to the fixed-size of the memory.
• 3: bias. An optional 1-D tensor of shape [num_units].
• 4: state (in). A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
• 5: rank. The rank of the SVD approximation.
• 6: fused_activation_function. An optional FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation.

Outputs:

• 0: state (out). A 2-D tensor of the same OperandCode as the inputs, with shape [batch_size, (memory_size - 1) * num_units * rank].
• 1: output. A 2-D tensor of the same OperandCode as the inputs, with shape [batch_size, num_units].

Available since API level 27.

Computes hyperbolic tangent of input tensor element-wise.

The output is calculated using this formula:

output = tanh(input)

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since API level 29)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input.

Outputs:

• 0: The output tensor of same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale must be 1.f / 128 and the zeroPoint must be 128.

Available since API level 27.

Constructs a tensor by tiling a given tensor.

This operation creates a new tensor by replicating inputmultiples times. The output tensor's i-th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the i-th dimension. For example, tiling [a b c d] by [2] produces [a b c d a b c d].

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: input, an n-D tensor specifying the input.
• 1: multiples, a 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The length of multiples must be n.

Outputs:

• 0: A tiled tensor of the same OperandCode and rank as input.

Available since API level 29.

Finds values and indices of the k largest entries for the last dimension.

Resulting values in each dimensions are sorted in descending order. If two values are equal, the one with larger index appears first.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: from 1

Inputs:

• 0: input, an n-D tensor specifying the input.
• 1: k, an ANEURALNETWORKS_INT32 scalar, specifying the number of top elements to look for along the last dimension.

Outputs:

• 0: An n-D tensor of the same type as the input, containing the k largest elements along each last dimensional slice.
• 1: An n-D tensor of type ANEURALNETWORKS_TENSOR_INT32 containing the indices of values within the last dimension of input.

Available since API level 29.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since API level 29)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be transposed.
• 1: An optional 1-D Tensor of ANEURALNETWORKS_TENSOR_INT32, the permutation of the dimensions of the input tensor.

Outputs:

• 0: A tensor of the same OperandCode as input0.

Available since API level 28.

Performs the tranpose of 2-D convolution operation.

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is actually the transpose (gradient) of ANEURALNETWORKS_CONV_2D rather than an actual deconvolution.

The output dimensions are functions of the filter dimensions, stride, and padding.

Supported tensor OperandCode configurations:

• 32 bit Floating point :
• * ANEURALNETWORKS_TENSOR_FLOAT32 for input, filter, output, and bias.
• Quantized:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).

Available since API level 29:

• Quantized with symmetric per channel quantization for the filter:
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM for input, and output.
• * ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL for filter.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (scale set to 0.0,
• * each value scaling is separate and equal to input.scale * filter.scales[channel]).

Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. With the default data layout NHWC, the data is stored in the order of: [batch, height, width, channels]. Alternatively, the data layout could be NCHW, the data storage order of: [batch, channels, height, width].

Both explicit padding and implicit padding are supported.

Inputs (explicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (extraParams.channelQuant.channelDim) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias should be of the same type. For input tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias must be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the left, in the ‘width’ dimension.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the right, in the ‘width’ dimension.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the top, in the ‘height’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the padding on the bottom, in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 8: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 9: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 10: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
• 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], specifying the filter. For tensor of type ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL the channel dimension (extraParams.channelQuant.channelDim) must be set to 0.
• 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input tensor of type ANEURALNETWORKS_TENSOR_FLOAT32 or ANEURALNETWORKS_TENSOR_FLOAT16, the bias should be of the same type. For input tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the bias should be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale == input_scale * filter_scale. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, the bias must be of ANEURALNETWORKS_TENSOR_INT32, with zeroPoint of 0 and bias_scale of 0. The actual scale of each value 'i' is equal to bias_scale[i] = input_scale * filter_scale[i].
• 3: An ANEURALNETWORKS_TENSOR_INT32 tensor, specifying the output tensor shape.
• 4: An ANEURALNETWORKS_INT32 scalar, specifying the implicit padding scheme, has to be one of the PaddingCode values.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘width’ dimension.
• 6: An ANEURALNETWORKS_INT32 scalar, specifying the stride when walking through input in the ‘height’ dimension.
• 7: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result.
• 8: An ANEURALNETWORKS_BOOL scalar, set to true to specify NCHW data layout for input0 and output0. Set to false for NHWC.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale (for filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL this condition must be true for all filter scales).

Available since API level 29.

A recurrent neural network specified by an LSTM cell.

Performs (fully) dynamic unrolling of input.

This Op unrolls the input along the time dimension, and implements the following operation for each element in the sequence s = 1...sequence_length: outputs[s] = projection(state = activation(LSTMOp(inputs[s])))

Where LSTMOp is the LSTM op as in ANEURALNETWORKS_LSTM, the "projection" is an optional projection layer from state and output and the “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: 3, either time-major or batch-major.

All input and output tensors must be of the same type.

Inputs:

• 0: The input ( $x_t$). A 3-D tensor of shape: If time-major: [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size] where “max_size” is the number of timesteps (sequence length), “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
• 1: The input-to-input weights ( $W_{xi}$). Optional. A 2-D tensor of shape [num_units, input_size], where “num_units” corresponds to the number of cell units.
• 2: The input-to-forget weights ( $W_{xf}$). A 2-D tensor of shape [num_units, input_size].
• 3: The input-to-cell weights ( $W_{xc}$). A 2-D tensor of shape [num_units, input_size].
• 4: The input-to-output weights ( $W_{xo}$). A 2-D tensor of shape [num_units, input_size].
• 5: The recurrent-to-input weights ( $W_{hi}$). Optional. A 2-D tensor of shape [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., “num_units”), or the second dimension of the “projection_weights”, if defined.
• 6: The recurrent-to-forget weights ( $W_{hf}$). A 2-D tensor of shape [num_units, output_size].
• 7: The recurrent-to-cell weights ( $W_{hc}$). A 2-D tensor of shape [num_units, output_size].
• 8: The recurrent-to-output weights ( $W_{ho}$). A 2-D tensor of shape [num_units, output_size].
• 9: The cell-to-input weights ( $W_{ci}$). Optional. A 1-D tensor of shape [num_units].
• 10:The cell-to-forget weights ( $W_{cf}$). Optional. A 1-D tensor of shape [num_units].
• 11:The cell-to-output weights ( $W_{co}$). Optional. A 1-D tensor of shape [num_units].
• 12:The input gate bias ( $b_i$). Optional. A 1-D tensor of shape [num_units].
• 13:The forget gate bias ( $b_f$). A 1-D tensor of shape [num_units].
• 14:The cell bias ( $b_c$). A 1-D tensor of shape [num_units].
• 15:The output gate bias ( $b_o$). A 1-D tensor of shape [num_units].
• 16:The projection weights ( $W_{proj}$). Optional. A 2-D tensor of shape [output_size, num_units].
• 17:The projection bias ( $b_{proj}$). Optional. A 1-D tensor of shape [output_size].
• 18:The output state (in) ( $h_{t-1}$). A 2-D tensor of shape [batch_size, output_size].
• 19:The cell state (in) ( $C_{t-1}$). A 2-D tensor of shape [batch_size, num_units].
• 20:The activation function ( $g$). A value indicating the activation function:
  • 0: None;
  • 1: Relu;
  • 3: Relu6;
  • 4: Tanh;
  • 6: Sigmoid.
• 21:The clipping threshold ( $t_{cell}$) for the cell state, such that values are bound within [-cell_clip, cell_clip]. If set to 0.0 then clipping is disabled.
• 22:The clipping threshold ( $t_{proj}$) for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
• 23:Time-major if true, batch-major if false.
• 24:The input layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at input gate.
• 25:The forget layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at forget gate.
• 26:The cell layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at cell gate.
• 27:The output layer normalization weights. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at output gate.

Outputs:

• 0: The output ( $o_t$). A 3-D tensor of shape: If time-major: [max_time, batch_size, output_size] If batch-major: [batch_size, max_time, output_size]

Available since API level 29.

A recurrent neural network layer that applies a basic RNN cell to a sequence of inputs.

This layer unrolls the input along the sequence dimension, and implements the following operation for each element in the sequence s = 1...sequence_length: outputs[s] = state = activation(inputs[s] * input_weights’ + state * recurrent_weights’ + bias)

Where:

• “input_weights” is a weight matrix that multiplies the inputs;
• “recurrent_weights” is a weight matrix that multiplies the current “state” which itself is the output from the previous time step computation;
• “bias” is a bias vector (added to each output vector in the batch);
• “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

The input tensors must all be the same type.

Inputs:

• 0: input. A 3-D tensor. The shape is defined by the input 6 (timeMajor). If it is set to 1, then the input has a shape [maxTime, batchSize, inputSize], otherwise the input has a shape [batchSize, maxTime, inputSize].
• 1: weights. A 2-D tensor of shape [numUnits, inputSize].
• 2: recurrent_weights. A 2-D tensor of shape [numUnits, numUnits].
• 3: bias. A 1-D tensor of shape [numUnits].
• 4: hidden state A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden state input for the first time step of the computation.
• 5: fusedActivationFunction. A FuseCode value indicating the activation function. If “NONE” is specified then it results in a linear activation.
• 6: timeMajor An ANEURALNETWORKS_INT32 scalar specifying the shape format of input and output tensors. Must be set to either 0 or 1. Outputs:
• 0: output. A 3-D tensor. The shape is defined by the input 6 (timeMajor). If it is set to 1, then the output has a shape [maxTime, batchSize, numUnits], otherwise the output has a shape [batchSize, maxTime, numUnits].

Available since API level 29.

SAME padding.

Padding on both ends are the "same": padding_to_beginning = total_padding / 2 padding_to_end = (total_padding + 1)/2. i.e., for even number of padding, padding to both ends are exactly the same; for odd number of padding, padding to the ending is bigger than the padding to the beginning by 1.

total_padding is a function of input, stride and filter size. It could be computed as follows: out_size = (input + stride - 1) / stride; needed_input = (out_size - 1) * stride + filter_size total_padding = max(0, needed_input - input_size) The computation is the same for the horizontal and vertical directions.

VALID padding.

No padding. When the input size is not evenly divisible by the filter size, the input at the end that could not fill the whole filter tile will simply be ignored.

Prefer returning a single answer as fast as possible, even if this causes more power consumption.

Prefer executing in a way that minimizes battery drain.

This is desirable for compilations that will be executed often.

Prefer maximizing the throughput of successive frames, for example when processing successive frames coming from the camera.

Failure caused by invalid function arguments, invalid model definition, invalid execution definition or invalid data at execution time.

Failure caused by object being in the wrong state.

Operation was succesful.

Failure caused by failed model execution.

Failure caused by insufficient buffer size provided to a model output.

Failure caused by not enough available memory.

Failure caused by a device not being available.

Failure caused by unexpected null argument.

Failure caused by not being able to map a file into memory.

This may be caused by a file descriptor not being mappable, or an AHardwareBuffer not supported by the device. Mitigate by reading its content into memory.