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.

NNAPI specification available in Android O-MR1, Android NNAPI feature level 1.

NNAPI specification available in Android P, Android NNAPI feature level 2.

NNAPI specification available in Android Q, Android NNAPI feature level 3.

NNAPI specification available in Android R, Android NNAPI feature level 4.

NNAPI specification available in Android S, Android NNAPI feature level 5.

After Android S, the NNAPI specification can be updated between Android API releases.

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 NNAPI feature level 3.

An IEEE 754 16 bit floating point scalar value.

Available since NNAPI feature level 3.

A 32 bit floating point scalar value.

A signed 32 bit integer scalar value.

A reference to a model.

ANeuralNetworksModel_setOperandValueFromModel must be used to set the value for an Operand of this type.

Available since NNAPI feature level 4.

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 NNAPI feature level 3.

A tensor of IEEE 754 16 bit floating point values.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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 than zero.

Available since NNAPI feature level 3.

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 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 [-128, 127].

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

Available since NNAPI feature level 4.

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 than zero.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 4)

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.

Outputs:

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

Available since NNAPI feature level 3.

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}

Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero dimension is only compatible with 0 or 1. The size of the output dimension is zero if either of corresponding input dimension is zero.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 4)

Supported tensor rank: up to 4

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode, and compatible dimensions as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scales and zeroPoint can be different from input0 scale and zeroPoint.
• 2: An ANEURALNETWORKS_INT32 scalar, and has to be one of the FuseCode values. Specifies the activation to invoke on the result. For a ANEURALNETWORKS_TENSOR_INT32 tensor, the FuseCode must be "NONE".

Outputs:

• 0: The sum, a tensor of the same OperandCode as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. If input is 1-dimensional, the output shape is [1].

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. If input is 1-dimensional, the output shape is [1].

Available since NNAPI feature level 3.

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_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

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. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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. Zero num_rois is supported for this tensor.
• 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 or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED. Zero num_rois is supported for this tensor.
• 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. Zero num_rois is supported for this tensor.
• 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]. For type of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, the scale must be 0.125 and the zero point must be 0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 2.

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

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

  INPUT  (INPUT_REVERSED)
    |         |

| FW_LSTM BW_LSTM |

| | FW_OUT BW_OUT

An op with cross-linking takes two inputs and feeds them into the RNN cells in the following way:

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

| FW_LSTM BW_LSTM |

| | FW_OUT BW_OUT

The cross-linking mode is enabled iff auxiliary input and auxiliary weights are present. 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 input.

Since NNAPI feature level 4 parallel linking mode is supported. The mode is enabled if auxiliary input is present but auxiliary weights are omitted. In this case, the cell feeds inputs into the RNN in the following way:

  INPUT (AUX_INPUT_REVERSED)
    |         |

| FW_LSTM BW_LSTM |

| | 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 corresponding inputs.

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, input_size] If batch-major: [batch_size, max_time, input_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 [fw_num_units, input_size], where “fw_num_units” corresponds to the number of forward cell units.
• 2: The forward input-to-forget weights. A 2-D tensor of shape [fw_num_units, input_size].
• 3: The forward input-to-cell weights. A 2-D tensor of shape [fw_num_units, input_size].
• 4: The forward input-to-output weights. A 2-D tensor of shape [fw_num_units, input_size].
• 5: The forward recurrent-to-input weights. Optional. A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size” corresponds to either the number of cell units (i.e., fw_num_units), or the second dimension of the “fw_projection_weights”, if defined.
• 6: The forward recurrent-to-forget weights. A 2-D tensor of shape [fw_num_units, fw_output_size].
• 7: The forward recurrent-to-cell weights. A 2-D tensor of shape [fw_num_units, fw_output_size].
• 8: The forward recurrent-to-output weights. A 2-D tensor of shape [fw_num_units, fw_output_size].
• 9: The forward cell-to-input weights. Optional. A 1-D tensor of shape [fw_num_units].
• 10: The forward cell-to-forget weights. Optional. A 1-D tensor of shape [fw_num_units].
• 11: The forward cell-to-output weights. Optional. A 1-D tensor of shape [fw_num_units].
• 12: The forward input gate bias. Optional. A 1-D tensor of shape [fw_num_units].
• 13: The forward forget gate bias. A 1-D tensor of shape [fw_num_units].
• 14: The forward cell gate bias. A 1-D tensor of shape [fw_num_units].
• 15: The forward output gate bias. A 1-D tensor of shape [fw_num_units].
• 16: The forward projection weights. Optional. A 2-D tensor of shape [fw_output_size, fw_num_units].
• 17: The forward projection bias. Optional. A 1-D tensor of shape [fw_output_size].
• 18: The backward input-to-input weights. Optional. A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units” corresponds to the number of backward cell units.
• 19: The backward input-to-forget weights. A 2-D tensor of shape [bw_num_units, input_size].
• 20: The backward input-to-cell weights. A 2-D tensor of shape [bw_num_units, input_size].
• 21: The backward input-to-output weights. A 2-D tensor of shape [bw_num_units, input_size].
• 22: The backward recurrent-to-input weights. Optional. A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size” corresponds to either the number of cell units (i.e., “bw_num_units”), or the second dimension of the “bw_projection_weights”, if defined.
• 23: The backward recurrent-to-forget weights. A 2-D tensor of shape [bw_num_units, bw_output_size].
• 24: The backward recurrent-to-cell weights. A 2-D tensor of shape [bw_num_units, bw_output_size].
• 25: The backward recurrent-to-output weights. A 2-D tensor of shape [bw_num_units, bw_output_size].
• 26: The backward cell-to-input weights. Optional. A 1-D tensor of shape [bw_num_units].
• 27: The backward cell-to-forget weights. Optional. A 1-D tensor of shape [bw_num_units].
• 28: The backward cell-to-output weights. Optional. A 1-D tensor of shape [bw_num_units].
• 29: The backward input gate bias. Optional. A 1-D tensor of shape [bw_num_units].
• 30: The backward forget gate bias. A 1-D tensor of shape [bw_num_units].
• 31: The backward cell gate bias. A 1-D tensor of shape [bw_num_units].
• 32: The backward output gate bias. A 1-D tensor of shape [bw_num_units].
• 33: The backward projection weights. Optional. A 2-D tensor of shape [bw_output_size, bw_num_units].
• 34: The backward projection bias. Optional. A 1-D tensor of shape [bw_output_size].
• 35: The forward input activation state. A 2-D tensor of shape [batch_size, bw_output_size].
• 36: The forward input cell state. A 2-D tensor of shape [batch_size, bw_num_units].
• 37: The backward input activation state. A 2-D tensor of shape [batch_size, bw_output_size].
• 38: The backward input cell state. A 2-D tensor of shape [batch_size, bw_num_units].
• 39: The auxiliary input. Optional. A 3-D tensor of shape [max_time, batch_size, aux_input_size], where “batch_size” corresponds to the batching dimension, and “aux_input_size” is the size of the auxiliary input. Optional. See the docs above for the usage modes explanation.
• 40: The forward auxiliary input-to-input weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [fw_num_units, aux_input_size].
• 41: The forward auxiliary input-to-forget weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [fw_num_units, aux_input_size].
• 42: The forward auxiliary input-to-cell weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [fw_num_units, aux_input_size].
• 43: The forward auxiliary input-to-output weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [fw_num_units, aux_input_size].
• 44: The backward auxiliary input-to-input weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [bw_num_units, aux_input_size].
• 45: The backward auxiliary input-to-forget weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [bw_num_units, aux_input_size].
• 46: The backward auxiliary input-to-cell weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [bw_num_units, aux_input_size].
• 47: The backward auxiliary input-to-output weights. Optional. See the docs above for the usage modes explanation. A 2-D tensor of shape [bw_num_units, aux_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 ANEURALNETWORKS_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 ANEURALNETWORKS_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.
• 53: The forward input layer normalization weights. Optional. A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs to activation at input gate.
• 54: The forward forget layer normalization weights. Optional. A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs to activation at forget gate.
• 55: The forward cell layer normalization weights. Optional. A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs to activation at cell gate.
• 56: The forward output layer normalization weights. Optional. A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs to activation at output gate.
• 57: The backward input layer normalization weights. Optional. A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs to activation at input gate.
• 58: The backward forget layer normalization weights. Optional. A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs to activation at forget gate.
• 59: The backward cell layer normalization weights. Optional. A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs to activation at cell gate.
• 60: The backward output layer normalization weights. Optional. A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs to activation at output gate.

Outputs:

• 0: The forward output. A 3-D tensor of shape: If time-major and not merge_outputs: [max_time, batch_size, fw_output_size] If time-major and merge_outputs: [max_time, batch_size, fw_output_size + bw_output_size] If batch-major and not merge_outputs: [batch_size, max_time, fw_output_size] If batch-major and merge_outputs: [batch_size, max_time, fw_output_size + bw_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, bw_output_size] If batch-major: [batch_size, max_time, bw_output_size]
• 2: The forward activation state output. A 2-D tensor of shape [batch_size, fw_output_size] containing an activation state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then outputs 3-5 must be present as well. Available since NNAPI feature level 4.
• 3: The forward cell state output. A tensor of shape [batch_size, fw_cell_size] containing a cell state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then outputs 2, 4, 5 must be present as well. Available since NNAPI feature level 4.
• 4: The backward activation state output. A 2-D tensor of shape [batch_size, bw_output_size] containing an activation state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then outputs 2, 3, 5 must be present as well. Available since NNAPI feature level 4.
• 5: The backward cell state output. A tensor of shape [batch_size, bw_cell_size] containing a cell state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then outputs 2-4 must be present as well. Available since NNAPI feature level 4.

Available since NNAPI feature level 3.

Important: As of NNAPI feature level 3, there is no way to get the output state tensors out and NNAPI does not maintain internal states. This operator does not support the usage pattern in which multiple cells are chained and state tensors are propagated.

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 supports cross-linking via 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 cross-linking 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

The cross-linking mode is enabled iff auxiliary input and auxiliary weights are present. 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 input.

Since NNAPI feature level 4 parallel linking mode is supported. The mode is enabled if auxiliary input is present but auxiliary weights are omitted. In this case, the cell feeds inputs into the RNN in the following way:

  INPUT (AUX_INPUT_REVERSED)
    |         |

| 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 corresponding 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 defined by the input 6 (timeMajor). If it is set to true, then the input has a shape [maxTime, batchSize, auxInputSize], otherwise the input has a shape [batchSize, maxTime, auxInputSize]. Can be omitted. See the docs above for the usage modes explanation.
• 10:fwAuxWeights. A 2-D tensor of shape [fwNumUnits, auxInputSize]. Can be omitted. See the docs above for the usage modes explanation.
• 11:bwAuxWeights. A 2-D tensor of shape [bwNumUnits, auxInputSize]. Can be omitted. See the docs above for the usage modes explanation.
• 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].
• 2: The forward hidden state output. A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then output 3 must be present as well. Available since NNAPI feature level 4.
• 3: The backward hidden state output. A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then output 2 must be present as well. Available since NNAPI feature level 4.

Available since NNAPI feature level 3.

Important: As of NNAPI feature level 3, there is no way to get the output state tensors out and NNAPI does not maintain internal states. This operator does not support the usage pattern in which multiple cells are chained and state tensors are propagated.

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

This op applies NMS algorithm to each class. In each loop of execution, the box with maximum score gets selected and removed from the pending set. The scores of the rest of boxes are lowered according to the intersection-over-union (IOU) overlapping with the previously selected boxes and a specified NMS kernel method. Any boxes with score less than a threshold are removed from the pending set.

Three NMS kernels are supported:

• Hard: score_new = score_old * (1 if IoU < threshold else 0)
• Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU)
• Gaussian: score_new = score_old * exp(- IoU^2 / sigma)

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. Zero num_rois is supported for this tensor.
• 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. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of -128 and scale of 0.125. Zero num_rois is supported for this tensor.
• 2: A 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_FLOAT32 scalar, score_threshold. Boxes with scores lower than the threshold are filtered before sending to the NMS algorithm.
• 4: 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.
• 5: An ANEURALNETWORKS_INT32 scalar, specifying the NMS kernel method, options are 0:hard, 1:linear, 2:gaussian.
• 6: An ANEURALNETWORKS_FLOAT32 scalar, specifying the IoU threshold in hard and linear NMS kernel. This field is ignored if gaussian kernel is selected.
• 7: An ANEURALNETWORKS_FLOAT32 scalar, specifying the sigma in gaussian NMS kernel. This field is ignored if gaussian kernel is not selected.
• 8: An ANEURALNETWORKS_FLOAT32 scalar, nms_score_threshold. Boxes with scores lower than the threshold are dropped during the score updating phase in soft NMS.

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, guaranteed. For type of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 [num_output_rois], specifying the batch index of each box. Boxes with the same batch index are grouped together.

Available since NNAPI feature level 3.

Casts a tensor to a 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 Since NNAPI feature level 4, casting tensors of the following OperandCode to the same OperandCode is supported:
• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT16_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT16_SYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM

Supported tensor rank: from 1

Inputs:

• 0: A tensor.

Outputs:

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

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (full support since NNAPI feature level 3, see the input section)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4

Inputs:

• 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. Before NNAPI feature level 3, all input tensors of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM must have the same scale and zeroPoint as the output tensor. Input tensors of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED are allowed to have different scale and zeroPoint. Since NNAPI feature level 3, zero-sized tensors are supported.
• 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]. Since NNAPI feature level 3, for a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint values can be different from input tensors. Before NNAPI feature level 3 they have to be the same as for the input tensors.

Available since NNAPI feature level 1.

Performs a 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 NNAPI feature level 3:

• 16 bit floating point:
• * ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.
• 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]).

Available since NNAPI feature level 4:

• Quantized signed (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).
• Quantized signed with filter symmetric per channel quantization (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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]. NCHW is supported since NNAPI feature level 3.

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. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 (ANeuralNetworksSymmPerChannelQuantParams::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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 (ANeuralNetworksSymmPerChannelQuantParams::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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. Before NNAPI feature level 3, for output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale

Available since NNAPI feature level 1.

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 NNAPI feature level 3:

• 16 bit floating point:
• * ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.
• 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]).

Available since NNAPI feature level 4:

• Quantized signed (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).
• Quantized signed with filter symmetric per channel quantization (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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]. NCHW is supported since NNAPI feature level 3.

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 (ANeuralNetworksSymmPerChannelQuantParams::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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.

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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.
• 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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. Before NNAPI feature level 3, for output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size, depth/(block_size*block_size)]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

Dequantizes the input tensor.

The formula is:

output = (input - zeroPoint) * scale.

Supported input tensor OperandCode:

• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported output tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32.

Supported tensor rank: up to 4

Inputs:

• 0: A tensor. Since NNAPI feature level 3, this tensor may be zero-sized.

Outputs:

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

Available since NNAPI feature level 1.

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: A scalar, score_threshold. Boxes with scores lower than the threshold are filtered before sending to the NMS algorithm. The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT32.
• 12: A scalar, specifying the IoU threshold for hard NMS. The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT32.
• 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 NNAPI feature level 3.

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.

For inputs of ANEURALNETWORKS_TENSOR_INT32, performs "floor division" ("//" in Python). For example, 5 // 2 = 2 -5 // 2 = -3

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}

Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero dimension is only compatible with 0 or 1. The size of the output dimension is zero if either of corresponding input dimension is zero.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_INT32 tensor, the FuseCode must be "NONE".

Outputs:

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

Available since NNAPI feature level 2.

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

The output is calculated using the following formula:

ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1))

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: from 1.

Inputs:

• 0: A tensor, specifying the input. May be zero-sized.
• 1: A scalar, specifying the alpha parameter. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the alpha value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the alpha value must be of ANEURALNETWORKS_FLOAT32.

Outputs:

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

Available since NNAPI feature level 4.

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_FLOAT16 (since NNAPI feature level 4)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input1.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

Creates a tensor filled with a scalar value.

Supported output tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32

Supported tensor rank: from 1.

Inputs:

• 0: A 1-D tensor, specifying the desired output tensor shape.
• 1: A scalar, specifying the value to fill the output tensors with. For output tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the scalar must be of ANEURALNETWORKS_FLOAT16. For output tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the scalar must be of ANEURALNETWORKS_FLOAT32. For output tensor of ANEURALNETWORKS_TENSOR_INT32, the scalar must be of ANEURALNETWORKS_INT32.

Outputs:

• 0: The output tensor.

Available since NNAPI feature level 4.

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

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• 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 NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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". Since NNAPI feature level 3, zero batch_size is supported for this tensor.
• 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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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]. Before NNAPI feature level 3, for output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the following condition must be satisfied: output_scale > input_scale * filter_scale.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 ANEURALNETWORKS_TENSOR_QUANT8_ASYMM or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, this tensor should be of ANEURALNETWORKS_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 or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 [num_output_rois], specifying the batch index of each box. Boxes with the same batch index are grouped together.

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

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:

• 16 bit floating point:
• * ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.
• 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).
• Quantized signed (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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]).
• Quantized signed with filter symmetric per channel quantization (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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 (ANeuralNetworksSymmPerChannelQuantParams::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 ANEURALNETWORKS_TENSOR_FLOAT16, the bias must be of the same type. For filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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 a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 3.

Computes hard-swish activation on the input tensor element-wise.

Hard swish activation is introduced in https://arxiv.org/pdf/1905.02244.pdf

The output is calculated using the following formula:

h-swish(x) = x * max(0, min(6, (x + 3))) / 6

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED

Supported tensor rank: from 1.

Inputs:

• 0: A tensor, specifying the input. May be zero-sized.

Outputs:

• 0: The output tensor of same shape and type as input0. Scale and zero point of this tensor may be different from the input tensor's parameters.

Available since NNAPI feature level 4.

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 …]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint must be the same as input2.
• 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 NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, this tensor should be of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, with zeroPoint of -128 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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from input0 scale and zeroPoint.
• 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]. For type of ANEURALNETWORKS_TENSOR_QUANT16_ASYMM, the scale must be 0.125 and the zero point must be 0.

Available since NNAPI feature level 3.

Executes one of the two referenced models as determined by a boolean value.

The inputs and outputs of the two referenced models must agree with the signature of this operation. That is, if the operation has (3 + n) inputs and m outputs, both models must have n inputs and m outputs with the same types, ranks (if specified), dimensions (if specified), scales, zeroPoints, and other operand parameters as the corresponding operation inputs and outputs.

Inputs:

• 0: A value of type ANEURALNETWORKS_TENSOR_BOOL8 and shape [1] that determines which of the two referenced models to execute. The operand must have fully specified dimensions.
• 1: A ANEURALNETWORKS_MODEL reference to the model to be executed if the condition is true.
• 2: A ANEURALNETWORKS_MODEL reference to the model to be executed if the condition is false.
• 3 ~ (n + 2): Inputs to be passed to the model selected for execution.

Outputs:

• 0 ~ (m - 1): Outputs produced by the selected model.

Available since NNAPI feature level 4.

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: A scalar, specifying gamma, the scale applied to the normalized tensor. The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT32.
• 2: A scalar, specifying beta, the offset applied to the normalized tensor. The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT32.
• 3: A scalar, specifying epsilon, the small value added to variance to avoid dividing by zero. The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT32.
• 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 NNAPI feature level 3.

Applies L2 normalization along the axis 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))

By default the axis dimension is the last dimension of the input tensor.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4 Tensors with rank less than 4 are only supported since NNAPI feature level 3.

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 NNAPI feature level 3.

Outputs:

• 0: A tensor of the same OperandCode and same shape as input0. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale must be 1.f / 128 and the zeroPoint must be 128. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the scale must be 1.f / 128 and the zeroPoint must be 0.NOTE: Before NNAPI feature level 4, if the elements along an axis are all zeros, the result is undefined. Since NNAPI feature level 4, if the elements along an axis are all zeros, the result is logical zero.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• 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]. NCHW is supported since NNAPI feature level 3.

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. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Outputs:

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

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported tensor rank: up to 4 Tensors with rank less than 4 are only supported since NNAPI feature level 3.

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: A scalar, specifying the bias, must not be zero. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the bias value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the bias value must be of ANEURALNETWORKS_FLOAT32.
• 3: A scalar, specifying the scale factor, alpha. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the alpha value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the alpha value must be of ANEURALNETWORKS_FLOAT32.
• 4: A scalar, specifying the exponent, beta. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the beta value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the beta value must be of ANEURALNETWORKS_FLOAT32.
• 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 NNAPI feature level 3.

Outputs:

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

Available since NNAPI feature level 1.

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 NNAPI feature level 3.

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 NNAPI feature level 3.

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 NNAPI feature level 3.

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 NNAPI feature level 3.

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_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input. Since NNAPI feature level 3, this tensor may be zero-sized.

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. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the scale must be 1.f / 256 and the zeroPoint must be -128.

Available since NNAPI feature level 1.

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: A scalar, specifying the positive scaling factor for the exponent, beta. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT16, the beta value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the beta value must be of ANEURALNETWORKS_FLOAT32.
• 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 NNAPI feature level 3.

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

Supported input tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• 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 NNAPI feature level 3). 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 NNAPI feature level 1. The offset value for sparse projections was added in NNAPI feature level 3.

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 NNAPI feature level 3 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 cell-to-input weights ( $W_{ci}$), cell-to-forget weights ( $W_{cf}$) and cell-to-output weights ( $W_{co}$) either all have values or neither of them have values (i.e., all set to null). If they have values, the peephole optimization is used.
• The input-to-input weights ( $W_{xi}$), recurrent-to-input weights ( $W_{hi}$) and input gate bias ( $b_i$) either all have values, or none of them have values. 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*} In case peephole optimization is used and CIFG is not used cell-to-input ( $W_{ci}$) weights must be present. Otherwise, the cell-to-input weights must have no value.
• 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.
• (NNAPI feature level 3 or later) The four layer normalization weights either all have values or none of them have values. Additionally, if CIFG is used, input layer normalization weights tensor is omitted and the other layer normalization weights either all have values or none of them have values. Layer normalization is used when the values of all the layer normalization weights 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 NNAPI feature level 3)
• 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 NNAPI feature level 3 this scalar must be of type ANEURALNETWORKS_FLOAT32. Since NNAPI feature level 3, 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 NNAPI feature level 3 this scalar must be of type ANEURALNETWORKS_FLOAT32. Since NNAPI feature level 3, 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 NNAPI feature level 3 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 NNAPI feature level 1.

Returns the element-wise maximum of two tensors.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and compatible dimensions with input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scales and zeroPoint can be different from input0 scale and zeroPoint.

Outputs:

• 0: A tensor of the same OperandCode as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 3.

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_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

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. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Inputs (implicit padding):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. Must be in the range [-rank(input_tensor), rank(input_tensor)).NOTE: When the operation was introduced, the documentation incorrectly stated that if dimensions were empty, the operation would reduce across all dimensions. This behavior was never implemented.
• 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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0. If all dimensions are reduced and keep_dims is false, the output shape is [1].

Available since NNAPI feature level 2.

Returns the element-wise minimum of two tensors.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: from 1.

Inputs:

• 0: A tensor.
• 1: A tensor of the same OperandCode and compatible dimensions with input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scales and zeroPoint can be different from input0 scale and zeroPoint.

Outputs:

• 0: A tensor of the same OperandCode as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 3.

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.

Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero dimension is only compatible with 0 or 1. The size of the output dimension is zero if either of corresponding input dimension is zero.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_INT32 tensor, the FuseCode must be "NONE".

Outputs:

• 0: The product, a tensor of the same OperandCode as input0. For output tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the following condition must be satisfied: output_scale > input1_scale * input2_scale.

Available since NNAPI feature level 1.

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 NNAPI feature level 3.

Pads a tensor.

This operation pads a tensor according to the specified paddings.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4) (full support since NNAPI feature level 3, see the output section)

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] For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.NOTE: Before NNAPI feature level 3, the pad value for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM is undefined. Since NNAPI feature level 3, the pad value is always the logical zero.

Available since NNAPI feature level 2.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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_FLOAT16, the pad value must be of ANEURALNETWORKS_FLOAT16. For input tensor of ANEURALNETWORKS_TENSOR_FLOAT32, the pad value must be of ANEURALNETWORKS_FLOAT32. For input tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the pad value must 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] For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scales and zeroPoint can be different from input0 scale and zeroPoint.

Available since NNAPI feature level 3.

Quantizes the input tensor.

The formula for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM output tensor is:

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

The formula for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED output tensor is:

output = max(-128, min(127, round(input / scale) + zeroPoint)

Supported input tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32

Supported output tensor OperandCode:

• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: from 1

Inputs:

• 0: A tensor, may be zero-sized.

Outputs:

• 0: The output tensor of same shape as input0, but with ANEURALNETWORKS_TENSOR_QUANT8_ASYMM or. ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED.

Available since NNAPI feature level 3.

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, outputSize] 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, outputSize] 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, outputSize] 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, outputSize] 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.

Quantized version of ANEURALNETWORKS_LSTM.

The input and the output use asymmetric quantized types, while the rest use symmetric ones.

Inputs:

• 0: The input to the LSTM cell. Type: ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED Shape: [batchSize, inputSize]
• 1: The input-to-input weights. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, inputSize]
• 2: The input-to-forget weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, inputSize]
• 3: The input-to-cell weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, inputSize]
• 4: The input-to-output weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, inputSize]
• 5: The recurrent-to-input weights. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, outputSize]
• 6: The recurrent-to-forget weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, outputSize]
• 7: The recurrent-to-cell weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, outputSize]
• 8: The recurrent-to-output weights. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [numUnits, outputSize]
• 9: The cell-to-input weights (for peephole). Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 10: The cell-to-forget weights (for peephole). Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 11: The cell-to-output weights (for peephole). Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 12: The input gate bias. Quantized with scale being the product of input and weights scales and zeroPoint equal to 0. Optional. Type: ANEURALNETWORKS_TENSOR_INT32 Shape: [numUnits]
• 13: The forget gate bias. Quantized with scale being the product of input and weights scales and zeroPoint equal to 0. Type: ANEURALNETWORKS_TENSOR_INT32 Shape: [numUnits]
• 14: The cell bias. Quantized with scale being the product of input and weights scales and zeroPoint equal to 0. Type: ANEURALNETWORKS_TENSOR_INT32 Shape: [numUnits]
• 15: The output gate bias. Quantized with scale being the product of input and weights scales and zeroPoint equal to 0. Type: ANEURALNETWORKS_TENSOR_INT32 Shape: [numUnits]
• 16: The projection weights. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT8_SYMM Shape: [outputSize, numUnits]
• 17: The projection bias. Quantized with scale being the product of input and weights scales and zeroPoint equal to 0. Optional. Type: ANEURALNETWORKS_TENSOR_INT32 Shape: [outputSize]
• 18: The output from the previous time step. Type: ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED Shape: [batchSize, outputSize]
• 19: The cell state from the previous time step. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [batchSize, numUnits]
• 20: The input layer normalization weights. Used to rescale normalized inputs to activation at input gate. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 21: The forget layer normalization weights. Used to rescale normalized inputs to activation at forget gate. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 22: The cell layer normalization weights. Used to rescale normalized inputs to activation at cell gate. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 23: The output layer normalization weights. Used to rescale normalized inputs to activation at output gate. Optional. Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [numUnits]
• 24: The cell clip. If provided the cell state is clipped by this value prior to the cell output activation. Optional. Type: ANEURALNETWORKS_FLOAT32.
• 25: The projection clip. If provided and projection is enabled, this is used for clipping the projected values. Optional. Type: ANEURALNETWORKS_FLOAT32.
• 26: The scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate. Type: ANEURALNETWORKS_FLOAT32.
• 27: The scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate. Type: ANEURALNETWORKS_FLOAT32.
• 28: The scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate. Type: ANEURALNETWORKS_FLOAT32.
• 29: The scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate. Type: ANEURALNETWORKS_FLOAT32.
• 30: The zero point of the hidden state, i.e. input to projection. Type: ANEURALNETWORKS_INT32.
• 31: The scale of the hidden state, i.e. input to projection. Type: ANEURALNETWORKS_FLOAT32.

Outputs:

• 0: The output state (out). Type: ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED Shape: [batchSize, outputSize]
• 1: The cell state (out). Type: ANEURALNETWORKS_TENSOR_QUANT16_SYMM Shape: [batchSize, numUnits]
• 2: The output. This is effectively the same as the current "output state (out)" value. Type: ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED Shape: [batchSize, outputSize]

Available since NNAPI feature level 4.

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. If both provided seeds are 0, both will be randomly generated. Outputs:
• 0: A 2-D ANEURALNETWORKS_TENSOR_INT32 tensor with shape [batches, samples], containing the drawn samples.

Available since NNAPI feature level 3.

Returns the rank of a tensor.

The rank of a tensor is the number of dimensions in it. Also known as "order", "degree", "ndims".

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_INT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT16_SYMM
• ANEURALNETWORKS_TENSOR_BOOL8
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL
• ANEURALNETWORKS_TENSOR_QUANT16_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_SYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED

Supported tensor rank: from 1.

Inputs:

• 0: The input tensor.

Outputs:

• 0: A scalar of ANEURALNETWORKS_INT32, specifying the rank of the input tensor.

Available since NNAPI feature level 4.

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. If all dimensions are reduced and keep_dims is false, the output shape is [1].

Available since NNAPI feature level 3.

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. If all dimensions are reduced and keep_dims is false, the output shape is [1].

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. If all dimensions are reduced and keep_dims is false, the output shape is [1]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. If all dimensions are reduced and keep_dims is false, the output shape is [1]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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. If all dimensions are reduced and keep_dims is false, the output shape is [1].

Available since NNAPI feature level 3.

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. If all dimensions are reduced and keep_dims is false, the output shape is [1].

Available since NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input. Since NNAPI feature level 3, this tensor may be zero-sized.

Outputs:

• 0: The output tensor of same shape as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input. Since NNAPI feature level 3, this tensor may be zero-sized.

Outputs:

• 0: The output tensor of the same shape as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input. Since NNAPI feature level 3, this tensor may be zero-sized.

Outputs:

• 0: The output tensor of same shape as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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.If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a shape of [-1] flattens into 1-D. At most one component of shape can be -1.

Outputs:

• 0: The output tensor, of shape specified by the input shape. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

Both resizing by shape and resizing by scale are supported.

Inputs (resizing by shape):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Since NNAPI feature level 3, zero batches is supported for this tensor.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the output height 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 NNAPI feature level 3.
• 4: Align corners. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Available since NNAPI feature level 4.
• 5: Half pixel centers. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the pixel centers are assumed to be at (0.5, 0.5). This is the default behavior of image.resize in TF 2.0. If this parameter is True, then align_corners parameter must be False. Available since NNAPI feature level 4.

Inputs (resizing by scale, since NNAPI feature level 3):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Zero batches is supported for this tensor.
• 1: A scalar, specifying width_scale, the scaling factor of the width dimension from the input tensor to the output tensor. The output width is calculated as new_width = floor(width * width_scale). The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 otherwise.
• 2: A scalar, specifying height_scale, the scaling factor of the height dimension from the input tensor to the output tensor. The output height is calculated as new_height = floor(height * height_scale). The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 otherwise.
• 3: An optional ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0.
• 4: Align corners. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Available since NNAPI feature level 4.
• 5: Half pixel centers. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the pixel centers are assumed to be at (0.5, 0.5). This is the default behavior of image.resize in TF 2.0. If this parameter is True, then align_corners parameter must be False. Available since NNAPI feature level 4.

Outputs:

• 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 resizing by shape and resizing by scale are supported.

Inputs (resizing by shape):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Zero batches is supported for this tensor.
• 1: An ANEURALNETWORKS_INT32 scalar, specifying the output width of the output tensor.
• 2: An ANEURALNETWORKS_INT32 scalar, specifying the output height of the output tensor.
• 3: An ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0.
• 4: Align corners. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Available since NNAPI feature level 4.
• 5: Half pixel centers. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the pixel centers are assumed to be at (0.5, 0.5). This is the default behavior of image.resize in TF 2.0. If this parameter is True, then align_corners parameter must be False. Available since NNAPI feature level 4.

Inputs (resizing by scale):

• 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. Zero batches is supported for this tensor.
• 1: A scalar, specifying width_scale, the scaling factor of the width dimension from the input tensor to the output tensor. The output width is calculated as new_width = floor(width * width_scale). The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 otherwise.
• 2: A scalar, specifying height_scale, the scaling factor of the height dimension from the input tensor to the output tensor. The output height is calculated as new_height = floor(height * height_scale). The scalar must be of ANEURALNETWORKS_FLOAT16 if input0 is of ANEURALNETWORKS_TENSOR_FLOAT16 and of ANEURALNETWORKS_FLOAT32 otherwise.
• 3: An ANEURALNETWORKS_BOOL scalar, default to false. Set to true to specify NCHW data layout for input0 and output0.
• 4: Align corners. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Available since NNAPI feature level 4.
• 5: Half pixel centers. An optional ANEURALNETWORKS_BOOL scalar, default to false. If True, the pixel centers are assumed to be at (0.5, 0.5). This is the default behavior of image.resize in TF 2.0. If this parameter is True, then align_corners parameter must be False. Available since NNAPI feature level 4.

Outputs:

• 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3)
• 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 NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. Zero num_rois is supported for this tensor.
• 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. Zero num_rois is supported for this tensor.
• 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]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from the input0 scale and zeroPoint.

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, 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]. For input0 of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scales and zeroPoint can be different from input1 scale and zeroPoint.

Outputs:

• 0: A tensor of the same type and shape as input1 and input2. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: from 1

Inputs:

• 0: An n-D tensor to take slice from, may be zero-sized.
• 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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, its scale and zeroPoint has to be same as the input0 scale and zeroPoint.

Available since NNAPI feature level 3.

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_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4. Tensors with rank other than 2 or 4 are only supported since NNAPI feature level 3.

Inputs:

• 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since NNAPI feature level 3, this tensor may be zero-sized.
• 1: A scalar, specifying the positive scaling factor for the exponent, beta. If input0 is of ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM or ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the scalar must be of ANEURALNETWORKS_FLOAT32. If input0 is of ANEURALNETWORKS_TENSOR_FLOAT16, then the scalar must be of ANEURALNETWORKS_FLOAT16.
• 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 NNAPI feature level 3.

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. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the scale must be 1.f / 256 and the zeroPoint must be -128.

Available since NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4) (full support since NNAPI feature level 3, see the output section)

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]. NCHW is supported since NNAPI feature level 3.

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 NNAPI feature level 3.

Outputs:

• 0: A tensor of the same OperandCode as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.NOTE: Before NNAPI feature level 3, the pad value for ANEURALNETWORKS_TENSOR_QUANT8_ASYMM is undefined. Since NNAPI feature level 3, the pad value is always the logical zero.

Available since NNAPI feature level 2.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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]. NCHW is supported since NNAPI feature level 3.

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 NNAPI feature level 3.

Outputs:

• 0: The output 4-D tensor, of shape [batches, height/block_size, width/block_size, depth_in*block_size*block_size]. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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 NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0. If all input dimensions are equal to 1 and are to be squeezed, the output shape is [1].

Available since NNAPI feature level 2.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0. If shrink_axis_mask is true for all input dimensions, the output shape is [1].

Available since NNAPI feature level 2.

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}

Since NNAPI feature level 3, generic zero-sized input tensor is supported. Zero dimension is only compatible with 0 or 1. The size of the output dimension is zero if either of corresponding input dimension is zero.

Supported tensor OperandCode:

• ANEURALNETWORKS_TENSOR_FLOAT16 (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)
• ANEURALNETWORKS_TENSOR_INT32 (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_INT32 tensor, the FuseCode must be "NONE".

Outputs:

• 0: A tensor of the same OperandCode as input0. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 2.

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 NNAPI feature level 3)
• 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 NNAPI feature level 1.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM (since NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4.

Inputs:

• 0: A tensor, specifying the input. Since NNAPI feature level 3, this tensor may be zero-sized.

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. For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, the scale must be 1.f / 128 and the zeroPoint must be 0.

Available since NNAPI feature level 1.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 3.

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
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.
• 1: An n-D tensor of type ANEURALNETWORKS_TENSOR_INT32 containing the indices of values within the last dimension of input.

Available since NNAPI feature level 3.

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 NNAPI feature level 3)
• ANEURALNETWORKS_TENSOR_FLOAT32
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
• ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED (since NNAPI feature level 4)

Supported tensor rank: up to 4

Inputs:

• 0: An n-D tensor, specifying the tensor to be transposed. Since NNAPI feature level 3, this tensor may be zero-sized.
• 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. For a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint must be the same as input0.

Available since NNAPI feature level 2.

Performs the transpose 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:

• 16 bit floating point:
• * ANEURALNETWORKS_TENSOR_FLOAT16 for input, filter, output, and bias.
• 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).
• 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]).

Available since NNAPI feature level 4:

• Quantized signed (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED for input, filter, and output.
• * ANEURALNETWORKS_TENSOR_INT32 for bias (with scale set to
• * input.scale * filter.scale).
• Quantized signed with filter symmetric per channel quantization (since NNAPI feature level 4):
• * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED 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. Since API level 29, zero batches is supported for this tensor.
• 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 (ANeuralNetworksSymmPerChannelQuantParams::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 and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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. Since API level 29, zero batches is supported for this tensor.
• 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 (ANeuralNetworksSymmPerChannelQuantParams::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 filter tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, 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 a ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED tensor, the scale and zeroPoint can be different from inputs' scale and zeroPoint.

Available since NNAPI feature level 3.

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, input_size] If batch-major: [batch_size, max_time, input_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 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. Optional. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at input gate.
• 25:The forget layer normalization weights. Optional. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at forget gate.
• 26:The cell layer normalization weights. Optional. A 1-D tensor of shape [num_units]. Used to rescale normalized inputs to activation at cell gate.
• 27:The output layer normalization weights. Optional. 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]
• 1: A tensor of shape [batch_size, output_size] containing a hidden state from the last time step in the sequence. This output is optional and can be omitted. If this output is present then output #2 must be present as well. Available since NNAPI feature level 4.
• 2: A tensor of shape [batch_size, cell_size] containing a cell state from the last time step in the sequence. This output is optional and can be omitted. Available since NNAPI feature level 4.

Available since NNAPI feature level 3.

Important: As of NNAPI feature level 3, there is no way to get the output state tensors out and NNAPI does not maintain internal states. This operator does not support the usage pattern in which multiple cells are chained and state tensors are propagated.

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].
• 1: A tensor of shape [batchSize, numUnits] containing hidden state from the last time step in the sequence. This output is optional and can be omitted. Available since NNAPI feature level 4.

Available since NNAPI feature level 3.

Important: As of NNAPI feature level 3, there is no way to get the output state tensors out and NNAPI does not maintain internal states. This operator does not support the usage pattern in which multiple cells are chained and state tensors are propagated.

Executes the body model until the condition model outputs false.

The inputs to this operation are the condition model, the body model, and operand values for the first iteration of the loop. The values are implicitly split into three groups of input-output, state-only, and input-only values, as described below.

The outputs of this operation are the final values of input-output operands.

Both the condition and body model receive (m + k + n) inputs.

• The first m (m >= 1) inputs are input-output operands. For the first iteration, these are initialized from the corresponding inputs of the WHILE operation. In subsequent iterations, their values come from the corresponding outputs of the body model produced during the previous iteration.
• The next k (k >= 0) inputs are state-only operands. They are similar to the input-output operands, except that their values are no longer available after the loop terminates.
• The last n (n >= 0) inputs are input-only operands. Their values come from the corresponding inputs of the WHILE operation.

The body model produces (m + k) outputs.

• The first m outputs are input-output operands. They become the outputs of the WHILE operation when a termination condition is reached.
• The last k outputs are state-only operands. Their values are no longer available after the loop terminates.

The numbers m, k, and n are inferred by the runtime as follows: m = (WHILE operation output count) k = (body model output count) - m n = (body model input count) - m - k

The pseudo-code below illustrates the flow of a WHILE operation with inputs condition, body, initial_input_output, initial_state, input_only (m = 1, k = 1, n = 1):

input_output = initial_input_output
state = initial_state
while condition(input_output, state, input_only):
    input_output, state = body(input_output, state, input_only)
return input_output

To prevent infinite loops, there is an implicit execution timeout associated with each loop ("loop timeout duration"). See ANeuralNetworksExecution_setLoopTimeout.

Inputs:

• 0: A ANEURALNETWORKS_MODEL reference to the condition model. The model must have (m + k + n) inputs with the same types, ranks (if specified), dimensions (if specified), scales, zeroPoints, and other operand parameters as the corresponding inputs of the WHILE operation and exactly one output of ANEURALNETWORKS_TENSOR_BOOL8 and shape [1]. The output operand must have fully specified dimensions.
• 1: A ANEURALNETWORKS_MODEL reference to the body model. The model must have (m + k + n) inputs and (m + k) outputs with the same types, ranks (if specified), dimensions (if specified), scales, zeroPoints, and other operand parameters as the corresponding inputs and outputs of the WHILE operation.
• (m inputs): Initial values for input-output operands.
• (k inputs): Initial values for state-only operands.
• (n inputs): Values for input-only operands.

Outputs:

• 0 ~ (m - 1): Outputs produced by the loop.

Available since NNAPI feature level 4.

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, dilation and filter size. It could be computed as follows: out_size = (input + stride - 1) / stride effective_filter_size = (filter_size - 1) * dilation + 1 needed_input = (out_size - 1) * stride + effective_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.

Failure indicating an object is in a dead state.

Available since NNAPI feature level 4.

Failure because a deadline could not be met for a task, and future deadlines will likely also not be met for the same task even after a short delay.

Available since NNAPI feature level 4.

Failure because a deadline could not be met for a task, but future deadlines may still be met for the same task after a short delay.

Available since NNAPI feature level 4.

Operation was successful.

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 because of a resource limitation within the driver, and future calls for the same task will likely also fail even after a short delay.

Available since NNAPI feature level 4.

Failure because of a resource limitation within the driver, but future calls for the same task may still succeed after a short delay.

Available since NNAPI feature level 4.

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.