--- /dev/null
+#ifndef __ANDROID_NN_API_H__
+#define __ANDROID_NN_API_H__
+
+#include <stdint.h>
+#include <stddef.h>
+
+#define EXTERN_C extern "C"
+
+typedef enum
+{
+ ANEURALNETWORKS_NO_ERROR = 0,
+ ANEURALNETWORKS_OUT_OF_MEMORY = 1,
+ ANEURALNETWORKS_INCOMPLETE = 2,
+ ANEURALNETWORKS_UNEXPECTED_NULL = 3,
+ ANEURALNETWORKS_BAD_DATA = 4,
+ ANEURALNETWORKS_OP_FAILED = 5,
+ ANEURALNETWORKS_UNMAPPABLE = 5,
+ ANEURALNETWORKS_BAD_STATE = 6,
+} ResultCode;
+
+typedef enum
+{
+ /** The following entries are used to declare scalars. */
+
+ /** A 32 bit floating point scalar value. */
+ ANEURALNETWORKS_FLOAT32 = 0,
+ /** A signed 32 bit integer scalar value. */
+ ANEURALNETWORKS_INT32 = 1,
+ /** An unsigned 32 bit integer scalar value. */
+ ANEURALNETWORKS_UINT32 = 2,
+
+ /** The following entries are used to declare tensors. */
+
+ /** A tensor of 32 bit floating point values. */
+ ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
+ /** A tensor of 32 bit integer values. */
+ ANEURALNETWORKS_TENSOR_INT32 = 4,
+ /** A tensor of 8 bit 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
+ * - zero_value: an 32 bit integer
+ *
+ * The formula is:
+ * real_value = (integer_value - zero_value) * scale.
+ */
+ ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
+} OperandCode;
+
+typedef struct
+{
+ /** The data type, e.g ANEURALNETWORKS_INT8. */
+ int32_t type;
+
+ /** The number of dimensions. It should be 0 for scalars. */
+ uint32_t dimensionCount;
+ /** The dimensions of the tensor. It should be nullptr for scalars. */
+ const uint32_t* dimensions;
+
+ /** These two fields are only used for quantized tensors.
+ * They should be zero for scalars and non-fixed point tensors.
+ * The dequantized value of each entry is (value - offset) * scale.
+ */
+ float scale;
+ int32_t zeroPoint;
+} ANeuralNetworksOperandType;
+
+typedef enum
+{
+ /** Adds two tensors, element-wise.
+ *
+ * Takes two input tensors of identical type and compatible dimensions. The
+ * output is the sum of both input tensors, optionally modified by an
+ * activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the output is the maximum size along each dimension of the
+ * input operands. It starts with the trailing dimensions, and works its way
+ * forward.
+ *
+ * Example:
+ *
+ * input1.dimension = {4, 1, 2}
+ * input2.dimension = {5, 4, 3, 1}
+ * output.dimension = {5, 4, 3, 2}
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The sum, a tensor of the same type as input0.
+ */
+ ANEURALNETWORKS_ADD = 0,
+ /** 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[batch, row, col, channel] =
+ * sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
+ * dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
+ * dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
+ * dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
+ * dimension.
+ * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the output stride in the ‘height’
+ * dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth].
+ */
+ ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
+ /** Concatenates the input tensors along the given dimension.
+ *
+ * The input tensors must have identical type and the same dimensions except
+ * the dimension along the concatenation axis.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ...,
+ * Dm] n+1: An INT32 value, specifying the concatenation axis. n+2: An INT32
+ * value, and has to be one of the {@link FuseCode} values. Specifies the
+ * activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output, a tensor of the same type as the input tensors.
+ * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+ */
+ ANEURALNETWORKS_CONCATENATION = 2,
+ /** Performs an 2-D convolution operation.
+ *
+ * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
+ * batch of images, applying the filter to each window of each image of the
+ * appropriate size.
+ *
+ * The output dimensions are functions of the filter dimensions, stride, and
+ * padding.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * sum_{i, j} (
+ * input[batch, row + i, col + j, k] *
+ * filter[channel, row + i, col + j, k] +
+ * bias[channel]
+ * )
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
+ * the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width,
+ * depth_in], specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
+ * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
+ * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
+ * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’
+ * dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’
+ * dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’
+ * dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’
+ * dimension.
+ * * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the output stride in the ‘height’
+ * dimension.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth_out].
+ */
+ ANEURALNETWORKS_CONV_2D = 3,
+ /** Performs a depthwise 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_in]
+ * containing in_channels 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[di, dj, k, q]
+ * )
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
+ * the input.
+ * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width,
+ * depth_in], specifying the filter.
+ * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+ * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
+ * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
+ * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
+ * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
+ * * 3: An INT32 value, specifying the padding on the left, in the ‘width’
+ * dimension.
+ * * 4: An INT32 value, specifying the padding on the right,in the ‘width’
+ * dimension.
+ * * 5: An INT32 value, specifying the padding on the top, in the ‘height’
+ * dimension.
+ * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’
+ * dimension.
+ * * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+ * * 8: An INT32 value, specifying the output stride in the ‘height’
+ * dimension.
+ * * 9: An INT32 value, specifying the depthwise multiplier.
+ * * 10: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth_out].
+ */
+ ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
+ * the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size * block_size must be a divisor of the input depth.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+ * width*block_size, depth/(block_size*block_size)].
+ */
+ ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
+ /** Dequantizes the input tensor.
+ *
+ * The formula is:
+ *
+ * output = (input - zero_value) * scale.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0, but with type
+ * {@link ANEURALNETWORKS_TENSOR_FLOAT32}.
+ */
+ ANEURALNETWORKS_DEQUANTIZE = 6,
+
+ /**
+ * Looks up items from a given tensor.
+ *
+ * Each item in the output is a raw copy of the corresponding item in
+ * the input “values”. If the given “lookup” indices are out of bounds,
+ * the op will fail and an error will be reported.
+ *
+ * Inputs:
+ * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
+ * then the shape would be [lookup_dimension, values_dimension], where
+ * “lookup_dimension” corresponds to the indexing dimension in the lookup
+ * table, and “values_dimension” to the contents.
+ * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
+ * “lookup_size” is the number of elements to look for, and each entry
+ * corresponds to the first dimension of the “values” tensor.
+ *
+ * Output:
+ * * 0: A n-D tensor of type X and the same rank and shape as the “values”
+ * tensor, except for the first dimension which has size “lookup_size”.
+ */
+ ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
+
+ /** Computes element-wise floor() on the input tensor.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ *
+ * Outputs:
+ * * 0: The output, a tensor of the same type and dimensions as input0.
+ */
+ ANEURALNETWORKS_FLOOR = 8,
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. If rank is greater than 2, then it
+ * gets flattened to a 2-D Tensor. The 2-D Tensor is handled as if dimensions
+ * corresponded to shape [batch_size, input_size], where “batch_size”
+ * corresponds to the batching dimension, and “input_size” is the size of the
+ * input.
+ * * 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 {@link ANEURALNETWORKS_TENSOR_FLOAT32} type, the
+ * bias should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input
+ * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the bias should
+ * be of {@link ANEURALNETWORKS_TENSOR_INT32}.
+ * * 3: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape [batch_size, num_units].
+ */
+ ANEURALNETWORKS_FULLY_CONNECTED = 9,
+
+ /**
+ * Looks up values of a hash table with given keys.
+ *
+ * Inputs:
+ * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
+ * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
+ * ascending order.
+ * * 2: Values. A tensor with shape [ n … ].
+ *
+ * Outputs:
+ * * 0: Output. A tensor with shape [ k …].
+ * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
+ * hits or not.
+ */
+ ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,
+
+ /** Applies L2 normalization along the depth dimension.
+ *
+ * The values in the output tensor are computed as:
+ *
+ * output[batch, row, col, channel] =
+ * input[batch, row, col, channel] /
+ * sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+ *
+ * For x with more dimensions, independently normalizes each 1-D slice along
+ * dimension dim.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth].
+ */
+ ANEURALNETWORKS_L2_NORMALIZATION = 11,
+
+ /** 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[batch, row, col, channel] =
+ * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) /
+ * sum(1))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
+ * dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
+ * dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
+ * dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
+ * dimension.
+ * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the output stride in the ‘height’
+ * dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth].
+ */
+ ANEURALNETWORKS_L2_POOL_2D = 12,
+ /** 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)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ * * 1: An INT32 value, specifying the radius of the normalization window.
+ * * 2: A FLOAT32 value, specifying the bias, must not be zero.
+ * * 3: A FLOAT32 value, specifying the scale factor, alpha.
+ * * 4: A FLOAT32 value, specifying the exponent, beta.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,
+ /** Computes sigmoid activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = 1 / (1 + exp(-input))
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_LOGISTIC = 14,
+
+ /**
+ * Projects an input to a bit vector via locality senstive hashing.
+ *
+ * Inputs:
+ * * 0: Hash functions. Dim.size == 2, DataType: Float.
+ * Tensor[0].Dim[0]: Number of hash functions.
+ * Tensor[0].Dim[1]: Number of seeds per hash functions.
+ * Tensor[0].Dim[1] <= 32 in sparse case.
+ *
+ * * 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(=1).
+ * Computed bit vector is considered to be sparse.
+ * Each output element is an int32 made up of multiple bits computed
+ * from hash functions.
+ *
+ * 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.
+ */
+ ANEURALNETWORKS_LSH_PROJECTION = 15,
+
+ /**
+ * Long short-term memory unit (LSTM) recurrent network layer.
+ *
+ * The default non-peephole implementation is based on:
+ * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+ * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+ * Computation, 9(8):1735-1780, 1997.
+ *
+ * The peephole implementation 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.
+ *
+ * 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 class has the following independently optional inputs:
+ * * If input gate (if CIFG): “input_to_forget_weights”,
+ * “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
+ * * If no peephole connections: “cell_to_input_weights”,
+ * “cell_to_forget_weights”, “cell_to_output_weights”.
+ * * If no projection layer: “projection_weights” and “projection_bias”.
+ * * If no projection bias: “projection_bias”.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: Input.
+ * A 2-D tensor of type T, of shape [batch_size, input_size], where
+ * “batch_size” corresponds to the batching dimension, and “input_size”
+ * is the size of the input.
+ * * 1: input_to_input_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of cell units.
+ * * 2: input_to_forget_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 3: input_to_cell_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 4: input_to_output_weights.
+ * A 2-D tensor of type T, of shape [num_units, input_size].
+ * * 5: recurrent_to_input_weights.
+ * A 2-D tensor of type T, 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: recurrent_to_forget_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 7: recurrent_to_cell_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 8: recurrent_to_output_weights.
+ * A 2-D tensor of type T, of shape [num_units, output_size].
+ * * 9: cell_to_input_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 10:cell_to_forget_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 11:cell_to_output_weights.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 12:input_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 13:forget_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 14:cell_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 15:output_gate_bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ * * 16:projection_weights.
+ * A 2-D tensor of type T, of shape [output_size, num_units].
+ * * 17:projection_bias.
+ * A 1-D tensor of type T, of shape [output_size].
+ *
+ * Parameters:
+ * * 18:fused_activation_function.
+ * An (optional) ActivationFunctionType indicating the activation
+ * function.
+ * If “NONE” is specified then it results in a linear activation.
+ * * 19:cell_clip.
+ * A 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.
+ * * 20:proj_clip.
+ * A 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.
+ *
+ * Outputs:
+ * * 0: scratch_buffer.
+ * A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
+ * * 1: output_state.
+ * A 2-D tensor of type T, of shape [batch_size, output_size].
+ * * 2: cell_state.
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ * * 3: output.
+ * A 2-D tensor of type T, of shape [batch_size, output_size]. This is
+ * effectively the same as the current “output_state” value.
+ */
+ ANEURALNETWORKS_LSTM = 16,
+
+ /** 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[batch, row, col, channel] =
+ * max_{i, j} (input[batch, row + i, col + j, channel])
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ * * 1: An INT32 value, specifying the padding on the left, in the ‘width’
+ * dimension.
+ * * 2: An INT32 value, specifying the padding on the right,in the ‘width’
+ * dimension.
+ * * 3: An INT32 value, specifying the padding on the top, in the ‘height’
+ * dimension.
+ * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’
+ * dimension.
+ * * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+ * * 6: An INT32 value, specifying the output stride in the ‘height’
+ * dimension.
+ * * 7: An INT32 value, specifying the filter width.
+ * * 8: An INT32 value, specifying the filter height.
+ * * 9: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, out_height, out_width,
+ * depth].
+ */
+ ANEURALNETWORKS_MAX_POOL_2D = 17,
+
+ /** Multiplies two tensors, element-wise.
+ *
+ * Takes two input tensors of identical type and compatible dimensions. The
+ * output is the product of both input tensors, optionally modified by an
+ * activation function.
+ *
+ * Two dimensions are compatible when:
+ * 1. they are equal, or
+ * 2. one of them is 1
+ *
+ * The size of the resulting output is the maximum size along each dimension
+ * of the input operands. It starts with the trailing dimensions, and works
+ * its way forward.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4
+ *
+ * Inputs:
+ * * 0: A tensor.
+ * * 1: A tensor of the same type, and compatible dimensions as input0.
+ * * 2: An INT32 value, and has to be one of the {@link FuseCode} values.
+ * Specifies the activation to invoke on the result of each addition.
+ *
+ * Outputs:
+ * * 0: The product, a tensor of the same type as input0.
+ */
+ ANEURALNETWORKS_MUL = 18,
+ /** Computes rectified linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = max(0, input)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU = 19,
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU1 = 20,
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_RELU6 = 21,
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the tensor to be reshaped.
+ * * 1: A 1-D tensor of type {@link ANEURALNETWORKS_TENSOR_INT32}, defining
+ * the shape of the output tensor. The number of elements implied by shape
+ * must be the same as the number of elements in the input tensor.
+ *
+ * Outputs:
+ * * 0: The output tensor, of shape specified by the input shape.
+ */
+ ANEURALNETWORKS_RESHAPE = 22,
+ /** Resizes images to given size using the bilinear interpretation.
+ *
+ * Resized images will be distorted if their original aspect ratio is not the
+ * same as input.
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the
+ * input.
+ * * 1: An INT32 value, specifying the output width of the output tensor.
+ * * 2: An INT32 value, specifying the output height of the output tensor.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batches, new_height, new_width,
+ * depth].
+ */
+ ANEURALNETWORKS_RESIZE_BILINEAR = 23,
+
+ /**
+ * 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of units.
+ * * 2: recurrent_weights.
+ * A 2-D tensor of type T, of shape [num_units, num_units], with columns
+ * corresponding to the weights from each unit.
+ * * 3: bias.
+ * A 1-D tensor of type T, of shape [num_units].
+ *
+ * For FLOAT32 input tensor, bias must also be FLOAT32.
+ * For UINT8 input tensor, bias must be INT32.
+ *
+ * Parameters
+ * * 4: fused_activation_function.
+ * An (optional) ActivationFunctionType indicating the activation
+ * function. If “NONE” is specified then it results in a linear
+ * activation.
+ *
+ * * 5: Hidden state.
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ *
+ * Outputs:
+ * * 0: output.
+ * A 2-D tensor of type T, of shape [batch_size, num_units]. This is
+ * effectively the same as the current state value.
+ */
+ ANEURALNETWORKS_RNN = 24,
+
+ /** 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)}
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 2 or 4.
+ *
+ * Inputs:
+ * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+ * * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_SOFTMAX = 25,
+
+ /** 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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
+ *
+ * Supported tensor rank: 4, with "NHWC" data layout.
+ *
+ * Inputs:
+ * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying
+ * the input.
+ * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+ * block_size must be a divisor of both the input height and width.
+ *
+ * Outputs:
+ * * 0: The output 4-D tensor, of shape [batch, height/block_size,
+ * width/block_size, depth*block_size*block_size].
+ */
+ ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
+
+ /**
+ * 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, "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 types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: input.
+ * A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+ * “num_units” corresponds to the number of units.
+ * * 2: weights_time.
+ * A 2-D tensor of type T, of shape [num_units, memory_size], where
+ * “memory_size” corresponds to the fixed-size of the memory.
+ * * 3: bias.
+ * A optional 1-D tensor of type T, of shape [num_units].
+ *
+ * For FLOAT32 input tensor, bias must also be FLOAT32.
+ * For UINT8 input tensor, bias must be INT32.
+ *
+ * Parameters:
+ * * 4: rank.
+ * The rank of the SVD approximation.
+ * * 5: fused_activation_function.
+ * An (optional) ActivationFunctionType indicating the activation
+ * function. If “NONE” is specified then it results in a linear activation.
+ *
+ * Outputs:
+ * * 0: state.
+ * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) *
+ * num_units * rank].
+ * * 1: output.
+ * A 2-D tensor of type T, of shape [batch_size, num_units].
+ */
+ ANEURALNETWORKS_SVDF = 27,
+
+ /** Computes hyperbolic tangent of input tensor element-wise.
+ *
+ * The output is calculated using this formula:
+ *
+ * output = tanh(input)
+ *
+ * Supported tensor types:
+ * * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
+ *
+ * Supported tensor rank: up to 4.
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape as input0.
+ */
+ ANEURALNETWORKS_TANH = 28,
+} OperationCode;
+
+typedef int32_t ANeuralNetworksOperationType;
+
+typedef enum
+{
+ /** NO fused activation function. */
+ ANEURALNETWORKS_FUSED_NONE = 0,
+ /** Fused ReLU activation function. */
+ ANEURALNETWORKS_FUSED_RELU = 1,
+ /** Fused ReLU1 activation function. */
+ ANEURALNETWORKS_FUSED_RELU1 = 2,
+ /** Fused ReLU6 activation function. */
+ ANEURALNETWORKS_FUSED_RELU6 = 3,
+} FuseCode;
+
+//
+// Event
+//
+struct ANeuralNetworksEvent;
+
+EXTERN_C ResultCode ANeuralNetworksEvent_wait(ANeuralNetworksEvent* event);
+EXTERN_C ResultCode ANeuralNetworksEvent_free(ANeuralNetworksEvent* event);
+
+//
+// Memory
+//
+struct ANeuralNetworksMemory;
+
+EXTERN_C ResultCode ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, ANeuralNetworksMemory** memory);
+EXTERN_C ResultCode ANeuralNetworksMemory_free(ANeuralNetworksMemory* memory);
+
+//
+// Model
+//
+struct ANeuralNetworksModel;
+
+EXTERN_C ResultCode ANeuralNetworksModel_create(ANeuralNetworksModel** model);
+EXTERN_C ResultCode ANeuralNetworksModel_addOperand(ANeuralNetworksModel* model, const ANeuralNetworksOperandType *type);
+EXTERN_C ResultCode ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel* model, int32_t index, const void* buffer, size_t length);
+EXTERN_C ResultCode ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel* model, int32_t index, const ANeuralNetworksMemory* memory, size_t offset, size_t length);
+EXTERN_C ResultCode ANeuralNetworksModel_addOperation(ANeuralNetworksModel* model, ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs);
+EXTERN_C ResultCode ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount, const uint32_t* outputs);
+EXTERN_C ResultCode ANeuralNetworksModel_finish(ANeuralNetworksModel* model);
+EXTERN_C ResultCode ANeuralNetworksModel_free(ANeuralNetworksModel* model);
+
+//
+// Compilation
+//
+struct ANeuralNetworksCompilation;
+
+EXTERN_C ResultCode ANeuralNetworksCompilation_create(ANeuralNetworksModel* model, ANeuralNetworksCompilation** compilation);
+EXTERN_C ResultCode ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation* compilation);
+
+// ANeuralNetworksCompilation_free is omitted as T/F Lite interpreter currently does not need it
+
+//
+// Execution
+//
+struct ANeuralNetworksExecution;
+
+EXTERN_C ResultCode ANeuralNetworksExecution_create(ANeuralNetworksCompilation* compilation, ANeuralNetworksExecution** execution);
+EXTERN_C ResultCode ANeuralNetworksExecution_setInput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length);
+EXTERN_C ResultCode ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution* execution, int32_t index, const ANeuralNetworksOperandType* type, const void* buffer, size_t length);
+EXTERN_C ResultCode ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event);
+EXTERN_C ResultCode ANeuralNetworksExecution_free(ANeuralNetworksExecution* execution);
+
+#endif // __ANDROID_NN_API_H__
projects / platform / core / ml / nnfw.git / commitdiff