return nnapi_is_available;
}
-// nn api types
+// NN api types based on NNAPI header file
+// https://developer.android.com/ndk/reference/group/neural-networks
/**
* Operand types.
* ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, and ANEURALNETWORKS_INT32.
*/
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,
};
* The type of operations that can be added to a model.
*/
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 sensitive 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://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 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,
+ ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
+ ANEURALNETWORKS_DIV = 30,
+ ANEURALNETWORKS_MEAN = 31,
+ ANEURALNETWORKS_PAD = 32,
+ ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33,
+ ANEURALNETWORKS_SQUEEZE = 34,
+ ANEURALNETWORKS_STRIDED_SLICE = 35,
+ ANEURALNETWORKS_SUB = 36,
+ ANEURALNETWORKS_TRANSPOSE = 37,
};
/**
*
*/
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,
};
* Execution preferences.
*/
enum {
- /**
- * Prefer executing in a way that minimizes battery drain.
- * This is desirable for compilations that will be executed often.
- */
ANEURALNETWORKS_PREFER_LOW_POWER = 0,
- /**
- * Prefer returning a single answer as fast as possible, even if this causes
- * more power consumption.
- */
ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
- /**
- * Prefer maximizing the throughput of successive frames, for example when
- * processing successive frames coming from the camera.
- */
ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
};
projects / platform / upstream / tensorflow.git / commitdiff