From c5187bb81db7d22570aa691c0a46c5a5116fa62c Mon Sep 17 00:00:00 2001 From: =?utf8?q?=EC=9D=B4=EC=83=81=EA=B7=9C/=EB=8F=99=EC=9E=91=EC=A0=9C?= =?utf8?q?=EC=96=B4Lab=28SR=29/Principal=20Engineer/=EC=82=BC=EC=84=B1?= =?utf8?q?=EC=A0=84=EC=9E=90?= Date: Mon, 9 Jul 2018 10:37:09 +0900 Subject: [PATCH] Update NeuralNetworks.h from android-p-prevew-4 (#1905) Related Issue: #1904 Android-p-preview-3 announced that it will have fixed NNAPI v1.1. However I would like to update NeuralNetworks.h from android-p-preview-4's. Because - it updated comments (style, description, ...). - it fixed a mistake included in android-p-preview-3. Signed-off-by: Sanggyu Lee --- include/NeuralNetworks.h | 1457 ++++++++++++++++++++++++++++------------------ 1 file changed, 884 insertions(+), 573 deletions(-) diff --git a/include/NeuralNetworks.h b/include/NeuralNetworks.h index 7b98c31..6414af6 100644 --- a/include/NeuralNetworks.h +++ b/include/NeuralNetworks.h @@ -59,27 +59,24 @@ __BEGIN_DECLS * and {@link ANEURALNETWORKS_INT32}. */ typedef enum { - /** The following entries are used to declare scalars. */ - /** A 32 bit floating point scalar value. */ - ANEURALNETWORKS_FLOAT32 = 0, + ANEURALNETWORKS_FLOAT32 = 0, /** A signed 32 bit integer scalar value. */ - ANEURALNETWORKS_INT32 = 1, + ANEURALNETWORKS_INT32 = 1, /** An unsigned 32 bit integer scalar value. */ - ANEURALNETWORKS_UINT32 = 2, - - /** The following entries are used to declare tensors. */ + ANEURALNETWORKS_UINT32 = 2, /** A tensor of 32 bit floating point values. */ - ANEURALNETWORKS_TENSOR_FLOAT32 = 3, + 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. + 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: + * 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: an 32 bit integer, in range [0, 255]. + * - zeroPoint: a 32 bit integer, in range [0, 255]. * * The formula is: * real_value = (integer_value - zeroPoint) * scale. @@ -93,17 +90,20 @@ typedef enum { * The type of operations that can be added to a model. */ typedef enum { - /** Adds two tensors, element-wise. + /** + * 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. + * Takes two input tensors of identical {@link 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. + * 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: * @@ -111,7 +111,7 @@ typedef enum { * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -119,95 +119,119 @@ typedef enum { * * 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. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The sum, a tensor of the same type as input0. + * * 0: The sum, a tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_ADD = 0, - /** Performs a 2-D average pooling operation. + /** + * Performs a 2-D average pooling operation. * - * The output dimensions are functions of the filter dimensions, stride, and padding. + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * - * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels) - * data layout. + * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, + * and Channels) data layout. * * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An 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 stride when walking through input - * in the ‘width’ dimension. - * * 6: An INT32 value, specifying the stride when walking through input - * 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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the * {@link PaddingCode} values. - * * 2: An INT32 value, specifying the stride when walking through input - * in the ‘width’ dimension. - * * 3: An INT32 value, specifying the stride when walking through input - * in the ‘height’ dimension. - * * 4: An INT32 value, specifying the filter width. - * * 5: An INT32 value, specifying the filter height. - * * 6: An INT32 value, and has to be one of the {@link FuseCode} values. - * Specifies the activation to invoke on the result of each addition. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. + * * 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. + /** + * 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. + * The input tensors must have identical {@link OperandCode} and the same + * dimensions except the dimension along the concatenation axis. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]. - * For inputs of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, all - * input tensors must have the same scale and zeroPoint. - * * n: An INT32 value, specifying the concatenation axis. + * * 0 ~ n-1: The list of n input tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, all input tensors + * must have the same scale and zeroPoint. + * * n: An {@link ANEURALNETWORKS_INT32} scalar, specifying the + * concatenation axis. * * Outputs: - * * 0: The output, a tensor of the same type as the input tensors. - * The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. + * * 0: The output, a tensor of the same {@link OperandCode} as the input + * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. */ ANEURALNETWORKS_CONCATENATION = 2, - /** Performs an 2-D convolution operation. + /** + * 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 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 output dimensions are functions of the filter dimensions, stride, and + * padding. * * The values in the output tensor are computed as: * @@ -218,7 +242,7 @@ typedef enum { * bias[channel] * ) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -227,62 +251,77 @@ typedef enum { * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. - * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], - * specifying the filter. + * * 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}, with zeroPoint of 0 and - * bias_scale == input_scale * filter_scale. - * * 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 stride when walking through input - * in the ‘width’ dimension. - * * 8: An INT32 value, specifying the stride when walking through input - * 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. + * For input tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias + * should also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input + * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias + * should be of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale == input_scale * filter_scale. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. - * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in], - * specifying the filter. - * * 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}, with zeroPoint of 0 and + * * 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}, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. - * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the * {@link PaddingCode} values. - * * 4: An INT32 value, specifying the stride when walking through input - * in the ‘width’ dimension. - * * 5: An INT32 value, specifying the stride when walking through input - * in the ‘height’ dimension. - * * 6: An INT32 value, and has to be one of the {@link FuseCode} values. - * Specifies the activation to invoke on the result of each addition. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. - * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following - * condition must be satisfied: output_scale > input_scale * filter_scale. + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale. */ ANEURALNETWORKS_CONV_2D = 3, - /** Performs a depthwise 2-D convolution operation. + /** + * 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. + * 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 output dimensions are functions of the filter dimensions, stride, and + * padding. * * The values in the output tensor are computed as: * @@ -292,7 +331,7 @@ typedef enum { * filter[1, di, dj, k * channel_multiplier + q] * ) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -301,105 +340,123 @@ typedef enum { * 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. + * * 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 {@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}, with zeroPoint of 0 and + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. - * * 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 stride when walking through input - * in the ‘width’ dimension. - * * 8: An INT32 value, specifying the stride when walking through input - * 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. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise + * multiplier. + * * 10: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input. + * * 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 {@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}, with zeroPoint of 0 and + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. - * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the * {@link PaddingCode} values. - * * 4: An INT32 value, specifying the stride when walking through input - * in the ‘width’ dimension. - * * 5: An INT32 value, specifying the stride when walking through input - * in the ‘height’ dimension. - * * 6: An INT32 value, specifying the depthwise multiplier. - * * 7: An INT32 value, and has to be one of the {@link FuseCode} values. - * Specifies the activation to invoke on the result of each addition. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the depthwise + * multiplier. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out]. - * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following - * condition must be satisfied: output_scale > input_scale * filter_scale. + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale. */ ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4, - /** Rearranges data from depth into blocks of spatial data. + /** + * 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. + * 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. + * 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 + * 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: + * Supported tensor {@link OperandCode}: * * {@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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link 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. * * Outputs: - * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size, - * depth/(block_size*block_size)]. + * * 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. + /** + * Dequantizes the input tensor. * * The formula is: * * output = (input - zeroPoint) * scale. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * * 0: A tensor of type {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. + * * 0: A tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}. * * Outputs: - * * 0: The output tensor of same shape as input0, but with type + * * 0: The output tensor of same shape as input0, but with * {@link ANEURALNETWORKS_TENSOR_FLOAT32}. */ ANEURALNETWORKS_DEQUANTIZE = 6, - /** Looks up sub-tensors in the input tensor. + /** + * 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). @@ -411,15 +468,15 @@ typedef enum { * to create the output tensor. * * For example, if Values has shape of [40, 200, 300] and - * Lookups has shape of [3], we would expect all three values - * found in Lookups to be between 0 and 39. The resulting tensor will + * 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 will fail - * and an error will be reported. + * If a value in Lookups is out of bounds, the operation must fail + * and an error must be reported. * * Inputs: - * * 0: Lookups. A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_INT32} type. + * * 0: Lookups. A 1-D tensor of {@link 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. @@ -431,9 +488,10 @@ typedef enum { */ ANEURALNETWORKS_EMBEDDING_LOOKUP = 7, - /** Computes element-wise floor() on the input tensor. + /** + * Computes element-wise floor() on the input tensor. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 @@ -442,47 +500,56 @@ typedef enum { * * 0: A tensor. * * Outputs: - * * 0: The output tensor, of the same type and dimensions as the input tensor. + * * 0: The output tensor, of the same {@link OperandCode} and dimensions as + * the input tensor. */ ANEURALNETWORKS_FLOOR = 8, - /** Denotes a fully (densely) connected layer, which connects all elements in the input - * tensor with each element in the output tensor. + /** + * 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: + * Supported tensor {@link OperandCode}: * * {@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}, with zeroPoint of 0 and + * * 0: A tensor of at least rank 2, specifying the input. If rank is + * greater than 2, then it gets flattened to a 2-D Tensor. The + * (flattened) 2-D Tensor is reshaped (if necessary) to + * [batch_size, input_size], where "input_size" corresponds to the + * number of inputs to the layer, matching the second dimension of + * weights, and "batch_size" is calculated by dividing the number of + * elements by "input_size". + * * 1: A 2-D tensor, specifying the weights, of shape + * [num_units, input_size], where "num_units" corresponds to the number + * of output nodes. + * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input + * tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, the bias should + * also be of {@link ANEURALNETWORKS_TENSOR_FLOAT32}. For input tensor + * of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link ANEURALNETWORKS_TENSOR_INT32}, with zeroPoint of 0 and * bias_scale == input_scale * filter_scale. - * * 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. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output tensor, of shape [batch_size, num_units]. - * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following - * condition must be satisfied: output_scale > input_scale * filter_scale. + * * 0: The output tensor, of shape [batch_size, num_units]. For output + * tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, the following + * condition must be satisfied: + * output_scale > input_scale * filter_scale. */ ANEURALNETWORKS_FULLY_CONNECTED = 9, - /** Looks up sub-tensors in the input tensor using a key-value map. + /** + * 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 @@ -496,37 +563,41 @@ typedef enum { * 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 will have zero values. + * 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], we're concatenating three slices, so the resulting tensor - * will have the shape of [3, 200, 300]. If the first entry in - * Lookups has the value 123456, we'll look for that value in Keys tensor. - * If the sixth entry of Keys contains 123456, we'll select the sixth - * slice of Values. If no entry in Keys has 123456, a slice of zeroes - * will be concatenated. + * 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. * * Inputs: - * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape [ k ]. - * * 1: Keys. A 1-D {@link 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. + * * 0: Lookups. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with + * shape [ k ]. + * * 1: Keys. A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with shape + * [ n ]; Keys and Values pair represent a map, i.e., the ith element + * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values + * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in + * ascending order. + * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension + * must be n. * * Outputs: * * 0: Output. A tensor with shape [ k …]. * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup * hits (True) or not (False). - * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f. + * Stored as {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. * A non-zero byte represents True, a hit. A zero indicates otherwise. */ ANEURALNETWORKS_HASHTABLE_LOOKUP = 10, - /** Applies L2 normalization along the depth dimension. + /** + * Applies L2 normalization along the depth dimension. * * The values in the output tensor are computed as: * @@ -534,31 +605,37 @@ typedef enum { * input[batch, row, col, channel] / * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) * - * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim. + * For input tensor with more dimensions, independently normalizes each 1-D + * slice along dimension dim. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * - * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels). + * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, + * Height, Width, and Channels). * * Inputs: * * 0: A 4-D tensor, of shape [batches, height, width, depth]. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. + * * 0: The output 4-D tensor, of the same shape as input + * [batches, height, width, depth]. */ ANEURALNETWORKS_L2_NORMALIZATION = 11, - /** Performs an 2-D L2 pooling operation. + /** + * Performs an 2-D L2 pooling operation. * - * The output dimensions are functions of the filter dimensions, stride, and padding. + * 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)) + * sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / + * sum(1)) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: 4, with "NHWC" data layout. @@ -566,74 +643,96 @@ typedef enum { * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An 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 stride when walking through input - * in the ‘width’ dimension. - * * 6: An INT32 value, specifying the stride when walking through input - * 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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the * {@link PaddingCode} values. - * * 2: An INT32 value, specifying the stride when walking through input - * in the ‘width’ dimension. - * * 3: An INT32 value, specifying the stride when walking through input - * in the ‘height’ dimension. - * * 4: An INT32 value, specifying the filter width. - * * 5: An INT32 value, specifying the filter height. - * * 6: An INT32 value, and has to be one of the {@link FuseCode} values. - * Specifies the activation to invoke on the result of each addition. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. + * * 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. + /** + * 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 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) + * 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: + * Supported tensor {@link OperandCode}: * * {@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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the radius of + * the normalization window. + * * 2: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the bias, must + * not be zero. + * * 3: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the scale + * factor, alpha. + * * 4: An {@link ANEURALNETWORKS_FLOAT32} scalar, 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. + /** + * Computes sigmoid activation on the input tensor element-wise. * * The output is calculated using this formula: * * output = 1 / (1 + exp(-input)) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -644,7 +743,7 @@ typedef enum { * * Outputs: * * 0: The output tensor of same shape as input0. - * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, * the scale must be 1.f / 256 and the zeroPoint must be 0. */ ANEURALNETWORKS_LOGISTIC = 14, @@ -660,18 +759,19 @@ typedef enum { * * * 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. + * 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. + * 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. + * 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: @@ -691,9 +791,12 @@ typedef enum { * \f{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; \\ + * 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. \\ * \f} @@ -705,7 +808,8 @@ typedef enum { * * \f$o_t\f$ is the output, * * \f$h_t\f$ is the output state, * * \f$\sigma\f$ is the logistic sigmoid function, - * * \f$g\f$ is the cell input and cell output activation function, usually \f$tahn\f$, + * * \f$g\f$ is the cell input and cell output activation function, usually + * \f$tahn\f$, * * \f$W_{xi}\f$ is the input-to-input weight matrix, * * \f$W_{hi}\f$ is the recurrent to input weight matrix, * * \f$W_{ci}\f$ is the cell-to-input weight matrix, @@ -725,27 +829,32 @@ typedef enum { * * \f$b_{proj}\f$ is the projection bias, * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and * * \f$t_{proj}\f$ is the threshold for clipping the projected output. - * * \f$\odot\f$ is the + * * \f$\odot\f$ 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. * * The operation has the following independently optional inputs: - * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$), - * cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values, - * or none of them have values (i.e., all set to null). If they have no - * values, coupling of input and forget gates (CIFG) is used, in which case - * the input gate (\f$i_t\f$) is calculated using the following equation instead. + * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights + * (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate + * bias (\f$b_i\f$) either all have values, or none of them have values + * (i.e., all set to null). If they have no values, coupling of input and + * forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$) + * is calculated using the following equation instead. * \f{eqnarray*}{ * i_t = 1 - f_t * \f} - * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights (\f$W_{cf}\f$), and cell-to-output - * weights (\f$W_{co}\f$) either all have values or none of them have values. - * If they have values, the peephole optimization is used. - * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection - * layer, and should otherwise have no value. - * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a value if the - * recurrent projection layer exists, and should otherwise have no value. + * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights + * (\f$W_{co}\f$) either both have values or neither of them have values. + * If they have values, the peephole optimization is used. Additionally, + * if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also + * required to have values for peephole optimization. + * * The projection weights (\f$W_{proj}\f$) is required only for the + * recurrent projection layer, and should otherwise have no value. + * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a + * value if the recurrent projection layer exists, and should otherwise + * have no value. * * References: * @@ -757,8 +866,8 @@ typedef enum { * 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. + * 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.) * @@ -766,56 +875,74 @@ typedef enum { * http://arxiv.org/pdf/1503.04069.pdf * Greff et al. "LSTM: A Search Space Odyssey" * - * Supported tensor types (type T): + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Inputs: * * 0: The input (\f$x_t\f$). - * 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 (\f$W_{xi}\f$). Optional. - * A 2-D tensor of type T, of shape [num_units, input_size], where - * “num_units” corresponds to the number of cell units. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, input_size], where “num_units” corresponds to the + * number of cell units. * * 2: The input-to-forget weights (\f$W_{xf}\f$). - * A 2-D tensor of type T, of shape [num_units, input_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, input_size]. * * 3: The input-to-cell weights (\f$W_{xc}\f$). - * A 2-D tensor of type T, of shape [num_units, input_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, input_size]. * * 4: The input-to-output weights (\f$W_{xo}\f$). - * A 2-D tensor of type T, of shape [num_units, input_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, input_size]. * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. - * 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 (\f$W_{hf}\f$). - * A 2-D tensor of type T, of shape [num_units, output_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, output_size]. * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). - * A 2-D tensor of type T, of shape [num_units, output_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, output_size]. * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). - * A 2-D tensor of type T, of shape [num_units, output_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units, output_size]. * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 12:The input gate bias (\f$b_i\f$). Optional. - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 13:The forget gate bias (\f$b_f\f$). - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 14:The cell bias (\f$b_c\f$). - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 15:The output gate bias (\f$b_o\f$). - * A 1-D tensor of type T, of shape [num_units]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 16:The projection weights (\f$W_{proj}\f$). Optional. - * A 2-D tensor of type T, of shape [output_size, num_units]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [output_size, num_units]. * * 17:The projection bias (\f$b_{proj}\f$). Optional. - * A 1-D tensor of type T, of shape [output_size]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [output_size]. * * 18:The output state (in) (\f$h_{t-1}\f$). - * A 2-D tensor of type T, of shape [batch_size, output_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, output_size]. * * 19:The cell state (in) (\f$C_{t-1}\f$). - * A 2-D tensor of type T, of shape [batch_size, num_units]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units]. * * 20:The activation function (\f$g\f$). * A value indicating the activation function: * - * * 21:The clipping threshold (\f$t_{cell}\f$) 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 (\f$t_{proj}\f$) for the output from the projection layer, such - * that values are bound within [-proj_clip, proj_clip]. If set to 0.0 + * * 21:The clipping threshold (\f$t_{cell}\f$) 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 (\f$t_{proj}\f$) 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: The scratch buffer. - * A 2-D tensor of type T, of shape [batch_size, num_units * 4] with - * CIFG, or [batch_size, num_units * 3] without CIFG. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units * 4] with CIFG, or + * [batch_size, num_units * 3] without CIFG. * * 1: The output state (out) (\f$h_t\f$). - * A 2-D tensor of type T, of shape [batch_size, output_size]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, output_size]. * * 2: The cell state (out) (\f$C_t\f$). - * A 2-D tensor of type T, of shape [batch_size, num_units]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units]. * * 3: The output (\f$o_t\f$). - * A 2-D tensor of type T, of shape [batch_size, output_size]. This is - * effectively the same as the current “output state (out)” value. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, output_size]. This is effectively the same as the + * current “output state (out)” value. */ ANEURALNETWORKS_LSTM = 16, - /** Performs an 2-D max pooling operation. + /** + * Performs an 2-D max pooling operation. * - * The output dimensions are functions of the filter dimensions, stride, and padding. + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -864,51 +997,68 @@ typedef enum { * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An 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 stride when walking through input - * in the ‘width’ dimension. - * * 6: An INT32 value, specifying the stride when walking through input - * 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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 8: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 9: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Inputs (implicit padding): - * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input. - * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the implicit + * padding scheme, has to be one of the * {@link PaddingCode} values. - * * 2: An INT32 value, specifying the stride when walking through input - * in the ‘width’ dimension. - * * 3: An INT32 value, specifying the stride when walking through input - * in the ‘height’ dimension. - * * 4: An INT32 value, specifying the filter width. - * * 5: An INT32 value, specifying the filter height. - * * 6: An INT32 value, and has to be one of the {@link FuseCode} values. - * Specifies the activation to invoke on the result of each addition. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link ANEURALNETWORKS_INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * width. + * * 5: An {@link ANEURALNETWORKS_INT32} scalar, specifying the filter + * height. + * * 6: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth]. + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. */ ANEURALNETWORKS_MAX_POOL_2D = 17, - /** Multiplies two tensors, element-wise. + /** + * 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. + * Takes two input tensors of identical {@link 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. + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -916,24 +1066,28 @@ typedef enum { * * 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. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * * 0: The product, a tensor of the same type as input0. - * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, the following - * condition must be satisfied: output_scale > input1_scale * input2_scale. + * * 0: The product, a tensor of the same {@link OperandCode} as input0. + * For output tensor of {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input1_scale * input2_scale. */ ANEURALNETWORKS_MUL = 18, - /** Computes rectified linear activation on the input tensor element-wise. + /** + * Computes rectified linear activation on the input tensor element-wise. * * The output is calculated using this formula: * * output = max(0, input) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -947,13 +1101,14 @@ typedef enum { */ ANEURALNETWORKS_RELU = 19, - /** Computes rectified linear 1 activation on the input tensor element-wise. + /** + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -967,13 +1122,14 @@ typedef enum { */ ANEURALNETWORKS_RELU1 = 20, - /** Computes rectified linear 6 activation on the input tensor element-wise. + /** + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -987,12 +1143,13 @@ typedef enum { */ ANEURALNETWORKS_RELU6 = 21, - /** Reshapes a tensor. + /** + * Reshapes a tensor. * - * Given tensor, this operation returns a tensor that has the same values as tensor, - * but with a newly specified shape. + * Given tensor, this operation returns a tensor that has the same values as + * tensor, but with a newly specified shape. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -1000,32 +1157,38 @@ typedef enum { * * 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. + * * 1: A 1-D tensor of {@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. + /** + * Resizes images to given size using the bilinear interpretation. * - * Resized images will be distorted if their output aspect ratio is not the same as - * input aspect ratio. + * 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 types: + * Supported tensor {@link OperandCode}: * * {@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 height of the output tensor. - * * 2: An INT32 value, specifying the output width of the output tensor. + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * height of the output tensor. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, specifying the output + * width of the output tensor. * * Outputs: - * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth]. + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. */ ANEURALNETWORKS_RESIZE_BILINEAR = 23, @@ -1033,7 +1196,8 @@ typedef enum { * A basic recurrent neural network layer. * * This layer implements the operation: - * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias) + * outputs = state = activation(inputs * input_weights + + * state * recurrent_weights + bias) * * Where: * * “input_weights” is a weight matrix that multiplies the inputs; @@ -1044,41 +1208,49 @@ typedef enum { * * “activation” is the function passed as the “fused_activation_function” * argument (if not “NONE”). * - * Supported tensor types (Type T): + * Supported tensor {@link OperandCode}: * * {@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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32} 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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]. + * A 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [num_units]. * * 4: hidden state (in). - * A 2-D tensor of type T, of shape [batch_size, num_units]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units]. * * 5: fused_activation_function. - * An optional {@link FuseCode} value indicating the activation - * function. If “NONE” is specified then it results in a linear - * activation. + * An optional {@link 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 type T, of shape [batch_size, num_units]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units]. * * * 1: output. - * A 2-D tensor of type T, of shape [batch_size, num_units]. This is - * effectively the same as the current state value. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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. + /** + * 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: * @@ -1086,7 +1258,7 @@ typedef enum { * exp((input[batch, i] - max(input[batch, :])) * beta) / * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * @@ -1094,41 +1266,46 @@ typedef enum { * * Inputs: * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. - * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta. + * * 1: An {@link ANEURALNETWORKS_FLOAT32} scalar, specifying the positive + * scaling factor for the exponent, beta. * * Outputs: * * 0: The output tensor of same shape as input0. - * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} type, + * For {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}, * the scale must be 1.f / 256 and the zeroPoint must be 0. */ ANEURALNETWORKS_SOFTMAX = 25, - /** Rearranges blocks of spatial data, into depth. + /** + * 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. + * 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. + * 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: + * Supported tensor {@link OperandCode}: * * {@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. + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link 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. * * Outputs: - * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size, - * depth*block_size*block_size]. + * * 0: The output 4-D tensor, of shape [batches, height/block_size, + * width/block_size, depth_in*block_size*block_size]. */ ANEURALNETWORKS_SPACE_TO_DEPTH = 26, @@ -1145,21 +1322,22 @@ typedef enum { * 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 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")); + * 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); + * 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 @@ -1170,45 +1348,53 @@ typedef enum { * Each rank adds a dimension to the weights matrices by means of stacking * the filters. * - * Supported tensor types (type T): + * Supported tensor {@link OperandCode}: * * {@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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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 type T, of shape [num_units]. + * An optional 1-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, + * of shape [num_units]. * * 4: state (in). - * A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, (memory_size - 1) * num_units * rank]. * * 5: rank. * The rank of the SVD approximation. * * 6: fused_activation_function. - * An optional {@link FuseCode} value indicating the activation function. - * If “NONE” is specified then it results in a linear activation. + * An optional {@link 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 type T, of shape [batch_size, (memory_size - 1) * num_units * rank]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, 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]. + * A 2-D tensor of {@link ANEURALNETWORKS_TENSOR_FLOAT32}, of shape + * [batch_size, num_units]. */ ANEURALNETWORKS_SVDF = 27, - /** Computes hyperbolic tangent of input tensor element-wise. + /** + * Computes hyperbolic tangent of input tensor element-wise. * * The output is calculated using this formula: * * output = tanh(input) * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4. @@ -1225,87 +1411,95 @@ typedef enum { /** * 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 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 types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: 4 * * Inputs: - * 0: An n-D tensor, specifying the tensor to be reshaped - * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the - * input tensor. All values must be >= 1. + * * 0: An n-D tensor, specifying the tensor to be reshaped + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the block + * sizes for each spatial dimension of the input tensor. All values + * must be >= 1. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29, /** * Element-wise division of two tensors. * - * Takes two input tensors of identical type and compatible dimensions. The output - * is the result of dividing the first input tensor by the second, optionally - * modified by an activation function. + * Takes two input tensors of identical {@link OperandCode} and compatible + * dimensions. The output is the result of dividing the first input tensor + * by the second, optionally modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * - * The size of the output is the maximum size along each dimension of the input operands. - * It starts with the trailing dimensions, and works its way forward. + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, specifying the first input. - * 1: A tensor of the same type, and compatible dimensions as input0. - * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. - * Specifies the activation to invoke on the result of each addition. + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_DIV = 30, /** * 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. + * Reduces the input tensor along the given dimensions to reduce. Unless + * keep_dims is true, the rank of the tensor is reduced by 1 for each entry + * in axis. If keep_dims is true, the reduced dimensions are retained with + * length 1. * - * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with - * a single element is returned. + * If dimensions to reduce have no entries, all dimensions are reduced, and + * a tensor with a single element is returned. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * 0: A tensor, specifying the input. - * 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default), - * reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)). - * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1. + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}. The dimensions + * to reduce. If None (the default), reduces all dimensions. Must be in + * the range [-rank(input_tensor), rank(input_tensor)). + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, keep_dims. If positive, + * retains reduced dimensions with length 1. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_MEAN = 31, @@ -1314,21 +1508,30 @@ typedef enum { * * This operation pads a tensor according to the specified paddings. * - * Supported tensor types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, specifying the tensor to be padded. - * 1: A 2-D Tensor of type 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 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. + * * 0: An n-D tensor, specifying the tensor to be padded. + * * 1: A 2-D Tensor of {@link 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 type as input0. + * * 0: A tensor of the same {@link 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] */ ANEURALNETWORKS_PAD = 32, @@ -1336,149 +1539,169 @@ typedef enum { /** * 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. + * 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 types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: 4 * * Inputs: - * 0: An n-D tensor, specifying the input. - * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the - * input tensor. All values must be >= 1. - * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the - * input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}. - * 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. + * * 0: An n-D tensor, specifying the input. + * * 1: A 1-D Tensor of {@link 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 {@link 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 {rank(input0), 2}. + * 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. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33, /** * Removes dimensions of size 1 from the shape of a tensor. * - * Given a tensor input, this operation returns a tensor of the same type 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). + * Given a tensor input, this operation returns a tensor of the same + * {@link 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 types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, the tensor to be squeezed. - * 1: An optional 1-D tensor of type 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 will be reported if squeezing a dimension that - * is not 1. + * * 0: An n-D tensor, the tensor to be squeezed. + * * 1: An optional 1-D tensor of {@link 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 type as input0. Contains the same data as input, but has one or more - * dimensions of size 1 removed. + * * 0: A tensor of the same {@link OperandCode} as input0. Contains the + * same data as input, but has one or more dimensions of size 1 + * removed. */ ANEURALNETWORKS_SQUEEZE = 34, /** * 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. + * 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 types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, specifying the tensor to be sliced. - * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input - * tensor to be sliced. The length must be of rank(input0). - * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input - * tensor to be sliced. The length must be of rank(input0). - * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input - * tensor to be sliced. The length must be of rank(input0). - * 4: An INT32 value, begin_mask. 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: An INT32 value, end_mask. 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: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is - * set, it implies that the ith specification shrinks the dimensionality by 1. A slice of - * size 1 starting from begin[i] in the dimension will be preserved. + * * 0: An n-D tensor, specifying the tensor to be sliced. + * * 1: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of + * the dimensions of the input tensor to be sliced. The length must be + * of rank(input0). + * * 2: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of + * the dimensions of the input tensor to be sliced. The length must be + * of rank(input0). + * * 3: A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of + * the dimensions of the input tensor to be sliced. The length must be + * of rank(input0). + * * 4: An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. 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: An {@link ANEURALNETWORKS_INT32} scalar, end_mask. 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: An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32 + * mask. If the ith bit of shrink_axis_mask is set, it implies that the + * ith specification shrinks the dimensionality by 1. A slice of size 1 + * starting from begin[i] in the dimension must be preserved. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_STRIDED_SLICE = 35, /** * Element-wise subtraction of two tensors. * - * Takes two input tensors of identical type and compatible dimensions. The output - * is the result of subtracting the second input tensor from the first one, optionally - * modified by an activation function. + * Takes two input tensors of identical {@link 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. + * 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: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, specifying the first input. - * 1: A tensor of the same type, and compatible dimensions as input0. - * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. - * Specifies the activation to invoke on the result of each addition. + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandCode}, and compatible dimensions + * as input0. + * * 2: An {@link ANEURALNETWORKS_INT32} scalar, and has to be one of the + * {@link FuseCode} values. Specifies the activation to + * invoke on the result. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_SUB = 36, /** - * Transposes the input tensor, permuting the dimensions according to the perm tensor. + * 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. + * 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 types: + * Supported tensor {@link OperandCode}: * * {@link ANEURALNETWORKS_TENSOR_FLOAT32} * * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4 * * Inputs: - * 0: An n-D tensor, specifying the tensor to be transposed. - * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the - * input tensor. + * * 0: An n-D tensor, specifying the tensor to be transposed. + * * 1: An optional 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, + * the permutation of the dimensions of the input tensor. * * Outputs: - * 0: A tensor of the same type as input0. + * * 0: A tensor of the same {@link OperandCode} as input0. */ ANEURALNETWORKS_TRANSPOSE = 37, } OperationCode; @@ -1561,8 +1784,8 @@ typedef enum { ANEURALNETWORKS_UNEXPECTED_NULL = 3, ANEURALNETWORKS_BAD_DATA = 4, ANEURALNETWORKS_OP_FAILED = 5, - ANEURALNETWORKS_UNMAPPABLE = 5, ANEURALNETWORKS_BAD_STATE = 6, + ANEURALNETWORKS_UNMAPPABLE = 7, } ResultCode; /** @@ -1603,6 +1826,12 @@ typedef struct ANeuralNetworksMemory ANeuralNetworksMemory; *
  • {@link ANeuralNetworksModel_addOperand}
    • * * + * This forms a graph in which each operation and operand is a node, a + * directed edge from an operand to an operation indicates that the + * operand is an input to the operation, and a directed edge from an + * operation to an operand indicates that the operand is an output + * from the operation. This graph must be acyclic. + * * A model is completed by calling {@link ANeuralNetworksModel_finish}. * A model is destroyed by calling {@link ANeuralNetworksModel_free}. * @@ -1658,10 +1887,10 @@ typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; *

    To use:

    * + *

    An output buffer or memory region must not overlap with any + * other output buffer or memory region, with an input buffer or + * memory region, or with an operand value in a memory object + * ({@link ANeuralNetworksModel_setOperandValueFromMemory}).

    + * *

    An execution cannot be modified once {@link ANeuralNetworksExecution_startCompute} * has been called on it.

    * @@ -1682,18 +1916,55 @@ typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation; * thread to use {@link ANeuralNetworksEvent_wait} at the same time.

    * *

    It is also the application's responsibility to ensure that there are no other - * uses of the request after calling {@link ANeuralNetworksExecution_free}.

    + * uses of the execution after calling {@link ANeuralNetworksExecution_free}.

    */ typedef struct ANeuralNetworksExecution ANeuralNetworksExecution; /** * ANeuralNetworksOperandType describes the type of an operand. * This structure is used to describe both scalars and tensors. + * + * A tensor operand type must have a specified rank (number of + * dimensions) but may have any of its dimensions unspecified. + * + * A tensor operand type with all dimensions specified is "fully + * specified". Whenever possible (i.e., whenever the dimensions are + * known at model construction time), a tensor operand type should be + * (but is not required to be) fully specified, in order to enable the + * best possible performance. + * + * If a tensor operand's type is not fully specified, the dimensions + * of the operand are deduced from the operand types and values of the + * operation for which that operand is an output. + * + *

    In the following situations, a tensor operand type must be fully + * specified:

    + * + * A tensor operand type with some number of unspecified dimensions is + * represented by setting each unspecified dimension to 0. */ typedef struct ANeuralNetworksOperandType { /** The data type, e.g ANEURALNETWORKS_INT8. */ int32_t type; - /** The number of dimensions. It should be 0 for scalars. */ + /** The number of dimensions (rank). It should be 0 for scalars. */ uint32_t dimensionCount; /** The dimensions of the tensor. It should be nullptr for scalars. */ const uint32_t* dimensions; @@ -1808,17 +2079,35 @@ int ANeuralNetworksModel_finish(ANeuralNetworksModel* model); * * The order in which the operands are added is important. The first one added * to a model will have the index value 0, the second 1, etc. These indexes are - * used as operand identifiers in {@link ANeuralNetworksModel_addOperation}, + * used as operand identifiers in + * {@link ANeuralNetworksModel_addOperation}, + * {@link ANeuralNetworksModel_identifyInputsAndOutputs}, + * {@link ANeuralNetworksModel_setOperandValue}, + * {@link ANeuralNetworksModel_setOperandValueFromMemory}, * {@link ANeuralNetworksExecution_setInput}, * {@link ANeuralNetworksExecution_setInputFromMemory}, * {@link ANeuralNetworksExecution_setOutput}, * {@link ANeuralNetworksExecution_setOutputFromMemory} and * {@link ANeuralNetworksExecution_setOperandValue}. * - * To build a model that can accomodate inputs of various sizes, as you may want - * to do for a CNN, set the size of the dimensions that will vary at run time to 0. - * If you do so, provide the full dimensions when calling - * {@link ANeuralNetworksExecution_setInput} or {@link ANeuralNetworksExecution_setInputFromMemory}. + *

    Every operand must be referenced in exactly one of the following + * ways: