diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal index 2e48ba016f..3eda14f686 100644 --- a/neuralnetworks/1.2/types.hal +++ b/neuralnetworks/1.2/types.hal @@ -38,6 +38,8 @@ enum OperandType : @1.0::OperandType { * * Values of this operand type are either true or false. A zero value * represents false; any other value represents true. + * + * Available since API level 29. */ BOOL = 6, /** @@ -48,41 +50,49 @@ enum OperandType : @1.0::OperandType { * realValue = integerValue * scale. * * scale is a 32 bit floating point with value greater than zero. + * + * Available since API level 29. */ TENSOR_QUANT16_SYMM = 7, - /** A tensor of IEEE 754 16 bit floating point values. */ + /** + * A tensor of IEEE 754 16 bit floating point values. + * + * Available since API level 29. + */ TENSOR_FLOAT16 = 8, /** * A tensor of 8 bit boolean values. * * Values of this operand type are either true or false. A zero value * represents false; any other value represents true. + * + * Available since API level 29. */ TENSOR_BOOL8 = 9, - /** An IEEE 754 16 bit floating point scalar value. */ + /** + * An IEEE 754 16 bit floating point scalar value. + * + * Available since API level 29. + */ FLOAT16 = 10, /** * A tensor of 8 bit signed integers that represent real numbers. * - * This tensor is associated with additional fields that are - * used to convert the 8 bit signed integer to the real value and vice versa. + * This tensor is associated with additional fields that can + * be used to convert the 8 bit signed integer to the real value and vice versa. * These fields are: * - channelDim: a 32 bit unsigned integer indicating channel dimension. * - scales: an array of positive 32 bit floating point values. * The size of the scales array must be equal to dimensions[channelDim]. - * These fields are located inside Operand's extraParams union, inside the - * SymmPerChannelQuantParams struct. * - * An Operand of this type must use the 'channelQuant' variant of its - * extraParams field. + * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0). * - * The channel dimension of this tensor must be known, i.e. - * dimensions[channelDim] must be non-zero. - * - * The formula for real values: + * The formula is: * realValue[..., C, ...] = * integerValue[..., C, ...] * scales[C] * where C is an index in the Channel dimension. + * + * Available since API level 29. */ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11, /** @@ -95,6 +105,8 @@ enum OperandType : @1.0::OperandType { * * The formula is: * real_value = (integer_value - zeroPoint) * scale. + * + * Available since API level 29. */ TENSOR_QUANT16_ASYMM = 12, /** @@ -105,8 +117,24 @@ enum OperandType : @1.0::OperandType { * realValue = integerValue * scale. * * scale is a 32 bit floating point with value greater than zero. + * + * Available since API level 29. */ TENSOR_QUANT8_SYMM = 13, + /* + * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to + * OEM operation and data types. + * + * OEM specific scalar value. + * OEM = 10000, + */ + /* + * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to + * OEM operation and data types. + * + * A tensor of OEM specific values. + * TENSOR_OEM_BYTE = 10001, + */ /* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF * OperandTypeRange::FUNDAMENTAL_MAX. */ @@ -132,64 +160,4295 @@ enum OperandTypeRange : uint32_t { * * The type of an operation in a model. */ -enum OperationType : @1.1::OperationType { - // TODO(b/116445845): Sync docs when all ops are implemented. +enum OperationType : int32_t { + /** + * Adds two tensors, element-wise. + * + * Takes two input tensors of identical {@link OperandType} and compatible + * dimensions. The output is the sum of both input tensors, optionally + * modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its + * way forward. + * + * Example: + * + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType}, and compatible dimensions + * as input0. + * * 2: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: The sum, a tensor of the same {@link OperandType} as input0. + * + * Available since API level 27. + */ + ADD = @1.1::OperationType:ADD, + + /** + * Performs a 2-D average pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * sum_{di, dj}( + * input[b, strides[1] * i + di, strides[2] * j + dj, channel] + * ) / sum(1) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 8: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 9: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 2: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 5: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 6: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * + * Available since API level 27. + */ + AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D, + + /** + * Concatenates the input tensors along the given dimension. + * + * The input tensors must have identical {@link OperandType} and the same + * dimensions except the dimension along the concatenation axis. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API + * level 29, see the input section) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0 ~ n-1: The list of n input tensors, of shape + * [D0, D1, ..., Daxis(i), ..., Dm]. + * Before API level 29, all input tensors of + * {@link OperandType::TENSOR_QUANT8_ASYMM} + * must have the same scale and zeroPoint as the output tensor. + * * n: An {@link OperandType::INT32} scalar, specifying the + * concatenation axis. + * + * Outputs: + * * 0: The output, a tensor of the same {@link OperandType} as the input + * tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm]. + * + * Available since API level 27. + */ + CONCATENATION = @1.1::OperationType:CONCATENATION, + + /** + * Performs an 2-D convolution operation. + * + * The CONV_2D op sweeps a 2-D filter that can mix channels together over a + * batch of images, applying the filter to each window of each image of the + * appropriate size. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * sum_{di, dj, k} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, k] * + * filter[channel, di, dj, k] + * ) + bias[channel] + * + * Supported tensor {@link OperandType} configurations: + * * 32 bit Floating point : + * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * Available since API level 29: + * * Quantized with symetric per channel quantization for the filter: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * * 16 bit Floating point: + * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. For tensor of type + * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel + * dimension (extraParams.channelQuant.channelDim) must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 12 (dilation factor for height) must be specified as well. + * Available since API level 29. + * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 11 (dilation factor for width) must be specified as well. + * Available since API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. For tensor of type + * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel + * dimension (extraParams.channelQuant.channelDim) must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 4: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 9 (dilation factor for height) must be specified as well. + * Available since API level 29. + * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 8 (dilation factor for width) must be specified as well. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale (for + * filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} + * this condition must be true for all filter scales). + * + * Available since API level 27. + */ + CONV_2D = @1.1::OperationType:CONV_2D, + + /** + * Performs a depthwise 2-D convolution operation. + * + * Given an input tensor of shape [batches, height, width, depth_in] and a + * filter tensor of shape [1, filter_height, filter_width, depth_out] + * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV + * applies a different filter to each input channel (expanding from 1 + * channel to channel_multiplier channels for each), then concatenates the + * results together. + * + * The output has depth_out = depth_in * depth_multiplier channels. + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, k * channel_multiplier + q] = + * sum_{di, dj} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, k] * + * filter[1, di, dj, k * channel_multiplier + q] + * ) + bias[k * channel_multiplier + q] + * + * Supported tensor {@link OperandType} configurations: + * * 32 bit Floating point : + * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * Available since API level 29: + * * Quantized with symetric per channel quantization for the filter: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. For tensor of type + * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel + * dimension (extraParams.channelQuant.channelDim) must be set to 3. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise + * multiplier. + * * 10: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 11: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 13 (dilation factor for height) must be specified as well. + * Available since API level 29. + * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 12 (dilation factor for width) must be specified as well. + * Available since API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], + * specifying the filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 4: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise + * multiplier. + * * 7: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 8: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on width dimension. If this input is set, + * input 10 (dilation factor for height) must be specified as well. + * Available since API level 29. + * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation + * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped + * cells between each filter element on height dimension. If this input is set, + * input 9 (dilation factor for width) must be specified as well. + * Available since API level 29. + + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale (for + * filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} + * this condition must be true for all filter scales). + * + * Available since API level 27. + */ + DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D, + + /** + * Rearranges data from depth into blocks of spatial data. + * + * More specifically, this op outputs a copy of the input tensor where + * values from the depth dimension are moved in spatial blocks to the height + * and width dimensions. The value block_size indicates the input block size + * and how the data is moved. + * + * Chunks of data of size block_size * block_size from depth are rearranged + * into non-overlapping blocks of size block_size x block_size. + * + * The width of the output tensor is input_depth * block_size, whereas the + * height is input_height * block_size. The depth of the input tensor must + * be divisible by block_size * block_size + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the block_size. + * block_size must be >=1 and block_size * block_size must be a divisor + * of the input depth. + * * 2: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batch, height*block_size, + * width*block_size, depth/(block_size*block_size)]. + * + * Available since API level 27. + */ + DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE, + + /** + * Dequantizes the input tensor. + * + * The formula is: + * + * output = (input - zeroPoint) * scale. + * + * Supported input tensor {@link OperandType}: + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29) + * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29) + * + * Supported output tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32}. + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since API level 27. + */ + DEQUANTIZE = @1.1::OperationType:DEQUANTIZE, + + /** + * Looks up sub-tensors in the input tensor. + * + * This operator takes for input a tensor of values (Values) and + * a one-dimensional tensor of selection indices (Lookups). + * The output tensor is the concatenation of sub-tensors of Values as + * selected by Lookups. + * + * Think of Values as being sliced along its first dimension: + * The entries in Lookups select which slices are concatenated together + * to create the output tensor. + * + * For example, if Values has shape of [40, 200, 300] and + * Lookups has shape of [3], all three values found in Lookups are + * expected to be between 0 and 39. The resulting tensor must + * have shape of [3, 200, 300]. + * + * If a value in Lookups is out of bounds, the operation must fail + * and an error must be reported. + * + * Supported value tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported value tensor rank: from 2 + * + * Inputs: + * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}. + * The values are indices into the first dimension of Values. + * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are + * extracted. + * + * Output: + * * 0: A n-D tensor with the same rank and shape as the Values + * tensor, except for the first dimension which has the same size + * as Lookups' only dimension. + * + * Available since API level 27. + */ + EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP, + + /** + * Computes element-wise floor() on the input tensor. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor, of the same {@link OperandType} and dimensions as + * the input tensor. + * + * Available since API level 27. + */ + FLOOR = @1.1::OperationType:FLOOR, + + /** + * 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 {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor of at least rank 2, specifying the input. If rank is + * greater than 2, then it gets flattened to a 2-D Tensor. The + * (flattened) 2-D Tensor is reshaped (if necessary) to + * [batch_size, input_size], where "input_size" corresponds to the + * number of inputs to the layer, matching the second dimension of + * weights, and "batch_size" is calculated by dividing the number of + * elements by "input_size". + * * 1: A 2-D tensor, specifying the weights, of shape + * [num_units, input_size], where "num_units" corresponds to the number + * of output nodes. + * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input + * tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should + * also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor + * of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} 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 OperandType::TENSOR_QUANT8_ASYMM}, the following + * condition must be satisfied: + * output_scale > input_scale * filter_scale. + * + * Available since API level 27. + */ + FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED, + + /** + * Looks up sub-tensors in the input tensor using a key-value map. + * + * This operator takes for input a tensor of values (Values), + * a one-dimensional tensor of selection values (Lookups) and + * a one-dimensional tensor that maps these values to Values + * indexes. The output tensor is the concatenation of sub-tensors of + * Values as selected by Lookups via Keys. + * + * Think of Values as being sliced along its outer-most dimension. + * The output is a concatenation of selected slices, with one slice + * for each entry of Lookups. The slice selected is the one at the + * same index as the Maps entry that matches the value in Lookups. + * + * For a hit, the corresponding sub-tensor of Values is included + * in the Output tensor. For a miss, the corresponding sub-tensor in + * Output must have zero values. + * + * For example, if Values has shape of [40, 200, 300], + * Keys should have a shape of [40]. If Lookups tensor has shape + * of [3], three slices are being concatenated, so the resulting tensor + * must have the shape of [3, 200, 300]. If the first entry in Lookups + * has the value 123456, that value must be located in Keys tensor. + * If the sixth entry of Keys contains 123456, the sixth slice of Values + * must be selected. If no entry in Keys has 123456, a slice of zeroes + * must be concatenated. + * + * Supported value tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported value tensor rank: from 2 + * + * Inputs: + * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with + * shape [ k ]. + * * 1: Keys. A 1-D {@link OperandType::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 OperandType::TENSOR_QUANT8_ASYMM} with offset 0 + * and scale 1.0f. + * A non-zero byte represents True, a hit. A zero indicates otherwise. + * + * Available since API level 27. + */ + HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP, + + /** + * Applies L2 normalization along the depth dimension. + * + * The values in the output tensor are computed as: + * + * output[batch, row, col, channel] = + * input[batch, row, col, channel] / + * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) + * + * For input tensor with rank less than 4, independently normalizes each + * 1-D slice along dimension dim. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since API level 29. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be normalized. + * * 1: An optional {@link OperandType::INT32} scalar, default to -1, + * specifying the dimension normalization would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since API level 29. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} and same shape as input0. + * + * Available since API level 27. + */ + L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION, + + /** + * Performs an 2-D L2 pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, c] = + * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) / + * sum(1)) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 8: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 9: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 2: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 5: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 6: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * + * Available since API level 27. + */ + L2_POOL_2D = @1.1::OperationType:L2_POOL_2D, + + /** + * Applies Local Response Normalization along the depth dimension. + * + * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the + * last dimension), and each vector is normalized independently. Within a + * given vector, each component is divided by the weighted, squared sum of + * inputs within depth_radius. + * + * The output is calculated using this formula: + * + * sqr_sum[a, b, c, d] = sum( + * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)) + * output = input / pow((bias + alpha * sqr_sum), beta) + * + * For input tensor with rank less than 4, independently normalizes each + * 1-D slice along specified dimension. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * Tensors with rank less than 4 are only supported since API level 29. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the radius of + * the normalization window. + * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must + * not be zero. + * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale + * factor, alpha. + * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent, + * beta. + * * 5: An optional {@link OperandType::INT32} scalar, default to -1, + * specifying the dimension normalization would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since API level 29. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 27. + */ + LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION, + + /** + * Computes sigmoid activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = 1 / (1 + exp(-input)) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 256 and the zeroPoint must be 0. + * + * Available since API level 27. + */ + LOGISTIC = @1.1::OperationType:LOGISTIC, + + /** + * Projects an input to a bit vector via locality senstive hashing. + * + * Supported input tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported input tensor rank: from 1 + * + * Inputs: + * * 0: Hash functions. Dim.size == 2, DataType: Float. + * Tensor[0].Dim[0]: Number of hash functions. + * Tensor[0].Dim[1]: Number of projected output bits generated by each + * hash function. + * If the projection type is Sparse: + * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32 + * + * * 1: Input. Dim.size >= 1, no restriction on DataType. + * * 2: Weight. Optional. Dim.size == 1, DataType: Float. + * If not set, each input element is considered to have the same weight + * of 1.0. + * Tensor[1].Dim[0] == Tensor[2].Dim[0] + * * 3: Type: + * Sparse: + * Value LSHProjectionType_SPARSE(=3) (since API level 29). + * Computed bit vector is considered to be sparse. + * Each output element is an int32 made up of multiple bits + * computed from hash functions. + * + * NOTE: To avoid collisions across hash functions, an offset value + * of k * (1 << Tensor[0].Dim[1]) will be added to each signature, + * where k is the index of the hash function. + * + * Value LSHProjectionType_SPARSE_DEPRECATED(=1). + * Legacy behavior that does not include the offset value. + * + * Dense: + * Value LSHProjectionType_DENSE(=2). + * Computed bit vector is considered to be dense. Each output + * element represents a bit and can take the value of either + * 0 or 1. + * + * Outputs: + * * 0: If the projection type is Sparse: + * Output.Dim == { Tensor[0].Dim[0] } + * A tensor of int32 that represents hash signatures, + * + * If the projection type is Dense: + * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } + * A flattened tensor that represents projected bit vectors. + * + * Available since API level 27. + * The offset value for sparse projections was added in API level 29. + */ + LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION, + + /** + * Performs a single time step in a Long Short-Term Memory (LSTM) layer + * + * The LSTM operation is described by the following equations. + * + * \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; \\ + * h_t =& & \\ + * & o_t \odot g(C_t) & otherwise. \\ + * \f} + * Where: + * * \f$x_t\f$ is the input, + * * \f$i_t\f$ is the input gate, + * * \f$f_t\f$ is the forget gate, + * * \f$C_t\f$ is the cell state, + * * \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$W_{xi}\f$ 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, + * * \f$b_i\f$ is the input gate bias, + * * \f$W_{xf}\f$ is the input-to-forget weight matrix, + * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix, + * * \f$W_{cf}\f$ is the cell-to-forget weight matrix, + * * \f$b_f\f$ is the forget gate bias, + * * \f$W_{xc}\f$ is the input-to-cell weight matrix, + * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix, + * * \f$b_c\f$ is the cell bias, + * * \f$W_{xo}\f$ is the input-to-output weight matrix, + * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix, + * * \f$W_{co}\f$ is the cell-to-output weight matrix, + * * \f$b_o\f$ is the output gate bias, + * * \f$W_{proj}\f$ is the projection weight matrix, + * * \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 + * + * Hadamard product that takes two matrices and produces another + * matrix, each element of which is the product of the corresponding + * elements of the input matrices. + * + * Since API level 29 LSTM supports layer normalization. + * In case layer normalization is used, the inputs to internal activation + * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered + * following an approach from section 3.1 from + * https://arxiv.org/pdf/1607.06450.pdf + * + * The operation has the following independently optional inputs: + * * The input-to-input weights (\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-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. + * * (API level >= 29) The four layer normalization weights either all have + * values or none of them have values. Layer normalization is used when + * values are present. + * + * References: + * + * The default non-peephole non-CIFG implementation is based on: + * http://www.bioinf.jku.at/publications/older/2604.pdf + * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural + * Computation, 9(8):1735-1780, 1997. + * + * The peephole implementation and projection layer is based on: + * https://research.google.com/pubs/archive/43905.pdf + * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory + * recurrent neural network architectures for large scale acoustic + * modeling." INTERSPEECH, 2014. + * (However, the concept of peephole optimization was introduced in work + * prior to this paper.) + * + * The coupling of input and forget gate (CIFG) is based on: + * http://arxiv.org/pdf/1503.04069.pdf + * Greff et al. "LSTM: A Search Space Odyssey" + * + * The layer normalization is based on: + * https://arxiv.org/pdf/1607.06450.pdf + * Jimmy Ba et al. "Layer Normalization" + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * All input and output tensors must be of the same type. + * + * Inputs: + * * 0: The input (\f$x_t\f$). + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 2: The input-to-forget weights (\f$W_{xf}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 3: The input-to-cell weights (\f$W_{xc}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 4: The input-to-output weights (\f$W_{xo}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 12:The input gate bias (\f$b_i\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 13:The forget gate bias (\f$b_f\f$). + * A 1-D tensor of shape [num_units]. + * * 14:The cell bias (\f$b_c\f$). + * A 1-D tensor of shape [num_units]. + * * 15:The output gate bias (\f$b_o\f$). + * A 1-D tensor of shape [num_units]. + * * 16:The projection weights (\f$W_{proj}\f$). Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 17:The projection bias (\f$b_{proj}\f$). Optional. + * A 1-D tensor of shape [output_size]. + * * 18:The output state (in) (\f$h_{t-1}\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 19:The cell state (in) (\f$C_{t-1}\f$). + * A 2-D tensor 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. + * Until API level 29 this scalar must be of type {@link + * FLOAT32}. Since API level 29, if all the input + * tensors have type {@link OperandType::TENSOR_FLOAT32}, this + * scalar must be of the type {@link OperandType::FLOAT32}, + * otherwise if all the input tensors have the type {@link + * TENSOR_FLOAT16}, this scalar must be of type {@link + * FLOAT16}. + * * 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. + * Until API level 29 this scalar must be of type {@link + * FLOAT32}. Since API level 29, if all the input + * tensors have type {@link OperandType::TENSOR_FLOAT32}, this + * scalar must be of the type {@link OperandType::FLOAT32}, + * otherwise if all the input tensors have the type {@link + * TENSOR_FLOAT16}, this scalar must be of type {@link + * FLOAT16}. + * Since API level 29 there are additional inputs to this op: + * * 23:The input layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 24:The forget layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 25:The cell layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 26:The output layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The scratch buffer. + * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or + * [batch_size, num_units * 4] without CIFG. + * * 1: The output state (out) (\f$h_t\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 2: The cell state (out) (\f$C_t\f$). + * A 2-D tensor of shape [batch_size, num_units]. + * * 3: The output (\f$o_t\f$). + * A 2-D tensor of shape [batch_size, output_size]. This is effectively + * the same as the current “output state (out)” value. + * + * Available since API level 27. + */ + LSTM = @1.1::OperationType:LSTM, + + /** + * Performs an 2-D max pooling operation. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, channel] = + * max_{di, dj} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, channel] + * ) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 2: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 8: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 9: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 10: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 2: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 3: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the filter + * width. + * * 5: An {@link OperandType::INT32} scalar, specifying the filter + * height. + * * 6: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 7: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth]. + * + * Available since API level 27. + */ + MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D, + + /** + * Multiplies two tensors, element-wise. + * + * Takes two input tensors of identical {@link OperandType} and compatible + * dimensions. The output is the product of both input tensors, optionally + * modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the resulting output is the maximum size along each dimension + * of the input operands. It starts with the trailing dimensions, and works + * its way forward. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType}, and compatible dimensions + * as input0. + * * 2: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: The product, a tensor of the same {@link OperandType} as input0. + * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the following condition must be satisfied: + * output_scale > input1_scale * input2_scale. + * + * Available since API level 27. + */ + MUL = @1.1::OperationType:MUL, + + /** + * Computes rectified linear activation on the input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = max(0, input) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 27. + */ + RELU = @1.1::OperationType:RELU, + + /** + * 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 {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 27. + */ + RELU1 = @1.1::OperationType:RELU1, + + /** + * 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 {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 27. + */ + RELU6 = @1.1::OperationType:RELU6, + + /** + * Reshapes a tensor. + * + * Given tensor, this operation returns a tensor that has the same values as + * tensor, but with a newly specified shape. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the tensor to be reshaped. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the + * shape of the output tensor. The number of elements implied by shape + * must be the same as the number of elements in the input tensor. + * + * Outputs: + * * 0: The output tensor, of shape specified by the input shape. + * + * Available since API level 27. + */ + RESHAPE = @1.1::OperationType:RESHAPE, + + /** + * Resizes images to given size using the bilinear interpretation. + * + * Resized images must be distorted if their output aspect ratio is not the + * same as input aspect ratio. The corner pixels of output may not be the + * same as corner pixels of input. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying + * the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the output + * height of the output tensor. + * * 2: An {@link OperandType::INT32} scalar, specifying the output + * width of the output tensor. + * * 3: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, new_height, new_width, depth]. + * + * Available since API level 27. + */ + RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR, + + /** + * A basic recurrent neural network layer. + * + * This layer implements the operation: + * outputs = state = activation(inputs * input_weights + + * state * recurrent_weights + bias) + * + * Where: + * * “input_weights” is a weight matrix that multiplies the inputs; + * * “recurrent_weights” is a weight matrix that multiplies the current + * “state” which itself is the output from the previous time step + * computation; + * * “bias” is a bias vector (added to each output vector in the batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: weights. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of units. + * * 2: recurrent_weights. + * A 2-D tensor of shape [num_units, num_units], with columns + * corresponding to the weights from each unit. + * * 3: bias. + * A 1-D tensor of shape [num_units]. + * * 4: hidden state (in). + * A 2-D tensor of shape [batch_size, num_units]. + * * 5: fused_activation_function. + * An optional {@link FusedActivationFunc} value indicating the + * activation function. If “NONE” is specified then it results in a + * linear activation. + * + * Outputs: + * * 0: hidden state (out). + * A 2-D tensor of shape [batch_size, num_units]. + * + * * 1: output. + * A 2-D tensor of shape [batch_size, num_units]. This is effectively + * the same as the current state value. + * + * Available since API level 27. + */ + RNN = @1.1::OperationType:RNN, + + /** + * Computes the softmax activation on the input tensor element-wise, per + * batch, by normalizing the input vector so the maximum coefficient is + * zero. + * + * The output is calculated using this formula: + * + * output[batch, i] = + * exp((input[batch, i] - max(input[batch, :])) * beta) / + * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} + * + * For input tensor with rank other than 2, the activation will be applied + * independently on each 1-D slice along specified dimension. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4. + * Tensors with rank other than 2 or 4 are only supported since API level 29. + * + * Inputs: + * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. + * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive + * scaling factor for the exponent, beta. + * * 2: An optional {@link OperandType::INT32} scalar, default to -1, + * specifying the dimension the activation would be performed on. + * Negative index is used to specify axis from the end (e.g. -1 for + * the last axis). Must be in the range [-n, n). + * Available since API level 29. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 256 and the zeroPoint must be 0. + * + * Available since API level 27. + */ + SOFTMAX = @1.1::OperationType:SOFTMAX, + + /** + * Rearranges blocks of spatial data, into depth. + * + * More specifically, this op outputs a copy of the input tensor where + * values from the height and width dimensions are moved to the depth + * dimension. The value block_size indicates the input block size and how + * the data is moved. + * + * Chunks of data of size block_size * block_size from depth are rearranged + * into non-overlapping blocks of size block_size x block_size. + * + * The depth of the output tensor is input_depth * block_size * block_size. + * The input tensor's height and width must be divisible by block_size. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: An {@link OperandType::INT32} scalar, specifying the block_size. + * block_size must be >=1 and block_size must be a divisor of both the + * input height and width. + * * 2: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: The output 4-D tensor, of shape [batches, height/block_size, + * width/block_size, depth_in*block_size*block_size]. + * + * Available since API level 27. + */ + SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH, + + /** + * SVDF op is a kind of stateful layer derived from the notion that a + * densely connected layer that's processing a sequence of input frames can + * be approximated by using a singular value decomposition of each of its + * nodes. The implementation is based on: + * + * https://research.google.com/pubs/archive/43813.pdf + * + * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. + * “Compressing Deep Neural Networks using a Rank-Constrained Topology”. + * INTERSPEECH, 2015. + * + * It processes the incoming input using a 2-stage filtering mechanism: + * * stage 1 performs filtering on the "features" dimension, whose outputs + * get pushed into a memory of fixed-size memory_size. + * * stage 2 performs filtering on the "time" dimension of the memory_size + * memoized outputs of stage 1. + * + * Specifically, for rank 1, this layer implements the operation: + * + * memory = push(conv1d(inputs, weights_feature, feature_dim, + * "PADDING_VALID")); + * outputs = activation(memory * weights_time + bias); + * + * Where: + * * “weights_feature” is a weights matrix that processes the inputs (by + * convolving the input with every “feature filter”), and whose outputs + * get pushed, stacked in order, into the fixed-size “memory” (the oldest + * entry gets dropped); + * * “weights_time” is a weights matrix that processes the “memory” (by a + * batched matrix multiplication on the num_units); + * * “bias” is an optional bias vector (added to each output vector in the + * batch); and + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Each rank adds a dimension to the weights matrices by means of stacking + * the filters. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * All input tensors must be the same type. + * + * Inputs: + * * 0: input. + * A 2-D tensor of shape [batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 1: weights_feature. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of units. + * * 2: weights_time. + * A 2-D tensor of shape [num_units, memory_size], where “memory_size” + * corresponds to the fixed-size of the memory. + * * 3: bias. + * An optional 1-D tensor of shape [num_units]. + * * 4: state (in). + * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank]. + * * 5: rank. + * The rank of the SVD approximation. + * * 6: fused_activation_function. + * An optional {@link FusedActivationFunc} value indicating the + * activation function. If “NONE” is specified then it results in a + * linear activation. + * + * Outputs: + * * 0: state (out). + * A 2-D tensor of the same {@link OperandType} as the inputs, with shape + * [batch_size, (memory_size - 1) * num_units * rank]. + * * 1: output. + * A 2-D tensor of the same {@link OperandType} as the inputs, with shape + * [batch_size, num_units]. + * + * Available since API level 27. + */ + SVDF = @1.1::OperationType:SVDF, + + /** + * Computes hyperbolic tangent of input tensor element-wise. + * + * The output is calculated using this formula: + * + * output = tanh(input) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) + * + * Supported tensor rank: up to 4. + * + * Inputs: + * * 0: A tensor, specifying the input. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * For {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the scale must be 1.f / 128 and the zeroPoint must be 128. + * + * Available since API level 27. + */ + TANH = @1.1::OperationType:TANH, + + /** + * BatchToSpace for N-dimensional tensors. + * + * This operation reshapes the batch dimension (dimension 0) into M + 1 + * dimensions of shape block_shape + [batch], interleaves these blocks back + * into the grid defined by the spatial dimensions [1, ..., M], to obtain a + * result with the same rank as the input. + * + * This is the reverse of SpaceToBatch. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be reshaped + * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block + * sizes for each spatial dimension of the input tensor. All values + * must be >= 1. + * * 2: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND, + + /** + * Element-wise division of two tensors. + * + * Takes two input tensors of identical {@link OperandType} and compatible + * dimensions. The output is the result of dividing the first input tensor + * by the second, optionally modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandType}, and compatible dimensions + * as input0. + * * 2: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + DIV = @1.1::OperationType:DIV, + + /** + * Computes the mean of elements across dimensions of a tensor. + * + * Reduces the input tensor along the given dimensions to reduce. Unless + * keep_dims is true, the rank of the tensor is reduced by 1 for each entry + * in axis. If keep_dims is true, the reduced dimensions are retained with + * length 1. + * + * If dimensions to reduce have no entries, all dimensions are reduced, and + * a tensor with a single element is returned. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A 1-D Tensor of {@link OperandType::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 OperandType::INT32} scalar, keep_dims. If positive, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + MEAN = @1.1::OperationType:MEAN, + + /** + * Pads a tensor with zeros. + * + * This operation pads a tensor according to the specified paddings. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::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 {@link OperandType::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 {@link OperandType} as input0. The + * output tensor has the same rank as input0, and each + * dimension of the output tensor has the same size as the + * corresponding dimension of the input tensor plus the size + * of the padding: + * output0.dimension[i] = + * padding[i, 0] + input0.dimension[i] + padding[i, 1] + * + * Available since API level 28. + */ + PAD = @1.1::OperationType:PAD, + + /** + * SpaceToBatch for N-Dimensional tensors. + * + * This operation divides "spatial" dimensions [1, ..., M] of the input into + * a grid of blocks of shape block_shape, and interleaves these blocks with + * the "batch" dimension (0) such that in the output, the spatial dimensions + * [1, ..., M] correspond to the position within the grid, and the batch + * dimension combines both the position within a spatial block and the + * original batch position. Prior to division into blocks, the spatial + * dimensions of the input are optionally zero padded according to paddings. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: An n-D tensor, specifying the input. + * * 1: A 1-D Tensor of {@link OperandType::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 OperandType::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. + * * 3: An optional {@link OperandType::BOOL} scalar, default to false. + * Set to true to specify NCHW data layout for input0 and output0. + * Available since API level 29. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND, + + /** + * Removes dimensions of size 1 from the shape of a tensor. + * + * Given a tensor input, this operation returns a tensor of the same + * {@link OperandType} 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 {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::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 {@link OperandType::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 {@link OperandType} as input0. Contains the + * same data as input, but has one or more dimensions of size 1 + * removed. + * + * Available since API level 28. + */ + SQUEEZE = @1.1::OperationType:SQUEEZE, + + /** + * Extracts a strided slice of a tensor. + * + * Roughly speaking, this op extracts a slice of size (end - begin) / stride + * from the given input tensor. Starting at the location specified by begin + * the slice continues by adding stride to the index until all dimensions + * are not less than end. Note that a stride can be negative, which causes a + * reverse slice. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be sliced. + * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The + * starts of the dimensions of the input tensor to be sliced. The + * length must be of rank(input0). + * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The + * ends of the dimensions of the input tensor to be sliced. The length + * must be of rank(input0). + * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The + * strides of the dimensions of the input tensor to be sliced. The + * length must be of rank(input0). The entries must be non-zero. + * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit + * of begin_mask is set, begin[i] is ignored and the fullest possible + * range in that dimension is used instead. + * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of + * end_mask is set, end[i] is ignored and the fullest possible range in + * that dimension is used instead. + * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the + * ith bit of shrink_axis_mask is set, the ith dimension specification + * shrinks the dimensionality by 1, taking on the value at index + * begin[i]. In this case, the ith specification must define a + * slice of size 1, e.g. begin[i] = x, end[i] = x + 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k), + * where k is the number of bits set in shrink_axis_mask. + * + * Available since API level 28. + */ + STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE, + + /** + * Element-wise subtraction of two tensors. + * + * Takes two input tensors of identical {@link OperandType} and compatible + * dimensions. The output is the result of subtracting the second input + * tensor from the first one, optionally modified by an activation function. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input1.dimension = {4, 1, 2} + * input2.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the first input. + * * 1: A tensor of the same {@link OperandType}, and compatible dimensions + * as input0. + * * 2: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + SUB = @1.1::OperationType:SUB, + + /** + * Transposes the input tensor, permuting the dimensions according to the + * perm tensor. + * + * The returned tensor's dimension i corresponds to the input dimension + * perm[i]. If perm is not given, it is set to (n-1...0), where n is the + * rank of the input tensor. Hence by default, this operation performs a + * regular matrix transpose on 2-D input Tensors. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::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 {@link OperandType::TENSOR_INT32}, + * the permutation of the dimensions of the input tensor. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 28. + */ + TRANSPOSE = @1.1::OperationType:TRANSPOSE, + + /** + * Computes the absolute value of a tensor, element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ ABS = 38, + + /** + * Returns the index of the largest element along an axis. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor specifying the input. Must be non-empty. + * * 1: An {@link OperandType::INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor. + * + * Available since API level 29. + */ + // There is no underscore in ARG_MAX to avoid name conflict with + // the macro defined in libc/kernel/uapi/linux/limits.h. ARGMAX = 39, - ARGMIN = 40, + + /** + * Returns the index of the smallest element along an axis. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor specifying the input. Must be non-empty. + * * 1: An {@link OperandType::INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor. + * + * Available since API level 29. + */ + ARGMIN = 40, // See ARGMAX for naming discussion. + + /** + * Transform axis-aligned bounding box proposals using bounding box deltas. + * + * Given the positions of bounding box proposals and the corresponding + * bounding box deltas for each class, return the refined bounding box + * regions. The resulting bounding boxes are cliped against the edges of + * the image. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the + * bounding box proposals, each line with format [x1, y1, x2, y2]. + * For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM}, + * the zeroPoint must be 0 and the scale must be 0.125. + * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the + * bounding box delta for each region of interest and each class. The + * bounding box deltas are organized in the following order + * [dx, dy, dw, dh], where dx and dy is the relative correction factor + * for the center position of the bounding box with respect to the width + * and height, dw and dh is the log-scale relative correction factor + * for the width and height. For input0 of type + * {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be + * of {@link OperandType::TENSOR_QUANT8_ASYMM}. + * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches], specifying the number of output boxes for each batch. + * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of + * each image in the batch, each line with format + * [image_height, image_width]. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0, with shape + * [num_rois, num_classes * 4], specifying the coordinates of each + * output bounding box for each class, with format [x1, y1, x2, y2]. + * + * Available since API level 29. + */ AXIS_ALIGNED_BBOX_TRANSFORM = 41, + + /** + * Performs a forward LSTM on the input followed by a backward LSTM. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: 3, either time-major or batch-major. + * + * All input and output tensors must be of the same type. + * + * + * Inputs: + * * 0: The input. + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * where "max_time" is the number of timesteps (sequence length), + * "batch_size" corresponds to the batching dimension, and + * "input_size" is the size of the input. + * * 1: The forward input-to-input weights. Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 2: The forward input-to-forget weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 3: The forward input-to-cell weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 4: The forward input-to-output weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 5: The forward recurrent-to-input weights. Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 6: The forward recurrent-to-forget weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 7: The forward recurrent-to-cell weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 8: The forward recurrent-to-output weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 9: The forward cell-to-input weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 10: The forward cell-to-forget weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 11: The forward cell-to-output weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 12: The forward input gate bias. Optional. + * A 1-D tensor of shape [num_units]. + * * 13: The forward forget gate bias. + * A 1-D tensor of shape [num_units]. + * * 14: The forward cell gate bias. + * A 1-D tensor of shape [num_units]. + * * 15: The forward output gate bias. + * A 1-D tensor of shape [num_units]. + * * 16: The forward projection weights. Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 17: The forward projection bias. Optional. + * A 1-D tensor of shape [output_size]. + * * 18: The backward input-to-input weights. Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 19: The backward input-to-forget weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 20: The backward input-to-cell weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 21: The backward input-to-output weights. + * A 2-D tensor of shape [num_units, input_size]. + * * 22: The backward recurrent-to-input weights. Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 23: The backward recurrent-to-forget weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 24: The backward recurrent-to-cell weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 25: The backward recurrent-to-output weights. + * A 2-D tensor of shape [num_units, output_size]. + * * 26: The backward cell-to-input weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 27: The backward cell-to-forget weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 28: The backward cell-to-output weights. Optional. + * A 1-D tensor of shape [num_units]. + * * 29: The backward input gate bias. Optional. + * A 1-D tensor of shape [num_units]. + * * 30: The backward forget gate bias. + * A 1-D tensor of shape [num_units]. + * * 31: The backward cell gate bias. + * A 1-D tensor of shape [num_units]. + * * 32: The backward output gate bias. + * A 1-D tensor of shape [num_units]. + * * 33: The backward projection weights. Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 34: The backward projection bias. Optional. + * A 1-D tensor of shape [output_size]. + * * 35: The forward input activation state. + * A 2-D tensor of shape [batch_size, output_size]. + * * 36: The forward input cell state. + * A 2-D tensor of shape [batch_size, num_units]. + * * 37: The backward input activation state. + * A 2-D tensor of shape [batch_size, output_size]. + * * 38: The backward input cell state. + * A 2-D tensor of shape [batch_size, num_units]. + * * 39: The auxiliary input. Optional. + * A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size” + * corresponds to the batching dimension, and “input_size” is the size + * of the input. + * * 40: The forward auxiliary input-to-input weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 41: The forward auxiliary input-to-forget weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 42: The forward auxiliary input-to-cell weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 43: The forward auxiliary input-to-output weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 44: The backward auxiliary input-to-input weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 45: The backward auxiliary input-to-forget weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 46: The backward auxiliary input-to-cell weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 47: The backward auxiliary input-to-output weights. Optional. + * A 2-D tensor of shape [num_units, input_size]. + * * 48: The activation function. + * A value indicating the activation function: + * + * * 49: The clipping threshold for the cell state, such + * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 + * then clipping is disabled. + * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32}, + * this scalar must be of the type {@link OperandType::FLOAT32}, + * otherwise if all the input tensors have the type {@link + * TENSOR_FLOAT16}, this scalar must be of type {@link + * FLOAT16}. + * * 50: The clipping threshold for the output from the + * projection layer, such that values are bound within + * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32}, + * this scalar must be of the type {@link OperandType::FLOAT32}, + * otherwise if all the input tensors have the type {@link + * TENSOR_FLOAT16}, this scalar must be of type {@link + * FLOAT16}. + * * 51: merge_outputs + * An {@link OperandType::BOOL} scalar specifying if the outputs + * from forward and backward cells should be merged. + * * 52: time_major + * An {@link OperandType::BOOL} scalar specifying the shape format + * of input and output tensors. + * + * Outputs: + * * 0: The forward output. + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * * 1: The backward output. Unused if merge_outputs is true. + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * + * Available since API level 29. + */ BIDIRECTIONAL_SEQUENCE_LSTM = 42, + + /** + * A recurrent neural network layer that applies a basic RNN cell to a + * sequence of inputs in forward and backward directions. + * + * This Op unrolls the input along the sequence dimension, and implements + * the following operation for each element in the sequence s = + * 1...sequence_length: + * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ + + * fw_state * fw_recurrent_weights’ + fw_bias) + * + * And for each element in sequence t = sequence_length : 1 + * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ + + * bw_state * bw_recurrent_weights’ + bw_bias) + * + * Where: + * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs; + * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the + * current “state” which itself is the output from the previous time step + * computation; + * * “{fw,bw}_bias” is a bias vector (added to each output vector in the + * batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * The op also supports an auxiliary input. Regular cell feeds one input + * into the two RNN cells in the following way: + * + * INPUT (INPUT_REVERSED) + * | | + * --------------------- + * | FW_RNN BW_RNN | + * --------------------- + * | | + * FW_OUT BW_OUT + * + * An op with an auxiliary input takes two inputs and feeds them into the + * RNN cells in the following way: + * + * AUX_INPUT (AUX_INPUT_REVERSED) + * | | + * INPUT | (INPUT_R'D.)| + * | | | | + * ----------------------- + * | \ / \ / | + * | FW_RNN BW_RNN | + * ----------------------- + * | | + * FW_OUT BW_OUT + * + * While stacking this op on top of itself, this allows to connect both + * forward and backward outputs from previous cell to the next cell's + * inputs. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to true, then the input has a shape [maxTime, batchSize, + * inputSize], otherwise the input has a shape [batchSize, maxTime, + * inputSize]. + * * 1: fwWeights. + * A 2-D tensor of shape [fwNumUnits, inputSize]. + * * 2: fwRecurrentWeights. + * A 2-D tensor of shape [fwNumUnits, fwNumUnits]. + * * 3: fwBias. + * A 1-D tensor of shape [fwNumUnits]. + * * 4: fwHiddenState. + * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 5: bwWeights. + * A 2-D tensor of shape [bwNumUnits, inputSize]. + * * 6: bwRecurrentWeights. + * A 2-D tensor of shape [bwNumUnits, bwNumUnits]. + * * 7: bwBias. + * A 1-D tensor of shape [bwNumUnits]. + * * 8: bwHiddenState + * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 9: auxInput. + * A 3-D tensor. The shape is the same as of the input 0. + * * 10:fwAuxWeights. + * A 2-D tensor of shape [fwNumUnits, inputSize]. + * * 11:bwAuxWeights. + * A 2-D tensor of shape [bwNumUnits, inputSize]. + * * 12:fusedActivationFunction. + * A {@link FusedActivationFunc} value indicating the activation function. If + * “NONE” is specified then it results in a linear activation. + * * 13:timeMajor + * An {@link OperandType::BOOL} scalar specifying the shape format + * of input and output tensors. + * * 14:mergeOutputs + * An {@link OperandType::BOOL} scalar specifying if the outputs + * from forward and backward cells are separate (if set to false) or + * concatenated (if set to true). + * Outputs: + * * 0: fwOutput. + * A 3-D tensor. The first two dimensions of the shape are defined by + * the input 6 (timeMajor) and the third dimension is defined by the + * input 14 (mergeOutputs). If timeMajor is set to true, then the first + * two dimensions are [maxTime, batchSize], otherwise they are set to + * [batchSize, maxTime]. If mergeOutputs is set to true, then the third + * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set + * to fwNumUnits. + * * 1: bwOutput. + * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then + * this tensor is not produced. The shape is defined by the input 6 + * (timeMajor). If it is set to true, then the shape is set to + * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to + * [batchSize, maxTime, bwNumUnits]. + * + * Available since API level 29. + */ BIDIRECTIONAL_SEQUENCE_RNN = 43, + + /** + * Greedily selects a subset of bounding boxes in descending order of score. + * + * This op applies hard NMS algorithm to each class. In each loop of + * execution, the box with maximum score gets selected, and any boxes with + * the intersection-over-union (IOU) greater than a threshold are removed + * from the pending set. + * + * Axis-aligned bounding boxes are represented by its upper-left corner + * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid + * bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Inputs: + * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score + * of each bounding box proposal. The boxes are grouped by batches in the + * first dimension. + * * 1: A 2-D Tensor specifying the bounding boxes of shape + * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2]. + * The boxes are grouped by batches in the first dimension. The sequential + * order of the boxes corresponds with input0. For input0 of type + * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of + * {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and + * scale of 0.125. + * * 2: A 1-D Tensor of shape [batches], specifying the number of boxes + * for each image in the batch. + * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes + * with scores lower than the threshold are filtered before sending + * to the NMS algorithm. + * * 4: An {@link OperandType::FLOAT32} scalar, specifying the IoU + * threshold. + * * 5: An {@link OperandType::INT32} scalar, specifying the maximum + * number of selected bounding boxes for each image. Set to a negative + * value for unlimited number of output bounding boxes. + * + * Outputs: + * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape + * [num_output_rois], specifying the score of each output box. The boxes + * are grouped by batches, but the sequential order in each batch is not + * guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the scale and zero point must be the same as input0. + * * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape + * [num_output_rois, 4], specifying the coordinates of each + * output bounding box with the same format as input1. The sequential + * order of the boxes corresponds with output0. For type of + * {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be + * 0.125 and the zero point must be 0. + * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [num_output_rois], specifying the class of each output box. The + * sequential order of the boxes corresponds with output0. + * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches], specifying the number of output boxes for each image. + * + * Available since API level 29. + */ BOX_WITH_NMS_LIMIT = 44, + + /** + * Casts a tensor to a new type. + * + * This operation ignores the scale and zeroPoint of quanized tensors, + * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input + * as a tensor of uint8 values. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: A tensor with the same shape as input0. + * + * Available since API level 29. + */ CAST = 45, + + /** + * Shuffle the channels of the input tensor. + * + * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE + * divide the channel dimension into num_groups groups, and reorganize the + * channels by grouping channels with the same index in each group. + * + * Along the channel dimension, the output is calculated using this formula: + * + * output_channel[k * num_groups + g] = input_channel[g * group_size + k] + * + * where group_size = num_channels / num_groups + * + * The number of channels must be divisible by num_groups. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be shuffled. + * * 1: An {@link OperandType::INT32} scalar, specifying the number of + * groups. + * * 2: An {@link OperandType::INT32} scalar, specifying the dimension + * channel shuffle would be performed on. Negative index is used to + * specify axis from the end (e.g. -1 for the last axis). Must be in + * the range [-n, n). + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} and same shape as input0. + * + * Available since API level 29. + */ CHANNEL_SHUFFLE = 46, + + /** + * Apply postprocessing steps to bounding box detections. + * + * Bounding box detections are generated by applying transformation on a set + * of predefined anchors with the bounding box deltas from bounding box + * regression. A final step of hard NMS is applied to limit the number of + * returned boxes. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying + * the score of each anchor with each class. Class 0 for each + * [batches, num_anchors, 0] is background and will be ignored. + * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with + * the first four values in length_box_encoding specifying the bounding + * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw], + * where dy and dx is the linear-scale relative correction factor for the + * center position of the bounding box with respect to the width and height, + * dh and dw is the log-scale relative correction factor for the width and + * height. All the entries in length_box_encoding beyond the first four + * values are ignored in this operation. + * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each + * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and + * ctr_x are the center position of the box, and h and w are the height + * and the width. + * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling + * factor for dy in bounding box deltas. + * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling + * factor for dx in bounding box deltas. + * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling + * factor for dh in bounding box deltas. + * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling + * factor for dw in bounding box deltas. + * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular + * multi-class NMS algorithm that do NMS separately for each class, + * set to false for a faster algorithm that only do one single NMS + * using the highest class score.. + * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying + * the maximum number of boxes for the output. Boxes with the lowest + * scores are discarded to meet the limit. + * * 9: An {@link OperandType::INT32} scalar, only used when input7 is + * set to false, specifying the maximum number of classes per detection. + * * 10: An {@link OperandType::INT32} scalar, only used when input7 is + * set to true, specifying the maximum number of detections when + * applying NMS algorithm for each single class. + * * 11: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes + * with scores lower than the threshold are filtered before sending + * to the NMS algorithm. + * * 12: An {@link OperandType::FLOAT32} scalar, specifying the IoU + * threshold for hard NMS. + * * 13: An {@link OperandType::BOOL} scalar, set to true to include + * background class in the list of label map for the output, set + * to false to not include the background. When the background + * class is included, it has label 0 and the output classes start + * at 1 in the label map, otherwise, the output classes start at 0. + * + * Outputs: + * * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape + * [batches, max_num_detections], specifying the score of each output + * detections. + * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the + * coordinates of each output bounding box, with format + * [y1, x1, y2, x2]. + * * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches, max_num_detections], specifying the class label for each + * output detection. + * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches], + * specifying the number of valid output detections for each batch. + * + * Available since API level 29. + */ DETECTION_POSTPROCESSING = 47, + + /** + * For input tensors x and y, computes x == y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ EQUAL = 48, + + /** + * Computes exponential of x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ EXP = 49, + + /** + * Inserts a dimension of 1 into a tensor's shape. + * + * Given a tensor input, this operation inserts a dimension of 1 at the + * given dimension index of input's shape. The dimension index starts at + * zero; if you specify a negative dimension index, it is counted backward + * from the end. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: An {@link OperandType::INT32} scalar specifying the dimension + * index to expand. Must be in the range [-(n + 1), (n + 1)). + * + * Outputs: + * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as + * input0. + * + * Available since API level 29. + */ EXPAND_DIMS = 50, + + /** + * Gathers values along an axis. + * + * Produces an output tensor with shape + * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] + * where: + * # Vector indices (output is rank(input0)). + * output[a_0, ..., a_n, i, b_0, ..., b_n] = + * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n] + * + * # Higher rank indices (output is rank(input0) + rank(indices) - 1). + * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = + * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor from which to gather values. + * * 1: An {@link OperandType::INT32} scalar specifying the axis. + * Negative index is used to specify axis from the end + * (e.g. -1 for the last axis). Must be in the range [-n, n). + * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices. + * The values must be in the bounds of the corresponding dimensions + * of input0. + * + * Outputs: + * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0. + * + * Available since API level 29. + */ GATHER = 51, + + /** + * Generate aixs-aligned bounding box proposals. + * + * Bounding box proposals are generated by applying transformation on a set + * of predefined anchors with the bounding box deltas from bounding box + * regression. A final step of hard NMS is applied to limit the number of + * returned boxes. + * + * Axis-aligned bounding boxes are represented by its upper-left corner + * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid + * bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Inputs: + * * 0: A 4-D Tensor specifying the score of each anchor at each + * location. With "NHWC" data layout, the tensor shape is + * [batches, height, width, num_anchors]. With "NCHW" data layout, + * the tensor shape is [batches, num_anchors, height, width]. + * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data + * layout, the tensor shape is [batches, height, width, num_anchors * 4]. + * With "NCHW" data layout, the tensor shape is + * [batches, num_anchors * 4, height, width]. The box deltas are encoded + * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale + * relative correction factor for the center position of the bounding box + * with respect to the width and height, dw and dh is the log-scale + * relative correction factor for the width and height. The last + * dimensions is the channel dimension. + * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each + * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type + * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of + * {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125. + * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of + * each image in the batch, with format [image_height, image_width]. + * * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 6: An {@link OperandType::INT32} scalar, specifying the maximum + * number of boxes before going into the hard NMS algorithm. Boxes + * with the lowest scores are discarded to meet the limit. Set to + * a non-positive value for unlimited number. + * * 7: An {@link OperandType::INT32} scalar, specifying the maximum + * number of boxes returning from the hard NMS algorithm. Boxes + * with the lowest scores are discarded to meet the limit. Set to + * a non-positive value for unlimited number. + * * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU + * threshold for hard NMS. + * * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with + * height or width lower than the absolute threshold are filtered out. + * * 10: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and input1. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0, of shape + * [num_output_rois], specifying the score of each output box. + * The boxes are grouped by batches, but the sequential order in + * each batch is not guaranteed. For type of + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scale and zero + * point must be the same as input0. + * * 1: A tensor of the same {@link OperandType} as input1, of shape + * [num_output_rois, 4], specifying the coordinates of each output + * bounding box for each class, with format [x1, y1, x2, y2]. + * The sequential order of the boxes corresponds with output0. + * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the + * scale must be 0.125 and the zero point must be 0. + * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches], specifying the number of output boxes for each image. + * + * Available since API level 29. + */ GENERATE_PROPOSALS = 52, + + /** + * For input tensors x and y, computes x > y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ GREATER = 53, + /** + * For input tensors x and y, computes x >= y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ GREATER_EQUAL = 54, + + /** + * Performs a grouped 2-D convolution operation. + * + * Given an input tensor of shape [batches, height, width, depth_in] and a + * filter tensor of shape [depth_out, filter_height, filter_width, depth_group] + * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV + * applies a group of different filters to each input channel group, then + * concatenates the results together. + * + * Specifically, the input channels are divided into num_groups groups, each with + * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional + * filters are also divided into num_groups groups, i.e. depth_out is divisible + * by num_groups. GROUPED_CONV applies each group of filters to the corresponding + * input channel group, and the result are concatenated together. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * The values in the output tensor are computed as: + * + * output[b, i, j, g * channel_multiplier + q] = + * sum_{di, dj, dk} ( + * input[b, strides[1] * i + di, strides[2] * j + dj, + * g * depth_group + dk] * + * filter[g * channel_multiplier + q, di, dj, dk] + * ) + bias[channel] + * + * where channel_multiplier = depth_out / num_groups + * + * Supported tensor {@link OperandType} configurations: + * * 32 bit Floating point : + * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. + * + * * 16 bit Floating point: + * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. + * + * * Quantized: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. + * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to + * * * input.scale * filter.scale). + * + * * Quantized with symetric per channel quantization for the filter: + * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output. + * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter. + * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0, + * * * each value scaling is separate and equal to input.scale * filter.scales[channel]). + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input, where depth_in = num_groups * depth_group. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_group], specifying + * the filter, where depth_out must be divisible by num_groups. For + * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link OperandType::INT32} scalar, specifying the number of + groups. + * * 10: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 11: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input, where depth_in = num_groups * depth_group. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_group], specifying + * the filter, where depth_out must be divisible by num_groups. For + * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} + * the channel dimension must be set to 0. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same + * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint + * of 0 and bias_scale == input_scale * filter_scale. For filter tensor + * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias + * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of + * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to + * bias_scale[i] = input_scale * filter_scale[i]. + * * 3: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 4: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the number of + * groups. + * * 7: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 8: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale (for + * filter tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} + * this condition must be true for all filter scales). + * + * Available since API level 29. + */ GROUPED_CONV_2D = 55, + + /** + * Localize the maximum keypoints from heatmaps. + * + * This operation approximates the accurate maximum keypoint scores and + * indices after bicubic upscaling by using Taylor expansion up to the + * quadratic term. + * + * The bounding box is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A valid bounding box should satisfy x1 <= x2 and y1 <= y2. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D Tensor of shape + * [num_boxes, heatmap_size, heatmap_size, num_keypoints], + * specifying the heatmaps, the height and width of heatmaps should + * be the same, and must be greater than or equal to 2. + * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes, + * each with format [x1, y1, x2, y2]. For input0 of type + * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should + * be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint + * of 0 and scale of 0.125. + * * 2: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0, with shape + * [num_boxes, num_keypoints], specifying score of the keypoints. + * * 1: A tensor of the same {@link OperandType} as input1, with shape + * [num_boxes, num_keypoints, 2], specifying the location of + * the keypoints, the second dimension is organized as + * [keypoint_x, keypoint_y]. + * + * Available since API level 29. + */ HEATMAP_MAX_KEYPOINT = 56, + + /** + * Applies instance normalization to the input tensor. + * + * The values in the output tensor are computed as: + * + * output[b, h, w, c] = + * (input[b, h, w, c] - mean[b, c]) * gamma / + * sqrt(var[b, c] + epsilon) + beta + * + * Where the mean and variance are computed across the spatial dimensions: + * + * mean[b, c] = + * sum_{h, w}(input[b, h, w, c]) / sum(1) + * + * var[b, c] = + * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: An n-D tensor, specifying the tensor to be normalized. + * * 1: An {@link OperandType::FLOAT32} scalar, specifying gamma, the + * scale applied to the normalized tensor. + * * 2: An {@link OperandType::FLOAT32} scalar, specifying beta, the + * offset applied to the normalized tensor. + * * 3: An {@link OperandType::FLOAT32} scalar, specifying epsilon, the + * small value added to variance to avoid dividing by zero. + * * 4: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} and same shape as input0. + * + * Available since API level 29. + */ INSTANCE_NORMALIZATION = 57, + + /** + * For input tensors x and y, computes x < y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ LESS = 58, + + /** + * For input tensors x and y, computes x <= y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ LESS_EQUAL = 59, + + /** + * Computes natural logarithm of x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ LOG = 60, + + /** + * Returns the truth value of x AND y element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions + * compatible with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ LOGICAL_AND = 61, + + /** + * Computes the truth value of NOT x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ LOGICAL_NOT = 62, + + /** + * Returns the truth value of x OR y element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions + * compatible with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ LOGICAL_OR = 63, + + /** + * Computes the log softmax activations given logits. + * + * The output is calculated using this formula: + * + * output = logits * beta - log(reduce_sum(exp(logits * beta), axis)) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor specifying the input logits. + * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive + * scaling factor for the exponent, beta. + * * 2: An {@link OperandType::INT32} scalar specifying the axis to + * reduce across. Negative index is used to specify axis from the + * end (e.g. -1 for the last axis). Must be in the range [-n, n). + * + * Outputs: + * * 0: The output tensor of the same {@link OperandType} and shape as + * input0. + * + * Available since API level 29. + */ LOG_SOFTMAX = 64, + + /** + * Returns the element-wise maximum of two tensors. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and compatible dimensions + * with input0. + * + * Outputs: + * * 0: The sum, a tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ MAXIMUM = 65, + + /** + * Returns the element-wise minimum of two tensors. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and compatible dimensions + * with input0. + * + * Outputs: + * * 0: The sum, a tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ MINIMUM = 66, + + /** + * Computes numerical negative value element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ NEG = 67, + + /** + * For input tensors x and y, computes x != y elementwise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * This operation supports broadcasting. + * + * Inputs: + * * 0: A tensor. + * * 1: A tensor of the same {@link OperandType} and dimensions compatible + * with input0. + * + * Outputs: + * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. + * + * Available since API level 29. + */ NOT_EQUAL = 68, + + /** + * Pads a tensor with the given constant value according to the specified + * paddings. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::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 {@link OperandType::TENSOR_INT32}, the paddings + * for each spatial dimension of the input tensor. The shape of the + * tensor must be {rank(input0), 2}. + * padding[i, 0] specifies the number of elements to be padded in the + * front of dimension i. + * padding[i, 1] specifies the number of elements to be padded after + * the end of dimension i. + * * 2: An scalar specifying the value to use for padding input0. + * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the + * pad value should be of {@link OperandType::FLOAT32}. + * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, + * the pad value should be of {@link OperandType::INT32}. The + * scale and zeroPoint are assumed to be the same as in input0. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. The + * output tensor has the same rank as input0, and each + * dimension of the output tensor has the same size as the + * corresponding dimension of the input tensor plus the size + * of the padding: + * output0.dimension[i] = + * padding[i, 0] + input0.dimension[i] + padding[i, 1] + * + * Available since API level 29. + */ PAD_V2 = 69, + + /** + * Computes the power of one value to another. + * + * Given a tensor base and a tensor exponent, this operation computes + * base^exponent elementwise. + * + * This operations supports broadcasting. The size of the output is the + * maximum size along each dimension of the input operands. It starts with + * the trailing dimensions, and works its way forward. + * + * For example: + * base.dimension = {4, 1, 2} + * exponent.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor specifying the base. + * * 1: A tensor specifying the exponent. + * + * Outputs: + * * 0: An output tensor. + * + * Available since API level 29. + */ POW = 70, + + /** + * Parametric Rectified Linear Unit. + * + * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha + * is a learned array with the same {@link OperandType} and compatible + * dimensions as input x. + * + * Two dimensions are compatible when: + * 1. they are equal, or + * 2. one of them is 1 + * + * The size of the output is the maximum size along each dimension of the + * input operands. It starts with the trailing dimensions, and works its way + * forward. + * + * Example: + * input.dimension = {4, 1, 2} + * alpha.dimension = {5, 4, 3, 1} + * output.dimension = {5, 4, 3, 2} + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor, specifying the input. + * * 1: A tensor of the same {@link OperandType}, and compatible dimensions + * as input0, specifying the alpha. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ PRELU = 71, + + /** + * Quantizes the input tensor. + * + * The formula is: + * + * output = max(0, min(255, round(input / scale) + zeroPoint) + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0, but with + * {@link OperandType::TENSOR_QUANT8_ASYMM}. + * + * Available since API level 29. + */ QUANTIZE = 72, + + /** + * A version of quantized LSTM, using 16 bit quantization for internal + * state. + * + * There is no projection layer, so cell state size is equal to the output + * size. + * + * Inputs: + * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [numBatches, inputSize] specifying the input to the LSTM + * cell. Tensor is quantized with a fixed quantization range of + * [-1, 127/128] (scale = 1/128, zeroPoint = 128). + * * 1: The input-to-input weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-input part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 2: The input-to-forget weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-forget part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 3: The input-to-cell weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-cell part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 4: The input-to-output weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying input-to-output part of + * weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 5: The recurrent-to-input weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying recurrent-to-input part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 6: The recurrent-to-forget weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying recurrent-to-forget + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 7: The recurrent-to-cell weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying recurrent-to-cell part + * of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 8: The recurrent-to-output weights. + * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [outputSize, inputSize] specifying recurrent-to-output + * part of weights for fully-connected layer inside the LSTM cell. + * Quantization zero point and scale must be the same across all the + * weights. + * * 9: The input gate bias. + * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 10:The forget gate bias. + * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 11:The cell bias. + * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 12:The output gate bias. + * A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape + * [outputSize] specifying the bias for the fully-connected layer + * inside the LSTM cell. Bias is quantized with scale being a product + * of input and weights scales and zeroPoint equal to 0. + * * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] specifying the cell state from the + * previous time step of the LSTM cell. It is quantized using a + * quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / + * 32768, zeroPoint = 0). + * * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] specifying the output of the LSTM + * cell from previous time-step. Tensor is quantized with a fixed + * quantization range of [-1, 127/128] (scale = 1/128, zeroPoint = + * 128). + * + * + * Outputs: + * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM} + * and shape [numBatches, outputSize] which contains a cell state from + * the current time step. Tensor is quantized using a quantization + * range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint = + * 0). + * * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} + * and shape [numBathes, outputSize] which contains the output value. + * Tensor is quantized with a fixed quantization range of [-1, 127/128] + * (scale = 1/128, zeroPoint = 128). + */ QUANTIZED_16BIT_LSTM = 73, + + /** + * Draws samples from a multinomial distribution. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Inputs: + * * 0: A 2-D tensor with shape [batches, classes], specifying the + * unnormalized log-probabilities for all classes. + * * 1: A scalar {@link OperandType::INT32}, specifying the number of + * independent samples to draw for each row slice. + * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2], + * specifying seeds used to initialize the random distribution. + * Outputs: + * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape + * [batches, samples], containing the drawn samples. + * + * Available since API level 29. + */ RANDOM_MULTINOMIAL = 74, + + /** + * Reduces a tensor by computing the "logical and" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_ALL = 75, + + /** + * Reduces a tensor by computing the "logical or" of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_BOOL8} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_ANY = 76, + + /** + * Reduces a tensor by computing the maximum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_MAX = 77, + + /** + * Reduces a tensor by computing the minimum of elements along given + * dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_MIN = 78, + + /** + * Reduces a tensor by multiplying elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_PROD = 79, + + /** + * Reduces a tensor by summing elements along given dimensions. + * + * If keep_dims is true, the reduced dimensions are + * retained with length 1. Otherwise, the rank of the tensor is reduced by + * 1 for each entry in dimensions. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: up to 4 + * + * Inputs: + * * 0: An n-D tensor. + * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions + * to reduce. Dimension values must be in the range [-n, n). + * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, + * retains reduced dimensions with length 1. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. + * + * Available since API level 29. + */ REDUCE_SUM = 80, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by average pooling sampling points from bilinear interpolation. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * No rounding is applied in this operation. The sampling points are unified + * distributed in the pooling bin and their values are calculated by bilinear + * interpolation. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line with format [x1, y1, x2, y2]. + * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, + * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, + * with zeroPoint of 0 and scale of 0.125. + * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches], specifying the number of output boxes for each batch. + * * 3: An {@link OperandType::INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link OperandType::INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: An {@link OperandType::INT32} scalar, specifying the number of + * sampling points in height dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_height/out_height). + * * 8: An {@link OperandType::INT32} scalar, specifying the number of + * sampling points in width dimension used to compute the output. + * Set to 0 for adaptive value of ceil(roi_width/out_width). + * * 9: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * + * Available since API level 29. + */ ROI_ALIGN = 81, + + /** + * Select and scale the feature map of each region of interest to a unified + * output size by max-pooling. + * + * The region of interest is represented by its upper-left corner coordinate + * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. + * A spatial scaling factor is applied to map into feature map coordinate. + * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. + * + * Rounding is applied in this operation to ensure integer boundary for + * regions of interest and pooling bins. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Inputs: + * * 0: A 4-D tensor, specifying the feature map. + * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of + * the regions of interest, each line with format [x1, y1, x2, y2]. + * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, + * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, + * with zeroPoint of 0 and scale of 0.125. + * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape + * [batches], specifying the number of output boxes for each batch. + * * 3: An {@link OperandType::INT32} scalar, specifying the output + * height of the output tensor. + * * 4: An {@link OperandType::INT32} scalar, specifying the output + * width of the output tensor. + * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the height of original image to the height of feature map. + * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio + * from the width of original image to the width of feature map. + * * 7: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: A tensor of the same {@link OperandType} as input0. The output + * shape is [num_rois, out_height, out_width, depth]. + * + * Available since API level 29. + */ ROI_POOLING = 82, + + /** + * Computes reciprocal of square root of x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ RSQRT = 83, + + /** + * Using a tensor of booleans c and input tensors x and y select values + * elementwise from both input tensors: + * + * O[i] = C[i] ? x[i] : y[i]. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a + * mask that chooses, based on the value at each element, whether the + * corresponding element in the output should be taken from input1 (if + * true) or input2 (if false). + * * 1: An input tensor of the same shape as input0. + * * 2: An input tensor of the same shape and type as input1. + * + * Outputs: + * * 0: A tensor of the same type and shape as input1 and input2. + * + */ SELECT = 84, + + /** + * Computes sin of x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ SIN = 85, + + /** + * Extracts a slice of specified size from the input tensor starting at a + * specified location. + * + * The starting location is specified as a 1-D tensor containing offsets + * for each dimension. The size is specified as a 1-D tensor containing + * either size of a slice along corresponding dimension or -1. In the latter + * case, all the remaining elements in dimension are included in the slice. + * Slice size in each dimension cannot be zero. + * + * A sum of begin offset and a size of a slice must not exceed size of a + * corresponding dimension. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to take slice from. + * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying + * the beginning indices of the slice in each dimension. + * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying + * the size of the slice in each dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input containing the slice. + * + * Available since API level 29. + */ SLICE = 86, + + /** + * Splits a tensor along a given axis into num_splits subtensors. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: An n-D tensor to split. + * * 1: An {@link OperandType::INT32} scalar specifying the axis along + * which to split. + * * 2: An {@link OperandType::INT32} scalar indicating the number of + * splits along given axis. Must evenly divide axis size. + * + * Outputs: + * * 0 ~ (num_splits - 1): Resulting subtensors. + * + * Available since API level 29. + */ SPLIT = 87, + + /** + * Computes square root of x element-wise. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: from 1. + * + * Inputs: + * * 0: A tensor. + * + * Outputs: + * * 0: The output tensor of same shape as input0. + * + * Available since API level 29. + */ SQRT = 88, + + /** + * Constructs a tensor by tiling a given tensor. + * + * This operation creates a new tensor by replicating `input` `multiples` + * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]` + * elements, and the values of `input` are replicated `multiples[i]` times + * along the i-th dimension. + * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}. + * The length of multiples must be n. + * + * Outputs: + * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`. + * + * Available since API level 29. + */ TILE = 89, + + /** + * Finds values and indices of the k largest entries for the last dimension. + * + * Resulting values in each dimensions are sorted in descending order. If + * two values are equal, the one with larger index appears first. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_INT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: from 1 + * + * Inputs: + * * 0: input, an n-D tensor specifying the input. + * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of + * top elements to look for along the last dimension. + * + * Outputs: + * * 0: An n-D tensor of the same type as the input, containing the k + * largest elements along each last dimensional slice. + * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32} + * containing the indices of values within the last dimension of input. + * + * Available since API level 29. + */ TOPK_V2 = 90, + + /** + * Performs the tranpose of 2-D convolution operation. + * + * This operation is sometimes called "deconvolution" after Deconvolutional + * Networks, but is actually the transpose (gradient) of + * {@link OperandType::CONV_2D} rather than an actual deconvolution. + * + * The output dimensions are functions of the filter dimensions, stride, and + * padding. + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * * {@link OperandType::TENSOR_QUANT8_ASYMM} + * + * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. + * With the default data layout NHWC, the data is stored in the order of: + * [batch, height, width, channels]. Alternatively, the data layout could + * be NCHW, the data storage order of: [batch, channels, height, width]. + * + * Both explicit padding and implicit padding are supported. + * + * Inputs (explicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_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 type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the + * same type. For input tensor of type + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An {@link OperandType::INT32} scalar, specifying the padding on + * the left, in the ‘width’ dimension. + * * 4: An {@link OperandType::INT32} scalar, specifying the padding on + * the right, in the ‘width’ dimension. + * * 5: An {@link OperandType::INT32} scalar, specifying the padding on + * the top, in the ‘height’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the padding on + * the bottom, in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 8: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 9: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 10: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Inputs (implicit padding): + * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], + * specifying the input. + * * 1: A 4-D tensor, of shape + * [depth_out, filter_height, filter_width, depth_in], specifying the + * filter. + * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input + * tensor of type {@link OperandType::TENSOR_FLOAT32} or + * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the + * same type. For input tensor of type + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be + * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and + * bias_scale == input_scale * filter_scale. + * * 3: An {@link OperandType::TENSOR_INT32} tensor, specifying the output + * tensor shape. + * * 4: An {@link OperandType::INT32} scalar, specifying the implicit + * padding scheme, has to be one of the + * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. + * * 5: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘width’ dimension. + * * 6: An {@link OperandType::INT32} scalar, specifying the stride when + * walking through input in the ‘height’ dimension. + * * 7: An {@link OperandType::INT32} scalar, and has to be one of the + * {@link FusedActivationFunc} values. Specifies the activation to + * invoke on the result. + * * 8: An {@link OperandType::BOOL} scalar, set to true to specify + * NCHW data layout for input0 and output0. Set to false for NHWC. + * + * Outputs: + * * 0: The output 4-D tensor, of shape + * [batches, out_height, out_width, depth_out]. For output tensor of + * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition + * must be satisfied: output_scale > input_scale * filter_scale. + * + * Available since API level 29. + */ TRANSPOSE_CONV_2D = 91, + + /** + * A recurrent neural network specified by an LSTM cell. + * + * Performs (fully) dynamic unrolling of input. + * + * This Op unrolls the input along the time dimension, and implements the + * following operation for each element in the sequence + * s = 1...sequence_length: + * outputs[s] = projection(state = activation(LSTMOp(inputs[s]))) + * + * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM}, + * the "projection" is an optional projection layer from state and output + * and the “activation” is the function passed as the + * “fused_activation_function” argument (if not “NONE”). + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * Supported tensor rank: 3, either time-major or batch-major. + * + * All input and output tensors must be of the same type. + * + * Inputs: + * * 0: The input (\f$x_t\f$). + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * where “max_size” is the number of timesteps (sequence length), + * “batch_size” corresponds to the batching dimension, and + * “input_size” is the size of the input. + * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. + * A 2-D tensor of shape [num_units, input_size], where “num_units” + * corresponds to the number of cell units. + * * 2: The input-to-forget weights (\f$W_{xf}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 3: The input-to-cell weights (\f$W_{xc}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 4: The input-to-output weights (\f$W_{xo}\f$). + * A 2-D tensor of shape [num_units, input_size]. + * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. + * A 2-D tensor of shape [num_units, output_size], where “output_size” + * corresponds to either the number of cell units (i.e., “num_units”), + * or the second dimension of the “projection_weights”, if defined. + * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). + * A 2-D tensor of shape [num_units, output_size]. + * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 12:The input gate bias (\f$b_i\f$). Optional. + * A 1-D tensor of shape [num_units]. + * * 13:The forget gate bias (\f$b_f\f$). + * A 1-D tensor of shape [num_units]. + * * 14:The cell bias (\f$b_c\f$). + * A 1-D tensor of shape [num_units]. + * * 15:The output gate bias (\f$b_o\f$). + * A 1-D tensor of shape [num_units]. + * * 16:The projection weights (\f$W_{proj}\f$). Optional. + * A 2-D tensor of shape [output_size, num_units]. + * * 17:The projection bias (\f$b_{proj}\f$). Optional. + * A 1-D tensor of shape [output_size]. + * * 18:The output state (in) (\f$h_{t-1}\f$). + * A 2-D tensor of shape [batch_size, output_size]. + * * 19:The cell state (in) (\f$C_{t-1}\f$). + * A 2-D tensor 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 then clipping is disabled. + * * 23:Time-major if true, batch-major if false. + * * 24:The input layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at input gate. + * * 25:The forget layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at forget gate. + * * 26:The cell layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at cell gate. + * * 27:The output layer normalization weights. + * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs + * to activation at output gate. + * + * Outputs: + * * 0: The output (\f$o_t\f$). + * A 3-D tensor of shape: + * If time-major: [max_time, batch_size, output_size] + * If batch-major: [batch_size, max_time, output_size] + * + * Available since API level 29. + */ UNIDIRECTIONAL_SEQUENCE_LSTM = 92, + + /** + * A recurrent neural network layer that applies a basic RNN cell to a + * sequence of inputs. + * + * This layer unrolls the input along the sequence dimension, and implements + * the following operation + * for each element in the sequence s = 1...sequence_length: + * outputs[s] = state = activation(inputs[s] * input_weights’ + state * + * recurrent_weights’ + bias) + * + * Where: + * * “input_weights” is a weight matrix that multiplies the inputs; + * * “recurrent_weights” is a weight matrix that multiplies the current + * “state” which itself is the output from the previous time step + * computation; + * * “bias” is a bias vector (added to each output vector in the batch); + * * “activation” is the function passed as the “fused_activation_function” + * argument (if not “NONE”). + * + * Supported tensor {@link OperandType}: + * * {@link OperandType::TENSOR_FLOAT16} + * * {@link OperandType::TENSOR_FLOAT32} + * + * The input tensors must all be the same type. + * + * Inputs: + * * 0: input. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to 1, then the input has a shape [maxTime, batchSize, + * inputSize], otherwise the input has a shape [batchSize, maxTime, + * inputSize]. + * * 1: weights. + * A 2-D tensor of shape [numUnits, inputSize]. + * * 2: recurrent_weights. + * A 2-D tensor of shape [numUnits, numUnits]. + * * 3: bias. + * A 1-D tensor of shape [numUnits]. + * * 4: hidden state + * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden + * state input for the first time step of the computation. + * * 5: fusedActivationFunction. + * A {@link FusedActivationFunc} value indicating the activation function. If + * “NONE” is specified then it results in a linear activation. + * * 6: timeMajor + * An {@link OperandType::INT32} scalar specifying the shape format + * of input and output tensors. Must be set to either 0 or 1. + * Outputs: + * * 0: output. + * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If + * it is set to 1, then the output has a shape [maxTime, batchSize, + * numUnits], otherwise the output has a shape [batchSize, maxTime, + * numUnits]. + * + * Available since API level 29. + */ UNIDIRECTIONAL_SEQUENCE_RNN = 93, + /** + * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to + * OEM operation and data types. + * + * This operation is OEM specific. It should only be used for OEM + * applications. + */ + OEM_OPERATION = @1.1::OperationType:OEM_OPERATION, /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF * OperationTypeRange::FUNDAMENTAL_MAX. */