/* * Copyright (C) 2018 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package android.hardware.neuralnetworks@1.2; import @1.0::DataLocation; import @1.0::ErrorStatus; import @1.0::OperandLifeTime; import @1.0::OperandType; import @1.0::PerformanceInfo; import @1.1::OperationType; import android.hidl.safe_union@1.0::Monostate; enum Constant : uint32_t { /** * The byte size of the cache token. */ BYTE_SIZE_OF_CACHE_TOKEN = 32, /** * The maximum number of files for each type of cache in compilation caching. */ MAX_NUMBER_OF_CACHE_FILES = 32, }; enum OperandType : @1.0::OperandType { /** * An 8 bit boolean scalar value. * * Values of this operand type are either true or false. A zero value * represents false; any other value represents true. */ BOOL = 6, /** * A tensor of 16 bit signed integers that represent real numbers. * * Attached to this tensor is a number representing real value scale that is * used to convert the 16 bit number to a real value in the following way: * realValue = integerValue * scale. * * scale is a 32 bit floating point with value greater than zero. */ TENSOR_QUANT16_SYMM = 7, /** * A tensor of IEEE 754 16 bit floating point values. */ 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. */ TENSOR_BOOL8 = 9, /** * An IEEE 754 16 bit floating point scalar value. */ FLOAT16 = 10, /** * A tensor of 8 bit signed integers that represent real numbers. * * This tensor is associated with additional fields that can * be used to convert the 8 bit signed integer to the real value and vice versa. * These fields are: * - channelDim: a 32 bit unsigned integer indicating channel dimension. * - scales: an array of positive 32 bit floating point values. * The size of the scales array must be equal to dimensions[channelDim]. * * {@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type. * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0). * * The formula is: * realValue[..., C, ...] = * integerValue[..., C, ...] * scales[C] * where C is an index in the Channel dimension. */ TENSOR_QUANT8_SYMM_PER_CHANNEL = 11, /** * A tensor of 16 bit unsigned integers that represent real numbers. * * Attached to this tensor are two numbers that can be used to convert the * 16 bit integer to the real value and vice versa. These two numbers are: * - scale: a 32 bit floating point value greater than zero. * - zeroPoint: a 32 bit integer, in range [0, 65535]. * * The formula is: * real_value = (integer_value - zeroPoint) * scale. */ TENSOR_QUANT16_ASYMM = 12, /** * A tensor of 8 bit signed integers that represent real numbers. * * Attached to this tensor is a number representing real value scale that is * used to convert the 8 bit number to a real value in the following way: * realValue = integerValue * scale. * * scale is a 32 bit floating point with value greater than zero. */ TENSOR_QUANT8_SYMM = 13, /* * DEPRECATED. Since HAL version 1.2, extensions are the preferred * alternative to OEM operation and data types. * * OEM specific scalar value. * OEM = 10000, */ /* * DEPRECATED. Since HAL version 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. */ /* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF * OperandTypeRange::OEM_MAX. */ }; /** * The range of operand values in the OperandType enum. */ enum OperandTypeRange : uint32_t { BASE_MIN = 0, FUNDAMENTAL_MIN = 0, FUNDAMENTAL_MAX = 13, OEM_MIN = 10000, OEM_MAX = 10001, BASE_MAX = 0xFFFF, }; /** * Operation types. * * The type of an operation in a model. */ 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} * * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero * dimension is only compatible with 0 or 1. The size of the output * dimension is zero if either of corresponding input dimension is zero. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scales and zeroPoint can be different from input0 scale and zeroPoint. * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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_FLOAT16} (since HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * (full support since HAL version 1.2, 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 HAL version 1.2, all input tensors of * {@link OperandType::TENSOR_QUANT8_ASYMM} * must have the same scale and zeroPoint as the output tensor. * Since HAL version 1.2, zero-sized tensors are supported. * * 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]. * Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint values can be different from * input tensors. Before HAL version 1.2 they have to be the same as for the * input tensors. */ CONCATENATION = @1.1::OperationType:CONCATENATION, /** * Performs a 2-D convolution operation. * * The CONV_2D op sweeps a 2-D filter that can mix channels together over a * batch of images, applying the filter to each window of each image of the * appropriate size. * * The output dimensions are functions of the filter dimensions, stride, and * padding. * * The values in the output tensor are computed as: * * output[b, i, j, channel] = * sum_{di, dj, k} ( * input[b, strides[1] * i + di, strides[2] * j + dj, k] * * filter[channel, di, dj, k] * ) + bias[channel] * * Supported tensor {@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 HAL version 1.2: * * 16 bit floating point: * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. * * * Quantized with symmetric 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]. * NCHW is supported since HAL version 1.2. * * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], * specifying the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 1: A 4-D tensor, of shape * [depth_out, filter_height, filter_width, depth_in], specifying the * filter. * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} * the channel dimension (SymmPerChannelQuantParams::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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], * specifying the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 1: A 4-D tensor, of shape * [depth_out, filter_height, filter_width, depth_in], specifying the * filter. * For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} * the channel dimension (SymmPerChannelQuantParams::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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth_out]. * Before HAL version 1.2, for output tensor of * {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition must * be satisfied: output_scale > input_scale * filter_scale */ 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 HAL version 1.2: * * 16 bit floating point: * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. * * * Quantized with symmetric 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]. * NCHW is supported since HAL version 1.2. * * 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 (SymmPerChannelQuantParams::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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, * the following condition must be satisfied: * output_scale > input_scale * filter_scale */ 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 HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape [batch, height*block_size, * width*block_size, depth/(block_size*block_size)]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2) * * Supported output tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32}. * * Supported tensor rank: up to 4 * * Inputs: * * 0: A tensor. * Since HAL version 1.2, this tensor may be zero-sized. * * Outputs: * * 0: A tensor with the same shape as input0. */ 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} (since HAL version 1.2) * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2) * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input1. */ EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP, /** * Computes element-wise floor() on the input tensor. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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. */ 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 HAL version 1.2) * * {@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". * Since HAL version 1.2, zero batch_size is supported for this tensor. * * 1: A 2-D tensor, specifying the weights, of shape * [num_units, input_size], where "num_units" corresponds to the number * of output nodes. * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input * tensor of {@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]. Before HAL version 1.2, for * output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following * condition must be satisfied: output_scale > input_scale * filter_scale. */ 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 …]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input2. * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup * hits (True) or not (False). * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 * and scale 1.0f. * A non-zero byte represents True, a hit. A zero indicates otherwise. */ HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP, /** * Applies L2 normalization along the axis dimension. * * The values in the output tensor are computed as: * * output[batch, row, col, channel] = * input[batch, row, col, channel] / * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) * * By default the axis dimension is the last dimension of the input tensor. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2) * * Supported tensor rank: up to 4 * Tensors with rank less than 4 are only supported since HAL version 1.2. * * 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 HAL version 1.2. * * Outputs: * * 0: A tensor of the same {@link OperandType} and same shape as input0. * For {@link OperandType::TENSOR_QUANT8_ASYMM}, * the scale must be 1.f / 128 and the zeroPoint must be 128. */ 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 HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth]. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * Supported tensor rank: up to 4 * Tensors with rank less than 4 are only supported since HAL version 1.2. * * 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: A scalar, specifying the bias, must not be zero. * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the bias * value must be of {@link OperandType::FLOAT16}. * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias * value must be of {@link OperandType::FLOAT32}. * * 3: A scalar, specifying the scale factor, alpha. * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the * alpha value must be of {@link OperandType::FLOAT16}. * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the * alpha value must be of {@link OperandType::FLOAT32}. * * 4: A scalar, specifying the exponent, beta. * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta * value must be of {@link OperandType::FLOAT16}. * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta * value must be of {@link OperandType::FLOAT32}. * * 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 HAL version 1.2. * * Outputs: * * 0: The output tensor of same shape as input0. */ 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_FLOAT16} (since HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the input. * Since HAL version 1.2, this tensor may be zero-sized. * * 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. */ 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 HAL version 1.2) * * {@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 HAL version 1.2). * 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. * The offset value for sparse projections was added in HAL version 1.2. */ 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 HAL version 1.2 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 cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights * (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all * have values or neither of them have values (i.e., all set to null). If * they have values, the peephole optimization is used. * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values, * or none of them have values. If they have no values, coupling of input * and forget gates (CIFG) is used, in which case the input gate * (\f$i_t\f$) is calculated using the following equation instead. * \f{eqnarray*}{ * i_t = 1 - f_t * \f} * In case peephole optimization is used and CIFG is not used * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the * cell-to-input weights must have no value. * * 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. * * (HAL version 1.2 or later) The four layer normalization weights either all have * values or none of them have values. Additionally, if CIFG is used, * input layer normalization weights tensor is omitted and the other layer * normalization weights either all have values or none of them have * values. Layer normalization is used when the values of all the layer * normalization weights are present. * * References: * * The default non-peephole non-CIFG implementation is based on: * http://www.bioinf.jku.at/publications/older/2604.pdf * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural * Computation, 9(8):1735-1780, 1997. * * The peephole implementation and projection layer is based on: * https://research.google.com/pubs/archive/43905.pdf * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory * recurrent neural network architectures for large scale acoustic * modeling." INTERSPEECH, 2014. * (However, the concept of peephole optimization was introduced in work * prior to this paper.) * * The coupling of input and forget gate (CIFG) is based on: * http://arxiv.org/pdf/1503.04069.pdf * Greff et al. "LSTM: A Search Space Odyssey" * * The layer normalization is based on: * https://arxiv.org/pdf/1607.06450.pdf * Jimmy Ba et al. "Layer Normalization" * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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 HAL version 1.2 this scalar must be of type {@link * OperandType::FLOAT32}. Since HAL version 1.2, 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 * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link * OperandType::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 HAL version 1.2 this scalar must be of type {@link * OperandType::FLOAT32}. Since HAL version 1.2, 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 * OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link * OperandType::FLOAT16}. * Since HAL version 1.2 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. */ 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_FLOAT16} (since HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * Both explicit padding and implicit padding are supported. * * Inputs (explicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Inputs (implicit padding): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, out_height, out_width, depth]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. * * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero * dimension is only compatible with 0 or 1. The size of the output * dimension is zero if either of corresponding input dimension is zero. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the input. * Since HAL version 1.2, this tensor may be zero-sized. * * Outputs: * * 0: The output tensor of same shape as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the input. * Since HAL version 1.2, this tensor may be zero-sized. * * Outputs: * * 0: The output tensor of the same shape as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the input. * Since HAL version 1.2, this tensor may be zero-sized. * * Outputs: * * 0: The output tensor of same shape as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@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. * * If one component of shape is the special value -1, the size of that * dimension is computed so that the total size remains constant. In * particular, a shape of [-1] flattens into 1-D. At most one component * of shape can be -1. * * Outputs: * * 0: The output tensor, of shape specified by the input shape. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2) * * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. * With the default data layout NHWC, the data is stored in the order of: * [batch, height, width, channels]. Alternatively, the data layout could * be NCHW, the data storage order of: [batch, channels, height, width]. * NCHW is supported since HAL version 1.2. * * Both resizing by shape and resizing by scale are supported. * * Inputs (resizing by shape): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. * Since HAL version 1.2, zero batches is supported for this tensor. * * 1: An {@link OperandType::INT32} scalar, specifying the output * width of the output tensor. * * 2: An {@link OperandType::INT32} scalar, specifying the output * height 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 HAL version 1.2. * * Inputs (resizing by scale, since HAL version 1.2): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. Zero batches is supported for this tensor. * * 1: A scalar, specifying width_scale, the scaling factor of the width * dimension from the input tensor to the output tensor. The output * width is calculated as new_width = floor(width * width_scale). * The scalar must be of {@link OperandType::FLOAT16} if input0 is * of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} otherwise. * * 2: A scalar, specifying height_scale, the scaling factor of the height * dimension from the input tensor to the output tensor. The output * height is calculated as new_height = floor(height * height_scale). * The scalar must be of {@link OperandType::FLOAT16} if input0 is * of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} otherwise. * * 3: An optional {@link OperandType::BOOL} scalar, default to false. * Set to true to specify NCHW data layout for input0 and output0. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, new_height, new_width, depth]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@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. */ 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_FLOAT16} (since HAL version 1.2) * * {@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 HAL version 1.2. * * Inputs: * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. * Since HAL version 1.2, this tensor may be zero-sized. * * 1: A scalar, specifying the positive scaling factor for the exponent, * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of * {@link OperandType::FLOAT32}. * If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the * scalar must be of {@link OperandType::FLOAT16}. * * 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 HAL version 1.2. * * 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. */ 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 HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * 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 HAL version 1.2. * * Outputs: * * 0: The output 4-D tensor, of shape [batches, height/block_size, * width/block_size, depth_in*block_size*block_size]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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 HAL version 1.2) * * {@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]. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2) * * Supported tensor rank: up to 4. * * Inputs: * * 0: A tensor, specifying the input. * Since HAL version 1.2, this tensor may be zero-sized. * * 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. */ 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 HAL version 1.2) * * {@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]. * NCHW is supported since HAL version 1.2. * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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} * * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero * dimension is only compatible with 0 or 1. The size of the output * dimension is zero if either of corresponding input dimension is zero. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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. */ 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. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@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. Must be in the range * [-rank(input_tensor), rank(input_tensor)). * * NOTE: When the operation was introduced, the documentation * incorrectly stated that if dimensions were empty, the operation * would reduce across all dimensions. This behavior was never * implemented. * * * 2: An {@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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. */ MEAN = @1.1::OperationType:MEAN, /** * Pads a tensor. * * This operation pads a tensor according to the specified paddings. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * (full support since HAL version 1.2, see the output section) * * Supported tensor rank: up to 4 * * Inputs: * * 0: An n-D tensor, specifying the tensor to be padded. * * 1: A 2-D Tensor of {@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] * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * * NOTE: Before HAL version 1.2, the pad value for * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined. * Since HAL version 1.2, the pad value is always the logical zero. */ 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 HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} * (full support since HAL version 1.2, see the output section) * * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. * With the default data layout NHWC, the data is stored in the order of: * [batch, height, width, channels]. Alternatively, the data layout could * be NCHW, the data storage order of: [batch, channels, height, width]. * NCHW is supported since HAL version 1.2. * * 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 {M, 2}, where M is the number * of spatial dimensions. * padding[i, 0] specifies the number of element to be padded in the * front of dimension i. * padding[i, 1] specifies the number of element to be padded after the * end of dimension i. * * 3: An optional {@link OperandType::BOOL} scalar, default to false. * Set to true to specify NCHW data layout for input0 and output0. * Available since HAL version 1.2. * * Outputs: * * 0: A tensor of the same {@link OperandType} as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * * NOTE: Before HAL version 1.2, the pad value for * {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined. * Since HAL version 1.2, the pad value is always the logical zero. */ 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 HAL version 1.2) * * {@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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * If all input dimensions are equal to 1 and are to be squeezed, the * output shape is [1]. */ 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 HAL version 1.2) * * {@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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * If shrink_axis_mask is true for all input dimensions, the output * shape is [1]. */ 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} * * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero * dimension is only compatible with 0 or 1. The size of the output * dimension is zero if either of corresponding input dimension is zero. * * Supported tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2) * * {@link OperandType::TENSOR_FLOAT32} * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2) * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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 HAL version 1.2) * * {@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. * Since HAL version 1.2, this tensor may be zero-sized. * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. */ 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. * If input is 1-dimensional, the output shape is [1]. */ // There is no underscore in ARG_MAX to avoid name conflict with // the macro defined in libc/kernel/uapi/linux/limits.h. ARGMAX = 39, /** * 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. * If input is 1-dimensional, the output shape is [1]. */ 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} * * {@link OperandType::TENSOR_QUANT16_ASYMM} * * Inputs: * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the * bounding box proposals, each line with format [x1, y1, x2, y2]. * For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM}, * the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois * is supported for this tensor. * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the * bounding box delta for each region of interest and each class. The * bounding box deltas are organized in the following order * [dx, dy, dw, dh], where dx and dy is the relative correction factor * for the center position of the bounding box with respect to the width * and height, dw and dh is the log-scale relative correction factor * for the width and height. For input0 of type * {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be * of {@link OperandType::TENSOR_QUANT8_ASYMM}. Zero num_rois is * supported for this tensor. * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape * [num_rois], specifying the batch index of each box. Boxes with * the same batch index are grouped together. Zero num_rois is * supported for this tensor. * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of * each image in the batch, each line with format * [image_height, image_width]. * * Outputs: * * 0: A tensor of the same {@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]. * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the * scale must be 0.125 and the zero point must be 0. */ AXIS_ALIGNED_BBOX_TRANSFORM = 41, /** * A recurrent neural network layer that applies an LSTM cell to a * sequence of inputs in forward and backward directions. * * The op supports cross-linking via an auxiliary input. Regular cell feeds * one input into the two RNN cells in the following way: * * INPUT (INPUT_REVERSED) * | | * --------------------- * | FW_LSTM BW_LSTM | * --------------------- * | | * FW_OUT BW_OUT * * An op with cross-linking takes two inputs and feeds them into the RNN * cells in the following way: * * AUX_INPUT (AUX_INPUT_REVERSED) * | | * INPUT | (INPUT_R'D.)| * | | | | * ----------------------- * | \ / \ / | * | FW_LSTM BW_LSTM | * ----------------------- * | | * FW_OUT BW_OUT * * The cross-linking mode is enabled iff auxiliary input and auxiliary * weights are present. While stacking this op on top of itself, this * allows to connect both forward and backward outputs from previous cell * to the next cell's input. * * 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, input_size] * If batch-major: [batch_size, max_time, input_size] * where "max_time" is the number of timesteps (sequence length), * "batch_size" corresponds to the batching dimension, and * "input_size" is the size of the input. * * 1: The forward input-to-input weights. Optional. * A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units” * corresponds to the number of forward cell units. * * 2: The forward input-to-forget weights. * A 2-D tensor of shape [fw_num_units, input_size]. * * 3: The forward input-to-cell weights. * A 2-D tensor of shape [fw_num_units, input_size]. * * 4: The forward input-to-output weights. * A 2-D tensor of shape [fw_num_units, input_size]. * * 5: The forward recurrent-to-input weights. Optional. * A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size” * corresponds to either the number of cell units (i.e., fw_num_units), * or the second dimension of the “fw_projection_weights”, if defined. * * 6: The forward recurrent-to-forget weights. * A 2-D tensor of shape [fw_num_units, fw_output_size]. * * 7: The forward recurrent-to-cell weights. * A 2-D tensor of shape [fw_num_units, fw_output_size]. * * 8: The forward recurrent-to-output weights. * A 2-D tensor of shape [fw_num_units, fw_output_size]. * * 9: The forward cell-to-input weights. Optional. * A 1-D tensor of shape [fw_num_units]. * * 10: The forward cell-to-forget weights. Optional. * A 1-D tensor of shape [fw_num_units]. * * 11: The forward cell-to-output weights. Optional. * A 1-D tensor of shape [fw_num_units]. * * 12: The forward input gate bias. Optional. * A 1-D tensor of shape [fw_num_units]. * * 13: The forward forget gate bias. * A 1-D tensor of shape [fw_num_units]. * * 14: The forward cell gate bias. * A 1-D tensor of shape [fw_num_units]. * * 15: The forward output gate bias. * A 1-D tensor of shape [fw_num_units]. * * 16: The forward projection weights. Optional. * A 2-D tensor of shape [fw_output_size, fw_num_units]. * * 17: The forward projection bias. Optional. * A 1-D tensor of shape [fw_output_size]. * * 18: The backward input-to-input weights. Optional. * A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units” * corresponds to the number of backward cell units. * * 19: The backward input-to-forget weights. * A 2-D tensor of shape [bw_num_units, input_size]. * * 20: The backward input-to-cell weights. * A 2-D tensor of shape [bw_num_units, input_size]. * * 21: The backward input-to-output weights. * A 2-D tensor of shape [bw_num_units, input_size]. * * 22: The backward recurrent-to-input weights. Optional. * A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size” * corresponds to either the number of cell units (i.e., “bw_num_units”), * or the second dimension of the “bw_projection_weights”, if defined. * * 23: The backward recurrent-to-forget weights. * A 2-D tensor of shape [bw_num_units, bw_output_size]. * * 24: The backward recurrent-to-cell weights. * A 2-D tensor of shape [bw_num_units, bw_output_size]. * * 25: The backward recurrent-to-output weights. * A 2-D tensor of shape [bw_num_units, bw_output_size]. * * 26: The backward cell-to-input weights. Optional. * A 1-D tensor of shape [bw_num_units]. * * 27: The backward cell-to-forget weights. Optional. * A 1-D tensor of shape [bw_num_units]. * * 28: The backward cell-to-output weights. Optional. * A 1-D tensor of shape [bw_num_units]. * * 29: The backward input gate bias. Optional. * A 1-D tensor of shape [bw_num_units]. * * 30: The backward forget gate bias. * A 1-D tensor of shape [bw_num_units]. * * 31: The backward cell gate bias. * A 1-D tensor of shape [bw_num_units]. * * 32: The backward output gate bias. * A 1-D tensor of shape [bw_num_units]. * * 33: The backward projection weights. Optional. * A 2-D tensor of shape [bw_output_size, bw_num_units]. * * 34: The backward projection bias. Optional. * A 1-D tensor of shape [bw_output_size]. * * 35: The forward input activation state. * A 2-D tensor of shape [batch_size, bw_output_size]. * * 36: The forward input cell state. * A 2-D tensor of shape [batch_size, bw_num_units]. * * 37: The backward input activation state. * A 2-D tensor of shape [batch_size, bw_output_size]. * * 38: The backward input cell state. * A 2-D tensor of shape [batch_size, bw_num_units]. * * 39: The auxiliary input. Optional. * A 3-D tensor of shape [max_time, batch_size, 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 [fw_num_units, input_size]. * * 41: The forward auxiliary input-to-forget weights. Optional. * A 2-D tensor of shape [fw_num_units, input_size]. * * 42: The forward auxiliary input-to-cell weights. Optional. * A 2-D tensor of shape [fw_num_units, input_size]. * * 43: The forward auxiliary input-to-output weights. Optional. * A 2-D tensor of shape [fw_num_units, input_size]. * * 44: The backward auxiliary input-to-input weights. Optional. * A 2-D tensor of shape [bw_num_units, input_size]. * * 45: The backward auxiliary input-to-forget weights. Optional. * A 2-D tensor of shape [bw_num_units, input_size]. * * 46: The backward auxiliary input-to-cell weights. Optional. * A 2-D tensor of shape [bw_num_units, input_size]. * * 47: The backward auxiliary input-to-output weights. Optional. * A 2-D tensor of shape [bw_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 OperandType::TENSOR_FLOAT16}, this scalar must be * of type {@link OperandType::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 OperandType::TENSOR_FLOAT16}, this scalar must be * of type {@link OperandType::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. * * 53: The forward input layer normalization weights. Optional. * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs * to activation at input gate. * * 54: The forward forget layer normalization weights. Optional. * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs * to activation at forget gate. * * 55: The forward cell layer normalization weights. Optional. * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs * to activation at cell gate. * * 56: The forward output layer normalization weights. Optional. * A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs * to activation at output gate. * * 57: The backward input layer normalization weights. Optional. * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs * to activation at input gate. * * 58: The backward forget layer normalization weights. Optional. * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs * to activation at forget gate. * * 59: The backward cell layer normalization weights. Optional. * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs * to activation at cell gate. * * 60: The backward output layer normalization weights. Optional. * A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs * to activation at output gate. * * Outputs: * * 0: The forward output. * A 3-D tensor of shape: * If time-major and not merge_outputs: * [max_time, batch_size, fw_output_size] * If time-major and merge_outputs: * [max_time, batch_size, fw_output_size + bw_output_size] * If batch-major and not merge_outputs: * [batch_size, max_time, fw_output_size] * If batch-major and merge_outputs: * [batch_size, max_time, fw_output_size + bw_output_size] * * 1: The backward output. Unused if merge_outputs is true. * A 3-D tensor of shape: * If time-major: [max_time, batch_size, bw_output_size] * If batch-major: [batch_size, max_time, bw_output_size] */ 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 supports cross-linking via an auxiliary input. Regular cell feeds * one input into the two RNN cells in the following way: * * INPUT (INPUT_REVERSED) * | | * --------------------- * | FW_RNN BW_RNN | * --------------------- * | | * FW_OUT BW_OUT * * An op with cross-linking takes two inputs and feeds them into the RNN * cells in the following way: * * AUX_INPUT (AUX_INPUT_REVERSED) * | | * INPUT | (INPUT_R'D.)| * | | | | * ----------------------- * | \ / \ / | * | FW_RNN BW_RNN | * ----------------------- * | | * FW_OUT BW_OUT * * The cross-linking mode is enabled iff auxiliary input and auxiliary * weights are present. While stacking this op on top of itself, this * allows to connect both forward and backward outputs from previous cell * to the next cell's input. * * 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]. */ BIDIRECTIONAL_SEQUENCE_RNN = 43, /** * Greedily selects a subset of bounding boxes in descending order of score. * * This op applies NMS algorithm to each class. In each loop of execution, * the box with maximum score gets selected and removed from the pending set. * The scores of the rest of boxes are lowered according to the * intersection-over-union (IOU) overlapping with the previously selected * boxes and a specified NMS kernel method. Any boxes with score less * than a threshold are removed from the pending set. * * Three NMS kernels are supported: * * Hard: score_new = score_old * (1 if IoU < threshold else 0) * * Linear: score_new = score_old * (1 if IoU < threshold else 1 - IoU) * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma) * * Axis-aligned bounding boxes are represented by its upper-left corner * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid * bounding box should satisfy x1 <= x2 and y1 <= y2. * * Supported tensor {@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. Zero num_rois is supported for this tensor. * * 1: A 2-D Tensor specifying the bounding boxes of shape * [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2]. * The boxes are grouped by batches in the first dimension. The sequential * order of the boxes corresponds with input0. For input0 of type * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of * {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and * scale of 0.125. * Zero num_rois is supported for this tensor. * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape * [num_rois], specifying the batch index of each box. Boxes with * the same batch index are grouped together. * * 3: An {@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::INT32} scalar, specifying the maximum * number of selected bounding boxes for each image. Set to a negative * value for unlimited number of output bounding boxes. * * 5: An {@link OperandType::INT32} scalar, specifying the NMS * kernel method, options are 0:hard, 1:linear, 2:gaussian. * * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU * threshold in hard and linear NMS kernel. This field is ignored if * gaussian kernel is selected. * * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in * gaussian NMS kernel. This field is ignored if gaussian kernel is * not selected. * * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold. * Boxes with scores lower than the threshold are dropped during the * score updating phase in soft NMS. * * Outputs: * * 0: A 1-D Tensor of the same {@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}, * 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 * [num_output_rois], specifying the batch index of each box. Boxes * with the same batch index are grouped together. */ BOX_WITH_NMS_LIMIT = 44, /** * Casts a tensor to a 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. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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: A scalar, score_threshold. Boxes with scores lower than the * threshold are filtered before sending to the NMS algorithm. The * scalar must be of {@link OperandType::FLOAT16} if input0 is of * {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} if input0 is of * {@link OperandType::TENSOR_FLOAT32}. * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar * must be of {@link OperandType::FLOAT16} if input0 is of * {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} if input0 is of * {@link OperandType::TENSOR_FLOAT32}. * * 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. */ 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}. */ 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. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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]. * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, this * tensor should be of {@link OperandType::TENSOR_QUANT16_SYMM}, with * scale of 0.125. * * 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 input3, of shape * [num_output_rois, 4], specifying the coordinates of each output * bounding box for each class, with format [x1, y1, x2, y2]. * The sequential order of the boxes corresponds with output0. * For type of {@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 batch index of each box. Boxes * with the same batch index are grouped together. */ 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}. */ 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}. */ 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: * * 16 bit floating point: * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. * * * 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). * * * Quantized with symmetric 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 (channelDim at * {@link SymmPerChannelQuantParams}) 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 (SymmPerChannelQuantParams::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 * {@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 a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from input0 scale and zeroPoint. * * 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]. * For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the * scale must be 0.125 and the zero point must be 0. */ 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: A scalar, specifying gamma, the scale applied to the normalized * tensor. The scalar must be of {@link OperandType::FLOAT16} if * input0 is of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} if input0 is of * {@link OperandType::TENSOR_FLOAT32}. * * 2: A scalar, specifying beta, the offset applied to the normalized * tensor. The scalar must be of {@link OperandType::FLOAT16} if * input0 is of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} if input0 is of * {@link OperandType::TENSOR_FLOAT32}. * * 3: A scalar, specifying epsilon, the small value added to variance to * avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if * input0 is of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} if input0 is of * {@link OperandType::TENSOR_FLOAT32}. * * 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. */ 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}. */ 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}. */ 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. */ 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}. */ 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. */ 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}. */ 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: A scalar, specifying the positive scaling factor for the exponent, * beta. * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta * value must be of {@link OperandType::FLOAT16}. * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta * value must be of {@link OperandType::FLOAT32}. * * 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. */ 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} * * Supported tensor rank: from 1. * * Inputs: * * 0: A tensor. * * 1: A tensor of the same {@link OperandType} and compatible dimensions * with input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scales and zeroPoint can be different from input0 scale and zeroPoint. * * Outputs: * * 0: A tensor of the same {@link OperandType} as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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} * * Supported tensor rank: from 1. * * Inputs: * * 0: A tensor. * * 1: A tensor of the same {@link OperandType} and compatible dimensions * with input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scales and zeroPoint can be different from input0 scale and zeroPoint. * * Outputs: * * 0: A tensor of the same {@link OperandType} as input0. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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. */ 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}. */ 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: A scalar specifying the value to use for padding input0. * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the * pad value must be of {@link OperandType::FLOAT16}. * For input tensor of {@link OperandType::TENSOR_FLOAT32}, the * pad value must be of {@link OperandType::FLOAT32}. * For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, * the pad value must 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] * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scales and zeroPoint can be different from input0 scale and zeroPoint. */ PRELU = 71, /** * Quantizes the input tensor. * * The formula for {@link OperandType::TENSOR_QUANT8_ASYMM} output tensor is: * * output = max(0, min(255, round(input / scale) + zeroPoint) * * Supported input tensor {@link OperandType}: * * {@link OperandType::TENSOR_FLOAT16} * * {@link OperandType::TENSOR_FLOAT32} * * Supported output tensor {@link OperandType}: * * {@link OperandType::TENSOR_QUANT8_ASYMM} * * Supported tensor rank: from 1 * * Inputs: * * 0: A tensor, may be zero-sized. * * Outputs: * * 0: The output tensor of same shape as input0, but with * {@link OperandType::TENSOR_QUANT8_ASYMM}. */ 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, outputSize] specifying recurrent-to-input part * of weights for fully-connected layer inside the LSTM cell. * Quantization zero point and scale must be the same across all the * weights. * * 6: The recurrent-to-forget weights. * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} * and shape [outputSize, outputSize] specifying recurrent-to-forget * part of weights for fully-connected layer inside the LSTM cell. * Quantization zero point and scale must be the same across all the * weights. * * 7: The recurrent-to-cell weights. * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} * and shape [outputSize, outputSize] specifying recurrent-to-cell part * of weights for fully-connected layer inside the LSTM cell. * Quantization zero point and scale must be the same across all the * weights. * * 8: The recurrent-to-output weights. * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} * and shape [outputSize, outputSize] specifying recurrent-to-output * part of weights for fully-connected layer inside the LSTM cell. * Quantization zero point and scale must be the same across all the * weights. * * 9: The input gate bias. * A 1-D tensor of type {@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. If both * provided seeds are 0, both will be randomly generated. * Outputs: * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape * [batches, samples], containing the drawn samples. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. */ 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. * If all dimensions are reduced and keep_dims is false, the output * shape is [1]. */ 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} * * {@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. Zero num_rois is * supported for this tensor. * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape * [num_rois], specifying the batch index of each box. Boxes with * the same batch index are grouped together. Zero num_rois is * supported for this tensor. * * 3: An {@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]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from the input0 scale and zeroPoint. */ 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 * [num_rois], specifying the batch index of each box. Boxes with * the same batch index are grouped together. * * 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]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scales and zeroPoint can be different from input1 scale and zeroPoint. * * Outputs: * * 0: A tensor of the same type and shape as input1 and input2. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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. */ 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. * * 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, may be zero-sized. * * 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * its scale and zeroPoint has to be same as the input0 scale and zeroPoint. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. */ 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`. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ 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. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32} * containing the indices of values within the last dimension of input. */ TOPK_V2 = 90, /** * Performs the transpose 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} configurations: * * 16 bit floating point: * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias. * * * 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). * * * Quantized with symmetric 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 * [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 (SymmPerChannelQuantParams::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 must 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 {@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. For tensor of type * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel * dimension (SymmPerChannelQuantParams::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 should 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 must 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::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 a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint can be different from inputs' scale and zeroPoint. */ 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, input_size] * If batch-major: [batch_size, max_time, input_size] * where “max_time” is the number of timesteps (sequence length), * “batch_size” corresponds to the batching dimension, and * “input_size” is the size of the input. * * 1: The input-to-input weights (\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. Optional. * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs * to activation at input gate. * * 25:The forget layer normalization weights. Optional. * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs * to activation at forget gate. * * 26:The cell layer normalization weights. Optional. * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs * to activation at cell gate. * * 27:The output layer normalization weights. Optional. * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs * to activation at output gate. * * Outputs: * * 0: The output (\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] */ 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]. */ UNIDIRECTIONAL_SEQUENCE_RNN = 93, /** * Resizes images to given size using the nearest neighbor interpretation. * * Resized images must be distorted if their output aspect ratio is not the * same as input aspect ratio. The corner pixels of output may not be the * same as corner pixels of input. * * Supported tensor {@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 resizing by shape and resizing by scale are supported. * * Inputs (resizing by shape): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. Zero batches is supported for this tensor. * * 1: An {@link OperandType::INT32} scalar, specifying the output * width of the output tensor. * * 2: An {@link OperandType::INT32} scalar, specifying the output * height of the output tensor. * * 3: An {@link OperandType::BOOL} scalar, default to false. * Set to true to specify NCHW data layout for input0 and output0. * * Inputs (resizing by scale): * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying * the input. Zero batches is supported for this tensor. * * 1: A scalar, specifying width_scale, the scaling factor of the width * dimension from the input tensor to the output tensor. The output * width is calculated as new_width = floor(width * width_scale). * The scalar must be of {@link OperandType::FLOAT16} if input0 is * of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} otherwise. * * 2: A scalar, specifying height_scale, the scaling factor of the height * dimension from the input tensor to the output tensor. The output * height is calculated as new_height = floor(height * height_scale). * The scalar must be of {@link OperandType::FLOAT16} if input0 is * of {@link OperandType::TENSOR_FLOAT16} and of * {@link OperandType::FLOAT32} otherwise. * * 3: An {@link OperandType::BOOL} scalar, default to false. * Set to true to specify NCHW data layout for input0 and output0. * * Outputs: * * 0: The output 4-D tensor, of shape * [batches, new_height, new_width, depth]. * For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, * the scale and zeroPoint must be the same as input0. */ RESIZE_NEAREST_NEIGHBOR = 94, /** * 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. */ /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF * OperationTypeRange::OEM_MAX. */ }; /** * The range of values in the OperationType enum. */ enum OperationTypeRange : uint32_t { BASE_MIN = 0, FUNDAMENTAL_MIN = 0, FUNDAMENTAL_MAX = 94, OEM_MIN = 10000, OEM_MAX = 10000, BASE_MAX = 0xFFFF, }; /** * Device types. * * The type of NNAPI device. */ enum DeviceType : int32_t { // Leaving 0 unused as it means unknown type in NDK NNAPI. There is no // HAL equivalent of unknown type and a 1.2 HAL implementation must belong // to one of the categories below. /** The device does not fall into any category below. */ OTHER = 1, /** The device runs NNAPI models on single or multi-core CPU. */ CPU = 2, /** The device can run NNAPI models and also accelerate graphics APIs such * as OpenGL ES and Vulkan. */ GPU = 3, /** Dedicated accelerator for Machine Learning workloads. */ ACCELERATOR = 4, }; /** * The capabilities of a driver. * * Performance of an operation comes from the type of its first operand. * This represents performance for non extension operand types. */ struct Capabilities { /** * Driver performance when operating on float32 data but performing * calculations with range and/or precision as low as that of the IEEE * 754 16-bit floating-point format. */ PerformanceInfo relaxedFloat32toFloat16PerformanceScalar; PerformanceInfo relaxedFloat32toFloat16PerformanceTensor; /** * Driver performance when operating on a particular data type. * In the case of float32 data, this is used when the calculations * are not relaxed. */ struct OperandPerformance { OperandType type; PerformanceInfo info; }; /** * Performance by operand type. Must be sorted by OperandType. * If a particular OperandType is not present in operandPerformance, * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }. */ vec operandPerformance; }; /** * Describes one operation of the model's graph. */ struct Operation { /** * The operation type. * * Besides the values listed in {@link OperationType}, any value above * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted * as an extension type according to {@link Model::extensionNameToPrefix}. */ OperationType type; /** * Describes the table that contains the indexes of the inputs of the * operation. The offset is the index in the operandIndexes table. */ vec inputs; /** * Describes the table that contains the indexes of the outputs of the * operation. The offset is the index in the operandIndexes table. */ vec outputs; }; /** * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand. */ struct SymmPerChannelQuantParams { /** Array of scaling values for each channel. Each value must be greater than zero. */ vec scales; /** Index of the channel dimension */ uint32_t channelDim; }; /** * Describes one operand of the model's graph. */ struct Operand { /** * The data type. * * Besides the values listed in {@link OperandType}, any value above * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted * as an extension type according to {@link Model::extensionNameToPrefix}. */ OperandType type; /** * Dimensions of the operand. * * For a scalar operand, dimensions.size() must be 0. * * A tensor operand with all dimensions specified has "fully * specified" dimensions. Whenever possible (i.e., whenever the * dimensions are known at model construction time), a tensor * operand should have (but is not required to have) fully * specified dimensions, in order to enable the best possible * performance. * * If a tensor operand's dimensions are not fully specified, the * dimensions of the operand are deduced from the operand * dimensions and values of the operation for which that operand * is an output. * * In the following situations, a tensor operand's dimensions must * be fully specified: * * . The operand has lifetime CONSTANT_COPY or * CONSTANT_REFERENCE. * * . The operand has lifetime MODEL_INPUT. Fully * specified dimensions must either be present in the * Operand or they must be provided in the corresponding * RequestArgument. * EXCEPTION: If the input is optional and omitted * (by setting the hasNoValue field of the corresponding * RequestArgument to true) then it need not have fully * specified dimensions. * * A tensor operand with some number of unspecified dimensions is * represented by setting each unspecified dimension to 0. * * A tensor operand with unspecified rank is represented by providing * an empty dimensions vector. */ vec dimensions; /** * The number of times this operand appears as an operation input. * * (For example, if this operand appears once in one operation's * input list, and three times in another operation's input list, * then numberOfConsumers = 4.) */ uint32_t numberOfConsumers; /** * Quantized scale of the operand. * * Must be 0 when not applicable to an operand type. * * See {@link OperandType}. */ float scale; /** * Quantized zero-point offset of the operand. * * Must be 0 when not applicable to an operand type. * * See {@link OperandType}. */ int32_t zeroPoint; /** * How the operand is used. */ OperandLifeTime lifetime; /** * Where to find the data for this operand. * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or * NO_VALUE: * - All the fields must be 0. * If the lifetime is CONSTANT_COPY: * - location.poolIndex is 0. * - location.offset is the offset in bytes into Model.operandValues. * - location.length is set. * If the lifetime is CONSTANT_REFERENCE: * - location.poolIndex is set. * - location.offset is the offset in bytes into the specified pool. * - location.length is set. */ DataLocation location; /** * Additional parameters specific to a particular operand type. */ safe_union ExtraParams { /** * No additional parameters. */ Monostate none; /** * Symmetric per-channel quantization parameters. * * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL. */ SymmPerChannelQuantParams channelQuant; /** * Extension operand parameters. * * The framework treats this as an opaque data blob. * The format is up to individual extensions. */ vec extension; } extraParams; }; /** * A Neural Network Model. * * This includes not only the execution graph, but also constant data such as * weights or scalars added at construction time. The only information that * may not be known is the shape of the input tensors. */ struct Model { /** * All operands included in the model. */ vec operands; /** * All operations included in the model. * * The operations are sorted into execution order. Every operand * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be * written before it is read. */ vec operations; /** * Input indexes of the model. There must be at least one. * * Each value corresponds to the index of the operand in "operands". */ vec inputIndexes; /** * Output indexes of the model. There must be at least one. * * Each value corresponds to the index of the operand in "operands". */ vec outputIndexes; /** * A byte buffer containing operand data that were copied into the model. * * An operand's value must be located here if and only if Operand::lifetime * equals OperandLifeTime::CONSTANT_COPY. */ vec operandValues; /** * A collection of shared memory pools containing operand values. * * An operand's value must be located here if and only if Operand::lifetime * equals OperandLifeTime::CONSTANT_REFERENCE. */ vec pools; /** * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or * precision as low as that of the IEEE 754 16-bit floating-point format. * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the * range and precision of the IEEE 754 32-bit floating-point format. */ bool relaxComputationFloat32toFloat16; /** * The mapping between extension names and prefixes of operand and * operation type values. * * An operand or operation whose numeric type value is above * {@link OperandTypeRange::BASE_MAX} or * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted * as an extension operand. The low * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value * correspond to the type ID within the extension and the high * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode * the "prefix", which maps uniquely to the extension name. * * For example, if a model contains an operation whose value is * 0xAAAABBBB and extensionNameToPrefix contains an entry with * prefix=0xAAAA and name="vendor.test.test_extension", then * the operation should be interpreted as the operation 0xBBBB * of the extension named vendor.test.test_extension. * * This is a one-to-one correspondence. That is, there must be at most one * prefix corresponding to each extension name and at most one extension * name corresponding to each prefix. */ vec extensionNameToPrefix; /** * A correspondence between an extension name and a prefix of operand and * operation type values. */ struct ExtensionNameAndPrefix { /** * The extension name. * * See {@link Extension::name} for the format specification. */ string name; /** * The unique extension identifier within the model. * * See {@link Model::extensionNameToPrefix}. */ uint16_t prefix; }; /** * Numeric values of extension operand and operation types have the * following structure: * - 16 high bits represent the "prefix", which corresponds uniquely to the * extension name. * - 16 low bits represent the type ID within the extension. */ enum ExtensionTypeEncoding : uint8_t { HIGH_BITS_PREFIX = 16, LOW_BITS_TYPE = 16, }; }; /** * Describes the shape information of an output operand after execution. */ struct OutputShape { /** * Dimensions of the operand. */ vec dimensions; /** * Whether the provided buffer size is sufficient for the output. */ bool isSufficient; }; /** * Specifies whether or not to measure timing information during execution. */ enum MeasureTiming : int32_t { NO = 0, YES = 1, }; /** * Timing information measured during execution. Each time is a duration from * the beginning of some task to the end of that task, including time when that * task is not active (for example, preempted by some other task, or * waiting for some resource to become available). * * Times are measured in microseconds. * When a time is not available, it must be reported as UINT64_MAX. */ struct Timing { /** Execution time on device (not driver, which runs on host processor). */ uint64_t timeOnDevice; /** Execution time in driver (including time on device). */ uint64_t timeInDriver; }; /** * FmqRequestDatum is a single element of a serialized representation of an * execution request (a {@link @1.0::Request} object and a {@link MeasureTiming} * value) which is sent across FastMessageQueue. * * The serialized representation for a particular execution is referred to later * in these descriptions as a 'packet'. * * FastMessageQueue can only pass HIDL-defined types that do not involve nested * buffers, handles, or interfaces. * * The request is serialized as follows: * 1) 'packetInformation' * 2) For each input operand: * 2.1) 'inputOperandInformation' * 2.2) For each dimension element of the operand: * 2.2.1) 'inputOperandDimensionValue' * 3) For each output operand: * 3.1) 'outputOperandInformation' * 3.2) For each dimension element of the operand: * 3.2.1) 'outputOperandDimensionValue' * 4) For each pool: * 4.1) 'poolIdentifier' * 5) 'measureTiming' */ safe_union FmqRequestDatum { /** * Type to describe the high-level layout of the packet. */ struct PacketInformation { /** * How many elements the packet contains, including the * "packetInformation" datum. */ uint32_t packetSize; /** * Number of input operands. */ uint32_t numberOfInputOperands; /** * Number of output operands. */ uint32_t numberOfOutputOperands; /** * Number of pool identifiers. */ uint32_t numberOfPools; }; /** * Type representing the information for each operand. */ struct OperandInformation { /** * If true, the argument does not have a value. This can be used for * operations that take optional arguments. If true, the fields of * 'location' are set to 0, 'numberOfDimensions' is set to 0, and the * dimensions information is omitted from the serialization. */ bool hasNoValue; /** * The location within one of the memory pools passed in the Request. */ DataLocation location; /** * Number of subsequent elements that belong to the dimensions vector. */ uint32_t numberOfDimensions; }; /** * packetInformation is the first element of the packet and describes the * remainder of the packet. */ PacketInformation packetInformation; /** * Information for each input operand. */ OperandInformation inputOperandInformation; /** * Element of the dimensions vector. */ uint32_t inputOperandDimensionValue; /** * Information for each output operand. */ OperandInformation outputOperandInformation; /** * Element of the dimensions vector. */ uint32_t outputOperandDimensionValue; /** * Unique identifier for a pool. * * A {@link @1.0::Request} passes across one or more pools of shared memory * for the inputs and outputs of an execution. However, these memory pools * are not able to be sent across FastMessageQueue directly. Instead, the * producing side of the FMQ represents each different pool with a unique * identifier, and sends this identifier across the FMQ. Whenever the * consuming side of the FMQ needs the memory corresponding to this unique * identifier, it can pass the identifier to * {@link IBurstCallback::getMemories} to retreive the memory. Although this * HIDL Binder call is expensive compared to communication across FMQ, it is * only needed in the cases when the consumer does not recognize the unique * identifier. */ int32_t poolIdentifier; /** * Specifies whether or not to measure duration of the execution. The * duration runs from the time the driver dequeues the request from a * FastMessageQueue to the time the driver enqueues results to a * FastMessageQueue. */ MeasureTiming measureTiming; }; /** * FmqResultDatum is a single element of a serialized representation of the * values returned from an execution ({@link @1.0::ErrorStatus}, * vec<{@link OutputShape}>, and {@link Timing}) which is returned via * FastMessageQueue. * * The serialized representation for a particular execution is referred to later * in these descriptions as a 'packet'. * * FastMessageQueue can only pass HIDL-defined types that do not involve nested * buffers, handles, or interfaces. * * The execution return values ({@link @1.0::ErrorStatus} and * vec<{@link OutputShape}>) are serialized as follows: * 1) 'packetInformation' * 2) For each returned operand: * 2.1) 'operandInformation' * 2.2) For each dimension element of the operand: * 2.2.1) 'operandDimensionValue' * 3) 'executionTiming' */ safe_union FmqResultDatum { /** * Type to describe the high-level layout of the packet. */ struct PacketInformation { /** * How many elements the packet contains, including the * "packetInformation" datum. */ uint32_t packetSize; /** * Status of the execution. */ ErrorStatus errorStatus; /** * Number of returned operands. */ uint32_t numberOfOperands; }; /** * Type representing the information for each operand. */ struct OperandInformation { /** * Indicates whether the operand's output buffer is large enough to * store the operand's result data. */ bool isSufficient; /** * Number of subsequent elements that belong to the dimensions vector. */ uint32_t numberOfDimensions; }; /** * packetInformation is the first element of the packet and describes the * remainder of the packet. It additionally includes the status of the * execution. */ PacketInformation packetInformation; /** * Information for each returned operand. */ OperandInformation operandInformation; /** * Element of the dimensions vector. */ uint32_t operandDimensionValue; /** * Duration of execution. Unless measurement was requested and execution * succeeds, all times must be reported as UINT64_MAX. A driver may choose * to report any time as UINT64_MAX, indicating that measurement is not * available. */ Timing executionTiming; }; /** * Information about an extension. */ struct Extension { /** * The extension name. * * The name must consist of lowercase latin letters, numbers, periods, and * underscore signs. The name must contain at least one period. * * The name must start with the reverse domain name of the vendor. * * Example: com.google.test_extension */ string name; /** * Information about an extension operand type. */ struct OperandTypeInformation { /** * The extension operand type. */ uint16_t type; /** * Indicates whether the extension operand type represents a tensor or * a scalar. */ bool isTensor; /** * The byte size of the operand (if scalar) or of a single element (if * tensor). */ uint32_t byteSize; }; /** * Information about operand types defined by the extension. */ vec operandTypes; };