| /* |
| * 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. |
| * |
| * Available since API level 29. |
| */ |
| 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. |
| * |
| * Available since API level 29. |
| */ |
| TENSOR_QUANT16_SYMM = 7, |
| /** |
| * A tensor of IEEE 754 16 bit floating point values. |
| * |
| * Available since API level 29. |
| */ |
| TENSOR_FLOAT16 = 8, |
| /** |
| * A tensor of 8 bit boolean values. |
| * |
| * Values of this operand type are either true or false. A zero value |
| * represents false; any other value represents true. |
| * |
| * Available since API level 29. |
| */ |
| TENSOR_BOOL8 = 9, |
| /** |
| * An IEEE 754 16 bit floating point scalar value. |
| * |
| * Available since API level 29. |
| */ |
| FLOAT16 = 10, |
| /** |
| * A tensor of 8 bit signed integers that represent real numbers. |
| * |
| * This tensor is associated with additional fields that 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]. |
| * |
| * 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. |
| * |
| * Available since API level 29. |
| */ |
| 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. |
| * |
| * Available since API level 29. |
| */ |
| 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. |
| * |
| * Available since API level 29. |
| */ |
| TENSOR_QUANT8_SYMM = 13, |
| /* |
| * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to |
| * OEM operation and data types. |
| * |
| * OEM specific scalar value. |
| * OEM = 10000, |
| */ |
| /* |
| * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to |
| * OEM operation and data types. |
| * |
| * A tensor of OEM specific values. |
| * TENSOR_OEM_BYTE = 10001, |
| */ |
| /* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF |
| * OperandTypeRange::FUNDAMENTAL_MAX. |
| */ |
| /* 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 API level 29, 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 API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The sum, a tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 27. |
| */ |
| ADD = @1.1::OperationType:ADD, |
| |
| /** |
| * Performs a 2-D average pooling operation. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, channel] = |
| * sum_{di, dj}( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, channel] |
| * ) / sum(1) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * 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 API level 29, 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 API level 29. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. Since API level 29, 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 API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| * |
| * Available since API level 27. |
| */ |
| AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D, |
| |
| /** |
| * Concatenates the input tensors along the given dimension. |
| * |
| * The input tensors must have identical {@link OperandType} and the same |
| * dimensions except the dimension along the concatenation axis. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API |
| * level 29, see the input section) |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0 ~ n-1: The list of n input tensors, of shape |
| * [D0, D1, ..., Daxis(i), ..., Dm]. |
| * Before API level 29, all input tensors of |
| * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * must have the same scale and zeroPoint as the output tensor. |
| * Since API level 29, 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]. |
| * |
| * Available since API level 27. |
| */ |
| CONCATENATION = @1.1::OperationType:CONCATENATION, |
| |
| /** |
| * Performs an 2-D convolution operation. |
| * |
| * The CONV_2D op sweeps a 2-D filter that can mix channels together over a |
| * batch of images, applying the filter to each window of each image of the |
| * appropriate size. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, channel] = |
| * sum_{di, dj, k} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, k] * |
| * filter[channel, di, dj, k] |
| * ) + bias[channel] |
| * |
| * Supported tensor {@link OperandType} configurations: |
| * * 32 bit floating point: |
| * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. |
| * |
| * * Quantized: |
| * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. |
| * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to |
| * * * input.scale * filter.scale). |
| * |
| * Available since API level 29: |
| * * 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]. |
| * |
| * 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 API level 29, 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 (extraParams.channelQuant.channelDim) must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 9: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 10: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on width dimension. If this input is set, |
| * input 12 (dilation factor for height) must be specified as well. |
| * Available since API level 29. |
| * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on height dimension. If this input is set, |
| * input 11 (dilation factor for width) must be specified as well. |
| * Available since API level 29. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. Since API level 29, 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 (extraParams.channelQuant.channelDim) must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 7: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on width dimension. If this input is set, |
| * input 9 (dilation factor for height) must be specified as well. |
| * Available since API level 29. |
| * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on height dimension. If this input is set, |
| * input 8 (dilation factor for width) must be specified as well. |
| * Available since API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth_out]. Before API level 29, |
| * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the |
| * following condition must be satisfied: |
| * output_scale > input_scale * filter_scale |
| * |
| * Available since API level 27. |
| */ |
| CONV_2D = @1.1::OperationType:CONV_2D, |
| |
| /** |
| * Performs a depthwise 2-D convolution operation. |
| * |
| * Given an input tensor of shape [batches, height, width, depth_in] and a |
| * filter tensor of shape [1, filter_height, filter_width, depth_out] |
| * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV |
| * applies a different filter to each input channel (expanding from 1 |
| * channel to channel_multiplier channels for each), then concatenates the |
| * results together. |
| * |
| * The output has depth_out = depth_in * depth_multiplier channels. |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, k * channel_multiplier + q] = |
| * sum_{di, dj} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, k] * |
| * filter[1, di, dj, k * channel_multiplier + q] |
| * ) + bias[k * channel_multiplier + q] |
| * |
| * Supported tensor {@link OperandType} configurations: |
| * * 32 bit floating point: |
| * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias. |
| * |
| * * Quantized: |
| * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output. |
| * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to |
| * * * input.scale * filter.scale). |
| * |
| * Available since API level 29: |
| * * 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]. |
| * |
| * Both explicit padding and implicit padding are supported. |
| * |
| * Inputs (explicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], |
| * specifying the filter. For tensor of type |
| * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel |
| * dimension (extraParams.channelQuant.channelDim) must be set to 3. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise |
| * multiplier. |
| * * 10: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 11: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on width dimension. If this input is set, |
| * input 13 (dilation factor for height) must be specified as well. |
| * Available since API level 29. |
| * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on height dimension. If this input is set, |
| * input 12 (dilation factor for width) must be specified as well. |
| * Available since API level 29. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out], |
| * specifying the filter. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise |
| * multiplier. |
| * * 7: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 8: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on width dimension. If this input is set, |
| * input 10 (dilation factor for height) must be specified as well. |
| * Available since API level 29. |
| * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation |
| * factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped |
| * cells between each filter element on height dimension. If this input is set, |
| * input 9 (dilation factor for width) must be specified as well. |
| * Available since API level 29. |
| |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth_out]. Before API level 29, |
| * for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the |
| * following condition must be satisfied: |
| * output_scale > input_scale * filter_scale |
| * |
| * Available since API level 27. |
| */ |
| DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D, |
| |
| /** |
| * Rearranges data from depth into blocks of spatial data. |
| * |
| * More specifically, this op outputs a copy of the input tensor where |
| * values from the depth dimension are moved in spatial blocks to the height |
| * and width dimensions. The value block_size indicates the input block size |
| * and how the data is moved. |
| * |
| * Chunks of data of size block_size * block_size from depth are rearranged |
| * into non-overlapping blocks of size block_size x block_size. |
| * |
| * The width of the output tensor is input_depth * block_size, whereas the |
| * height is input_height * block_size. The depth of the input tensor must |
| * be divisible by block_size * block_size |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: An {@link OperandType::INT32} scalar, specifying the block_size. |
| * block_size must be >=1 and block_size * block_size must be a divisor |
| * of the input depth. |
| * * 2: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape [batch, height*block_size, |
| * width*block_size, depth/(block_size*block_size)]. |
| * |
| * Available since API level 27. |
| */ |
| DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE, |
| |
| /** |
| * Dequantizes the input tensor. |
| * |
| * The formula is: |
| * |
| * output = (input - zeroPoint) * scale. |
| * |
| * Supported input tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29) |
| * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29) |
| * |
| * Supported output tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32}. |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. Since API level 29, this tensor may be zero-sized. |
| * |
| * Outputs: |
| * * 0: A tensor with the same shape as input0. |
| * |
| * Available since API level 27. |
| */ |
| DEQUANTIZE = @1.1::OperationType:DEQUANTIZE, |
| |
| /** |
| * Looks up sub-tensors in the input tensor. |
| * |
| * This operator takes for input a tensor of values (Values) and |
| * a one-dimensional tensor of selection indices (Lookups). |
| * The output tensor is the concatenation of sub-tensors of Values as |
| * selected by Lookups. |
| * |
| * Think of Values as being sliced along its first dimension: |
| * The entries in Lookups select which slices are concatenated together |
| * to create the output tensor. |
| * |
| * For example, if Values has shape of [40, 200, 300] and |
| * Lookups has shape of [3], all three values found in Lookups are |
| * expected to be between 0 and 39. The resulting tensor must |
| * have shape of [3, 200, 300]. |
| * |
| * If a value in Lookups is out of bounds, the operation must fail |
| * and an error must be reported. |
| * |
| * Supported value tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported value tensor rank: from 2 |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}. |
| * The values are indices into the first dimension of Values. |
| * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are |
| * extracted. |
| * |
| * Output: |
| * * 0: A n-D tensor with the same rank and shape as the Values |
| * tensor, except for the first dimension which has the same size |
| * as Lookups' only dimension. |
| * |
| * Available since API level 27. |
| */ |
| EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP, |
| |
| /** |
| * Computes element-wise floor() on the input tensor. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor, of the same {@link OperandType} and dimensions as |
| * the input tensor. |
| * |
| * Available since API level 27. |
| */ |
| FLOOR = @1.1::OperationType:FLOOR, |
| |
| /** |
| * Denotes a fully (densely) connected layer, which connects all elements |
| * in the input tensor with each element in the output tensor. |
| * |
| * This layer implements the operation: |
| * |
| * outputs = activation(inputs * weights’ + bias) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor of at least rank 2, specifying the input. If rank is |
| * greater than 2, then it gets flattened to a 2-D Tensor. The |
| * (flattened) 2-D Tensor is reshaped (if necessary) to |
| * [batch_size, input_size], where "input_size" corresponds to the |
| * number of inputs to the layer, matching the second dimension of |
| * weights, and "batch_size" is calculated by dividing the number of |
| * elements by "input_size". Since API level 29, 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 API |
| * level 29, For output tensor of {@link |
| * OperandType::TENSOR_QUANT8_ASYMM}, the following condition must be |
| * satisfied: output_scale > input_scale * filter_scale. |
| * |
| * Available since API level 27. |
| */ |
| FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED, |
| |
| /** |
| * Looks up sub-tensors in the input tensor using a key-value map. |
| * |
| * This operator takes for input a tensor of values (Values), |
| * a one-dimensional tensor of selection values (Lookups) and |
| * a one-dimensional tensor that maps these values to Values |
| * indexes. The output tensor is the concatenation of sub-tensors of |
| * Values as selected by Lookups via Keys. |
| * |
| * Think of Values as being sliced along its outer-most dimension. |
| * The output is a concatenation of selected slices, with one slice |
| * for each entry of Lookups. The slice selected is the one at the |
| * same index as the Maps entry that matches the value in Lookups. |
| * |
| * For a hit, the corresponding sub-tensor of Values is included |
| * in the Output tensor. For a miss, the corresponding sub-tensor in |
| * Output must have zero values. |
| * |
| * For example, if Values has shape of [40, 200, 300], |
| * Keys should have a shape of [40]. If Lookups tensor has shape |
| * of [3], three slices are being concatenated, so the resulting tensor |
| * must have the shape of [3, 200, 300]. If the first entry in Lookups |
| * has the value 123456, that value must be located in Keys tensor. |
| * If the sixth entry of Keys contains 123456, the sixth slice of Values |
| * must be selected. If no entry in Keys has 123456, a slice of zeroes |
| * must be concatenated. |
| * |
| * Supported value tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported value tensor rank: from 2 |
| * |
| * Inputs: |
| * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with |
| * shape [ k ]. |
| * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape |
| * [ n ]; Keys and Values pair represent a map, i.e., the ith element |
| * in Keys (Keys[i]) is the key to select the ith sub-tensor in Values |
| * (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in |
| * ascending order. |
| * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension |
| * must be n. |
| * |
| * Outputs: |
| * * 0: Output. A tensor with shape [ k …]. |
| * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup |
| * hits (True) or not (False). |
| * Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 |
| * and scale 1.0f. |
| * A non-zero byte represents True, a hit. A zero indicates otherwise. |
| * |
| * Available since API level 27. |
| */ |
| HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP, |
| |
| /** |
| * Applies L2 normalization along the depth dimension. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[batch, row, col, channel] = |
| * input[batch, row, col, channel] / |
| * sqrt(sum_{c} pow(input[batch, row, col, c], 2)) |
| * |
| * For input tensor with rank less than 4, independently normalizes each |
| * 1-D slice along dimension dim. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) |
| * |
| * Supported tensor rank: up to 4 |
| * Tensors with rank less than 4 are only supported since API level 29. |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be normalized. |
| * * 1: An optional {@link OperandType::INT32} scalar, default to -1, |
| * specifying the dimension normalization would be performed on. |
| * Negative index is used to specify axis from the end (e.g. -1 for |
| * the last axis). Must be in the range [-n, n). |
| * Available since API level 29. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} and same shape as input0. |
| * For {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the scale must be 1.f / 128 and the zeroPoint must be 128. |
| * |
| * Available since API level 27. |
| */ |
| L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION, |
| |
| /** |
| * Performs an 2-D L2 pooling operation. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, c] = |
| * sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) / |
| * sum(1)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Both explicit padding and implicit padding are supported. |
| * |
| * Inputs (explicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. Since API level 29, 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 API level 29. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. Since API level 29, 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 API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| * |
| * Available since API level 27. |
| */ |
| L2_POOL_2D = @1.1::OperationType:L2_POOL_2D, |
| |
| /** |
| * Applies Local Response Normalization along the depth dimension. |
| * |
| * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the |
| * last dimension), and each vector is normalized independently. Within a |
| * given vector, each component is divided by the weighted, squared sum of |
| * inputs within depth_radius. |
| * |
| * The output is calculated using this formula: |
| * |
| * sqr_sum[a, b, c, d] = sum( |
| * pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)) |
| * output = input / pow((bias + alpha * sqr_sum), beta) |
| * |
| * For input tensor with rank less than 4, independently normalizes each |
| * 1-D slice along specified dimension. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * Tensors with rank less than 4 are only supported since API level 29. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. |
| * * 1: An {@link OperandType::INT32} scalar, specifying the radius of |
| * the normalization window. |
| * * 2: 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 API level 29. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 27. |
| */ |
| LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION, |
| |
| /** |
| * Computes sigmoid activation on the input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = 1 / (1 + exp(-input)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. Since API level 29, 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. |
| * |
| * Available since API level 27. |
| */ |
| LOGISTIC = @1.1::OperationType:LOGISTIC, |
| |
| /** |
| * Projects an input to a bit vector via locality senstive hashing. |
| * |
| * Supported input tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported input tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: Hash functions. Dim.size == 2, DataType: Float. |
| * Tensor[0].Dim[0]: Number of hash functions. |
| * Tensor[0].Dim[1]: Number of projected output bits generated by each |
| * hash function. |
| * If the projection type is Sparse: |
| * Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32 |
| * |
| * * 1: Input. Dim.size >= 1, no restriction on DataType. |
| * * 2: Weight. Optional. Dim.size == 1, DataType: Float. |
| * If not set, each input element is considered to have the same weight |
| * of 1.0. |
| * Tensor[1].Dim[0] == Tensor[2].Dim[0] |
| * * 3: Type: |
| * Sparse: |
| * Value LSHProjectionType_SPARSE(=3) (since API level 29). |
| * Computed bit vector is considered to be sparse. |
| * Each output element is an int32 made up of multiple bits |
| * computed from hash functions. |
| * |
| * NOTE: To avoid collisions across hash functions, an offset value |
| * of k * (1 << Tensor[0].Dim[1]) will be added to each signature, |
| * where k is the index of the hash function. |
| * |
| * Value LSHProjectionType_SPARSE_DEPRECATED(=1). |
| * Legacy behavior that does not include the offset value. |
| * |
| * Dense: |
| * Value LSHProjectionType_DENSE(=2). |
| * Computed bit vector is considered to be dense. Each output |
| * element represents a bit and can take the value of either |
| * 0 or 1. |
| * |
| * Outputs: |
| * * 0: If the projection type is Sparse: |
| * Output.Dim == { Tensor[0].Dim[0] } |
| * A tensor of int32 that represents hash signatures, |
| * |
| * If the projection type is Dense: |
| * Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } |
| * A flattened tensor that represents projected bit vectors. |
| * |
| * Available since API level 27. |
| * The offset value for sparse projections was added in API level 29. |
| */ |
| LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION, |
| |
| /** |
| * Performs a single time step in a Long Short-Term Memory (LSTM) layer |
| * |
| * The LSTM operation is described by the following equations. |
| * |
| * \f{eqnarray*}{ |
| * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\ |
| * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\ |
| * C_t =& clip(f_t \odot C_{t-1} + i_t \odot |
| * g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\ |
| * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\ |
| * & & \\ |
| * & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) |
| * & if\ there\ is\ a\ projection; \\ |
| * h_t =& & \\ |
| * & o_t \odot g(C_t) & otherwise. \\ |
| * \f} |
| * Where: |
| * * \f$x_t\f$ is the input, |
| * * \f$i_t\f$ is the input gate, |
| * * \f$f_t\f$ is the forget gate, |
| * * \f$C_t\f$ is the cell state, |
| * * \f$o_t\f$ is the output, |
| * * \f$h_t\f$ is the output state, |
| * * \f$\sigma\f$ is the logistic sigmoid function, |
| * * \f$g\f$ is the cell input and cell output activation function, usually |
| * \f$tahn\f$, |
| * * \f$W_{xi}\f$ is the input-to-input weight matrix, |
| * * \f$W_{hi}\f$ is the recurrent to input weight matrix, |
| * * \f$W_{ci}\f$ is the cell-to-input weight matrix, |
| * * \f$b_i\f$ is the input gate bias, |
| * * \f$W_{xf}\f$ is the input-to-forget weight matrix, |
| * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix, |
| * * \f$W_{cf}\f$ is the cell-to-forget weight matrix, |
| * * \f$b_f\f$ is the forget gate bias, |
| * * \f$W_{xc}\f$ is the input-to-cell weight matrix, |
| * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix, |
| * * \f$b_c\f$ is the cell bias, |
| * * \f$W_{xo}\f$ is the input-to-output weight matrix, |
| * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix, |
| * * \f$W_{co}\f$ is the cell-to-output weight matrix, |
| * * \f$b_o\f$ is the output gate bias, |
| * * \f$W_{proj}\f$ is the projection weight matrix, |
| * * \f$b_{proj}\f$ is the projection bias, |
| * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and |
| * * \f$t_{proj}\f$ is the threshold for clipping the projected output. |
| * * \f$\odot\f$ is the |
| * <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)"> |
| * Hadamard product</a> that takes two matrices and produces another |
| * matrix, each element of which is the product of the corresponding |
| * elements of the input matrices. |
| * |
| * Since API level 29 LSTM supports layer normalization. |
| * In case layer normalization is used, the inputs to internal activation |
| * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered |
| * following an approach from section 3.1 from |
| * https://arxiv.org/pdf/1607.06450.pdf |
| * |
| * The operation has the following independently optional inputs: |
| * * The 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. |
| * * (API level >= 29) 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 API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * All input and output tensors must be of the same type. |
| * |
| * Inputs: |
| * * 0: The input (\f$x_t\f$). |
| * A 2-D tensor of shape [batch_size, input_size], where “batch_size” |
| * corresponds to the batching dimension, and “input_size” is the size |
| * of the input. |
| * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional. |
| * A 2-D tensor of shape [num_units, input_size], where “num_units” |
| * corresponds to the number of cell units. |
| * * 2: The input-to-forget weights (\f$W_{xf}\f$). |
| * A 2-D tensor of shape [num_units, input_size]. |
| * * 3: The input-to-cell weights (\f$W_{xc}\f$). |
| * A 2-D tensor of shape [num_units, input_size]. |
| * * 4: The input-to-output weights (\f$W_{xo}\f$). |
| * A 2-D tensor of shape [num_units, input_size]. |
| * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional. |
| * A 2-D tensor of shape [num_units, output_size], where “output_size” |
| * corresponds to either the number of cell units (i.e., “num_units”), |
| * or the second dimension of the “projection_weights”, if defined. |
| * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$). |
| * A 2-D tensor of shape [num_units, output_size]. |
| * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$). |
| * A 2-D tensor of shape [num_units, output_size]. |
| * * 8: The recurrent-to-output weights (\f$W_{ho}\f$). |
| * A 2-D tensor of shape [num_units, output_size]. |
| * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 12:The input gate bias (\f$b_i\f$). Optional. |
| * A 1-D tensor of shape [num_units]. |
| * * 13:The forget gate bias (\f$b_f\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 14:The cell bias (\f$b_c\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 15:The output gate bias (\f$b_o\f$). |
| * A 1-D tensor of shape [num_units]. |
| * * 16:The projection weights (\f$W_{proj}\f$). Optional. |
| * A 2-D tensor of shape [output_size, num_units]. |
| * * 17:The projection bias (\f$b_{proj}\f$). Optional. |
| * A 1-D tensor of shape [output_size]. |
| * * 18:The output state (in) (\f$h_{t-1}\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. |
| * * 19:The cell state (in) (\f$C_{t-1}\f$). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * * 20:The activation function (\f$g\f$). |
| * A value indicating the activation function: |
| * <ul> |
| * <li>0: None; |
| * <li>1: Relu; |
| * <li>3: Relu6; |
| * <li>4: Tanh; |
| * <li>6: Sigmoid. |
| * </ul> |
| * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such |
| * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 |
| * then clipping is disabled. |
| * Until API level 29 this scalar must be of type {@link |
| * FLOAT32}. Since API level 29, if all the input |
| * tensors have type {@link OperandType::TENSOR_FLOAT32}, this |
| * scalar must be of the type {@link OperandType::FLOAT32}, |
| * otherwise if all the input tensors have the type {@link |
| * TENSOR_FLOAT16}, this scalar must be of type {@link |
| * FLOAT16}. |
| * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the |
| * projection layer, such that values are bound within |
| * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| * Until API level 29 this scalar must be of type {@link |
| * FLOAT32}. Since API level 29, if all the input |
| * tensors have type {@link OperandType::TENSOR_FLOAT32}, this |
| * scalar must be of the type {@link OperandType::FLOAT32}, |
| * otherwise if all the input tensors have the type {@link |
| * TENSOR_FLOAT16}, this scalar must be of type {@link |
| * FLOAT16}. |
| * Since API level 29 there are additional inputs to this op: |
| * * 23:The input layer normalization weights. |
| * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs |
| * to activation at input gate. |
| * * 24:The forget layer normalization weights. |
| * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs |
| * to activation at forget gate. |
| * * 25:The cell layer normalization weights. |
| * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs |
| * to activation at cell gate. |
| * * 26:The output layer normalization weights. |
| * A 1-D tensor of shape [num_units]. Used to rescale normalized inputs |
| * to activation at output gate. |
| * |
| * Outputs: |
| * * 0: The scratch buffer. |
| * A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or |
| * [batch_size, num_units * 4] without CIFG. |
| * * 1: The output state (out) (\f$h_t\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. |
| * * 2: The cell state (out) (\f$C_t\f$). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * * 3: The output (\f$o_t\f$). |
| * A 2-D tensor of shape [batch_size, output_size]. This is effectively |
| * the same as the current “output state (out)” value. |
| * |
| * Available since API level 27. |
| */ |
| LSTM = @1.1::OperationType:LSTM, |
| |
| /** |
| * Performs an 2-D max pooling operation. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, channel] = |
| * max_{di, dj} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, channel] |
| * ) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * 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 API level 29, 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 API level 29. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying |
| * the input. Since API level 29, 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 API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth]. |
| * |
| * Available since API level 27. |
| */ |
| MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D, |
| |
| /** |
| * Multiplies two tensors, element-wise. |
| * |
| * Takes two input tensors of identical {@link OperandType} and compatible |
| * dimensions. The output is the product of both input tensors, optionally |
| * modified by an activation function. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the resulting output is the maximum size along each dimension |
| * of the input operands. It starts with the trailing dimensions, and works |
| * its way forward. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Since API level 29, 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 rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: The product, a tensor of the same {@link OperandType} as input0. |
| * For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the following condition must be satisfied: |
| * output_scale > input1_scale * input2_scale. |
| * |
| * Available since API level 27. |
| */ |
| MUL = @1.1::OperationType:MUL, |
| |
| /** |
| * Computes rectified linear activation on the input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = max(0, input) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. Since API level 29, this tensor may |
| * be zero-sized. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 27. |
| */ |
| RELU = @1.1::OperationType:RELU, |
| |
| /** |
| * Computes rectified linear 1 activation on the input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = min(1.f, max(-1.f, input)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. Since API level 29, this tensor may |
| * be zero-sized. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 27. |
| */ |
| RELU1 = @1.1::OperationType:RELU1, |
| |
| /** |
| * Computes rectified linear 6 activation on the input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = min(6, max(0, input)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. Since API level 29, this tensor may |
| * be zero-sized. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 27. |
| */ |
| RELU6 = @1.1::OperationType:RELU6, |
| |
| /** |
| * Reshapes a tensor. |
| * |
| * Given tensor, this operation returns a tensor that has the same values as |
| * tensor, but with a newly specified shape. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the tensor to be reshaped. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the |
| * shape of the output tensor. The number of elements implied by shape |
| * must be the same as the number of elements in the input tensor. |
| * |
| * 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. |
| * |
| * Available since API level 27. |
| */ |
| RESHAPE = @1.1::OperationType:RESHAPE, |
| |
| /** |
| * Resizes images to given size using the bilinear interpretation. |
| * |
| * Resized images must be distorted if their output aspect ratio is not the |
| * same as input aspect ratio. The corner pixels of output may not be the |
| * same as corner pixels of input. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) |
| * |
| * 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. Since API level 29, 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 API level 29. |
| * |
| * Inputs (resizing by scale, since API level 29): |
| * * 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]. |
| * |
| * Available since API level 27. |
| */ |
| RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR, |
| |
| /** |
| * A basic recurrent neural network layer. |
| * |
| * This layer implements the operation: |
| * outputs = state = activation(inputs * input_weights + |
| * state * recurrent_weights + bias) |
| * |
| * Where: |
| * * “input_weights” is a weight matrix that multiplies the inputs; |
| * * “recurrent_weights” is a weight matrix that multiplies the current |
| * “state” which itself is the output from the previous time step |
| * computation; |
| * * “bias” is a bias vector (added to each output vector in the batch); |
| * * “activation” is the function passed as the “fused_activation_function” |
| * argument (if not “NONE”). |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * The input tensors must all be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor of shape [batch_size, input_size], where “batch_size” |
| * corresponds to the batching dimension, and “input_size” is the size |
| * of the input. |
| * * 1: weights. |
| * A 2-D tensor of shape [num_units, input_size], where “num_units” |
| * corresponds to the number of units. |
| * * 2: recurrent_weights. |
| * A 2-D tensor of shape [num_units, num_units], with columns |
| * corresponding to the weights from each unit. |
| * * 3: bias. |
| * A 1-D tensor of shape [num_units]. |
| * * 4: hidden state (in). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * * 5: fused_activation_function. |
| * An optional {@link FusedActivationFunc} value indicating the |
| * activation function. If “NONE” is specified then it results in a |
| * linear activation. |
| * |
| * Outputs: |
| * * 0: hidden state (out). |
| * A 2-D tensor of shape [batch_size, num_units]. |
| * |
| * * 1: output. |
| * A 2-D tensor of shape [batch_size, num_units]. This is effectively |
| * the same as the current state value. |
| * |
| * Available since API level 27. |
| */ |
| RNN = @1.1::OperationType:RNN, |
| |
| /** |
| * Computes the softmax activation on the input tensor element-wise, per |
| * batch, by normalizing the input vector so the maximum coefficient is |
| * zero. |
| * |
| * The output is calculated using this formula: |
| * |
| * output[batch, i] = |
| * exp((input[batch, i] - max(input[batch, :])) * beta) / |
| * sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)} |
| * |
| * For input tensor with rank other than 2, the activation will be applied |
| * independently on each 1-D slice along specified dimension. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4. |
| * Tensors with rank other than 2 or 4 are only supported since API level 29. |
| * |
| * Inputs: |
| * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since |
| * API level 29, 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 API level 29. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * For {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the scale must be 1.f / 256 and the zeroPoint must be 0. |
| * |
| * Available since API level 27. |
| */ |
| SOFTMAX = @1.1::OperationType:SOFTMAX, |
| |
| /** |
| * Rearranges blocks of spatial data, into depth. |
| * |
| * More specifically, this op outputs a copy of the input tensor where |
| * values from the height and width dimensions are moved to the depth |
| * dimension. The value block_size indicates the input block size and how |
| * the data is moved. |
| * |
| * Chunks of data of size block_size * block_size from depth are rearranged |
| * into non-overlapping blocks of size block_size x block_size. |
| * |
| * The depth of the output tensor is input_depth * block_size * block_size. |
| * The input tensor's height and width must be divisible by block_size. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input. |
| * * 1: An {@link OperandType::INT32} scalar, specifying the block_size. |
| * block_size must be >=1 and block_size must be a divisor of both the |
| * input height and width. |
| * * 2: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape [batches, height/block_size, |
| * width/block_size, depth_in*block_size*block_size]. |
| * |
| * Available since API level 27. |
| */ |
| SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH, |
| |
| /** |
| * SVDF op is a kind of stateful layer derived from the notion that a |
| * densely connected layer that's processing a sequence of input frames can |
| * be approximated by using a singular value decomposition of each of its |
| * nodes. The implementation is based on: |
| * |
| * https://research.google.com/pubs/archive/43813.pdf |
| * |
| * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada. |
| * “Compressing Deep Neural Networks using a Rank-Constrained Topology”. |
| * INTERSPEECH, 2015. |
| * |
| * It processes the incoming input using a 2-stage filtering mechanism: |
| * * stage 1 performs filtering on the "features" dimension, whose outputs |
| * get pushed into a memory of fixed-size memory_size. |
| * * stage 2 performs filtering on the "time" dimension of the memory_size |
| * memoized outputs of stage 1. |
| * |
| * Specifically, for rank 1, this layer implements the operation: |
| * |
| * memory = push(conv1d(inputs, weights_feature, feature_dim, |
| * "PADDING_VALID")); |
| * outputs = activation(memory * weights_time + bias); |
| * |
| * Where: |
| * * “weights_feature” is a weights matrix that processes the inputs (by |
| * convolving the input with every “feature filter”), and whose outputs |
| * get pushed, stacked in order, into the fixed-size “memory” (the oldest |
| * entry gets dropped); |
| * * “weights_time” is a weights matrix that processes the “memory” (by a |
| * batched matrix multiplication on the num_units); |
| * * “bias” is an optional bias vector (added to each output vector in the |
| * batch); and |
| * * “activation” is the function passed as the “fused_activation_function” |
| * argument (if not “NONE”). |
| * |
| * Each rank adds a dimension to the weights matrices by means of stacking |
| * the filters. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * All input tensors must be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 2-D tensor of shape [batch_size, input_size], where “batch_size” |
| * corresponds to the batching dimension, and “input_size” is the size |
| * of the input. |
| * * 1: weights_feature. |
| * A 2-D tensor of shape [num_units, input_size], where “num_units” |
| * corresponds to the number of units. |
| * * 2: weights_time. |
| * A 2-D tensor of shape [num_units, memory_size], where “memory_size” |
| * corresponds to the fixed-size of the memory. |
| * * 3: bias. |
| * An optional 1-D tensor of shape [num_units]. |
| * * 4: state (in). |
| * A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank]. |
| * * 5: rank. |
| * The rank of the SVD approximation. |
| * * 6: fused_activation_function. |
| * An optional {@link FusedActivationFunc} value indicating the |
| * activation function. If “NONE” is specified then it results in a |
| * linear activation. |
| * |
| * Outputs: |
| * * 0: state (out). |
| * A 2-D tensor of the same {@link OperandType} as the inputs, with shape |
| * [batch_size, (memory_size - 1) * num_units * rank]. |
| * * 1: output. |
| * A 2-D tensor of the same {@link OperandType} as the inputs, with shape |
| * [batch_size, num_units]. |
| * |
| * Available since API level 27. |
| */ |
| SVDF = @1.1::OperationType:SVDF, |
| |
| /** |
| * Computes hyperbolic tangent of input tensor element-wise. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = tanh(input) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) |
| * |
| * Supported tensor rank: up to 4. |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. Since API level 29, 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. |
| * |
| * Available since API level 27. |
| */ |
| TANH = @1.1::OperationType:TANH, |
| |
| /** |
| * BatchToSpace for N-dimensional tensors. |
| * |
| * This operation reshapes the batch dimension (dimension 0) into M + 1 |
| * dimensions of shape block_shape + [batch], interleaves these blocks back |
| * into the grid defined by the spatial dimensions [1, ..., M], to obtain a |
| * result with the same rank as the input. |
| * |
| * This is the reverse of SpaceToBatch. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be reshaped |
| * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block |
| * sizes for each spatial dimension of the input tensor. All values |
| * must be >= 1. |
| * * 2: An optional {@link OperandType::BOOL} scalar, default to false. |
| * Set to true to specify NCHW data layout for input0 and output0. |
| * Available since API level 29. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 28. |
| */ |
| BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND, |
| |
| /** |
| * Element-wise division of two tensors. |
| * |
| * Takes two input tensors of identical {@link OperandType} and compatible |
| * dimensions. The output is the result of dividing the first input tensor |
| * by the second, optionally modified by an activation function. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the output is the maximum size along each dimension of the |
| * input operands. It starts with the trailing dimensions, and works its way |
| * forward. |
| * |
| * Example: |
| * input1.dimension = {4, 1, 2} |
| * input2.dimension = {5, 4, 3, 1} |
| * output.dimension = {5, 4, 3, 2} |
| * |
| * Since API level 29, 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 API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the first input. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 28. |
| */ |
| DIV = @1.1::OperationType:DIV, |
| |
| /** |
| * Computes the mean of elements across dimensions of a tensor. |
| * |
| * Reduces the input tensor along the given dimensions to reduce. Unless |
| * keep_dims is true, the rank of the tensor is reduced by 1 for each entry |
| * in axis. If keep_dims is true, the reduced dimensions are retained with |
| * length 1. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. |
| * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. 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. |
| * |
| * Available since API level 28. |
| */ |
| MEAN = @1.1::OperationType:MEAN, |
| |
| /** |
| * Pads a tensor with zeros. |
| * |
| * This operation pads a tensor according to the specified paddings. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API |
| * level 29, 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] |
| * |
| * NOTE: Before API level 29, the pad value for |
| * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined. |
| * Since API level 29, the pad value is always the logical zero. |
| * |
| * Available since API level 28. |
| */ |
| PAD = @1.1::OperationType:PAD, |
| |
| /** |
| * SpaceToBatch for N-Dimensional tensors. |
| * |
| * This operation divides "spatial" dimensions [1, ..., M] of the input into |
| * a grid of blocks of shape block_shape, and interleaves these blocks with |
| * the "batch" dimension (0) such that in the output, the spatial dimensions |
| * [1, ..., M] correspond to the position within the grid, and the batch |
| * dimension combines both the position within a spatial block and the |
| * original batch position. Prior to division into blocks, the spatial |
| * dimensions of the input are optionally zero padded according to paddings. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the input. |
| * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block |
| * sizes for each spatial dimension of the input tensor. All values |
| * must be >= 1. |
| * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings |
| * for each spatial dimension of the input tensor. All values must be |
| * >= 0. The shape of the tensor must be {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 API level 29. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 28. |
| */ |
| SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND, |
| |
| /** |
| * Removes dimensions of size 1 from the shape of a tensor. |
| * |
| * Given a tensor input, this operation returns a tensor of the same |
| * {@link OperandType} with all dimensions of size 1 removed. If you don't |
| * want to remove all size 1 dimensions, you can remove specific size 1 |
| * dimensions by specifying the axes (input1). |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, the tensor to be squeezed. |
| * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The |
| * dimensions to squeeze. If specified only squeezes the dimensions |
| * listed. Otherwise, squeezes all dimensions. The dimension index |
| * starts at 0. An error must be reported if squeezing a dimension that |
| * is not 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. Contains the |
| * same data as input, but has one or more dimensions of size 1 |
| * removed. |
| * |
| * Available since API level 28. |
| */ |
| SQUEEZE = @1.1::OperationType:SQUEEZE, |
| |
| /** |
| * Extracts a strided slice of a tensor. |
| * |
| * Roughly speaking, this op extracts a slice of size (end - begin) / stride |
| * from the given input tensor. Starting at the location specified by begin |
| * the slice continues by adding stride to the index until all dimensions |
| * are not less than end. Note that a stride can be negative, which causes a |
| * reverse slice. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be sliced. |
| * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The |
| * starts of the dimensions of the input tensor to be sliced. The |
| * length must be of rank(input0). |
| * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The |
| * ends of the dimensions of the input tensor to be sliced. The length |
| * must be of rank(input0). |
| * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The |
| * strides of the dimensions of the input tensor to be sliced. The |
| * length must be of rank(input0). The entries must be non-zero. |
| * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit |
| * of begin_mask is set, begin[i] is ignored and the fullest possible |
| * range in that dimension is used instead. |
| * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of |
| * end_mask is set, end[i] is ignored and the fullest possible range in |
| * that dimension is used instead. |
| * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the |
| * ith bit of shrink_axis_mask is set, the ith dimension specification |
| * shrinks the dimensionality by 1, taking on the value at index |
| * begin[i]. In this case, the ith specification must define a |
| * slice of size 1, e.g. begin[i] = x, end[i] = x + 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k), |
| * where k is the number of bits set in shrink_axis_mask. |
| * |
| * Available since API level 28. |
| */ |
| STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE, |
| |
| /** |
| * Element-wise subtraction of two tensors. |
| * |
| * Takes two input tensors of identical {@link OperandType} and compatible |
| * dimensions. The output is the result of subtracting the second input |
| * tensor from the first one, optionally modified by an activation function. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the output is the maximum size along each dimension of the |
| * input operands. It starts with the trailing dimensions, and works its way |
| * forward. |
| * |
| * Example: |
| * input1.dimension = {4, 1, 2} |
| * input2.dimension = {5, 4, 3, 1} |
| * output.dimension = {5, 4, 3, 2} |
| * |
| * Since API level 29, 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 API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29) |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the first input. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0. |
| * * 2: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 28. |
| */ |
| SUB = @1.1::OperationType:SUB, |
| |
| /** |
| * Transposes the input tensor, permuting the dimensions according to the |
| * perm tensor. |
| * |
| * The returned tensor's dimension i corresponds to the input dimension |
| * perm[i]. If perm is not given, it is set to (n-1...0), where n is the |
| * rank of the input tensor. Hence by default, this operation performs a |
| * regular matrix transpose on 2-D input Tensors. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be transposed. |
| * Since API level 29, 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. |
| * |
| * Available since API level 28. |
| */ |
| TRANSPOSE = @1.1::OperationType:TRANSPOSE, |
| |
| /** |
| * Computes the absolute value of a tensor, element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| ABS = 38, |
| |
| /** |
| * Returns the index of the largest element along an axis. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor specifying the input. Must be non-empty. |
| * * 1: An {@link OperandType::INT32} scalar specifying the axis to |
| * reduce across. Negative index is used to specify axis from the |
| * end (e.g. -1 for the last axis). Must be in the range [-n, n). |
| * |
| * Outputs: |
| * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor. |
| * |
| * Available since API level 29. |
| */ |
| // There is no underscore in ARG_MAX to avoid name conflict with |
| // the macro defined in libc/kernel/uapi/linux/limits.h. |
| ARGMAX = 39, |
| |
| /** |
| * Returns the index of the smallest element along an axis. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor specifying the input. Must be non-empty. |
| * * 1: An {@link OperandType::INT32} scalar specifying the axis to |
| * reduce across. Negative index is used to specify axis from the |
| * end (e.g. -1 for the last axis). Must be in the range [-n, n). |
| * |
| * Outputs: |
| * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor. |
| * |
| * Available since API level 29. |
| */ |
| ARGMIN = 40, // See ARGMAX for naming discussion. |
| |
| /** |
| * Transform axis-aligned bounding box proposals using bounding box deltas. |
| * |
| * Given the positions of bounding box proposals and the corresponding |
| * bounding box deltas for each class, return the refined bounding box |
| * regions. The resulting bounding boxes are cliped against the edges of |
| * the image. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@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]. |
| * |
| * Available since API level 29. |
| */ |
| AXIS_ALIGNED_BBOX_TRANSFORM = 41, |
| |
| /** |
| * Performs a forward LSTM on the input followed by a backward LSTM. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 3, either time-major or batch-major. |
| * |
| * All input and output tensors must be of the same type. |
| * |
| * |
| * Inputs: |
| * * 0: The input. |
| * A 3-D tensor of shape: |
| * If time-major: [max_time, batch_size, 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: |
| * <ul> |
| * <li>0: None; |
| * <li>1: Relu; |
| * <li>3: Relu6; |
| * <li>4: Tanh; |
| * <li>6: Sigmoid. |
| * </ul> |
| * * 49: The clipping threshold for the cell state, such |
| * that values are bound within [-cell_clip, cell_clip]. If set to 0.0 |
| * then clipping is disabled. |
| * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32}, |
| * this scalar must be of the type {@link OperandType::FLOAT32}, |
| * otherwise if all the input tensors have the type {@link |
| * TENSOR_FLOAT16}, this scalar must be of type {@link |
| * FLOAT16}. |
| * * 50: The clipping threshold for the output from the |
| * projection layer, such that values are bound within |
| * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. |
| * If all the input tensors have type {@link OperandType::TENSOR_FLOAT32}, |
| * this scalar must be of the type {@link OperandType::FLOAT32}, |
| * otherwise if all the input tensors have the type {@link |
| * TENSOR_FLOAT16}, this scalar must be of type {@link |
| * FLOAT16}. |
| * * 51: merge_outputs |
| * An {@link OperandType::BOOL} scalar specifying if the outputs |
| * from forward and backward cells should be merged. |
| * * 52: time_major |
| * An {@link OperandType::BOOL} scalar specifying the shape format |
| * of input and output tensors. |
| * * 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] |
| * |
| * Available since API level 29. |
| */ |
| BIDIRECTIONAL_SEQUENCE_LSTM = 42, |
| |
| /** |
| * A recurrent neural network layer that applies a basic RNN cell to a |
| * sequence of inputs in forward and backward directions. |
| * |
| * This Op unrolls the input along the sequence dimension, and implements |
| * the following operation for each element in the sequence s = |
| * 1...sequence_length: |
| * fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ + |
| * fw_state * fw_recurrent_weights’ + fw_bias) |
| * |
| * And for each element in sequence t = sequence_length : 1 |
| * bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ + |
| * bw_state * bw_recurrent_weights’ + bw_bias) |
| * |
| * Where: |
| * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs; |
| * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the |
| * current “state” which itself is the output from the previous time step |
| * computation; |
| * * “{fw,bw}_bias” is a bias vector (added to each output vector in the |
| * batch); |
| * * “activation” is the function passed as the “fused_activation_function” |
| * argument (if not “NONE”). |
| * |
| * The op also supports an auxiliary input. Regular cell feeds one input |
| * into the two RNN cells in the following way: |
| * |
| * INPUT (INPUT_REVERSED) |
| * | | |
| * --------------------- |
| * | FW_RNN BW_RNN | |
| * --------------------- |
| * | | |
| * FW_OUT BW_OUT |
| * |
| * An op with an auxiliary input takes two inputs and feeds them into the |
| * RNN cells in the following way: |
| * |
| * AUX_INPUT (AUX_INPUT_REVERSED) |
| * | | |
| * INPUT | (INPUT_R'D.)| |
| * | | | | |
| * ----------------------- |
| * | \ / \ / | |
| * | FW_RNN BW_RNN | |
| * ----------------------- |
| * | | |
| * FW_OUT BW_OUT |
| * |
| * While stacking this op on top of itself, this allows to connect both |
| * forward and backward outputs from previous cell to the next cell's |
| * inputs. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * The input tensors must all be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If |
| * it is set to true, then the input has a shape [maxTime, batchSize, |
| * inputSize], otherwise the input has a shape [batchSize, maxTime, |
| * inputSize]. |
| * * 1: fwWeights. |
| * A 2-D tensor of shape [fwNumUnits, inputSize]. |
| * * 2: fwRecurrentWeights. |
| * A 2-D tensor of shape [fwNumUnits, fwNumUnits]. |
| * * 3: fwBias. |
| * A 1-D tensor of shape [fwNumUnits]. |
| * * 4: fwHiddenState. |
| * A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden |
| * state input for the first time step of the computation. |
| * * 5: bwWeights. |
| * A 2-D tensor of shape [bwNumUnits, inputSize]. |
| * * 6: bwRecurrentWeights. |
| * A 2-D tensor of shape [bwNumUnits, bwNumUnits]. |
| * * 7: bwBias. |
| * A 1-D tensor of shape [bwNumUnits]. |
| * * 8: bwHiddenState |
| * A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden |
| * state input for the first time step of the computation. |
| * * 9: auxInput. |
| * A 3-D tensor. The shape is the same as of the input 0. |
| * * 10:fwAuxWeights. |
| * A 2-D tensor of shape [fwNumUnits, inputSize]. |
| * * 11:bwAuxWeights. |
| * A 2-D tensor of shape [bwNumUnits, inputSize]. |
| * * 12:fusedActivationFunction. |
| * A {@link FusedActivationFunc} value indicating the activation function. If |
| * “NONE” is specified then it results in a linear activation. |
| * * 13:timeMajor |
| * An {@link OperandType::BOOL} scalar specifying the shape format |
| * of input and output tensors. |
| * * 14:mergeOutputs |
| * An {@link OperandType::BOOL} scalar specifying if the outputs |
| * from forward and backward cells are separate (if set to false) or |
| * concatenated (if set to true). |
| * Outputs: |
| * * 0: fwOutput. |
| * A 3-D tensor. The first two dimensions of the shape are defined by |
| * the input 6 (timeMajor) and the third dimension is defined by the |
| * input 14 (mergeOutputs). If timeMajor is set to true, then the first |
| * two dimensions are [maxTime, batchSize], otherwise they are set to |
| * [batchSize, maxTime]. If mergeOutputs is set to true, then the third |
| * dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set |
| * to fwNumUnits. |
| * * 1: bwOutput. |
| * A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then |
| * this tensor is not produced. The shape is defined by the input 6 |
| * (timeMajor). If it is set to true, then the shape is set to |
| * [maxTime, batchSize, bwNumUnits], otherwise the shape is set to |
| * [batchSize, maxTime, bwNumUnits]. |
| * |
| * Available since API level 29. |
| */ |
| BIDIRECTIONAL_SEQUENCE_RNN = 43, |
| |
| /** |
| * Greedily selects a subset of bounding boxes in descending order of score. |
| * |
| * This op applies 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}, |
| * 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. |
| * |
| * Available since API level 29. |
| */ |
| BOX_WITH_NMS_LIMIT = 44, |
| |
| /** |
| * Casts a tensor to a new type. |
| * |
| * This operation ignores the scale and zeroPoint of quanized tensors, |
| * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input |
| * as a tensor of uint8 values. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: A tensor with the same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| CAST = 45, |
| |
| /** |
| * Shuffle the channels of the input tensor. |
| * |
| * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE |
| * divide the channel dimension into num_groups groups, and reorganize the |
| * channels by grouping channels with the same index in each group. |
| * |
| * Along the channel dimension, the output is calculated using this formula: |
| * |
| * output_channel[k * num_groups + g] = input_channel[g * group_size + k] |
| * |
| * where group_size = num_channels / num_groups |
| * |
| * The number of channels must be divisible by num_groups. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be shuffled. |
| * * 1: An {@link OperandType::INT32} scalar, specifying the number of |
| * groups. |
| * * 2: An {@link OperandType::INT32} scalar, specifying the dimension |
| * channel shuffle would be performed on. Negative index is used to |
| * specify axis from the end (e.g. -1 for the last axis). Must be in |
| * the range [-n, n). |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} and same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| CHANNEL_SHUFFLE = 46, |
| |
| /** |
| * Apply postprocessing steps to bounding box detections. |
| * |
| * Bounding box detections are generated by applying transformation on a set |
| * of predefined anchors with the bounding box deltas from bounding box |
| * regression. A final step of hard NMS is applied to limit the number of |
| * returned boxes. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Inputs: |
| * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying |
| * the score of each anchor with each class. Class 0 for each |
| * [batches, num_anchors, 0] is background and will be ignored. |
| * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with |
| * the first four values in length_box_encoding specifying the bounding |
| * box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw], |
| * where dy and dx is the linear-scale relative correction factor for the |
| * center position of the bounding box with respect to the width and height, |
| * dh and dw is the log-scale relative correction factor for the width and |
| * height. All the entries in length_box_encoding beyond the first four |
| * values are ignored in this operation. |
| * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each |
| * predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and |
| * ctr_x are the center position of the box, and h and w are the height |
| * and the width. |
| * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling |
| * factor for dy in bounding box deltas. |
| * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling |
| * factor for dx in bounding box deltas. |
| * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling |
| * factor for dh in bounding box deltas. |
| * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling |
| * factor for dw in bounding box deltas. |
| * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular |
| * multi-class NMS algorithm that do NMS separately for each class, |
| * set to false for a faster algorithm that only do one single NMS |
| * using the highest class score.. |
| * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying |
| * the maximum number of boxes for the output. Boxes with the lowest |
| * scores are discarded to meet the limit. |
| * * 9: An {@link OperandType::INT32} scalar, only used when input7 is |
| * set to false, specifying the maximum number of classes per detection. |
| * * 10: An {@link OperandType::INT32} scalar, only used when input7 is |
| * set to true, specifying the maximum number of detections when |
| * applying NMS algorithm for each single class. |
| * * 11: 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. |
| * |
| * Available since API level 29. |
| */ |
| DETECTION_POSTPROCESSING = 47, |
| |
| /** |
| * For input tensors x and y, computes x == y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| EQUAL = 48, |
| |
| /** |
| * Computes exponential of x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| EXP = 49, |
| |
| /** |
| * Inserts a dimension of 1 into a tensor's shape. |
| * |
| * Given a tensor input, this operation inserts a dimension of 1 at the |
| * given dimension index of input's shape. The dimension index starts at |
| * zero; if you specify a negative dimension index, it is counted backward |
| * from the end. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: An {@link OperandType::INT32} scalar specifying the dimension |
| * index to expand. Must be in the range [-(n + 1), (n + 1)). |
| * |
| * Outputs: |
| * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as |
| * input0. |
| * |
| * Available since API level 29. |
| */ |
| EXPAND_DIMS = 50, |
| |
| /** |
| * Gathers values along an axis. |
| * |
| * Produces an output tensor with shape |
| * input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:] |
| * where: |
| * # Vector indices (output is rank(input0)). |
| * output[a_0, ..., a_n, i, b_0, ..., b_n] = |
| * input0[a_0, ..., a_n, indices[i], b_0, ..., b_n] |
| * |
| * # Higher rank indices (output is rank(input0) + rank(indices) - 1). |
| * output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = |
| * input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor from which to gather values. |
| * * 1: An {@link OperandType::INT32} scalar specifying the axis. |
| * Negative index is used to specify axis from the end |
| * (e.g. -1 for the last axis). Must be in the range [-n, n). |
| * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices. |
| * The values must be in the bounds of the corresponding dimensions |
| * of input0. |
| * |
| * Outputs: |
| * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| GATHER = 51, |
| |
| /** |
| * Generate aixs-aligned bounding box proposals. |
| * |
| * Bounding box proposals are generated by applying transformation on a set |
| * of predefined anchors with the bounding box deltas from bounding box |
| * regression. A final step of hard NMS is applied to limit the number of |
| * returned boxes. |
| * |
| * Axis-aligned bounding boxes are represented by its upper-left corner |
| * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid |
| * bounding box should satisfy x1 <= x2 and y1 <= y2. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Inputs: |
| * * 0: A 4-D Tensor specifying the score of each anchor at each |
| * location. With "NHWC" data layout, the tensor shape is |
| * [batches, height, width, num_anchors]. With "NCHW" data layout, |
| * the tensor shape is [batches, num_anchors, height, width]. |
| * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data |
| * layout, the tensor shape is [batches, height, width, num_anchors * 4]. |
| * With "NCHW" data layout, the tensor shape is |
| * [batches, num_anchors * 4, height, width]. The box deltas are encoded |
| * in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale |
| * relative correction factor for the center position of the bounding box |
| * with respect to the width and height, dw and dh is the log-scale |
| * relative correction factor for the width and height. The last |
| * dimensions is the channel dimension. |
| * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each |
| * predefined anchor, with format [x1, y1, x2, y2]. For input0 of type |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of |
| * {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125. |
| * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of |
| * each image in the batch, with format [image_height, image_width]. |
| * 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. |
| * |
| * Available since API level 29. |
| */ |
| GENERATE_PROPOSALS = 52, |
| |
| /** |
| * For input tensors x and y, computes x > y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| GREATER = 53, |
| /** |
| * For input tensors x and y, computes x >= y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| GREATER_EQUAL = 54, |
| |
| /** |
| * Performs a grouped 2-D convolution operation. |
| * |
| * Given an input tensor of shape [batches, height, width, depth_in] and a |
| * filter tensor of shape [depth_out, filter_height, filter_width, depth_group] |
| * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV |
| * applies a group of different filters to each input channel group, then |
| * concatenates the results together. |
| * |
| * Specifically, the input channels are divided into num_groups groups, each with |
| * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional |
| * filters are also divided into num_groups groups, i.e. depth_out is divisible |
| * by num_groups. GROUPED_CONV applies each group of filters to the corresponding |
| * input channel group, and the result are concatenated together. |
| * |
| * The output dimensions are functions of the filter dimensions, stride, and |
| * padding. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, i, j, g * channel_multiplier + q] = |
| * sum_{di, dj, dk} ( |
| * input[b, strides[1] * i + di, strides[2] * j + dj, |
| * g * depth_group + dk] * |
| * filter[g * channel_multiplier + q, di, dj, dk] |
| * ) + bias[channel] |
| * |
| * where channel_multiplier = depth_out / num_groups |
| * |
| * Supported tensor {@link OperandType} configurations: |
| * * 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 must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the left, in the ‘width’ dimension. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the right, in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the top, in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the padding on |
| * the bottom, in the ‘height’ dimension. |
| * * 7: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 8: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 9: An {@link OperandType::INT32} scalar, specifying the number of |
| groups. |
| * * 10: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 11: An {@link OperandType::BOOL} scalar, set to true to specify |
| * NCHW data layout for input0 and output0. Set to false for NHWC. |
| * |
| * Inputs (implicit padding): |
| * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], |
| * specifying the input, where depth_in = num_groups * depth_group. |
| * * 1: A 4-D tensor, of shape |
| * [depth_out, filter_height, filter_width, depth_group], specifying |
| * the filter, where depth_out must be divisible by num_groups. For |
| * tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} |
| * the channel dimension must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same |
| * type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint |
| * of 0 and bias_scale == input_scale * filter_scale. For filter tensor |
| * of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias |
| * should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of |
| * 0 and bias_scale of 0. The actual scale of each value 'i' is equal to |
| * bias_scale[i] = input_scale * filter_scale[i]. |
| * * 3: An {@link OperandType::INT32} scalar, specifying the implicit |
| * padding scheme, has to be one of the |
| * following values: {0 (NONE), 1 (SAME), 2 (VALID)}. |
| * * 4: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘width’ dimension. |
| * * 5: An {@link OperandType::INT32} scalar, specifying the stride when |
| * walking through input in the ‘height’ dimension. |
| * * 6: An {@link OperandType::INT32} scalar, specifying the number of |
| * groups. |
| * * 7: An {@link OperandType::INT32} scalar, and has to be one of the |
| * {@link FusedActivationFunc} values. Specifies the activation to |
| * invoke on the result. |
| * * 8: An {@link OperandType::BOOL} scalar, set to true to specify |
| * NCHW data layout for input0 and output0. Set to false for NHWC. |
| * |
| * Outputs: |
| * * 0: The output 4-D tensor, of shape |
| * [batches, out_height, out_width, depth_out]. |
| * |
| * Available since API level 29. |
| */ |
| GROUPED_CONV_2D = 55, |
| |
| /** |
| * Localize the maximum keypoints from heatmaps. |
| * |
| * This operation approximates the accurate maximum keypoint scores and |
| * indices after bicubic upscaling by using Taylor expansion up to the |
| * quadratic term. |
| * |
| * The bounding box is represented by its upper-left corner coordinate |
| * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. |
| * A valid bounding box should satisfy x1 <= x2 and y1 <= y2. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: A 4-D Tensor of shape |
| * [num_boxes, heatmap_size, heatmap_size, num_keypoints], |
| * specifying the heatmaps, the height and width of heatmaps should |
| * be the same, and must be greater than or equal to 2. |
| * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes, |
| * each with format [x1, y1, x2, y2]. For input0 of type |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should |
| * be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint |
| * of 0 and scale of 0.125. |
| * * 2: An {@link OperandType::BOOL} scalar, set to true to specify |
| * NCHW data layout for input0. Set to false for NHWC. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0, with shape |
| * [num_boxes, num_keypoints], specifying score of the keypoints. |
| * * 1: A tensor of the same {@link OperandType} as input1, with shape |
| * [num_boxes, num_keypoints, 2], specifying the location of |
| * the keypoints, the second dimension is organized as |
| * [keypoint_x, keypoint_y]. |
| * |
| * Available since API level 29. |
| */ |
| HEATMAP_MAX_KEYPOINT = 56, |
| |
| /** |
| * Applies instance normalization to the input tensor. |
| * |
| * The values in the output tensor are computed as: |
| * |
| * output[b, h, w, c] = |
| * (input[b, h, w, c] - mean[b, c]) * gamma / |
| * sqrt(var[b, c] + epsilon) + beta |
| * |
| * Where the mean and variance are computed across the spatial dimensions: |
| * |
| * mean[b, c] = |
| * sum_{h, w}(input[b, h, w, c]) / sum(1) |
| * |
| * var[b, c] = |
| * sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be normalized. |
| * * 1: 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. |
| * |
| * Available since API level 29. |
| */ |
| INSTANCE_NORMALIZATION = 57, |
| |
| /** |
| * For input tensors x and y, computes x < y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| LESS = 58, |
| |
| /** |
| * For input tensors x and y, computes x <= y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| LESS_EQUAL = 59, |
| |
| /** |
| * Computes natural logarithm of x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| LOG = 60, |
| |
| /** |
| * Returns the truth value of x AND y element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions |
| * compatible with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| LOGICAL_AND = 61, |
| |
| /** |
| * Computes the truth value of NOT x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| LOGICAL_NOT = 62, |
| |
| /** |
| * Returns the truth value of x OR y element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions |
| * compatible with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| LOGICAL_OR = 63, |
| |
| /** |
| * Computes the log softmax activations given logits. |
| * |
| * The output is calculated using this formula: |
| * |
| * output = logits * beta - log(reduce_sum(exp(logits * beta), axis)) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor specifying the input logits. |
| * * 1: 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. |
| * |
| * Available since API level 29. |
| */ |
| LOG_SOFTMAX = 64, |
| |
| /** |
| * Returns the element-wise maximum of two tensors. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and compatible dimensions |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| MAXIMUM = 65, |
| |
| /** |
| * Returns the element-wise minimum of two tensors. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and compatible dimensions |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| MINIMUM = 66, |
| |
| /** |
| * Computes numerical negative value element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| NEG = 67, |
| |
| /** |
| * For input tensors x and y, computes x != y elementwise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * This operation supports broadcasting. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * * 1: A tensor of the same {@link OperandType} and dimensions compatible |
| * with input0. |
| * |
| * Outputs: |
| * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}. |
| * |
| * Available since API level 29. |
| */ |
| NOT_EQUAL = 68, |
| |
| /** |
| * Pads a tensor with the given constant value according to the specified |
| * paddings. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor, specifying the tensor to be padded. |
| * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings |
| * for each spatial dimension of the input tensor. The shape of the |
| * tensor must be {rank(input0), 2}. |
| * padding[i, 0] specifies the number of elements to be padded in the |
| * front of dimension i. |
| * padding[i, 1] specifies the number of elements to be padded after |
| * the end of dimension i. |
| * * 2: An scalar specifying the value to use for padding input0. |
| * For input tensor of {@link OperandType::TENSOR_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] |
| * |
| * Available since API level 29. |
| */ |
| PAD_V2 = 69, |
| |
| /** |
| * Computes the power of one value to another. |
| * |
| * Given a tensor base and a tensor exponent, this operation computes |
| * base^exponent elementwise. |
| * |
| * This operations supports broadcasting. The size of the output is the |
| * maximum size along each dimension of the input operands. It starts with |
| * the trailing dimensions, and works its way forward. |
| * |
| * For example: |
| * base.dimension = {4, 1, 2} |
| * exponent.dimension = {5, 4, 3, 1} |
| * output.dimension = {5, 4, 3, 2} |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: A tensor specifying the base. |
| * * 1: A tensor specifying the exponent. |
| * |
| * Outputs: |
| * * 0: An output tensor. |
| * |
| * Available since API level 29. |
| */ |
| POW = 70, |
| |
| /** |
| * Parametric Rectified Linear Unit. |
| * |
| * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha |
| * is a learned array with the same {@link OperandType} and compatible |
| * dimensions as input x. |
| * |
| * Two dimensions are compatible when: |
| * 1. they are equal, or |
| * 2. one of them is 1 |
| * |
| * The size of the output is the maximum size along each dimension of the |
| * input operands. It starts with the trailing dimensions, and works its way |
| * forward. |
| * |
| * Example: |
| * input.dimension = {4, 1, 2} |
| * alpha.dimension = {5, 4, 3, 1} |
| * output.dimension = {5, 4, 3, 2} |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: A tensor, specifying the input. |
| * * 1: A tensor of the same {@link OperandType}, and compatible dimensions |
| * as input0, specifying the alpha. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| PRELU = 71, |
| |
| /** |
| * Quantizes the input tensor. |
| * |
| * The formula is: |
| * |
| * output = max(0, min(255, round(input / scale) + zeroPoint) |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: A tensor, may be zero-sized. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0, but with |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}. |
| * |
| * Available since API level 29. |
| */ |
| QUANTIZE = 72, |
| |
| /** |
| * A version of quantized LSTM, using 16 bit quantization for internal |
| * state. |
| * |
| * There is no projection layer, so cell state size is equal to the output |
| * size. |
| * |
| * Inputs: |
| * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [numBatches, inputSize] specifying the input to the LSTM |
| * cell. Tensor is quantized with a fixed quantization range of |
| * [-1, 127/128] (scale = 1/128, zeroPoint = 128). |
| * * 1: The input-to-input weights. |
| * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [outputSize, inputSize] specifying input-to-input part of |
| * weights for fully-connected layer inside the LSTM cell. |
| * Quantization zero point and scale must be the same across all the |
| * weights. |
| * * 2: The input-to-forget weights. |
| * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [outputSize, inputSize] specifying input-to-forget part of |
| * weights for fully-connected layer inside the LSTM cell. |
| * Quantization zero point and scale must be the same across all the |
| * weights. |
| * * 3: The input-to-cell weights. |
| * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [outputSize, inputSize] specifying input-to-cell part of |
| * weights for fully-connected layer inside the LSTM cell. |
| * Quantization zero point and scale must be the same across all the |
| * weights. |
| * * 4: The input-to-output weights. |
| * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [outputSize, inputSize] specifying input-to-output part of |
| * weights for fully-connected layer inside the LSTM cell. |
| * Quantization zero point and scale must be the same across all the |
| * weights. |
| * * 5: The recurrent-to-input weights. |
| * A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * and shape [outputSize, 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. |
| * Outputs: |
| * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape |
| * [batches, samples], containing the drawn samples. |
| * |
| * Available since API level 29. |
| */ |
| RANDOM_MULTINOMIAL = 74, |
| |
| /** |
| * Reduces a tensor by computing the "logical and" of elements along given |
| * dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_ALL = 75, |
| |
| /** |
| * Reduces a tensor by computing the "logical or" of elements along given |
| * dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_BOOL8} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_ANY = 76, |
| |
| /** |
| * Reduces a tensor by computing the maximum of elements along given |
| * dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_MAX = 77, |
| |
| /** |
| * Reduces a tensor by computing the minimum of elements along given |
| * dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_MIN = 78, |
| |
| /** |
| * Reduces a tensor by multiplying elements along given dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_PROD = 79, |
| |
| /** |
| * Reduces a tensor by summing elements along given dimensions. |
| * |
| * If keep_dims is true, the reduced dimensions are |
| * retained with length 1. Otherwise, the rank of the tensor is reduced by |
| * 1 for each entry in dimensions. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: up to 4 |
| * |
| * Inputs: |
| * * 0: An n-D tensor. |
| * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions |
| * to reduce. Dimension values must be in the range [-n, n). |
| * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true, |
| * retains reduced dimensions with length 1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same {@link OperandType} as input0. |
| * |
| * Available since API level 29. |
| */ |
| REDUCE_SUM = 80, |
| |
| /** |
| * Select and scale the feature map of each region of interest to a unified |
| * output size by average pooling sampling points from bilinear interpolation. |
| * |
| * The region of interest is represented by its upper-left corner coordinate |
| * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. |
| * A spatial scaling factor is applied to map into feature map coordinate. |
| * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. |
| * |
| * No rounding is applied in this operation. The sampling points are unified |
| * distributed in the pooling bin and their values are calculated by bilinear |
| * interpolation. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} (since API level 29) |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, specifying the feature map. |
| * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of |
| * the regions of interest, each line with format [x1, y1, x2, y2]. |
| * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, |
| * with zeroPoint of 0 and scale of 0.125. 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]. |
| * |
| * Available since API level 29. |
| */ |
| ROI_ALIGN = 81, |
| |
| /** |
| * Select and scale the feature map of each region of interest to a unified |
| * output size by max-pooling. |
| * |
| * The region of interest is represented by its upper-left corner coordinate |
| * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image. |
| * A spatial scaling factor is applied to map into feature map coordinate. |
| * A valid region of interest should satisfy x1 <= x2 and y1 <= y2. |
| * |
| * Rounding is applied in this operation to ensure integer boundary for |
| * regions of interest and pooling bins. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout. |
| * With the default data layout NHWC, the data is stored in the order of: |
| * [batch, height, width, channels]. Alternatively, the data layout could |
| * be NCHW, the data storage order of: [batch, channels, height, width]. |
| * |
| * Inputs: |
| * * 0: A 4-D tensor, specifying the feature map. |
| * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of |
| * the regions of interest, each line with format [x1, y1, x2, y2]. |
| * For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, |
| * this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM}, |
| * with zeroPoint of 0 and scale of 0.125. |
| * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape |
| * [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]. |
| * |
| * Available since API level 29. |
| */ |
| ROI_POOLING = 82, |
| |
| /** |
| * Computes reciprocal of square root of x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| RSQRT = 83, |
| |
| /** |
| * Using a tensor of booleans c and input tensors x and y select values |
| * elementwise from both input tensors: |
| * |
| * O[i] = C[i] ? x[i] : y[i]. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a |
| * mask that chooses, based on the value at each element, whether the |
| * corresponding element in the output should be taken from input1 (if |
| * true) or input2 (if false). |
| * * 1: An input tensor of the same shape as input0. |
| * * 2: An input tensor of the same shape and type as input1. |
| * |
| * Outputs: |
| * * 0: A tensor of the same type and shape as input1 and input2. |
| * |
| */ |
| SELECT = 84, |
| |
| /** |
| * Computes sin of x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| SIN = 85, |
| |
| /** |
| * Extracts a slice of specified size from the input tensor starting at a |
| * specified location. |
| * |
| * The starting location is specified as a 1-D tensor containing offsets |
| * for each dimension. The size is specified as a 1-D tensor containing |
| * either size of a slice along corresponding dimension or -1. In the latter |
| * case, all the remaining elements in dimension are included in the slice. |
| * Slice size in each dimension cannot be zero. |
| * |
| * A sum of begin offset and a size of a slice must not exceed size of a |
| * corresponding dimension. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor to take slice from, 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. |
| * |
| * Available since API level 29. |
| */ |
| SLICE = 86, |
| |
| /** |
| * Splits a tensor along a given axis into num_splits subtensors. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: An n-D tensor to split. |
| * * 1: An {@link OperandType::INT32} scalar specifying the axis along |
| * which to split. |
| * * 2: An {@link OperandType::INT32} scalar indicating the number of |
| * splits along given axis. Must evenly divide axis size. |
| * |
| * Outputs: |
| * * 0 ~ (num_splits - 1): Resulting subtensors. |
| * |
| * Available since API level 29. |
| */ |
| SPLIT = 87, |
| |
| /** |
| * Computes square root of x element-wise. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: from 1. |
| * |
| * Inputs: |
| * * 0: A tensor. |
| * |
| * Outputs: |
| * * 0: The output tensor of same shape as input0. |
| * |
| * Available since API level 29. |
| */ |
| SQRT = 88, |
| |
| /** |
| * Constructs a tensor by tiling a given tensor. |
| * |
| * This operation creates a new tensor by replicating `input` `multiples` |
| * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]` |
| * elements, and the values of `input` are replicated `multiples[i]` times |
| * along the i-th dimension. |
| * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: input, an n-D tensor specifying the input. |
| * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}. |
| * The length of multiples must be n. |
| * |
| * Outputs: |
| * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`. |
| * |
| * Available since API level 29. |
| */ |
| TILE = 89, |
| |
| /** |
| * Finds values and indices of the k largest entries for the last dimension. |
| * |
| * Resulting values in each dimensions are sorted in descending order. If |
| * two values are equal, the one with larger index appears first. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * |
| * Supported tensor rank: from 1 |
| * |
| * Inputs: |
| * * 0: input, an n-D tensor specifying the input. |
| * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of |
| * top elements to look for along the last dimension. |
| * |
| * Outputs: |
| * * 0: An n-D tensor of the same type as the input, containing the k |
| * largest elements along each last dimensional slice. |
| * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32} |
| * containing the indices of values within the last dimension of input. |
| * |
| * Available since API level 29. |
| */ |
| TOPK_V2 = 90, |
| |
| /** |
| * Performs the 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 OperandCode} 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 (extraParams.channelQuant.channelDim) must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the |
| * same type. For input tensor of type |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be |
| * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and |
| * bias_scale == input_scale * filter_scale. 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 (extraParams.channelQuant.channelDim) must be set to 0. |
| * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input |
| * tensor of type {@link OperandType::TENSOR_FLOAT32} or |
| * {@link OperandType::TENSOR_FLOAT16}, the bias should be of the |
| * same type. For input tensor of type |
| * {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be |
| * of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and |
| * bias_scale == input_scale * filter_scale. 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]. |
| * |
| * Available since API level 29. |
| */ |
| TRANSPOSE_CONV_2D = 91, |
| |
| /** |
| * A recurrent neural network specified by an LSTM cell. |
| * |
| * Performs (fully) dynamic unrolling of input. |
| * |
| * This Op unrolls the input along the time dimension, and implements the |
| * following operation for each element in the sequence |
| * s = 1...sequence_length: |
| * outputs[s] = projection(state = activation(LSTMOp(inputs[s]))) |
| * |
| * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM}, |
| * the "projection" is an optional projection layer from state and output |
| * and the “activation” is the function passed as the |
| * “fused_activation_function” argument (if not “NONE”). |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * Supported tensor rank: 3, either time-major or batch-major. |
| * |
| * All input and output tensors must be of the same type. |
| * |
| * Inputs: |
| * * 0: The input (\f$x_t\f$). |
| * A 3-D tensor of shape: |
| * If time-major: [max_time, batch_size, 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: |
| * <ul> |
| * <li>0: None; |
| * <li>1: Relu; |
| * <li>3: Relu6; |
| * <li>4: Tanh; |
| * <li>6: Sigmoid. |
| * </ul> |
| * * 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] |
| * |
| * Available since API level 29. |
| */ |
| UNIDIRECTIONAL_SEQUENCE_LSTM = 92, |
| |
| /** |
| * A recurrent neural network layer that applies a basic RNN cell to a |
| * sequence of inputs. |
| * |
| * This layer unrolls the input along the sequence dimension, and implements |
| * the following operation |
| * for each element in the sequence s = 1...sequence_length: |
| * outputs[s] = state = activation(inputs[s] * input_weights’ + state * |
| * recurrent_weights’ + bias) |
| * |
| * Where: |
| * * “input_weights” is a weight matrix that multiplies the inputs; |
| * * “recurrent_weights” is a weight matrix that multiplies the current |
| * “state” which itself is the output from the previous time step |
| * computation; |
| * * “bias” is a bias vector (added to each output vector in the batch); |
| * * “activation” is the function passed as the “fused_activation_function” |
| * argument (if not “NONE”). |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * |
| * The input tensors must all be the same type. |
| * |
| * Inputs: |
| * * 0: input. |
| * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If |
| * it is set to 1, then the input has a shape [maxTime, batchSize, |
| * inputSize], otherwise the input has a shape [batchSize, maxTime, |
| * inputSize]. |
| * * 1: weights. |
| * A 2-D tensor of shape [numUnits, inputSize]. |
| * * 2: recurrent_weights. |
| * A 2-D tensor of shape [numUnits, numUnits]. |
| * * 3: bias. |
| * A 1-D tensor of shape [numUnits]. |
| * * 4: hidden state |
| * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden |
| * state input for the first time step of the computation. |
| * * 5: fusedActivationFunction. |
| * A {@link FusedActivationFunc} value indicating the activation function. If |
| * “NONE” is specified then it results in a linear activation. |
| * * 6: timeMajor |
| * An {@link OperandType::INT32} scalar specifying the shape format |
| * of input and output tensors. Must be set to either 0 or 1. |
| * Outputs: |
| * * 0: output. |
| * A 3-D tensor. The shape is defined by the input 6 (timeMajor). If |
| * it is set to 1, then the output has a shape [maxTime, batchSize, |
| * numUnits], otherwise the output has a shape [batchSize, maxTime, |
| * numUnits]. |
| * |
| * Available since API level 29. |
| */ |
| UNIDIRECTIONAL_SEQUENCE_RNN = 93, |
| |
| /** |
| * 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]. |
| * |
| * Available since API level 29. |
| */ |
| 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> 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<uint32_t> inputs; |
| |
| /** |
| * Describes the table that contains the indexes of the outputs of the |
| * operation. The offset is the index in the operandIndexes table. |
| */ |
| vec<uint32_t> 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<float> 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<uint32_t> 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. |
| * |
| * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or |
| * TENSOR_INT32. |
| */ |
| float scale; |
| |
| /** |
| * Quantized zero-point offset of the operand. |
| * |
| * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM. |
| */ |
| 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<uint8_t> 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<Operand> 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<Operation> operations; |
| |
| /** |
| * Input indexes of the model. There must be at least one. |
| * |
| * Each value corresponds to the index of the operand in "operands". |
| */ |
| vec<uint32_t> inputIndexes; |
| |
| /** |
| * Output indexes of the model. There must be at least one. |
| * |
| * Each value corresponds to the index of the operand in "operands". |
| */ |
| vec<uint32_t> 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<uint8_t> 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<memory> 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<ExtensionNameAndPrefix> 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<uint32_t> 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<OperandTypeInformation> operandTypes; |
| }; |