Loading neuralnetworks/1.1/Android.bp 0 → 100644 +24 −0 Original line number Diff line number Diff line // This file is autogenerated by hidl-gen -Landroidbp. hidl_interface { name: "android.hardware.neuralnetworks@1.1", root: "android.hardware", vndk: { enabled: true, }, srcs: [ "types.hal", "IDevice.hal", ], interfaces: [ "android.hardware.neuralnetworks@1.0", "android.hidl.base@1.0", ], types: [ "Model", "Operation", "OperationType", ], gen_java: false, } neuralnetworks/1.1/IDevice.hal 0 → 100644 +106 −0 Original line number Diff line number Diff line /* * 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@1.1; import @1.0::ErrorStatus; import @1.0::IDevice; import @1.0::IPreparedModelCallback; /** * This interface represents a device driver. */ interface IDevice extends @1.0::IDevice { /** * Gets the supported operations in a model. * * getSupportedSubgraph indicates which operations of a model are fully * supported by the vendor driver. If an operation may not be supported for * any reason, getSupportedOperations must return false for that operation. * * @param model A model whose operations--and their corresponding * operands--are to be verified by the driver. * @return status Error status of the call, must be: * - NONE if successful * - DEVICE_UNAVAILABLE if driver is offline or busy * - GENERAL_FAILURE if there is an unspecified error * - INVALID_ARGUMENT if provided model is invalid * @return supportedOperations A list of supported operations, where true * indicates the operation is supported and * false indicates the operation is not * supported. The index of "supported" * corresponds with the index of the operation * it is describing. */ getSupportedOperations_1_1(Model model) generates (ErrorStatus status, vec<bool> supportedOperations); /** * Creates a prepared model for execution. * * prepareModel is used to make any necessary transformations or alternative * representations to a model for execution, possiblly including * transformations on the constant data, optimization on the model's graph, * or compilation into the device's native binary format. The model itself * is not changed. * * The model is prepared asynchronously with respect to the caller. The * prepareModel function must verify the inputs to the prepareModel function * are correct. If there is an error, prepareModel must immediately invoke * the callback with the appropriate ErrorStatus value and nullptr for the * IPreparedModel, then return with the same ErrorStatus. If the inputs to * the prepareModel function are valid and there is no error, prepareModel * must launch an asynchronous task to prepare the model in the background, * and immediately return from prepareModel with ErrorStatus::NONE. If the * asynchronous task fails to launch, prepareModel must immediately invoke * the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the * IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE. * * When the asynchronous task has finished preparing the model, it must * immediately invoke the callback function provided as an input to * prepareModel. If the model was prepared successfully, the callback object * must be invoked with an error status of ErrorStatus::NONE and the * produced IPreparedModel object. If an error occurred preparing the model, * the callback object must be invoked with the appropriate ErrorStatus * value and nullptr for the IPreparedModel. * * The only information that may be unknown to the model at this stage is * the shape of the tensors, which may only be known at execution time. As * such, some driver services may return partially prepared models, where * the prepared model can only be finished when it is paired with a set of * inputs to the model. Note that the same prepared model object can be * used with different shapes of inputs on different (possibly concurrent) * executions. * * Multiple threads can call prepareModel on the same model concurrently. * * @param model The model to be prepared for execution. * @param callback A callback object used to return the error status of * preparing the model for execution and the prepared model * if successful, nullptr otherwise. The callback object's * notify function must be called exactly once, even if the * model could not be prepared. * @return status Error status of launching a task which prepares the model * in the background; must be: * - NONE if preparation task is successfully launched * - DEVICE_UNAVAILABLE if driver is offline or busy * - GENERAL_FAILURE if there is an unspecified error * - INVALID_ARGUMENT if one of the input arguments is * invalid */ prepareModel_1_1(Model model, IPreparedModelCallback callback) generates (ErrorStatus status); }; neuralnetworks/1.1/types.hal 0 → 100644 +333 −0 Original line number Diff line number Diff line /* * 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@1.1; import @1.0::Operand; import @1.0::OperationType; /** * Operation types. * * The type of an operation in a model. */ enum OperationType : @1.0::OperationType { /** * BatchToSpace for N-D tensors. * * This operation reshapes the "batch" 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. * The spatial dimensions of this intermediate result are then optionally cropped * according to the amount to crop to produce the output. * This is the reverse of SpaceToBatch. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the * input tensor. All values must be >= 1. * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the * input tensor. All values must be >= 0. * * Outputs: * 0: A tensor of the same type as input0. */ BATCH_TO_SPACE_ND = 29, /** * Divides the second tensor from the first tensor, element-wise. * * Takes two input tensors of identical OperandType and compatible dimensions. The output * is the result of dividing the first input tensor by the second, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the input operands. * It starts with the trailing dimensions, and works its way forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor types: {@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 type, and compatible dimensions as input0. * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. * Specifies the activation to invoke on the result of each addition. * * Outputs: * 0: A tensor of the same type as input0. */ DIV = 30, /** * Computes the mean of elements across dimensions of a tensor. * * Reduces input tensor along the dimensions given in axis. Unless keep_dims is true, * the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is * true, the reduced dimensions are retained with length 1. * * If axis has no entries, all dimensions are reduced, and a tensor with a single * element is returned. * * Supported tensor types: {@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 type TENSOR_INT32. The dimensions to reduce. If None (the default), * reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)). * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1. * * Outputs: * 0: A tensor of the same type as input0. */ MEAN = 31, /** * Pads a tensor. * * This operation pads a tensor according to the specified paddings. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension * of the input tensor. * * Outputs: * 0: A tensor of the same type as input0. */ PAD = 32, /** * SpaceToBatch for N-D 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 types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the * input tensor. All values must be >= 1. * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the * input tensor. All values must be >= 0. * * Outputs: * 0: A tensor of the same type as input0. */ SPACE_TO_BATCH_ND = 33, /** * Removes dimensions of size 1 from the shape of a tensor. * * Given a tensor input, this operation returns a tensor of the same type with all * dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, * you can remove specific size 1 dimensions by specifying axis. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default), * squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension * index starts at 0. It is an error to squeeze a dimension that is not 1. * * Outputs: * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more * dimensions of size 1 removed. */ SQUEEZE = 34, /** * Extracts a strided slice of a tensor. * * This op extracts a slice of size (end-begin)/stride from the given input tensor. * Starting at the location specified by begin the slice continues by adding * stride to the index until all dimensions are not less than end. Note that a stride can * be negative, which causes a reverse slice. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input * tensor to be sliced. * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input * tensor to be sliced. * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input * tensor to be sliced. * * Outputs: * 0: A tensor of the same type as input0. */ STRIDED_SLICE = 35, /** * Subtracts the second tensor from the first tensor, element-wise. * * Takes two input tensors of identical type and compatible dimensions. The output * is the result of subtracting the second input tensor from the first one, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the input operands. * It starts with the trailing dimensions, and works its way forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor types: {@link 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 type, and compatible dimensions as input0. * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. * Specifies the activation to invoke on the result of each addition. * * Outputs: * 0: A tensor of the same type as input0. */ SUB = 36, /** * Transposes the input tensor, permuting the dimensions according to the perm tensor. * * The returned tensor's dimension i must correspond to the input dimension perm[i]. * If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. * Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input * tensor. * * Outputs: * 0: A tensor of the same type as input0. */ TRANSPOSE = 37, }; /** * Describes one operation of the model's graph. */ struct Operation { /** * The operation type. */ 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; }; /** * 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. */ vec<Operation> operations; /** * Input indexes of the model. * * Each value corresponds to the index of the operand in "operands". */ vec<uint32_t> inputIndexes; /** * Output indexes of the model. * * 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 data that were * registered by the model. * * An operand's value must be located here if and only if Operand::lifetime * equals OperandLifeTime::CONSTANT_REFERENCE. */ vec<memory> pools; }; Loading
neuralnetworks/1.1/Android.bp 0 → 100644 +24 −0 Original line number Diff line number Diff line // This file is autogenerated by hidl-gen -Landroidbp. hidl_interface { name: "android.hardware.neuralnetworks@1.1", root: "android.hardware", vndk: { enabled: true, }, srcs: [ "types.hal", "IDevice.hal", ], interfaces: [ "android.hardware.neuralnetworks@1.0", "android.hidl.base@1.0", ], types: [ "Model", "Operation", "OperationType", ], gen_java: false, }
neuralnetworks/1.1/IDevice.hal 0 → 100644 +106 −0 Original line number Diff line number Diff line /* * 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@1.1; import @1.0::ErrorStatus; import @1.0::IDevice; import @1.0::IPreparedModelCallback; /** * This interface represents a device driver. */ interface IDevice extends @1.0::IDevice { /** * Gets the supported operations in a model. * * getSupportedSubgraph indicates which operations of a model are fully * supported by the vendor driver. If an operation may not be supported for * any reason, getSupportedOperations must return false for that operation. * * @param model A model whose operations--and their corresponding * operands--are to be verified by the driver. * @return status Error status of the call, must be: * - NONE if successful * - DEVICE_UNAVAILABLE if driver is offline or busy * - GENERAL_FAILURE if there is an unspecified error * - INVALID_ARGUMENT if provided model is invalid * @return supportedOperations A list of supported operations, where true * indicates the operation is supported and * false indicates the operation is not * supported. The index of "supported" * corresponds with the index of the operation * it is describing. */ getSupportedOperations_1_1(Model model) generates (ErrorStatus status, vec<bool> supportedOperations); /** * Creates a prepared model for execution. * * prepareModel is used to make any necessary transformations or alternative * representations to a model for execution, possiblly including * transformations on the constant data, optimization on the model's graph, * or compilation into the device's native binary format. The model itself * is not changed. * * The model is prepared asynchronously with respect to the caller. The * prepareModel function must verify the inputs to the prepareModel function * are correct. If there is an error, prepareModel must immediately invoke * the callback with the appropriate ErrorStatus value and nullptr for the * IPreparedModel, then return with the same ErrorStatus. If the inputs to * the prepareModel function are valid and there is no error, prepareModel * must launch an asynchronous task to prepare the model in the background, * and immediately return from prepareModel with ErrorStatus::NONE. If the * asynchronous task fails to launch, prepareModel must immediately invoke * the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the * IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE. * * When the asynchronous task has finished preparing the model, it must * immediately invoke the callback function provided as an input to * prepareModel. If the model was prepared successfully, the callback object * must be invoked with an error status of ErrorStatus::NONE and the * produced IPreparedModel object. If an error occurred preparing the model, * the callback object must be invoked with the appropriate ErrorStatus * value and nullptr for the IPreparedModel. * * The only information that may be unknown to the model at this stage is * the shape of the tensors, which may only be known at execution time. As * such, some driver services may return partially prepared models, where * the prepared model can only be finished when it is paired with a set of * inputs to the model. Note that the same prepared model object can be * used with different shapes of inputs on different (possibly concurrent) * executions. * * Multiple threads can call prepareModel on the same model concurrently. * * @param model The model to be prepared for execution. * @param callback A callback object used to return the error status of * preparing the model for execution and the prepared model * if successful, nullptr otherwise. The callback object's * notify function must be called exactly once, even if the * model could not be prepared. * @return status Error status of launching a task which prepares the model * in the background; must be: * - NONE if preparation task is successfully launched * - DEVICE_UNAVAILABLE if driver is offline or busy * - GENERAL_FAILURE if there is an unspecified error * - INVALID_ARGUMENT if one of the input arguments is * invalid */ prepareModel_1_1(Model model, IPreparedModelCallback callback) generates (ErrorStatus status); };
neuralnetworks/1.1/types.hal 0 → 100644 +333 −0 Original line number Diff line number Diff line /* * 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@1.1; import @1.0::Operand; import @1.0::OperationType; /** * Operation types. * * The type of an operation in a model. */ enum OperationType : @1.0::OperationType { /** * BatchToSpace for N-D tensors. * * This operation reshapes the "batch" 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. * The spatial dimensions of this intermediate result are then optionally cropped * according to the amount to crop to produce the output. * This is the reverse of SpaceToBatch. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the * input tensor. All values must be >= 1. * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the * input tensor. All values must be >= 0. * * Outputs: * 0: A tensor of the same type as input0. */ BATCH_TO_SPACE_ND = 29, /** * Divides the second tensor from the first tensor, element-wise. * * Takes two input tensors of identical OperandType and compatible dimensions. The output * is the result of dividing the first input tensor by the second, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the input operands. * It starts with the trailing dimensions, and works its way forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor types: {@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 type, and compatible dimensions as input0. * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. * Specifies the activation to invoke on the result of each addition. * * Outputs: * 0: A tensor of the same type as input0. */ DIV = 30, /** * Computes the mean of elements across dimensions of a tensor. * * Reduces input tensor along the dimensions given in axis. Unless keep_dims is true, * the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is * true, the reduced dimensions are retained with length 1. * * If axis has no entries, all dimensions are reduced, and a tensor with a single * element is returned. * * Supported tensor types: {@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 type TENSOR_INT32. The dimensions to reduce. If None (the default), * reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)). * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1. * * Outputs: * 0: A tensor of the same type as input0. */ MEAN = 31, /** * Pads a tensor. * * This operation pads a tensor according to the specified paddings. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension * of the input tensor. * * Outputs: * 0: A tensor of the same type as input0. */ PAD = 32, /** * SpaceToBatch for N-D 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 types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the * input tensor. All values must be >= 1. * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the * input tensor. All values must be >= 0. * * Outputs: * 0: A tensor of the same type as input0. */ SPACE_TO_BATCH_ND = 33, /** * Removes dimensions of size 1 from the shape of a tensor. * * Given a tensor input, this operation returns a tensor of the same type with all * dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, * you can remove specific size 1 dimensions by specifying axis. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default), * squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension * index starts at 0. It is an error to squeeze a dimension that is not 1. * * Outputs: * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more * dimensions of size 1 removed. */ SQUEEZE = 34, /** * Extracts a strided slice of a tensor. * * This op extracts a slice of size (end-begin)/stride from the given input tensor. * Starting at the location specified by begin the slice continues by adding * stride to the index until all dimensions are not less than end. Note that a stride can * be negative, which causes a reverse slice. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input * tensor to be sliced. * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input * tensor to be sliced. * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input * tensor to be sliced. * * Outputs: * 0: A tensor of the same type as input0. */ STRIDED_SLICE = 35, /** * Subtracts the second tensor from the first tensor, element-wise. * * Takes two input tensors of identical type and compatible dimensions. The output * is the result of subtracting the second input tensor from the first one, optionally * modified by an activation function. * * Two dimensions are compatible when: * 1. they are equal, or * 2. one of them is 1 * * The size of the output is the maximum size along each dimension of the input operands. * It starts with the trailing dimensions, and works its way forward. * * Example: * input1.dimension = {4, 1, 2} * input2.dimension = {5, 4, 3, 1} * output.dimension = {5, 4, 3, 2} * * Supported tensor types: {@link 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 type, and compatible dimensions as input0. * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values. * Specifies the activation to invoke on the result of each addition. * * Outputs: * 0: A tensor of the same type as input0. */ SUB = 36, /** * Transposes the input tensor, permuting the dimensions according to the perm tensor. * * The returned tensor's dimension i must correspond to the input dimension perm[i]. * If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. * Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. * * Supported tensor types: {@link OperandType::TENSOR_FLOAT32} * {@link OperandType::TENSOR_QUANT8_ASYMM} * Supported tensor rank: up to 4 * * Inputs: * 0: An n-D tensor, specifying the input. * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input * tensor. * * Outputs: * 0: A tensor of the same type as input0. */ TRANSPOSE = 37, }; /** * Describes one operation of the model's graph. */ struct Operation { /** * The operation type. */ 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; }; /** * 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. */ vec<Operation> operations; /** * Input indexes of the model. * * Each value corresponds to the index of the operand in "operands". */ vec<uint32_t> inputIndexes; /** * Output indexes of the model. * * 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 data that were * registered by the model. * * An operand's value must be located here if and only if Operand::lifetime * equals OperandLifeTime::CONSTANT_REFERENCE. */ vec<memory> pools; };
