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ONE - On-device Neural Engine
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Files | |
| file | NeuralNetworks.h |
Data Structures | |
| struct | ANeuralNetworksOperandType |
Typedefs | |
| typedef struct ANeuralNetworksMemory | ANeuralNetworksMemory |
| typedef struct ANeuralNetworksModel | ANeuralNetworksModel |
| typedef struct ANeuralNetworksCompilation | ANeuralNetworksCompilation |
| typedef struct ANeuralNetworksExecution | ANeuralNetworksExecution |
| typedef struct ANeuralNetworksOperandType | ANeuralNetworksOperandType |
| typedef int32_t | ANeuralNetworksOperationType |
| typedef struct ANeuralNetworksEvent | ANeuralNetworksEvent |
| typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation |
ANeuralNetworksCompilation is an opaque type that can be used to compile a machine learning model.
To use:
ANeuralNetworksCompilation_create function. ANeuralNetworksCompilation_setPreference). ANeuralNetworksCompilation_finish. ANeuralNetworksExecution_create. ANeuralNetworksCompilation_free once all executions using the compilation have completed.A compilation is completed by calling ANeuralNetworksCompilation_finish. A compilation is destroyed by calling ANeuralNetworksCompilation_free.
A compilation cannot be modified once ANeuralNetworksCompilation_finish has been called on it.
It is the application's responsibility to make sure that only one thread modifies a compilation at a given time. It is however safe for more than one thread to use the compilation once ANeuralNetworksCompilation_finish has returned.
It is also the application's responsibility to ensure that there are no other uses of the compilation after calling ANeuralNetworksCompilation_free. This includes any execution object created using the compilation.
Definition at line 1480 of file NeuralNetworks.h.
| typedef struct ANeuralNetworksEvent ANeuralNetworksEvent |
ANeuralNetworksEvent is an opaque type that represents an event that will be signaled once an execution completes.
Definition at line 1542 of file NeuralNetworks.h.
| typedef struct ANeuralNetworksExecution ANeuralNetworksExecution |
ANeuralNetworksExecution is an opaque type that can be used to apply a machine learning model to a set of inputs.
To use:
ANeuralNetworksExecution_create function. ANeuralNetworksExecution_setInput or ANeuralNetworksExecution_setInputFromMemory. ANeuralNetworksExecution_setOutput or ANeuralNetworksExecution_setOutputFromMemory. ANeuralNetworksExecution_startCompute. ANeuralNetworksEvent_wait. ANeuralNetworksExecution_free.An execution cannot be modified once ANeuralNetworksExecution_startCompute has been called on it.
An execution can be applied to a model with ANeuralNetworksExecution_startCompute only once. Create new executions to do new evaluations of the model.
It is the application's responsibility to make sure that only one thread modifies an execution at a given time. It is however safe for more than one thread to use ANeuralNetworksEvent_wait at the same time.
It is also the application's responsibility to ensure that there are no other uses of the request after calling ANeuralNetworksExecution_free.
Definition at line 1515 of file NeuralNetworks.h.
| typedef struct ANeuralNetworksMemory ANeuralNetworksMemory |
ANeuralNetworksMemory is an opaque type that represents memory.
This type is used to represent shared memory, memory mapped files, and similar memories.
By using shared memory, a program can efficiently communicate to the runtime and drivers the tensors that define a model. See ANeuralNetworksModel_setOperandValueFromMemory. An application should typically create one shared memory object that contains every tensor needed to define a model. ANeuralNetworksMemory_createFromFd can be used to create shared memory from a file handle. ANeuralNetworksMemory_createShared can be used to directly created shared memory.
Memory objects can also be used to specify the input and output arguments of an execution. See ANeuralNetworksExecution_setInputFromMemory and ANeuralNetworksExecution_setOutputFromMemory.
Definition at line 1422 of file NeuralNetworks.h.
| typedef struct ANeuralNetworksModel ANeuralNetworksModel |
ANeuralNetworksModel is an opaque type that contains a description of the mathematical operations that constitute the model.
The model will be built by calling
A model is completed by calling ANeuralNetworksModel_finish. A model is destroyed by calling ANeuralNetworksModel_free.
A model cannot be modified once ANeuralNetworksModel_finish has been called on it.
It is the application's responsibility to make sure that only one thread modifies a model at a given time. It is however safe for more than one thread to use the model once ANeuralNetworksModel_finish has returned.
It is also the application's responsibility to ensure that there are no other uses of the model after calling ANeuralNetworksModel_free. This includes any compilation or execution object created using the model.
Definition at line 1448 of file NeuralNetworks.h.
| typedef struct ANeuralNetworksOperandType ANeuralNetworksOperandType |
ANeuralNetworksOperandType describes the type of an operand. This structure is used to describe both scalars and tensors.
| typedef int32_t ANeuralNetworksOperationType |
Definition at line 1536 of file NeuralNetworks.h.
| anonymous enum |
For ANeuralNetworksModel_setOperandValue, values with a length smaller or equal to this will be immediately copied into the model. The size is in bytes.
| Enumerator | |
|---|---|
| ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES | |
Definition at line 1400 of file NeuralNetworks.h.
| enum FuseCode |
Fused activation function types.
Definition at line 1317 of file NeuralNetworks.h.
| enum OperandCode |
Operand types.
The type of operands that can be added to a model.
Although we define many types, most operators accept just a few types. Most used are ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, and ANEURALNETWORKS_INT32.
Definition at line 63 of file NeuralNetworks.h.
| enum OperationCode |
Operation types.
The type of operations that can be added to a model.
| Enumerator | |
|---|---|
| ANEURALNETWORKS_ADD | Adds two tensors, element-wise. Takes two input tensors of identical type and compatible dimensions. The output is the sum of both input tensors, optionally modified by an activation function. Two dimensions are compatible when:
The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Example: input1.dimension = {4, 1, 2}
input2.dimension = {5, 4, 3, 1}
output.dimension = {5, 4, 3, 2}
Supported tensor types: Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_AVERAGE_POOL_2D | 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[batch, row, col, channel] =
sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
Supported tensor types: Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels) data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding):
Inputs (implicit padding):
Outputs:
|
| ANEURALNETWORKS_CONCATENATION | Concatenates the input tensors along the given dimension. The input tensors must have identical type and the same dimensions except the dimension along the concatenation axis. Supported tensor types: Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_CONV_2D | 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[batch, row, col, channel] =
sum_{i, j} (
input[batch, row + i, col + j, k] *
filter[channel, row + i, col + j, k] +
bias[channel]
)
Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding):
Inputs (implicit padding):
Outputs:
|
| ANEURALNETWORKS_DEPTHWISE_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]
)
Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding):
Inputs (explicit padding):
Outputs:
|
| ANEURALNETWORKS_DEPTH_TO_SPACE | 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 types: Supported tensor rank: 4, with "NHWC" data layout. Inputs:
Outputs:
|
| ANEURALNETWORKS_DEQUANTIZE | Dequantizes the input tensor. The formula is: output = (input - zeroPoint) * scale. Supported tensor types: Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_EMBEDDING_LOOKUP | 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], we would expect all three values found in Lookups to be between 0 and 39. The resulting tensor will have shape of [3, 200, 300]. If a value in Lookups is out of bounds, the operation will fail and an error will be reported. Inputs:
Output:
|
| ANEURALNETWORKS_FLOOR | Computes element-wise floor() on the input tensor. Supported tensor types: Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_FULLY_CONNECTED | Denotes a fully (densely) connected layer, which connects all elements in the input tensor with each element in the output tensor. This layer implements the operation: outputs = activation(inputs * weights’ + bias) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_HASHTABLE_LOOKUP | 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 will have zero values. For example, if Values has shape of [40, 200, 300], Keys should have a shape of [40]. If Lookups tensor has shape of [3], we're concatenating three slices, so the resulting tensor will have the shape of [3, 200, 300]. If the first entry in Lookups has the value 123456, we'll look for that value in Keys tensor. If the sixth entry of Keys contains 123456, we'll select the sixth slice of Values. If no entry in Keys has 123456, a slice of zeroes will be concatenated. Inputs:
Outputs:
|
| ANEURALNETWORKS_L2_NORMALIZATION | 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 more dimensions, independently normalizes each 1-D slice along dimension dim. Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels). Inputs:
Outputs:
|
| ANEURALNETWORKS_L2_POOL_2D | 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[batch, row, col, channel] =
sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding):
Inputs (implicit padding):
Outputs:
|
| ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION | 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)
Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Inputs:
Outputs:
|
| ANEURALNETWORKS_LOGISTIC | Computes sigmoid activation on the input tensor element-wise. The output is calculated using this formula: output = 1 / (1 + exp(-input)) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_LSH_PROJECTION | Projects an input to a bit vector via locality senstive hashing. Inputs:
Outputs:
|
| ANEURALNETWORKS_LSTM | Long short-term memory unit (LSTM) recurrent network layer. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997. The peephole implementation 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. 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 class has the following independently optional inputs:
Supported tensor types (type T): Inputs:
Outputs:
|
| ANEURALNETWORKS_MAX_POOL_2D | 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[batch, row, col, channel] =
max_{i, j} (input[batch, row + i, col + j, channel])
Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Both explicit padding and implicit padding are supported. Inputs (explicit padding):
Inputs (implicit padding):
Outputs:
|
| ANEURALNETWORKS_MUL | Multiplies two tensors, element-wise. Takes two input tensors of identical type and compatible dimensions. The output is the product of both input tensors, optionally modified by an activation function. Two dimensions are compatible when:
The size of the resulting output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Supported tensor types: Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_RELU | Computes rectified linear activation on the input tensor element-wise. The output is calculated using this formula: output = max(0, input) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_RELU1 | Computes rectified linear 1 activation on the input tensor element-wise. The output is calculated using this formula: output = min(1.f, max(-1.f, input)) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_RELU6 | Computes rectified linear 6 activation on the input tensor element-wise. The output is calculated using this formula: output = min(6, max(0, input)) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_RESHAPE | 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 types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_RESIZE_BILINEAR | Resizes images to given size using the bilinear interpretation. Resized images will be distorted if their output aspect ratio is not the same as input aspect ratio. Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Inputs:
Outputs:
|
| ANEURALNETWORKS_RNN | A basic recurrent neural network layer. This layer implements the operation: outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias) Where:
Supported tensor types (Type T): Inputs:
Outputs:
|
| ANEURALNETWORKS_SOFTMAX | 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)}
Supported tensor types: Supported tensor rank: 2 or 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_SPACE_TO_DEPTH | Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. The value block_size indicates the input block size and how the data is moved. Chunks of data of size block_size * block_size from depth are rearranged into non-overlapping blocks of size block_size x block_size. The depth of the output tensor is input_depth * block_size * block_size. The input tensor's height and width must be divisible by block_size. Supported tensor types: Supported tensor rank: 4, with "NHWC" data layout. Inputs:
Outputs:
|
| ANEURALNETWORKS_SVDF | 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:
Specifically, for rank 1, this layer implements the operation: memory = push(conv1d(inputs, weights_feature, feature_dim, "ANEURALNETWORKS_PADDING_VALID")); outputs = activation(memory * weights_time + bias); Where:
Each rank adds a dimension to the weights matrices by means of stacking the filters. Supported tensor types (type T): Inputs:
Outputs:
|
| ANEURALNETWORKS_TANH | Computes hyperbolic tangent of input tensor element-wise. The output is calculated using this formula: output = tanh(input) Supported tensor types: Supported tensor rank: up to 4. Inputs:
Outputs:
|
| ANEURALNETWORKS_DIV | Element-wise division of two tensors. Takes two input tensors of identical type and compatible dimensions. The output is the result of dividing the first input tensor by the second, optionally modified by an activation function. Two dimensions are compatible when:
The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Example: input1.dimension = {4, 1, 2} input2.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2} Supported tensor types: Supported tensor 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 Outputs: 0: A tensor of the same type as input0. |
| ANEURALNETWORKS_PAD | Pads a tensor. This operation pads a tensor according to the specified paddings. Supported tensor Supported tensor rank: up to 4 Inputs:
Outputs:
Available since API level 28. |
| ANEURALNETWORKS_STRIDED_SLICE | 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 Supported tensor rank: up to 4 Inputs:
Outputs:
|
| ANEURALNETWORKS_SUB | Element-wise subtraction of two tensors. Takes two input tensors of identical type and compatible dimensions. The output is the result of subtracting the second input tensor from the first one, optionally modified by an activation function. Two dimensions are compatible when:
The size of the output is the maximum size along each dimension of the input operands. It starts with the trailing dimensions, and works its way forward. Example: input1.dimension = {4, 1, 2} input2.dimension = {5, 4, 3, 1} output.dimension = {5, 4, 3, 2} Supported tensor types: Supported tensor 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 Outputs: 0: A tensor of the same type as input0. |
Definition at line 97 of file NeuralNetworks.h.
| enum PaddingCode |
Implicit padding algorithms.
Definition at line 1332 of file NeuralNetworks.h.
| enum PreferenceCode |
Execution preferences.
Definition at line 1363 of file NeuralNetworks.h.
| enum ResultCode |
Result codes.
Definition at line 1384 of file NeuralNetworks.h.
| int ANeuralNetworksCompilation_create | ( | ANeuralNetworksModel * | model, |
| ANeuralNetworksCompilation ** | compilation | ||
| ) |
Create a ANeuralNetworksCompilation to compile the given model.
This only creates the object. Compilation is only performed once ANeuralNetworksCompilation_finish is invoked.
ANeuralNetworksCompilation_finish should be called once all desired properties have been set on the compilation.
ANeuralNetworksModel_free should be called once the compilation is no longer needed.
The provided model must outlive the compilation.
The model must already have been finished by a call to ANeuralNetworksModel_finish.
See ANeuralNetworksCompilation for information on multithreaded usage.
| model | The ANeuralNetworksModel to be compiled. |
| compilation | The newly created object or NULL if unsuccessful. |
Definition at line 155 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, LOG, and m.
| int ANeuralNetworksCompilation_finish | ( | ANeuralNetworksCompilation * | compilation | ) |
Indicate that we have finished modifying a compilation. Required before calling ANeuralNetworksExecution_create.
An application is responsible to make sure that no other thread uses the compilation at the same time.
This function must only be called once for a given compilation.
See ANeuralNetworksCompilation for information on multithreaded usage.
| compilation | The compilation to be finished. |
Definition at line 191 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, CompilationBuilder::finish(), and LOG.
| void ANeuralNetworksCompilation_free | ( | ANeuralNetworksCompilation * | compilation | ) |
Destroy a compilation.
The compilation need not have been finished by a call to ANeuralNetworksModel_finish.
See ANeuralNetworksCompilation for information on multithreaded usage.
| compilation | The compilation to be destroyed. Passing NULL is acceptable and results in no operation. |
Definition at line 171 of file NeuralNetworks.cpp.
| int ANeuralNetworksCompilation_setPreference | ( | ANeuralNetworksCompilation * | compilation, |
| int32_t | preference | ||
| ) |
Sets the execution preference.
Provides guidance to the runtime when trade-offs are possible.
See ANeuralNetworksCompilation for information on multithreaded usage.
| compilation | The compilation to be modified. |
| preference | Either PREFER_LOW_POWER, PREFER_SINGLE_FAST_ANSWER, or PREFER_SUSTAINED_SPEED. |
Definition at line 179 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_NO_ERROR, ANEURALNETWORKS_UNEXPECTED_NULL, and LOG.
| void ANeuralNetworksEvent_free | ( | ANeuralNetworksEvent * | event | ) |
Destroys the event.
See ANeuralNetworksExecution for information on multithreaded usage.
Definition at line 330 of file NeuralNetworks.cpp.
| int ANeuralNetworksEvent_wait | ( | ANeuralNetworksEvent * | event | ) |
Waits until the execution completes.
More than one thread can wait on an event. When the execution completes, all threads will be released.
See ANeuralNetworksExecution for information on multithreaded usage.
Definition at line 319 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_NO_ERROR, ANEURALNETWORKS_UNEXPECTED_NULL, and LOG.
| int ANeuralNetworksExecution_create | ( | ANeuralNetworksCompilation * | compilation, |
| ANeuralNetworksExecution ** | execution | ||
| ) |
Create a ANeuralNetworksExecution to apply the given compilation. This only creates the object. Computation is only performed once ANeuralNetworksExecution_startCompute is invoked.
The provided compilation must outlive the execution.
See ANeuralNetworksExecution for information on multithreaded usage.
| compilation | The ANeuralNetworksCompilation to be evaluated. |
| execution | The newly created object or NULL if unsuccessful. |
Definition at line 202 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, CompilationBuilder::createExecution(), and LOG.
| void ANeuralNetworksExecution_free | ( | ANeuralNetworksExecution * | execution | ) |
Destroy an execution.
If called on an execution for which ANeuralNetworksExecution_startCompute has been called, the function will return immediately but will mark the execution to be deleted once the computation completes. The related ANeuralNetworksEvent will be signaled and the ANeuralNetworksEvent_wait will return ANEURALNETWORKS_ERROR_DELETED.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be destroyed. Passing NULL is acceptable and results in no operation. |
Definition at line 218 of file NeuralNetworks.cpp.
| int ANeuralNetworksExecution_setInput | ( | ANeuralNetworksExecution * | execution, |
| int32_t | index, | ||
| const ANeuralNetworksOperandType * | type, | ||
| const void * | buffer, | ||
| size_t | length | ||
| ) |
Associate a user buffer with an input of the model of the ANeuralNetworksExecution.
The provided buffer must outlive the execution.
If the input is optional, you can indicate that it is omitted by passing nullptr for buffer and 0 for length.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be modified. |
| index | The index of the input argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand. |
| type | The type of the operand. This should be used to specify the dimensions that were set to 0 when the operand was added to the model. All other properties of the type must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed. |
| buffer | The buffer containing the data. |
| length | The length in bytes of the buffer. |
Definition at line 226 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, and LOG.
| int ANeuralNetworksExecution_setInputFromMemory | ( | ANeuralNetworksExecution * | execution, |
| int32_t | index, | ||
| const ANeuralNetworksOperandType * | type, | ||
| const ANeuralNetworksMemory * | memory, | ||
| size_t | offset, | ||
| size_t | length | ||
| ) |
Associate part of a memory object with an input of the model of the ANeuralNetworksExecution.
The provided memory must outlive the execution.
If the input is optional, you can indicate that it is omitted by usingLink ANeuralNetworks_setInput} instead, passing nullptr for buffer and 0 for length.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be modified. |
| index | The index of the input argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand. |
| type | The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed. |
| memory | The memory containing the data. |
| offset | This specifies the location of the data whithin the memory. The offset is in bytes from the start of memory. |
| length | The size in bytes of the data value. |
Definition at line 245 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, LOG, m, and offset().
| int ANeuralNetworksExecution_setOutput | ( | ANeuralNetworksExecution * | execution, |
| int32_t | index, | ||
| const ANeuralNetworksOperandType * | type, | ||
| void * | buffer, | ||
| size_t | length | ||
| ) |
Associate a user buffer with an output of the model of the ANeuralNetworksExecution.
If the output is optional, you can indicate that it is omitted by passing nullptr for buffer and 0 for length.
The provided buffer must outlive the execution.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be modified. |
| index | The index of the output argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand. |
| type | The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed. |
| buffer | The buffer where the data is to be written. |
| length | The length in bytes of the buffer. |
Definition at line 261 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, and LOG.
| int ANeuralNetworksExecution_setOutputFromMemory | ( | ANeuralNetworksExecution * | execution, |
| int32_t | index, | ||
| const ANeuralNetworksOperandType * | type, | ||
| const ANeuralNetworksMemory * | memory, | ||
| size_t | offset, | ||
| size_t | length | ||
| ) |
Associate part of a memory object with an output of the model of the ANeuralNetworksExecution.
If the output is optional, you can indicate that it is omitted by usingLink ANeuralNetworks_setOutput} instead, passing nullptr for buffer and 0 for length.
The provided memory must outlive the execution.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be modified. |
| index | The index of the output argument we are setting. It is an index into the lists passed to ANeuralNetworksModel_identifyInputsAndOutputs. It is not the index associated with ANeuralNetworksModel_addOperand. |
| type | The type of the operand. This can be used to specify the dimensions that were set to 0 when the operand was added to the model. All other values must be the same as specified in the model. If the type is the same as specified when the model was built, NULL can be passed. |
| memory | The memory where the data is to be stored. |
| offset | This specifies the location of the data whithin the memory. The offset is in bytes from the start of memory. |
| length | The length in bytes of the data value. |
Definition at line 274 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, LOG, m, and offset().
| int ANeuralNetworksExecution_startCompute | ( | ANeuralNetworksExecution * | execution, |
| ANeuralNetworksEvent ** | event | ||
| ) |
Schedule evaluation of the execution.
Schedules evaluation of the execution. Once the model has been applied and the outputs are ready to be consumed, the returned event will be signaled. Use ANeuralNetworksEvent_wait to wait for that event.
Multiple executions can be scheduled and evaluated concurrently. The runtime makes no guarantee on the ordering of completion of executions. If it's important to the application, the application should enforce the ordering by using ANeuralNetworksEvent_wait.
ANeuralNetworksEvent_wait must be called to recuperate the resources used by the execution.
See ANeuralNetworksExecution for information on multithreaded usage.
| execution | The execution to be scheduled and executed. |
| event | The event that will be signaled on completion. event is set to NULL if there's an error. |
Definition at line 290 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_NO_ERROR, ANEURALNETWORKS_UNEXPECTED_NULL, and LOG.
| int ANeuralNetworksMemory_createFromFd | ( | size_t | size, |
| int | protect, | ||
| int | fd, | ||
| size_t | offset, | ||
| ANeuralNetworksMemory ** | memory | ||
| ) |
Creates a shared memory object from a file descriptor.
The shared memory is backed by a file descriptor via mmap. See ANeuralNetworksMemory for a description on how to use this shared memory.
| size | The requested size in bytes. Must not be larger than the file size. |
| prot | The desired memory protection for the mapping. It is either PROT_NONE or the bitwise OR of one or more of the following flags: PROT_READ, PROT_WRITE. |
| fd | The requested file descriptor. The file descriptor has to be mmap-able. The file descriptor will be duplicated. |
| offset | The offset to the beginning of the file of the area to map. The offset has to be aligned to a page size. |
| memory | The memory object to be created. Set to NULL if unsuccessful. |
Definition at line 29 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_NO_ERROR, ANEURALNETWORKS_OUT_OF_MEMORY, m, offset(), and size.
| void ANeuralNetworksMemory_free | ( | ANeuralNetworksMemory * | memory | ) |
Delete a memory object.
Destroys the object used by the run time to keep track of the memory. This will free the underlying actual memory if no other code has open handles to this memory.
| memory | The memory object to be freed. |
Definition at line 47 of file NeuralNetworks.cpp.
References m.
| int ANeuralNetworksModel_addOperand | ( | ANeuralNetworksModel * | model, |
| const ANeuralNetworksOperandType * | type | ||
| ) |
Add an operand to a model.
The order in which the operands are added is important. The first one added to a model will have the index value 0, the second 1, etc. These indexes are used as operand identifiers in ANeuralNetworksModel_addOperation, ANeuralNetworksExecution_setInput, ANeuralNetworksExecution_setInputFromMemory, ANeuralNetworksExecution_setOutput, ANeuralNetworksExecution_setOutputFromMemory and ANeuralNetworksExecution_setOperandValue.
To build a model that can accomodate inputs of various sizes, as you may want to do for a CNN, set the size of the dimensions that will vary at run time to 0. If you do so, provide the full dimensions when calling ANeuralNetworksExecution_setInput or ANeuralNetworksExecution_setInputFromMemory.
Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.
See ANeuralNetworksModel for information on multithreaded usage.
| model | The model to be modified. |
| type | The ANeuralNetworksOperandType that describes the shape of the operand. |
Definition at line 89 of file NeuralNetworks.cpp.
References ModelBuilder::addOperand(), ANEURALNETWORKS_UNEXPECTED_NULL, LOG, and m.
| int ANeuralNetworksModel_addOperation | ( | ANeuralNetworksModel * | model, |
| ANeuralNetworksOperationType | type, | ||
| uint32_t | inputCount, | ||
| const uint32_t * | inputs, | ||
| uint32_t | outputCount, | ||
| const uint32_t * | outputs | ||
| ) |
Add an operation to a model.
| model | The model to be modified. |
| type | The type of the operation. |
| inputCount | The number of entries in the inputs array. |
| inputs | An array of indexes identifying each operand. |
| outputCount | The number of entries in the outputs array. |
| outputs | An array of indexes identifying each operand. |
The operands specified by inputs and outputs must have been previously added by calls to ANeuralNetworksModel_addOperand.
Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.
See ANeuralNetworksModel for information on multithreaded usage.
Definition at line 127 of file NeuralNetworks.cpp.
References ModelBuilder::addOperation(), ANEURALNETWORKS_UNEXPECTED_NULL, LOG, and m.
| int ANeuralNetworksModel_create | ( | ANeuralNetworksModel ** | model | ) |
Create an empty ANeuralNetworksModel.
This only creates the object. Computation is performed once ANeuralNetworksExecution_startCompute is invoked.
The model should be constructed with calls to ANeuralNetworksModel_addOperation and ANeuralNetworksModel_addOperand
ANeuralNetworksModel_finish should be called once the model has been fully constructed.
ANeuralNetworksModel_free should be called once the model is no longer needed.
| model | The ANeuralNetworksModel to be created. Set to NULL if unsuccessful. |
Definition at line 54 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_NO_ERROR, ANEURALNETWORKS_OUT_OF_MEMORY, ANEURALNETWORKS_UNEXPECTED_NULL, LOG, and m.
| int ANeuralNetworksModel_finish | ( | ANeuralNetworksModel * | model | ) |
Indicate that we have finished modifying a model. Required before calling ANeuralNetworksCompilation_create.
An application is responsible to make sure that no other thread uses the model at the same time.
This function must only be called once for a given model.
See ANeuralNetworksModel for information on multithreaded usage.
| model | The model to be finished. |
Definition at line 78 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, ModelBuilder::finish(), LOG, and m.
| void ANeuralNetworksModel_free | ( | ANeuralNetworksModel * | model | ) |
Destroy a model.
The model need not have been finished by a call to ANeuralNetworksModel_finish.
See ANeuralNetworksModel for information on multithreaded usage.
| model | The model to be destroyed. Passing NULL is acceptable and results in no operation. |
Definition at line 71 of file NeuralNetworks.cpp.
References m.
| int ANeuralNetworksModel_identifyInputsAndOutputs | ( | ANeuralNetworksModel * | model, |
| uint32_t | inputCount, | ||
| const uint32_t * | inputs, | ||
| uint32_t | outputCount, | ||
| const uint32_t * | outputs | ||
| ) |
Specfifies which operands will be the model's inputs and outputs.
An operand cannot be used for both input and output. Doing so will return an error.
| model | The model to be modified. |
| inputCount | The number of entries in the inputs array. |
| inputs | An array of indexes identifying the input operands. |
| outputCount | The number of entries in the outputs array. |
| outputs | An array of indexes identifying the output operands. |
The operands specified by inputs and outputs must have been previously added by calls to ANeuralNetworksModel_addOperand.
Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.
See ANeuralNetworksModel for information on multithreaded usage.
Definition at line 142 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, ModelBuilder::identifyInputsAndOutputs(), LOG, and m.
| int ANeuralNetworksModel_setOperandValue | ( | ANeuralNetworksModel * | model, |
| int32_t | index, | ||
| const void * | buffer, | ||
| size_t | length | ||
| ) |
Sets an operand to a constant value.
Values of length smaller or equal to ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES are immediately copied into the model.
For values of length greater than ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES, a pointer to the buffer is stored within the model. The application is responsible for not changing the content of this region until all executions using this model have completed. As the data may be copied during processing, modifying the data after this call yields undefined results.
For large tensors, using ANeuralNetworksModel_setOperandValueFromMemory is likely to be more efficient.
To indicate that an optional operand should be considered missing, pass nullptr for buffer and 0 for length.
Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.
See ANeuralNetworksModel for information on multithreaded usage.
| model | The model to be modified. |
| index | The index of the model operand we're setting. |
| buffer | A pointer to the data to use. |
| length | The size in bytes of the data value. |
Definition at line 101 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, LOG, m, and ModelBuilder::setOperandValue().
| int ANeuralNetworksModel_setOperandValueFromMemory | ( | ANeuralNetworksModel * | model, |
| int32_t | index, | ||
| const ANeuralNetworksMemory * | memory, | ||
| size_t | offset, | ||
| size_t | length | ||
| ) |
Sets an operand to a value stored in a memory object.
The content of the memory is not copied. A reference to that memory is stored inside the model. The application is responsible for not changing the content of the memory region until all executions using this model have completed. As the data may be copied during processing, modifying the data after this call yields undefined results.
To indicate that an optional operand should be considered missing, use ANeuralNetworksModel_setOperandValue instead, passing nullptr for buffer.
Attempting to modify a model once ANeuralNetworksModel_finish has been called will return an error.
See ANeuralNetworksModel for information on multithreaded usage.
| model | The model to be modified. |
| index | The index of the model operand we're setting. |
| buffer | A pointer to the data to use. |
| memory | The memory containing the data. |
| offset | This specifies the location of the data within the memory. The offset is in bytes from the start of memory. |
| length | The size in bytes of the data value. |
Definition at line 113 of file NeuralNetworks.cpp.
References ANEURALNETWORKS_UNEXPECTED_NULL, LOG, m, offset(), and ModelBuilder::setOperandValueFromMemory().
| uint32_t ANeuralNetworksOperandType::dimensionCount |
The number of dimensions. It should be 0 for scalars.
Definition at line 1525 of file NeuralNetworks.h.
Referenced by ModelArgumentInfo::updateDimensionInfo().
| const uint32_t* ANeuralNetworksOperandType::dimensions |
The dimensions of the tensor. It should be nullptr for scalars.
Definition at line 1527 of file NeuralNetworks.h.
Referenced by ModelArgumentInfo::updateDimensionInfo().
| float ANeuralNetworksOperandType::scale |
These two fields are only used for quantized tensors. They should be zero for scalars and non-fixed point tensors. The dequantized value of each entry is (value - zeroPoint) * scale.
Definition at line 1532 of file NeuralNetworks.h.
| int32_t ANeuralNetworksOperandType::type |
The data type, e.g ANEURALNETWORKS_INT8.
Definition at line 1523 of file NeuralNetworks.h.
Referenced by ModelArgumentInfo::updateDimensionInfo().
| int32_t ANeuralNetworksOperandType::zeroPoint |
Definition at line 1533 of file NeuralNetworks.h.
1.9.8