Snap for 4579913 from 52d951b8ebf02a76e07398406119ba32dedf9c82 to pi-release (74bb0e8f) · Commits · e / os / android_hardware_interfaces

gnss/1.1/vts/functional/gnss_hal_test_cases.cpp

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	@@ -443,5 +443,9 @@ TEST_F(GnssHalTest, GnssDebugValuesSanityTest) {

	EXPECT_GE(data.position.ageSeconds, 0);		EXPECT_GE(data.position.ageSeconds, 0);
	}		}

			EXPECT_GE(data.time.timeEstimate, 1514764800000); // Jan 01 2018 00:00:00
			EXPECT_GT(data.time.timeUncertaintyNs, 0);
			EXPECT_GT(data.time.frequencyUncertaintyNsPerSec, 0);
	}		}
	}		}

neuralnetworks/1.1/Android.bp

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			// 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

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			/*
			* 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

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			/*
			* 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;
			};