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Merge "Remove the data type from the OperationType enum." into oc-mr1-dev

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Miao Wang 2017-08-16 23:47:06 +00:00 committed by Android (Google) Code Review
commit 5a9c0c3186
2 changed files with 52 additions and 43 deletions
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81
neuralnetworks/1.0/types.hal
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@ -32,7 +32,7 @@ enum OperandType : uint32_t {
UINT32 = 7,
TENSOR_FLOAT16 = 8,
TENSOR_FLOAT32 = 9,
TENSOR_SYMMETRICAL_QUANT8 = 10,
TENSOR_QUANT8_ASYMM = 10,
};
// The type of operations. Unlike the operation types found in
@ -41,39 +41,39 @@ enum OperandType : uint32_t {
// TODO: Currently they are the same. Add a conversion when finalizing the model.
// When modifying, be sure to update HAL_NUM_OPERATION_TYPES in HalIntefaces.h.
enum OperationType : uint32_t {
AVERAGE_POOL_FLOAT32 = 0,
CONCATENATION_FLOAT32 = 1,
CONV_FLOAT32 = 2,
DEPTHWISE_CONV_FLOAT32 = 3,
MAX_POOL_FLOAT32 = 4,
L2_POOL_FLOAT32 = 5,
DEPTH_TO_SPACE_FLOAT32 = 6,
SPACE_TO_DEPTH_FLOAT32 = 7,
LOCAL_RESPONSE_NORMALIZATION_FLOAT32 = 8,
SOFTMAX_FLOAT32 = 9,
RESHAPE_FLOAT32 = 10,
SPLIT_FLOAT32 = 11,
FAKE_QUANT_FLOAT32 = 12,
ADD_FLOAT32 = 13,
FULLY_CONNECTED_FLOAT32 = 14,
CAST_FLOAT32 = 15,
MUL_FLOAT32 = 16,
L2_NORMALIZATION_FLOAT32 = 17,
LOGISTIC_FLOAT32 = 18,
RELU_FLOAT32 = 19,
RELU6_FLOAT32 = 20,
RELU1_FLOAT32 = 21,
TANH_FLOAT32 = 22,
DEQUANTIZE_FLOAT32 = 23,
FLOOR_FLOAT32 = 24,
GATHER_FLOAT32 = 25,
RESIZE_BILINEAR_FLOAT32 = 26,
LSH_PROJECTION_FLOAT32 = 27,
LSTM_FLOAT32 = 28,
SVDF_FLOAT32 = 29,
RNN_FLOAT32 = 30,
N_GRAM_FLOAT32 = 31,
LOOKUP_FLOAT32 = 32,
AVERAGE_POOL = 0,
CONCATENATION = 1,
CONV = 2,
DEPTHWISE_CONV = 3,
MAX_POOL = 4,
L2_POOL = 5,
DEPTH_TO_SPACE = 6,
SPACE_TO_DEPTH = 7,
LOCAL_RESPONSE_NORMALIZATION = 8,
SOFTMAX = 9,
RESHAPE = 10,
SPLIT = 11,
FAKE_QUANT = 12,
ADD = 13,
FULLY_CONNECTED = 14,
CAST = 15,
MUL = 16,
L2_NORMALIZATION = 17,
LOGISTIC = 18,
RELU = 19,
RELU6 = 20,
RELU1 = 21,
TANH = 22,
DEQUANTIZE = 23,
FLOOR = 24,
GATHER = 25,
RESIZE_BILINEAR = 26,
LSH_PROJECTION = 27,
LSTM = 28,
SVDF = 29,
RNN = 30,
N_GRAM = 31,
LOOKUP = 32,
};
// Two special values that can be used instead of a regular poolIndex.
@ -102,9 +102,16 @@ struct PerformanceInfo {
float powerUsage; // in picoJoules
};
struct OperationTuple {
// The type of operation.
OperationType operationType;
// The input data type of operation.
OperandType operandType;
};
// The capabilities of a driver.
struct Capabilities {
vec<OperationType> supportedOperationTypes;
vec<OperationTuple> supportedOperationTuples;
// TODO Do the same for baseline model IDs
bool cachesCompilation;
// TODO revisit the data types and scales.
@ -142,8 +149,8 @@ struct Operand {
// Describes one operation of the graph.
struct Operation {
// The type of operation.
OperationType type;
// The tuple describing the operation type and input type.
OperationTuple opTuple;
// 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;

14
neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
View file

@ -66,8 +66,8 @@ TEST_F(NeuralnetworksHidlTest, StatusTest) {
// initialization
TEST_F(NeuralnetworksHidlTest, InitializeTest) {
Return<void> ret = device->initialize([](const Capabilities& capabilities) {
EXPECT_NE(nullptr, capabilities.supportedOperationTypes.data());
EXPECT_NE(0ull, capabilities.supportedOperationTypes.size());
EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
EXPECT_LT(0.0f, capabilities.bootupTime);
EXPECT_LT(0.0f, capabilities.float16Performance.execTime);
@ -92,7 +92,7 @@ Model createTestModel() {
const std::vector<Operand> operands = {
{
.type = OperandType::FLOAT32,
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 2, 2, 1},
.numberOfConsumers = 1,
.scale = 0.0f,
@ -102,7 +102,7 @@ Model createTestModel() {
.length = 0},
},
{
.type = OperandType::FLOAT32,
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 2, 2, 1},
.numberOfConsumers = 1,
.scale = 0.0f,
@ -112,7 +112,7 @@ Model createTestModel() {
.length = size},
},
{
.type = OperandType::FLOAT32,
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 2, 2, 1},
.numberOfConsumers = 0,
.scale = 0.0f,
@ -124,7 +124,9 @@ Model createTestModel() {
};
const std::vector<Operation> operations = {{
.type = OperationType::ADD_FLOAT32, .inputs = {operand1, operand2}, .outputs = {operand3},
.opTuple = {OperationType::ADD, OperandType::TENSOR_FLOAT32},
.inputs = {operand1, operand2},
.outputs = {operand3},
}};
const std::vector<uint32_t> inputIndexes = {operand1};