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// clang-format off
// Generated file (from: conv_float.mod.py). Do not edit
// clang-format off
// Generated file (from: conv_float.mod.py). Do not edit
// Generated from: conv_float.mod.py.
namespace conv_float {
// Generated conv_float test
#include "-"
// Generated model constructor
#include "-"
} // namespace conv_float
// Create the model
Model createTestModel() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 3, 3, 1},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 2, 2, 1},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 16},
},
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 16, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 4,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 20, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 24, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 28, .length = 4},
},
{
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1, 2, 2, 1},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::CONV_2D,
.inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5},
.outputs = {6},
}
};
const std::vector<uint32_t> inputIndexes = {0};
const std::vector<uint32_t> outputIndexes = {6};
std::vector<uint8_t> operandValues = {
0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 128, 62, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0
};
const std::vector<hidl_memory> pools = {};
return {
.operands = operands,
.operations = operations,
.inputIndexes = inputIndexes,
.outputIndexes = outputIndexes,
.operandValues = operandValues,
.pools = pools,
.relaxComputationFloat32toFloat16 = true,
};
}
bool is_ignored(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
std::vector<MixedTypedExample> examples = {
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
{{0, {1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
{}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
// int -> FLOAT32 map
{{0, {0.875f, 0.875f, 0.875f, 0.875f}}},
// int -> INT32 map
{},
// int -> QUANT8_ASYMM map
{}
}
}, // End of an example
};
TEST_F(NeuralnetworksHidlTest, conv_float) {
generated_tests::Execute(device,
conv_float::createTestModel,
conv_float::is_ignored,
conv_float::examples);
}
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