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Fix VTS tests.

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* Adds a specification of invalid scale and zero point for TENSOR_BOOL8.
This fixes vts failures for comparison ops.
* Removes (FUNDAMENTAL_MIN - 1) from invalid OperationTypes.
FUNDAMENTAL_MIN is equal to 0 and resulting -1 was statically casted to
uint32_t and passed 4294967295 as an invalid OperationType. However, our
validateOperation function interpreted this ID as an extension ID and
didn't fail.
* Adds mutateOperationOperandTypeSkip for QUANTIZE and DEQUANTIZE.
* Adds removeOperandSkip for BIDIRECTIONAL_SEQUENCE_RNN.

Fix: 121130841
Fix: 123247345
Test: VtsHalNeuralnetworksV1_2TargetTest --hal_service_instance=android.hardware.neuralnetworks@1.2::IDevice/sample-all
Change-Id: Iefb502c6b9301d5470eb4cdaa46d398f1a0e512a
This commit is contained in:
Lev Proleev 2019-01-30 17:14:40 +00:00
parent 3603de29f8
commit 923b8c5842
1 changed files with 32 additions and 2 deletions
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34
neuralnetworks/1.2/vts/functional/ValidateModel.cpp
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@ -157,6 +157,7 @@ static uint32_t getInvalidRank(OperandType type) {
case OperandType::UINT32:
case OperandType::BOOL:
return 1;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
@ -194,6 +195,7 @@ static float getInvalidScale(OperandType type) {
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
@ -230,6 +232,7 @@ static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
@ -283,6 +286,7 @@ static void mutateOperand(Operand* operand, OperandType type) {
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
newOperand.dimensions =
@ -339,6 +343,10 @@ static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, con
// TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - DEQUANTIZE input can be any of
// TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can
// be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
// - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
@ -357,8 +365,22 @@ static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, con
return true;
}
} break;
case OperationType::QUANTIZE:
case OperationType::RANDOM_MULTINOMIAL: {
if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32) {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
case OperationType::DEQUANTIZE: {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_QUANT8_ASYMM ||
type == OperandType::TENSOR_QUANT8_SYMM ||
type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
return true;
}
if (operand == operation.outputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
@ -397,7 +419,6 @@ static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Mode
///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
static const uint32_t invalidOperationTypes[] = {
static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MIN) - 1,
static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
@ -484,6 +505,15 @@ static bool removeOperandSkip(size_t operand, const Model& model) {
}
}
}
// BIDIRECTIONAL_SEQUENCE_RNN can have either on or two outputs
// depending on a mergeOutputs parameter
if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {
return true;
}
}
}
}
return false;
}