More tests for graph validation.
- detect cycle (CycleTest)
- detect bad execution order (mutateExecutionOrderTest)
- detect lifetime inconsistent with whether operand is written (mutateOperandLifeTimeTest)
- detect lifetime inconsistent with Model inputIndexes/outputIndexes (mutateOperandInputOutputTest)
- detect incorrect number of consumers (mutateOperandNumberOfConsumersTest)
- detect operand written multiple times (mutateOperandAddWriterTest)
- detect operand never written (mutateOperationRemoveWriteTest)
Bug: 66478689
Test: VtsHalNeuralnetworksV1_*TargetTest
Change-Id: Id4ba19660bbd31a16f8a675f7b6437f4d779e8da
diff --git a/neuralnetworks/1.2/vts/functional/Utils.cpp b/neuralnetworks/1.2/vts/functional/Utils.cpp
new file mode 100644
index 0000000..cc654f2
--- /dev/null
+++ b/neuralnetworks/1.2/vts/functional/Utils.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (C) 2019 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.
+ */
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+
+#include <functional>
+#include <numeric>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+
+uint32_t sizeOfData(V1_2::OperandType type) {
+ switch (type) {
+ case V1_2::OperandType::FLOAT32:
+ case V1_2::OperandType::INT32:
+ case V1_2::OperandType::UINT32:
+ case V1_2::OperandType::TENSOR_FLOAT32:
+ case V1_2::OperandType::TENSOR_INT32:
+ return 4;
+ case V1_2::OperandType::TENSOR_QUANT16_SYMM:
+ case V1_2::OperandType::TENSOR_FLOAT16:
+ case V1_2::OperandType::FLOAT16:
+ case V1_2::OperandType::TENSOR_QUANT16_ASYMM:
+ return 2;
+ case V1_2::OperandType::TENSOR_QUANT8_ASYMM:
+ case V1_2::OperandType::BOOL:
+ case V1_2::OperandType::TENSOR_BOOL8:
+ case V1_2::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case V1_2::OperandType::TENSOR_QUANT8_SYMM:
+ return 1;
+ default:
+ CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
+ return 0;
+ }
+}
+
+static bool isTensor(V1_2::OperandType type) {
+ switch (type) {
+ case V1_2::OperandType::FLOAT32:
+ case V1_2::OperandType::INT32:
+ case V1_2::OperandType::UINT32:
+ case V1_2::OperandType::FLOAT16:
+ case V1_2::OperandType::BOOL:
+ return false;
+ case V1_2::OperandType::TENSOR_FLOAT32:
+ case V1_2::OperandType::TENSOR_INT32:
+ case V1_2::OperandType::TENSOR_QUANT16_SYMM:
+ case V1_2::OperandType::TENSOR_FLOAT16:
+ case V1_2::OperandType::TENSOR_QUANT16_ASYMM:
+ case V1_2::OperandType::TENSOR_QUANT8_ASYMM:
+ case V1_2::OperandType::TENSOR_BOOL8:
+ case V1_2::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case V1_2::OperandType::TENSOR_QUANT8_SYMM:
+ return true;
+ default:
+ CHECK(false) << "Invalid OperandType " << static_cast<uint32_t>(type);
+ return false;
+ }
+}
+
+uint32_t sizeOfData(const V1_2::Operand& operand) {
+ const uint32_t dataSize = sizeOfData(operand.type);
+ if (isTensor(operand.type) && operand.dimensions.size() == 0) return 0;
+ return std::accumulate(operand.dimensions.begin(), operand.dimensions.end(), dataSize,
+ std::multiplies<>{});
+}
+
+} // namespace neuralnetworks
+} // namespace hardware
+} // namespace android