Moved validation to the specific classes.
It's hard to keep track of what's validated when the validations
are found in two files (NeuralNetworks.cpp + one of the other).
Move all the validations except for the initial nullptr check
to the specific file.
Moved some code from Memory.h into the .cpp file.
Adjusted some type size for setInput/Output/*
Adding new validations will be in the future CLs to make
review easier.
Bug: 63905942
Test: Ran local and VTS tests.
Change-Id: I2c448c9b0d3f2e878278f24eafce5720590403f3
diff --git a/runtime/NeuralNetworks.cpp b/runtime/NeuralNetworks.cpp
index d68dec4..1ef9cf1 100644
--- a/runtime/NeuralNetworks.cpp
+++ b/runtime/NeuralNetworks.cpp
@@ -213,54 +213,8 @@
using android::sp;
using namespace android::nn;
-// Validates the type. The used dimensions can be underspecified.
-static int ValidateOperandType(const ANeuralNetworksOperandType& type, const char* tag,
- bool allowPartial) {
- if (!allowPartial) {
- for (uint32_t i = 0; i < type.dimensionCount; i++) {
- if (type.dimensions[i] == 0) {
- LOG(ERROR) << tag << " OperandType invalid dimensions[" << i
- << "] = " << type.dimensions[i];
- return ANEURALNETWORKS_BAD_DATA;
- }
- }
- }
- if (!validCode(kNumberOfDataTypes, kNumberOfDataTypesOEM, type.type)) {
- LOG(ERROR) << tag << " OperandType invalid type " << type.type;
- return ANEURALNETWORKS_BAD_DATA;
- }
- /* TODO validate the quantization info.
- if (type.offset != 0.f && type.scale == 0.f) {
- LOG(ERROR) << ("%s OperandType invalid offset %f and scale %f", tag, type.offset,
- type.scale); return ANEURALNETWORKS_BAD_DATA;
- }
- if (type.scale != 0.f &&
- (type.type != ANEURALNETWORKS_FLOAT32)) {
- LOG(ERROR) << ("%s OperandType scale %f with float type %u", tag, type.scale,
- type.type); return ANEURALNETWORKS_BAD_DATA;
- }
- */
- return ANEURALNETWORKS_NO_ERROR;
-}
-
-static int ValidateOperandList(uint32_t count, const uint32_t* list, uint32_t operandCount,
- const char* tag) {
- for (uint32_t i = 0; i < count; i++) {
- if (list[i] >= operandCount) {
- LOG(ERROR) << tag << " invalid operand index at " << i << " = " << list[i]
- << ", operandCount " << operandCount;
- return ANEURALNETWORKS_BAD_DATA;
- }
- }
- return ANEURALNETWORKS_NO_ERROR;
-}
-
int ANeuralNetworksMemory_createFromFd(size_t size, int prot, int fd, size_t offset,
ANeuralNetworksMemory** memory) {
- if (fd < 0) {
- LOG(ERROR) << "ANeuralNetworksMemory_createFromFd invalid fd " << fd;
- return ANEURALNETWORKS_UNEXPECTED_NULL;
- }
*memory = nullptr;
std::unique_ptr<MemoryFd> m = std::make_unique<MemoryFd>();
if (m == nullptr) {
@@ -316,10 +270,6 @@
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model);
- int n = ValidateOperandType(*type, "ANeuralNetworksModel_addOperand", true);
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
return m->addOperand(*type);
}
@@ -354,21 +304,6 @@
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model);
- if (!validCode(kNumberOfOperationTypes, kNumberOfOperationTypesOEM, type)) {
- LOG(ERROR) << "ANeuralNetworksModel_addOperation invalid operations type " << type;
- return ANEURALNETWORKS_BAD_DATA;
- }
- int n = ValidateOperandList(inputCount, inputs, m->operandCount(),
- "ANeuralNetworksModel_addOperation inputs");
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
- n = ValidateOperandList(outputCount, outputs, m->operandCount(),
- "ANeuralNetworksModel_addOperation outputs");
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
-
return m->addOperation(type, inputCount, inputs, outputCount, outputs);
}
@@ -380,17 +315,6 @@
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
ModelBuilder* m = reinterpret_cast<ModelBuilder*>(model);
- int n = ValidateOperandList(inputCount, inputs, m->operandCount(),
- "ANeuralNetworksModel_setInputsAndOutputs inputs");
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
- n = ValidateOperandList(outputCount, outputs, m->operandCount(),
- "ANeuralNetworksModel_setInputsAndOutputs outputs");
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
-
return m->setInputsAndOutputs(inputCount, inputs, outputCount, outputs);
}
@@ -421,11 +345,6 @@
LOG(ERROR) << "ANeuralNetworksCompilation_setPreference passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- if (preference >= kNumberOfPreferences) {
- LOG(ERROR) << "ANeuralNetworksCompilation_setPreference invalid preference " << preference;
- return ANEURALNETWORKS_BAD_DATA;
- }
-
CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation);
return c->setPreference(preference);
}
@@ -435,8 +354,6 @@
LOG(ERROR) << "ANeuralNetworksCompilation_finish passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- // TODO validate the rest
-
CompilationBuilder* c = reinterpret_cast<CompilationBuilder*>(compilation);
return c->finish();
}
@@ -470,19 +387,8 @@
LOG(ERROR) << "ANeuralNetworksExecution_setInput passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- if (type != nullptr) {
- int n = ValidateOperandType(*type, "ANeuralNetworksExecution_setInput", false);
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
- }
- if (length > 0xFFFFFFFF) {
- LOG(ERROR) << "ANeuralNetworksExecution_setInput input exceeds max length " << length;
- return ANEURALNETWORKS_BAD_DATA;
- }
- uint32_t l = static_cast<uint32_t>(length);
ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution);
- return r->setInput(index, type, buffer, l);
+ return r->setInput(index, type, buffer, length);
}
int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
@@ -493,7 +399,6 @@
LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- // TODO validate the rest
const Memory* m = reinterpret_cast<const Memory*>(memory);
ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution);
@@ -507,20 +412,8 @@
LOG(ERROR) << "ANeuralNetworksExecution_setOutput passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- if (type != nullptr) {
- int n = ValidateOperandType(*type, "ANeuralNetworksExecution_setOutput", false);
- if (n != ANEURALNETWORKS_NO_ERROR) {
- return n;
- }
- }
- if (length > 0xFFFFFFFF) {
- LOG(ERROR) << "ANeuralNetworksExecution_setOutput input exceeds max length " << length;
- return ANEURALNETWORKS_BAD_DATA;
- }
- uint32_t l = static_cast<uint32_t>(length);
-
ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution);
- return r->setOutput(index, type, buffer, l);
+ return r->setOutput(index, type, buffer, length);
}
int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution* execution, int32_t index,
@@ -531,7 +424,6 @@
LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory passed a nullptr";
return ANEURALNETWORKS_UNEXPECTED_NULL;
}
- // TODO validate the rest
ExecutionBuilder* r = reinterpret_cast<ExecutionBuilder*>(execution);
const Memory* m = reinterpret_cast<const Memory*>(memory);
