diff options
Diffstat (limited to 'ArmnnPreparedModel_1_3.cpp')
| -rw-r--r-- | ArmnnPreparedModel_1_3.cpp | 47 |
1 files changed, 36 insertions, 11 deletions
diff --git a/ArmnnPreparedModel_1_3.cpp b/ArmnnPreparedModel_1_3.cpp index c7adc6c4..a27c7a39 100644 --- a/ArmnnPreparedModel_1_3.cpp +++ b/ArmnnPreparedModel_1_3.cpp @@ -101,7 +101,7 @@ bool ValidateRequestArgument(const RequestArgument& requestArg, const armnn::Ten for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d) { - if (requestArg.dimensions[d] != tensorInfo.GetShape()[d]) + if (requestArg.dimensions[d] != 0 && requestArg.dimensions[d] != tensorInfo.GetShape()[d]) { ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)", d, requestArg.dimensions[d], tensorInfo.GetShape()[d]); @@ -309,6 +309,13 @@ Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeFenced(const V1_3::Reque ALOGW("ArmnnPreparedModel_1_3::executeFenced parameter loopTimeoutDuration is set but not supported."); } + if (!android::nn::validateRequest(request, m_Model, /*allowUnspecifiedOutput=*/false)) + { + ALOGV("ArmnnPreparedModel_1_3::executeFenced outputs must be specified for fenced execution "); + cb(ErrorStatus::INVALID_ARGUMENT, hidl_handle(nullptr), nullptr); + return Void(); + } + ExecutionContext_1_3 ctx; if (measureTiming == MeasureTiming::YES) { @@ -319,12 +326,6 @@ Return<void> ArmnnPreparedModel_1_3<HalVersion>::executeFenced(const V1_3::Reque ALOGV("ArmnnPreparedModel_1_3::executeFenced(): %s", GetModelSummary(m_Model).c_str()); m_RequestCount++; - if (!android::nn::validateRequest(request, m_Model)) - { - cb(ErrorStatus::INVALID_ARGUMENT, hidl_handle(nullptr), nullptr); - return Void(); - } - if (!m_RequestInputsAndOutputsDumpDir.empty()) { ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(&cb)); @@ -442,7 +443,7 @@ Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForOu { const auto& outputArg = request.outputs[i]; - const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i); + armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i); const armnn::Tensor outputTensor = GetTensorForRequestArgument(outputArg, outputTensorInfo, memPools); if (outputTensor.GetMemoryArea() == nullptr) { @@ -450,16 +451,40 @@ Return<V1_3::ErrorStatus> ArmnnPreparedModel_1_3<HalVersion>::PrepareMemoryForOu return V1_3::ErrorStatus::GENERAL_FAILURE; } + unsigned int count = 0; + std::for_each(outputArg.dimensions.begin(), outputArg.dimensions.end(), [&](auto dim) + { + if (dim != 0) + { + outputTensorInfo.GetShape()[count] = dim; + } + else + { + outputTensorInfo.GetShape()[count] = outputArg.dimensions.size(); + } + + count++; + }); + const size_t outputSize = outputTensorInfo.GetNumBytes(); + + outputs.emplace_back(i, outputTensor); + outputShapes[i] = ComputeShape(outputTensorInfo); + + if (outputArg.location.length < outputSize) + { + ALOGW("ArmnnPreparedModel_1_3::Execute failed"); + outputShapes[i].isSufficient = false; + return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE; + } + const size_t bufferSize = memPools.at(outputArg.location.poolIndex).getHidlMemory().size(); if (bufferSize < outputSize) { ALOGW("ArmnnPreparedModel_1_3::Execute failed"); + outputShapes[i].isSufficient = false; return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE; } - - outputs.emplace_back(i, outputTensor); - outputShapes[i] = ComputeShape(outputTensorInfo); } return V1_3::ErrorStatus::NONE; |