commit a4983cec09a3e24bf4e99abd31aa11842e8b365f
author Finn Williams <Finn.Williams@arm.com> Thu Jul 23 12:55:12 2020 +0100
committer Teresa Charlin <teresa.charlinreyes@arm.com> Wed Aug 12 17:06:37 2020 +0100
tree 0a09be7cdad7ad5c15bb24c67abf0f4cd4d6e47e
parent bf866e2dc7bd5936788fe213b5c0f74483ec1532

IVGCVSW-4931 Update NN Driver to support dynamic tensors

 * Change NN Driver m_Network to now have ShapeInferenceMethod::InferAndValidate
 * Implement dynamic tensor support for:
    - ArgMinMax layer
    - Pooling2d layer
    - Activation layer
 * Skip dynamic tensor tests for any HAL other than 1.3

Change-Id: Icf66c968e49cdd4822b8c79c5f18b3f9e97dc53f
Signed-off-by: Finn Williams <Finn.Williams@Arm.com>
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>

ArmnnPreparedModel_1_3.cpp

diff --git a/ArmnnPreparedModel_1_3.cpp b/ArmnnPreparedModel_1_3.cpp
index c7adc6c..a27c7a3 100644
--- a/ArmnnPreparedModel_1_3.cpp
+++ b/ArmnnPreparedModel_1_3.cpp
@@ -101,7 +101,7 @@
 
         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 @@
         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 @@
     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 @@
     {
         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::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;