commit 9fd373954d64fbae72d1726bbdfc57a18a3a2f6d
author Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> Mon Jul 15 18:04:32 2019 +0100
committer Áron Virginás-Tar <aron.virginas-tar@arm.com> Tue Jul 16 13:31:37 2019 +0000
tree 18aec5d50a59ff103bae3a2c1dd5b475ad7694e3
parent 2b173126319343e49d1f081cfb58eacd96afc715

IVGCVSW-3455 Support dynamic output shape in hal_1_2::HalPolicy::ConvertDepthwiseConv2d

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Iba64a674d772a76ca071553cb423ed870fae9bfd

1.2/HalPolicy.cpp

diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index 69cc471..0c57636 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -340,7 +340,6 @@
     }
 
     const armnn::TensorInfo& inputInfo  = input.GetTensorInfo();
-    const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
 
     // ArmNN does not currently support non-fixed weights or bias
     // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
@@ -447,6 +446,22 @@
     desc.m_BiasEnabled = true;
     armnn::Optional<armnn::TensorInfo> biases(bias.GetInfo());
 
+    armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*output);
+    if (IsDynamicOutput(outputInfo))
+    {
+        try
+        {
+            ALOGD("Output shape not set, will infer from inputs");
+            outputInfo.SetShape(InferDepthwiseConvolution2dOutputShape(inputInfo.GetShape(),
+                                                                       weights.GetInfo().GetShape(),
+                                                                       desc));
+        }
+        catch (armnn::Exception& e)
+        {
+            return Fail("%s: Could not infer dynamic output shape: %s", __func__, e.what());
+        }
+    }
+
     bool isSupported = false;
     FORWARD_LAYER_SUPPORT_FUNC(__func__,
                                IsDepthwiseConvolutionSupported,
@@ -457,6 +472,7 @@
                                desc,
                                weights.GetInfo(),
                                biases);
+
     if (!isSupported)
     {
         return false;
@@ -464,6 +480,7 @@
 
     armnn::IConnectableLayer* startLayer =
         data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, armnn::Optional<armnn::ConstTensor>(bias));
+
     if (!startLayer)
     {
         return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__);
@@ -477,7 +494,12 @@
 
     input.Connect(startLayer->GetInputSlot(0));
 
-    return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data);
+    return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation,
+                                                            0,
+                                                            *endLayer,
+                                                            model,
+                                                            data,
+                                                            armnn::Optional<armnn::TensorInfo>(outputInfo));
 }
 
 bool HalPolicy::ConvertMaximum(const Operation& operation, const Model& model, ConversionData& data)