IVGCVSW-6873 Import inputs but don't export outputs fails.

Only one bool is used to indicate whether inputs should be imported.
However, its possible for the user to want to import inputs but not
export outputs. In addition it's possible for a user to enabled import
during optimize but then pass a memory source that does not require
import.

* Add m_ExportEnabled to INetwork.hpp.
* Modify Network::dNetwork to consider both m_ImportEnabled
  and m_ExportEnabled.
* Add ValidateSourcesMatchOptimizedNetwork to LoadedNetwork to validate
  import options between optimize and network load.
* Update the TfLite delegate consider exportEnabled flag in the
  optimizer.

!armnn-internal-tests:425350
Signed-off-by: Colm Donelan <colm.donelan@arm.com>
Change-Id: I776eab81595898e43f91ab40306962eae61329f4

2 files changed, 7 insertions, 4 deletions

diff --git a/python/pyarmnn/src/pyarmnn/_version.py b/python/pyarmnn/src/pyarmnn/_version.py
index 7c0940e7f3..d1b1ca290c 100644
--- a/python/pyarmnn/src/pyarmnn/_version.py
+++ b/python/pyarmnn/src/pyarmnn/_version.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: MIT
 import os
 
-version_info = (29, 0, 0)
+version_info = (30, 0, 0)
 
 __dev_version_env = os.getenv("PYARMNN_DEV_VER", "")
 
@@ -24,7 +24,7 @@ def check_armnn_version(installed_armnn_version: str, expected_armnn_version: st
     """Compares expected Arm NN version and Arm NN version used to build the package.
 
     Args:
-        installed_armnn_version (str): Arm NN version used to generate the package (e.g. 29.0.0)
+        installed_armnn_version (str): Arm NN version used to generate the package (e.g. 30.0.0)
         expected_armnn_version (str): Expected Arm NN version
 
     Returns:
diff --git a/python/pyarmnn/src/pyarmnn/swig/modules/armnn_network.i b/python/pyarmnn/src/pyarmnn/swig/modules/armnn_network.i
index a2f57a3aa9..55b6795c90 100644
--- a/python/pyarmnn/src/pyarmnn/swig/modules/armnn_network.i
+++ b/python/pyarmnn/src/pyarmnn/swig/modules/armnn_network.i
@@ -29,7 +29,7 @@ Contains:
                                that can not be reduced will be left in Fp32.
     m_ReduceFp32ToFp16 (bool): Reduces Fp32 network to Fp16 for faster processing. Layers
                                that can not be reduced will be left in Fp32.
-    m_ImportEnabled (bool):    Enable memory import.
+    m_ImportEnabled (bool):    Enable memory import of inport tensors.
     m_shapeInferenceMethod:    The ShapeInferenceMethod modifies how the output shapes are treated.
                                When ValidateOnly is selected, the output shapes are inferred from the input parameters
                                of the layer and any mismatch is reported.
@@ -38,6 +38,7 @@ Contains:
                                with tensors which rank or dimension sizes are not specified explicitly, however this
                                information can be calculated from the inputs.
     m_ModelOptions:            List of backends optimisation options.
+    m_ExportEnabled (bool):    Enable memory export of output tensors.
 
 ") OptimizerOptions;
 
@@ -51,7 +52,8 @@ struct OptimizerOptions
                      bool reduceFp32ToBf16 = false,
                      ShapeInferenceMethod shapeInferenceMethod = armnn::ShapeInferenceMethod::ValidateOnly,
                      bool importEnabled = false,
-                     std::vector<armnn::BackendOptions> modelOptions = {});
+                     std::vector<armnn::BackendOptions> modelOptions = {},
+                     bool exportEnabled = false);
 
     bool m_ReduceFp32ToBf16;
     bool m_ReduceFp32ToFp16;
@@ -59,6 +61,7 @@ struct OptimizerOptions
     ShapeInferenceMethod m_shapeInferenceMethod;
     bool m_ImportEnabled;
     std::vector<armnn::BackendOptions> m_ModelOptions;
+    bool m_ExportEnabled;
 };
 %model_options_clear;