* Generate unique output file names in CheckExecGraphInfoSerialization testcase.
When multiple instances of this test were executed in parallel the same
file was accessed by multiple threads which was the cause of flakiness.
* Enable ExecGraphTests.CheckExecGraphInfoSerialization on GPU
* Fix serialization of execution graph.
* Add exec graph comparison.
* Align execution graph serialization to old aproach.
* Revise error massages.
* Fixed execution graph comparison.
Now only attribute names are compared since values can differ on
different devices.
* Readability refactoring.
* Refactoring regarding readability.
* [IE] Add batched blob support
New `class BatchedBlob : public CompoundBlob` defined to allow to pass multiple blobs as 1 InferRequest input.
Motivation: There is the special user case when a number of plain images (e.g. `NV12Blob`) should be passed as one input for network which batch size > 1.
`class CompoundBlob` is not applicable for such cases due to:
1. `NV12Blob` is `CompoundBlob` which prevents to combine multiple NV12 images to a CompoundBlob
2. The default behavior in most of plugins - do not accept generic CompoundBlob as `SetBlob()` argument
Adding `SetBlob(name, vector<Blob::Ptr>...)` to `class IInferRequest`, `class InferRequest`, `class IInferRequestInternal`, ... - is not effective solution due to limited and specific use cases for `batched inputs`.
+ Apply rule-of-zero to CompoundBlob and inherited classes.
* Add "BATCHED_BLOB" optimization capability metric
* Add BatchedBlob usage to hello_nv12_input_classification
* Apply offline code review outcome:
1. Revert CompoundBlob public .ctors signatures
2. Remove 'workaround' .ctor for `BatchedBlob`
3. Revert tensor descriptors of `I420Blob` `NV12Blob` back to the 'fake' value.
* Code review fix
* Add functional tests for CPU, GPU, MULTI, HETERO
* update doc comment
* Apply code review change requests.
* Added transformation config to support automl efficientdet-4 model
* Added configuration file to convert Automl EfficientDet model
* Updated unit test for Pack
* Added instruction on how to convert EfficientDet Tensorflow model
* Updated documentation on how to convert EfficientDet model
* Updated a documentation with instruction on how to convert Automl EfficientDet.
There was some problem with replicatioon of simple loop body where
input was used as output as is.
Also was voided usage of special prefixes like "in_" for Loop body inputs.
Signed-off-by: Alexander Peskov <alexander.peskov@intel.com>
