How to deploy models where the shape of output tensor is not known about server

[deadeyegoodwin]

TRTIS only supports variable-sized dimension for batching, but this is a common request so we are planning on fixing it. Issue #8 is tracking this request so add upvotes there to indicate that you are interested in it.

[dcyoung]

Did you solve this issue? And if so, could you share your solution?

I am also interested in how to serve models with variably sized outputs.

[srihari-humbarwadi]

I was wrong, as the output shape is not variable, there is an upper bound for number of objects detected. So jus set the dims to this upper bound. That should work fine

[dcyoung]

Thanks for getting back @srihari-humbarwadi. Seems defining an upper bound is fine because your model type returns a fixed size tensor, but I'm still curious if variable sizes are supported in tensorrt-inference-server. Perhaps a dev can point me to the relevant docs??

For context:
I'm assuming your model (possibly from here ?) outputs tensors of fixed size, intending for boxes to be ignored based on the associated score.

However, returning a fixed size output is not ideal for performance reasons. While it doesn't matter much for simple result types, consider the case where the served model is a MaskRCNN and the return type includes a pixel mask for each detected object. Without an output signature with variable sized tensors, the payload size would be worst-case for every return. I like to support variable outputs to reduce the payload for the common case (where less than max objects are detected). For tf-serving, this involved modifying the output before exporting a saved model, such that the return type only includes results for object's whose score exceeds some threshold.

Is this behavior supported in tensorrt-inference-server

[dcyoung]

Thanks @deadeyegoodwin !

[tilaba]

hello，have you solved it?