Yunjay Hong

> The idea looks good to me. Could you show the example of `npy2h5.py`? I'm curious about the format of the post-process script. Of course. When I'm ready, I'll make...

Here is my first suggestion. Let's assume there is a model which has **4 tensors as input and 2 tensors for output**. .npy file naming convention (same convention except extension...

> I can't catch what the problem is. Maybe you want to store 1000 different inputs in one .h5 file? > Exactly. I assumed some backend driver can run multiple...

> Although this is about data generation, why is it `append` not `overwrite`? Maybe share some scenario may help. Ah! Sorry for lack of my explanation. The word `append` was...

Then with this format, let's move on to data converter (between npy, h5, and h5). > as the inputs and outputs are accessed by index ("0" for the first one),...

I think the goal of this issue is completed. Now, it's time to discuss how to convert from a data format to another format. I'll close this issue and make...

Discussion about the format and hierarchy of `h5` is currently done. But, it hasn't implemented yet, so I'll reopen and remain this as working in progress. Sorry for confussion. :sob:

#### [WIP] Let's define data spec for each data format (e.g. `npy` and `h5`). Let's assume there is a `model.tflite`, which takes two input tensors (their shapes are (1,5,5,3) and...

#### [WIP] How about extracting data generator? To remaining unified data spec for each inference driver, it'd be better to introduce data generator which takes the input tensors' shape and...

#### Define options of `tflite-infer` ```console # This is from my draft $ jay@YUNJAY tflite-infer -h usage: infer-tflite [OPTIONS] Command line tool for inferring tflite model. Input data for given...