[Experimental / WIP] Generate a miniscule model for each OP only, retain the results of test inference, and propagate to all OPs. STEP.3

[PINTO0309]

1. Content and background

• [Experimental / WIP] Generate a miniscule model for each OP only, retain the results of test inference, and propagate to all OPs. STEP.3
  • Third step
    • Add a process to perform the calculation of the maximum absolute error on the inference results of all OPs.
  • Fourth step - Will be separated into separate pull requests
    • Implement propagation of inference results for all operations other than the above.
  • Fifth step - Will be separated into separate pull requests
    • Instead of a processing format for inferring TensorFlow models, primitive operations are replaced with Numpy operations to make the process as fast as possible.
  • Sixth step - Will be separated into separate pull requests
    • Seek ways to eliminate accuracy errors without creating JSON files.
    • [DDNM] Support additional parameters to onnxsim #175

2. Summary of corrections

3. Before/After (If there is an operating log that can be used as a reference)

4. Issue number (only if there is a related issue)

• [Experimental / WIP] Generate a miniscule model for each OP only, retain the results of test inference, and propagate to all OPs. STEP.1,2 #184
• Implementation of strict mode #145