remove old inference module

micro_dl/cli/curator_script.py

@@ -9,7 +9,7 @@
 import iohub.ngff as ngff
 import argparse
 
-import micro_dl.inference.evaluation_metrics as metrics
+import micro_dl.evaluation.evaluation_metrics as metrics
 import micro_dl.utils.aux_utils as aux_utils
 # from waveorder.focus import focus_from_transverse_band
 

micro_dl/cli/metrics_script.py

@@ -8,7 +8,7 @@
 import argparse
 import pandas as pd
 
-import micro_dl.inference.evaluation_metrics as metrics
+import micro_dl.evaluation.evaluation_metrics as metrics
 import micro_dl.utils.aux_utils as aux_utils
 
 # %% read the below details from the config file

micro_dl/cli/readme.md

@@ -0,0 +1,15 @@
+# CLI
+
+## Exporting models to ONNX
+
+If you wish to run inference via usage of the ONNXruntime, models can be exported to onnx using the `micro_dl/cli/onnx_export_script.py`. See below for an example usage of this script with 5-input-stack model:
+
+```bash
+python micro_dl/cli/onnx_export_script.py --model_path path/to/your/pt_model.pt --stack_depth 5 --export_path intended/path/to/model/export.onnx --test_input path/to/test/input.npy
+```
+
+**Some Notes:**
+
+* For cpu sharing reasons, running an onnx model requires a dedicated node on hpc OR a non-distributed system (for example a personal laptop or other device).
+* Test inputs are optional, but help verify that the exported model can be run if exporting from intended usage device.
+* Models must be located in a lighting training logs directory with a valid `config.yaml` in order to be initialized. This can be "hacked" by locating the config in a directory called `checkpoints` beneath a valid config's directory.

micro_dl/evaluation/evaluation.py

@@ -1,5 +1,5 @@
 import numpy as np
-import micro_dl.inference.evaluation_metrics as inference_metrics
+import micro_dl.evaluation.evaluation_metrics as inference_metrics
 from torch.utils.tensorboard import SummaryWriter