fix regression tests (#1408)

* fix regression tests

* fix

* fix prepare_conversion_cfgs

* fix

---------

Co-authored-by: Louis Dupont <louis-dupont@live.fr>

src/super_gradients/training/models/conversion.py

@@ -1,6 +1,5 @@
 import os
 import pathlib
-from pathlib import Path
 
 import hydra
 import numpy as np
@@ -17,6 +16,7 @@
 from super_gradients.common.factories.transforms_factory import TransformsFactory
 from super_gradients.training import models
 from super_gradients.training.utils.sg_trainer_utils import parse_args
+from super_gradients.training.utils.utils import get_param
 
 logger = get_logger(__name__)
 
@@ -228,22 +228,20 @@ def prepare_conversion_cfgs(cfg: DictConfig):
     # CREATE THE EXPERIMENT CFG
 
     # Load the latest experiment config
-    # TODO: check if we can load the cfg from run
-    experiment_cfg = load_experiment_cfg(ckpt_root_dir=cfg.ckpt_root_dir, experiment_name=cfg.experiment_name)
+    run_id = get_param(cfg, "run_id")
+    if run_id is None:
+        run_id = get_latest_run_id(experiment_name=cfg.experiment_name, checkpoints_root_dir=cfg.ckpt_root_dir)
+    experiment_cfg = load_experiment_cfg(ckpt_root_dir=cfg.ckpt_root_dir, experiment_name=cfg.experiment_name, run_id=run_id)
 
     hydra.utils.instantiate(experiment_cfg)
     if cfg.checkpoint_path is None:
         logger.info(
             "checkpoint_params.checkpoint_path was not provided, so the model will be converted using weights from "
             "checkpoints_dir/training_hyperparams.ckpt_name "
         )
-        if cfg.run_id is None:
-            checkpoints_dir = Path(get_latest_run_id(experiment_name=cfg.experiment_name, checkpoints_root_dir=cfg.ckpt_root_dir))
-        else:
-            checkpoints_dir = Path(get_checkpoints_dir_path(experiment_name=cfg.experiment_name, ckpt_root_dir=cfg.ckpt_root_dir))
-            checkpoints_dir = os.path.join(checkpoints_dir, cfg.run_id)
-
+        checkpoints_dir = get_checkpoints_dir_path(experiment_name=cfg.experiment_name, ckpt_root_dir=cfg.ckpt_root_dir, run_id=run_id)
         cfg.checkpoint_path = os.path.join(checkpoints_dir, cfg.ckpt_name)
+
     cfg.out_path = cfg.out_path or cfg.checkpoint_path.replace(".pth", ".onnx")
     logger.info(f"Exporting checkpoint: {cfg.checkpoint_path} to ONNX.")
     return cfg, experiment_cfg

tests/recipe_training_tests/shortened_recipes_accuracy_test.py

@@ -3,7 +3,7 @@
 import os
 import torch
 
-from super_gradients.common.environment.checkpoints_dir_utils import get_checkpoints_dir_path
+from super_gradients.common.environment.checkpoints_dir_utils import get_checkpoints_dir_path, get_latest_run_id
 
 
 class ShortenedRecipesAccuracyTests(unittest.TestCase):
@@ -20,7 +20,9 @@ def test_shortened_cifar10_resnet_accuracy(self):
         self.assertTrue(self._reached_goal_metric(experiment_name="shortened_cifar10_resnet_accuracy_test", metric_value=0.9167, delta=0.05))
 
     def test_convert_shortened_cifar10_resnet(self):
-        ckpt_dir = get_checkpoints_dir_path(experiment_name="shortened_cifar10_resnet_accuracy_test")
+        experiment_name = "shortened_cifar10_resnet_accuracy_test"
+        run_id = get_latest_run_id(experiment_name=experiment_name)
+        ckpt_dir = get_checkpoints_dir_path(experiment_name=experiment_name, run_id=run_id)
         self.assertTrue(os.path.exists(os.path.join(ckpt_dir, "ckpt_best.onnx")))
 
     def test_shortened_coco2017_yolox_n_map(self):
@@ -34,7 +36,8 @@ def test_shortened_coco_dekr_32_ap_test(self):
 
     @classmethod
     def _reached_goal_metric(cls, experiment_name: str, metric_value: float, delta: float):
-        checkpoints_dir_path = get_checkpoints_dir_path(experiment_name=experiment_name)
+        run_id = get_latest_run_id(experiment_name=experiment_name)
+        checkpoints_dir_path = get_checkpoints_dir_path(experiment_name=experiment_name, run_id=run_id)
         sd = torch.load(os.path.join(checkpoints_dir_path, "ckpt_best.pth"))
         metric_val_reached = sd["acc"].cpu().item()
         diff = abs(metric_val_reached - metric_value)