From 43888e1b3eaa5902496ef1e191b58d94c224c220 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 2 Oct 2022 02:56:07 +0200
Subject: Fix train script

---
 training/run.py   |  6 +++++-
 training/utils.py | 23 ++++++++---------------
 2 files changed, 13 insertions(+), 16 deletions(-)

diff --git a/training/run.py b/training/run.py
index 99059d6..429b1a2 100644
--- a/training/run.py
+++ b/training/run.py
@@ -79,7 +79,11 @@ def run(config: DictConfig) -> Optional[float]:
 
     if config.test:
         log.info("Testing network...")
-        trainer.test(model, datamodule=datamodule)
+        ckpt_path = trainer.checkpoint_callback.best_model_path
+        if ckpt_path is None:
+            log.error("No best checkpoint path for model found")
+            return
+        trainer.test(model, datamodule=datamodule, ckpt_path=ckpt_path)
 
     log.info(f"Best checkpoint path:\n{trainer.checkpoint_callback.best_model_path}")
     utils.finish(logger)
diff --git a/training/utils.py b/training/utils.py
index f94a436..c8ea1be 100644
--- a/training/utils.py
+++ b/training/utils.py
@@ -1,5 +1,5 @@
 """Util functions for training with hydra and pytorch lightning."""
-from typing import Any, List, Type
+from typing import List, Type
 import warnings
 
 import hydra
@@ -105,10 +105,6 @@ def extras(config: DictConfig) -> None:
     OmegaConf.set_struct(config, True)
 
 
-def empty(*args: Any, **kwargs: Any) -> None:
-    pass
-
-
 @rank_zero_only
 def log_hyperparameters(
     config: DictConfig,
@@ -122,26 +118,23 @@ def log_hyperparameters(
     hparams["trainer"] = config["trainer"]
     hparams["model"] = config["model"]
     hparams["datamodule"] = config["datamodule"]
-    if "callbacks" in config:
-        hparams["callbacks"] = config["callbacks"]
 
     # save number of model parameters
-    hparams["model/params_total"] = sum(p.numel() for p in model.parameters())
-    hparams["model/params_trainable"] = sum(
+    hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
+    hparams["model/params/trainable"] = sum(
         p.numel() for p in model.parameters() if p.requires_grad
     )
-    hparams["model/params_not_trainable"] = sum(
+    hparams["model/params/non_trainable"] = sum(
         p.numel() for p in model.parameters() if not p.requires_grad
     )
+    hparams["callbacks"] = config.get("callbacks")
+    hparams["tags"] = config.get("tags")
+    hparams["ckpt_path"] = config.get("ckpt_path")
+    hparams["seed"] = config.get("seed")
 
     # send hparams to all loggers
     trainer.logger.log_hyperparams(hparams)
 
-    # disable logging any more hyperparameters for all loggers
-    # this is just a trick to prevent trainer from logging hparams of model,
-    # since we already did that above
-    trainer.logger.log_hyperparams = empty
-
 
 def finish(
     logger: List[Type[LightningLoggerBase]],
-- 
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