Functional WNet training

Author: C-Achard

napari_cellseg3d/code_models/worker_training.py

@@ -86,13 +86,6 @@
 # https://www.pythoncentral.io/pysidepyqt-tutorial-creating-your-own-signals-and-slots/
 # https://napari-staging-site.github.io/guides/stable/threading.html
 
-# TODO list for WNet training :
-# 1. Create a custom base worker for training to avoid code duplication
-# 2. Create a custom worker for WNet training
-# 3. Adapt UI for WNet training (Advanced tab + model choice on first tab)
-# 4. Adapt plots and TrainingReport for WNet training
-# 5. log_parameters function
-
 
 class TrainingWorkerBase(GeneratorWorker):
     """A basic worker abstract class, to run training jobs in.

napari_cellseg3d/code_plugins/plugin_model_training.py

@@ -1295,13 +1295,9 @@ def _display_results(self, images_dict, complete_missing=False):
                     self.result_layers[i].refresh()
                     self.result_layers[i].reset_contrast_limits()
 
-    def on_yield(self, report: TrainingReport):  # TODO refactor for dict
-        # logger.info(
-        #     f"\nCatching results : for epoch {data['epoch']},
-        #     loss is {data['losses']} and validation is {data['val_metrics']}"
-        # )
+    def on_yield(self, report: TrainingReport):
         if report == TrainingReport():
-            return
+            return  # skip empty reports
 
         if report.show_plot:
             try:
@@ -1375,7 +1371,7 @@ def _make_csv(self):
                 dice_metric = self.loss_1_values["Dice metric"]
                 self.df = pd.DataFrame(
                     {
-                        "epoch": size_column,
+                        "Epoch": size_column,
                         "Ncuts loss": ncuts_loss,
                         "Dice metric": dice_metric,
                         "Reconstruction loss": self.loss_2_values,
@@ -1384,7 +1380,7 @@ def _make_csv(self):
             except KeyError:
                 self.df = pd.DataFrame(
                     {
-                        "epoch": size_column,
+                        "Epoch": size_column,
                         "Ncuts loss": ncuts_loss,
                         "Reconstruction loss": self.loss_2_values,
                     }