Tooltips and show_results update

Author: C-Achard

napari_cellseg3d/code_models/worker_training.py

@@ -654,7 +654,7 @@ def train(self):
 
                 self.log(f"Ncuts loss: {ncuts_losses[-1]:.5f}")
                 self.log(f"Reconstruction loss: {rec_losses[-1]:.5f}")
-                self.log(f"Sum of losses: {total_losses[-1]:.5f}")
+                self.log(f"Weighted sum of losses: {total_losses[-1]:.5f}")
                 if epoch > 0:
                     self.log(
                         f"Ncuts loss difference: {ncuts_losses[-1] - ncuts_losses[-2]:.5f}"
@@ -663,7 +663,7 @@ def train(self):
                         f"Reconstruction loss difference: {rec_losses[-1] - rec_losses[-2]:.5f}"
                     )
                     self.log(
-                        f"Sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}"
+                        f"Weighted sum of losses difference: {total_losses[-1] - total_losses[-2]:.5f}"
                     )
 
                 # Update the learning rate
@@ -757,7 +757,7 @@ def train(self):
                         # aggregate the final mean dice result
                         metric = dice_metric.aggregate().item()
                         dice_values.append(metric)
-                        self.log(f"Validation Dice score: {metric}")
+                        self.log(f"Validation Dice score: {metric:.3f}")
                         if best_dice < metric <= 1:
                             best_dice = metric
                             # save the best model

napari_cellseg3d/code_plugins/plugin_model_training.py

@@ -1,7 +1,7 @@
 import shutil
 from functools import partial
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, List
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -149,7 +149,7 @@ def __init__(
         """Plot for dice metric"""
         self.plot_dock = None
         """Docked widget with plots"""
-        self.result_layers = []
+        self.result_layers: List[napari.layers.Layer] = []
         """Layers to display checkpoint"""
 
         self.plot_1_labels = {
@@ -323,9 +323,9 @@ def __init__(
     def _set_tooltips(self):
         # tooltips
         self.zip_choice.setToolTip(
-            "Checking this will save a copy of the results as a zip folder"
+            "Save a copy of the results as a zip folder"
         )
-        self.validation_percent_choice.tooltips = "Choose the proportion of images to retain for training.\nThe remaining images will be used for validation"
+        self.validation_percent_choice.tooltips = "The percentage of images to retain for training.\nThe remaining images will be used for validation"
         self.epoch_choice.tooltips = "The number of epochs to train for.\nThe more you train, the better the model will fit the training data"
         self.loss_choice.setToolTip(
             "The loss function to use for training.\nSee the list in the training guide for more info"
@@ -335,10 +335,10 @@ def _set_tooltips(self):
         )
         self.batch_choice.tooltips = (
             "The batch size to use for training.\n A larger value will feed more images per iteration to the model,\n"
-            " which is faster and possibly improves performance, but uses more memory"
+            " which is faster and can improve performance, but uses more memory on your selected device"
         )
         self.val_interval_choice.tooltips = (
-            "The number of epochs to perform before validating data.\n "
+            "The number of epochs to perform before validating on test data.\n "
             "The lower the value, the more often the score of the model will be computed and the more often the weights will be saved."
         )
         self.learning_rate_choice.setToolTip(
@@ -352,19 +352,19 @@ def _set_tooltips(self):
         )
         self.augment_choice.setToolTip(
             "Check this to enable data augmentation, which will randomly deform, flip and shift the intensity in images"
-            " to provide a more general dataset. \nUse this if you're extracting more than 10 samples per image"
+            " to provide a more diverse dataset"
         )
         [
             w.setToolTip("Size of the sample to extract")
             for w in self.patch_size_widgets
         ]
         self.patch_choice.setToolTip(
-            "Check this to automatically crop your images in smaller, cubic images for training."
-            "\nShould be used if you have a small dataset (and large images)"
+            "Check this to automatically crop your images into smaller, cubic images for training."
+            "\nShould be used if you have a few large images"
         )
         self.use_deterministic_choice.setToolTip(
             "Enable deterministic training for reproducibility."
-            "Using the same seed with all other parameters being similar should yield the exact same results between two runs."
+            "Using the same seed with all other parameters being similar should yield the exact same results across runs."
         )
         self.use_transfer_choice.setToolTip(
             "Use this you want to initialize the model with pre-trained weights or use your own weights."
@@ -1292,8 +1292,7 @@ def _display_results(self, images_dict, complete_missing=False):
                         "data"
                     ]
                     self.result_layers[i].refresh()
-                    clims = self.result_layers[i].contrast_limits
-                    [c.reset_contrast_limits_range() for c in clims]
+                    self.result_layers[i].reset_contrast_limits()
 
     def on_yield(self, report: TrainingReport):  # TODO refactor for dict
         # logger.info(