paper preperations and notebooks, optuna callbacks

util/module_mixins.py

@@ -5,6 +5,7 @@ from abc import ABC
 
 import torch
 import pandas as pd
+from matplotlib import pyplot as plt
 
 from ml_lib.modules.util import LightningBaseModule
 from util.loss_mixin import LossMixin
@@ -59,7 +60,7 @@ class ValMixin:
 
         target_y = torch.stack(tuple(sorted_batch_y.values())).long()
         if self.params.n_classes <= 2:
-            mean_sorted_y = torch.stack([x.mean(dim=0) if x.shape[0] > 1 else x for x in sorted_y.values()])
+            mean_sorted_y = torch.stack([x.mean(dim=0) if x.shape[0] > 1 else x.squeeze(-1) for x in sorted_y.values()])
             self.metrics.update(mean_sorted_y, target_y)
         else:
             y_max = torch.stack(
@@ -97,8 +98,11 @@ class ValMixin:
             sorted_y.update({file_name: torch.stack(sorted_y[file_name])})
 
         if self.params.n_classes <= 2:
-            mean_sorted_y = torch.stack([x.mean(dim=0) if x.shape[0] > 1 else x for x in sorted_y.values()]).squeeze()
+            mean_sorted_y = [x.mean(dim=0) if x.shape[0] > 1 else x.squeeze().unsqueeze(-1) for x in sorted_y.values()]
+            mean_sorted_y = torch.stack(mean_sorted_y).squeeze(1)
+            # mean_sorted_y = mean_sorted_y if mean_sorted_y.numel() > 1 else mean_sorted_y.unsqueeze(-1)
             max_vote_loss = self.bce_loss(mean_sorted_y.float(), sorted_batch_y.float())
+
             # Sklearn Scores
             additional_scores = self.additional_scores(dict(y=mean_sorted_y, batch_y=sorted_batch_y))
 
@@ -129,6 +133,7 @@ class ValMixin:
 
         for name, image in pl_images.items():
             self.logger.log_image(name, image, step=self.global_step)
+            plt.close(image)
         pass
 
 
@@ -162,12 +167,13 @@ class TestMixin:
             class_names = {val: key for val, key in
                            enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
         else:
-            pred = torch.stack([x.mean(dim=0) if x.shape[0] > 1 else x for x in sorted_y.values()]).squeeze()
+            pred = [x.mean(dim=0) if x.shape[0] > 1 else x.squeeze().unsqueeze(-1) for x in sorted_y.values()]
+            pred = torch.stack(pred).squeeze()
             pred = torch.where(pred > 0.5, 1, 0)
             class_names = {val: key for val, key in enumerate(['negative', 'positive'])}
 
 
-        df = pd.DataFrame(data=dict(filename=[Path(x).name for x in sorted_y.keys()],
+        df = pd.DataFrame(data=dict(filename=[Path(x).name.replace('.npy', '.wav') for x in sorted_y.keys()],
                                     prediction=[class_names[x.item()] for x in pred.cpu()]))
         result_file = Path(self.logger.log_dir / 'predictions.csv')
         if result_file.exists():
@@ -178,12 +184,13 @@ class TestMixin:
                 pass
         with result_file.open(mode='wb') as csv_file:
             df.to_csv(index=False, path_or_buf=csv_file)
-        with result_file.open(mode='rb') as csv_file:
-            try:
-                self.logger.neptunelogger.log_artifact(csv_file)
-            except:
-                print('No possible to send to neptune')
-                pass
+        if False:
+            with result_file.open(mode='rb') as csv_file:
+                try:
+                    self.logger.neptunelogger.log_artifact(csv_file)
+                except:
+                    print('No possible to send to neptune')
+                    pass
 
 
 class CombinedModelMixins(LossMixin,