Text labels

pyproject.toml

@@ -14,7 +14,7 @@ maggotuba-core = {git = "https://gitlab.pasteur.fr/nyx/MaggotUBA-core", tag = "v
 torch = "^1.11.0"
 numpy = "^1.19.3"
 protobuf = "3.9.2"
-taggingbackends = {git = "https://gitlab.pasteur.fr/nyx/TaggingBackends", tag = "v0.15.3"}
+taggingbackends = {git = "https://gitlab.pasteur.fr/nyx/TaggingBackends", tag = "v0.16"}
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

src/maggotuba/models/trainers.py

@@ -8,6 +8,7 @@ from taggingbackends.explorer import BackendExplorer, check_permissions
 import logging
 import json
 import re
+import os.path
 
 """
 This model borrows the pre-trained MaggotUBA encoder, substitute a dense layer
@@ -139,7 +140,9 @@ class MaggotTrainer:
             y = self.model(x.to(self.device))
             return y.cpu().numpy()
 
-    def prepare_dataset(self, dataset):
+    def prepare_dataset(self, dataset, training=False):
+        if training:
+            self.model.clf.config['training_dataset'] = str(dataset.path)
         try:
             dataset.batch_size
         except AttributeError:
@@ -203,7 +206,7 @@ class MaggotTrainer:
             optimizer.step()
 
     def train(self, dataset):
-        self.prepare_dataset(dataset)
+        self.prepare_dataset(dataset, training=True)
         criterion = self.init_model_for_training(dataset)
         # pre-train the classifier with static encoder weights
         if self._pretrain_classifier():
@@ -248,10 +251,26 @@ class MaggotTrainer:
                 assert pred.size == exp.size
                 predicted.append(pred)
                 expected.append(exp)
-            return np.concatenate(predicted), np.concatenate(expected)
+            predicted = np.concatenate(predicted)
+            expected = np.concatenate(expected)
+            predicted = [self.labels[label] for label in predicted]
+            expected = [dataset.labels[label] for label in expected]
+            return predicted, expected
 
-    def save(self):
+    def save(self, copy_dataset=True):
         self.model.save()
+        if copy_dataset:
+            # copy the compiled training dataset into the model directory
+            try:
+                dataset = self.model.clf.config['training_dataset']
+            except KeyError:
+                pass
+            else:
+                copy = self.model.clf.path / os.path.basename(dataset)
+                with open(copy, 'wb') as g:
+                    with open(dataset, 'rb') as f:
+                        g.write(f.read())
+                check_permissions(copy)
 
     @property
     def root_dir(self):