logging

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 = "v0.15.2"}
+taggingbackends = {git = "https://gitlab.pasteur.fr/nyx/TaggingBackends", branch = "dev"}
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

src/maggotuba/models/modules.py

@@ -30,6 +30,7 @@ class MaggotModule(nn.Module):
     @classmethod
     def load_config(cls, path):
         with open(path, "r") as f:
+            logging.debug(f"loading config file: {path}")
             return json.load(f)
 
     @property
@@ -74,6 +75,7 @@ class MaggotModule(nn.Module):
     def save_config(self, cfgfile=None):
         if cfgfile is None: cfgfile = self.cfgfile
         path = self.path / cfgfile
+        logging.debug(f"saving config to file: {path}")
         with open(path, "w") as f:
             json.dump(self.config, f, indent=2)
         check_permissions(path)
@@ -82,6 +84,7 @@ class MaggotModule(nn.Module):
     def save_model(self, ptfile=None):
         if ptfile is None: ptfile = self.ptfile
         path = self.path / ptfile
+        logging.debug(f"saving neural network state to file: {path}")
         torch.save(self.model.state_dict(), path)
         check_permissions(path)
         return path
@@ -176,6 +179,7 @@ class MaggotEncoder(MaggotModule):
         _reason = None
         if config.get('load_state', True):
             try:
+                logging.debug(f"loading neural network state: {path}")
                 encoder.load_state_dict(torch.load(path))
             except Exception as e:
                 _reason = e
@@ -331,9 +335,11 @@ class DeepLinear(nn.Module):
         return self.layers(x)
 
     def load(self, path):
+        logging.debug(f"loading neural network state: {path}")
         self.load_state_dict(torch.load(path))
 
     def save(self, path):
+        logging.debug(f"saving neural network state to file: {path}")
         torch.save(self.state_dict(), path)
         check_permissions(path)
 

src/maggotuba/models/train_model.py

@@ -51,6 +51,8 @@ def train_model(backend, layers=1, pretrained_model_instance="default",
     print(f"saving model \"{backend.model_instance}\"")
     model.save()
 
+    return dataset, model # for debugging purposes
+
 
 from taggingbackends.main import main