debug-level log messages

pyproject.toml

 [tool.poetry]
 name = "MaggotUBA-adapter"
-version = "0.16.3"
+version = "0.16.4"
 description = "Interface between MaggotUBA and the Nyx tagging UI"
 authors = ["François Laurent"]
 license = "MIT"
@@ -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", tag = "v0.15.3"}
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

src/maggotuba/models/modules.py

 import logging
 import os
 from pathlib import Path
+import numpy as np
 import torch
 from torch import nn
 import json
@@ -29,6 +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
@@ -73,6 +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)
@@ -81,6 +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
@@ -179,6 +183,8 @@ class MaggotEncoder(MaggotModule):
             except Exception as e:
                 _reason = e
                 config['load_state'] = False # for `was_pretrained` to properly work
+            else:
+                logging.debug(f"loading neural network state: {path}")
         else:
             _reason = '"load_state" is set to false'
         # if state file not found or config option "load_state" is False,
@@ -330,9 +336,11 @@ 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/trainers.py

@@ -386,17 +386,20 @@ def import_pretrained_model(backend, pretrained_model_instance):
     for file in pretrained_autoencoder_dir.iterdir():
         if not file.is_file():
             continue
+        logging.debug(f"copying file: {file}")
         dst = backend.model_dir() / file.name
         if file.name.endswith("config.json"):
             with open(file) as f:
                 config = json.load(f)
             dir = backend.model_dir().relative_to(backend.project_dir)
             config["log_dir"] = str(dir)
+            logging.debug(f"log_dir: \"{config['log_dir']}\"")
             with open(dst, "w") as f:
                 json.dump(config, f, indent=2)
             assert config_file is None
             config_file = dst
         else:
+            assert file.name != 'trained_classifier.pt'
             with open(file, "rb") as i:
                 with open(dst, "wb") as o:
                     o.write(i.read())