implements larvatagger.jl#58 at this project level

README.md

@@ -28,7 +28,7 @@ It was trained on a subset of 5000 files from the t5 and t15 databases. Spines w
 
 ## Usage
 
-For installation, see [TaggingBackends' README](https://gitlab.pasteur.fr/nyx/TaggingBackends/-/tree/dev#recommanded-installation-and-troubleshooting).
+For installation, see [TaggingBackends' README](https://gitlab.pasteur.fr/nyx/TaggingBackends/-/tree/dev#recommended-installation).
 
 A MaggotUBA-based tagger is typically called using the `poetry run tagging-backend` command from the backend's project (directory tree).
 

src/maggotuba/models/modules.py

+import os
 from pathlib import Path
 import torch
 from torch import nn
 import json
 import functools
 from behavior_model.models.neural_nets import Encoder
+from taggingbackends.explorer import check_permissions
 
 class MaggotModule(nn.Module):
     def __init__(self, path, cfgfile=None, ptfile=None):
@@ -66,12 +68,14 @@ class MaggotModule(nn.Module):
         path = self.path / cfgfile
         with open(path, "w") as f:
             json.dump(self.config, f, indent=2)
+        check_permissions(path)
         return path
 
     def save_model(self, ptfile=None):
         if ptfile is None: ptfile = self.ptfile
         path = self.path / ptfile
         torch.save(self.model.state_dict(), path)
+        check_permissions(path)
         return path
 
     def save(self):
@@ -81,6 +85,7 @@ class MaggotModule(nn.Module):
     def parameters(self, recurse=True):
         return self.model.parameters(recurse)
 
+
 class MaggotEncoder(MaggotModule):
     def __init__(self, path,
             cfgfile=None,
@@ -225,6 +230,7 @@ class DeepLinear(nn.Module):
 
     def save(self, path):
         torch.save(self.state_dict(), path)
+        check_permissions(path)
 
 class MaggotClassifier(MaggotModule):
     def __init__(self, path, behavior_labels=[], n_latent_features=None,

src/maggotuba/models/predict_model.py

@@ -26,7 +26,8 @@ def predict_model(backend, **kwargs):
     model_files = backend.list_model_files()
     config_file = [file for file in model_files if file.name.endswith("config.json")]
     n_config_files = len(config_file)
-    assert 1 < n_config_files
+    if n_config_files == 0:
+        raise RuntimeError(f"no such tagger found: {backend.model_instance}")
     config_file = [file
             for file in config_file
             if file.name.endswith("clf_config.json")

src/maggotuba/models/train_model.py

 from taggingbackends.data.labels import Labels
 from taggingbackends.data.dataset import LarvaDataset
+from taggingbackends.explorer import check_permissions
 from maggotuba.models.trainers import MaggotTrainer, MultiscaleMaggotTrainer, new_generator
 import json
 import glob
 
-def train_model(backend, layers=1, pretrained_model_instance="default", **kwargs):
+def train_model(backend, layers=1, pretrained_model_instance="default", subsets=(1, 0, 0), **kwargs):
     # make_dataset generated or moved the larva_dataset file into data/interim/{instance}/
     #larva_dataset_file = backend.list_interim_files("larva_dataset_*.hdf5") # recursive
     larva_dataset_file = glob.glob(str(backend.interim_data_dir() / "larva_dataset_*.hdf5")) # not recursive (faster)
     assert len(larva_dataset_file) == 1
-    dataset = LarvaDataset(larva_dataset_file[0], new_generator(), **kwargs)
+    # subsets=(1, 0, 0) => all data are training data; no validation or test subsets
+    dataset = LarvaDataset(larva_dataset_file[0], new_generator(), subsets=subsets, **kwargs)
     labels = dataset.labels
     assert 0 < len(labels)
     labels = labels if isinstance(labels[0], str) else [s.decode() for s in labels]
@@ -23,6 +25,7 @@ def train_model(backend, layers=1, pretrained_model_instance="default", **kwargs
         model = MultiscaleMaggotTrainer(config_files, labels, layers)
     # fine-tune and save the model
     model.train(dataset)
+    print(f"saving model \"{backend.model_instance}\"")
     model.save()
 
 # TODO: merge the below two functions
@@ -51,6 +54,7 @@ def import_pretrained_model(backend, pretrained_model_instance):
             with open(file, "rb") as i:
                 with open(dst, "wb") as o:
                     o.write(i.read())
+        check_permissions(dst)
     return config_file
 
 def import_pretrained_models(backend, model_instances):
@@ -77,6 +81,7 @@ def import_pretrained_models(backend, model_instances):
                 with open(file, "rb") as i:
                     with open(dst, "wb") as o:
                         o.write(i.read())
+            check_permissions(dst)
         assert config_file is not None
         config_files.append(config_file)
     return config_files