Training script to train models on a single domain.

pysegcnn/main/train_source.py

+"""Train a model on a single domain.
+
+Steps to launch a model run:
+
+    1. Configure the model run in :py:mod:`pysegcnn.main.config.py`
+        - configure the dataset   : ``src_ds_config``
+        - configure the split     : ``src_ds_config`
+        - configure the model     : ``model_config``
+    2. Save :py:mod:`pysegcnn.main.config.py`
+    3. In a terminal, navigate to the repository's root directory
+    4. Run
+
+    .. code-block:: bash
+
+        python pysegcnn/main/train.py
+
+
+License
+-------
+
+    Copyright (c) 2020 Daniel Frisinghelli
+
+    This source code is licensed under the GNU General Public License v3.
+
+    See the LICENSE file in the repository's root directory.
+
+"""
+
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# builtins
+from logging.config import dictConfig
+
+# locals
+from pysegcnn.core.trainer import (DatasetConfig, SplitConfig, ModelConfig,
+                                   StateConfig, LogConfig,
+                                   ClassificationNetworkTrainer)
+from pysegcnn.main.config import (src_ds_config, src_split_config,
+                                  model_config)
+from pysegcnn.core.logging import log_conf
+
+
+if __name__ == '__main__':
+
+    # (i) instanciate the source domain configurations
+    src_dc = DatasetConfig(**src_ds_config)   # source domain dataset
+    src_sc = SplitConfig(**src_split_config)  # source domain dataset split
+
+    # (ii) instanciate the model configuration
+    net_mc = ModelConfig(**model_config)
+
+    # (iii) instanciate the model state configuration
+    net_sc = StateConfig()
+
+    # (iv)) instanciate the source dataset
+    src_ds = src_dc.init_dataset()
+
+    # (v) instanciate the source training, validation and test dataset folds
+    src_folds = src_sc.train_val_test_split(src_ds)
+
+    # (vi) iterate over the different folds
+    for fold, src_fold in enumerate(src_folds):
+
+        # (vii) instanciate the model state file for the current fold
+        state_file = net_sc.init_state(src_dc, src_sc, net_mc, fold=fold)
+
+        # (viii) instanciate logging configuration
+        net_lc = LogConfig(state_file)
+        dictConfig(log_conf(net_lc.log_file))
+
+        # (ix) instanciate the source dataloaders
+        tra_dl, val_dl, tes_dl = src_sc.dataloaders(
+            *src_fold.values(), batch_size=net_mc.batch_size, shuffle=True,
+            drop_last=False)
+
+        # (x) instanciate the model
+        net, optimizer, checkpoint = net_mc.init_model(
+            len(src_ds.use_bands), len(src_ds.labels), state_file)
+
+        # (xi) instanciate the network trainer class
+        trainer = ClassificationNetworkTrainer(
+            model=net,
+            optimizer=optimizer,
+            state_file=net.state_file,
+            src_train_dl=tra_dl,
+            src_valid_dl=val_dl,
+            src_test_dl=tes_dl,
+            epochs=net_mc.epochs,
+            nthreads=net_mc.nthreads,
+            early_stop=net_mc.early_stop,
+            mode=net_mc.mode,
+            delta=net_mc.delta,
+            patience=net_mc.patience,
+            checkpoint_state=checkpoint,
+            save=net_mc.save
+            )
+
+        # (xii) train the model
+        training_state = trainer.train()