Moved training initialization to dedicated script.

pysegcnn/main/train.py

@@ -29,21 +29,84 @@ License
 # !/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+# builtins
+from logging.config import dictConfig
+
 # locals
-from pysegcnn.core.trainer import NetworkTrainer
+from pysegcnn.core.trainer import (DatasetConfig, SplitConfig, ModelConfig,
+                                   StateConfig, LogConfig,
+                                   MultispectralImageSegmentationTrainer)
 from pysegcnn.main.config import (src_ds_config, src_split_config,
                                   trg_ds_config, trg_split_config,
                                   model_config)
+from pysegcnn.core.logging import log_conf
 
 
 if __name__ == '__main__':
 
-    # instanciate the network trainer class
-    trainer = NetworkTrainer.init_network_trainer(src_ds_config,
-                                                  src_split_config,
-                                                  trg_ds_config,
-                                                  trg_split_config,
-                                                  model_config)
-
-    # (x) train model
+    # (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 target domain configuration
+    trg_dc = DatasetConfig(**trg_ds_config)   # target domain dataset
+    trg_sc = SplitConfig(**trg_split_config)  # target domain dataset split
+
+    # (iii) instanciate the model configuration
+    net_mc = ModelConfig(**model_config)
+
+    # (iv) instanciate the model state file
+    net_sc = StateConfig(src_dc, src_sc, trg_dc, trg_sc, net_mc)
+    state_file = net_sc.init_state()
+
+    # (v) instanciate logging configuration
+    net_lc = LogConfig(state_file)
+    dictConfig(log_conf(net_lc.log_file))
+
+    # (vi) instanciate the datasets to train the model on
+    src_ds = src_dc.init_dataset()
+    trg_ds = trg_dc.init_dataset()
+
+    # (vii) instanciate the training, validation and test datasets and
+    # dataloaders for the source domain
+    src_tra_ds, src_val_ds, src_tes_ds = src_sc.train_val_test_split(src_ds)
+    src_tra_dl, src_val_dl, src_tes_dl = src_sc.dataloaders(
+        src_tra_ds, src_val_ds, src_tes_ds, batch_size=net_mc.batch_size,
+        shuffle=True, drop_last=False)
+
+    # (viii) instanciate the training, validation and test datasets and
+    # dataloaders dor the target domain
+    trg_tra_ds, trg_val_ds, trg_tes_ds = trg_sc.train_val_test_split(trg_ds)
+    trg_tra_dl, trg_val_dl, trg_tes_dl = trg_sc.dataloaders(
+        trg_tra_ds, trg_val_ds, trg_tes_ds, batch_size=net_mc.batch_size,
+        shuffle=True, drop_last=False)
+
+    # (ix) instanciate the model
+    net, optimizer, checkpoint = net_mc.init_model(src_ds, state_file)
+
+    # (x) instanciate the network trainer class
+    trainer = MultispectralImageSegmentationTrainer(
+        model=net,
+        optimizer=optimizer,
+        state_file=net.state_file,
+        src_train_dl=src_tra_dl,
+        src_valid_dl=src_val_dl,
+        src_test_dl=src_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,
+        supervised=net_mc.supervised,
+        trg_train_dl=trg_tra_dl,
+        trg_valid_dl=trg_val_dl,
+        trg_test_dl=trg_tes_dl,
+        uda_loss_function=net_mc.init_uda_loss_function(),
+        uda_lambda=net_mc.uda_lambda,
+        uda_pos=net_mc.uda_pos)
+
+    # (xi) train the model
     training_state = trainer.train()