all debug and train running

lib/utils/logging.py

@@ -71,5 +71,25 @@ class Logger(LightningLoggerBase):
         self.testtubelogger.log_metrics(metrics, step=step)
         pass
 
+    def close(self):
+        self.testtubelogger.close()
+        self.neptunelogger.close()
+
     def log_config_as_ini(self):
         self.config.write(self.log_dir)
+
+    def save(self):
+        self.testtubelogger.save()
+        self.neptunelogger.save()
+
+    def finalize(self, status):
+        self.testtubelogger.finalize()
+        self.neptunelogger.finalize()
+        self.log_config_as_ini()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.finalize('success')
+        pass

main.py

@@ -9,7 +9,7 @@ import warnings
 
 import torch
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 
 from lib.modules.utils import LightningBaseModule
 from lib.utils.config import Config
@@ -43,7 +43,7 @@ main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, defa
 main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
 main_arg_parser.add_argument("--train_epochs", type=int, default=10, help="")
-main_arg_parser.add_argument("--train_batch_size", type=int, default=512, help="")
+main_arg_parser.add_argument("--train_batch_size", type=int, default=256, help="")
 main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")
 
 # Model
@@ -64,47 +64,54 @@ main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.geten
 args = main_arg_parser.parse_args()
 config = Config.read_namespace(args)
 
-# Logger
-# =============================================================================
-logger = Logger(config)
-
-# Checkpoint Callback
-# =============================================================================
-checkpoint_callback = ModelCheckpoint(
-            filepath=str(logger.log_dir / 'ckpt_weights'),
-            verbose=True,
-            period=1
-)
-
 
 if __name__ == "__main__":
 
-    # Model
+    # Logging
     # =============================================================================
-    # Init
+    # Logger
-    model: LightningBaseModule = config.model_class(config.model_paramters)
+    with Logger(config) as logger:
-    model.init_weights()
+        # Callbacks
+        # =============================================================================
+        # Checkpoint Saving
+        checkpoint_callback = ModelCheckpoint(
+                    filepath=str(logger.log_dir / 'ckpt_weights'),
+                    verbose=True, save_top_k=5,
+        )
+        # =============================================================================
+        # Early Stopping
+        # TODO: For This to work, one must set a validation step and End Eval and Score
+        early_stopping_callback = EarlyStopping(
+            monitor='val_loss',
+            min_delta=0.0,
+            patience=0,
+        )
 
-    # Trainer
+        # Model
-    # =============================================================================
+        # =============================================================================
-    trainer = Trainer(max_nb_epochs=config.train.epochs,
+        # Init
-                      show_progress_bar=True,
+        model: LightningBaseModule = config.model_class(config.model_paramters)
-                      weights_save_path=logger.log_dir,
+        model.init_weights()
-                      gpus=[0] if torch.cuda.is_available() else None,
-                      row_log_interval=model.data_len // 40,  # TODO: Better Value / Setting
-                      log_save_interval=model.data_len // 10,  # TODO: Better Value / Setting
-                      checkpoint_callback=checkpoint_callback,
-                      logger=logger,
-                      fast_dev_run=config.main.debug,
-                      early_stop_callback=None
-                      )
 
-    # Train it
+        # Trainer
-    trainer.fit(model)
+        # =============================================================================
+        trainer = Trainer(max_epochs=config.train.epochs,
+                          show_progress_bar=True,
+                          weights_save_path=logger.log_dir,
+                          gpus=[0] if torch.cuda.is_available() else None,
+                          row_log_interval=(model.data_len * 0.01),  # TODO: Better Value / Setting
+                          log_save_interval=(model.data_len * 0.04),  # TODO: Better Value / Setting
+                          checkpoint_callback=checkpoint_callback,
+                          logger=logger,
+                          fast_dev_run=config.main.debug,
+                          early_stop_callback=None
+                          )
 
-    # Save the last state & all parameters
+        # Train it
-    config.exp_path.mkdir(parents=True, exist_ok=True)  # Todo: do i need this?
+        trainer.fit(model)
-    trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
-    model.save_to_disk(logger.log_dir)
 
+        # Save the last state & all parameters
+        trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
+        model.save_to_disk(logger.log_dir)
+    pass
     # TODO: Eval here!