add test script

test.py

@@ -0,0 +1,101 @@
+# Copyright 2024, Theodor Westny. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+import warnings
+import torch
+
+from torch.multiprocessing import set_sharing_strategy
+from lightning.pytorch import Trainer, seed_everything
+from lightning.pytorch.loggers import Logger, CSVLogger
+
+from arguments import args
+from preamble import load_config, import_from_module
+
+torch.set_float32_matmul_precision('medium')
+warnings.filterwarnings("ignore", ".*Consider increasing the value of the `num_workers` argument*")
+warnings.filterwarnings("ignore", ".*Checkpoint directory*")
+
+set_sharing_strategy('file_system')
+
+# Load configuration and import modules
+config = load_config(args.config)
+TorchModel = import_from_module(config["model"]["module"], config["model"]["class"])
+LitDataModule = import_from_module(config["datamodule"]["module"], config["datamodule"]["class"])
+LitModel = import_from_module(config["litmodule"]["module"], config["litmodule"]["class"])
+
+
+def main(save_name: str) -> None:
+    ds = config["dataset"]
+    path = os.path.join("saved_models", ds, save_name)
+
+    # Check if checkpoint exists
+    if os.path.exists(path + ".ckpt"):
+        ckpt = path + ".ckpt"
+    elif os.path.exists(path + "-v1.ckpt"):
+        ckpt = path + "-v1.ckpt"
+    else:
+        raise NameError(f"Could not find model with name: {save_name}")
+
+    # Determine the number of devices, and accelerator
+    if torch.cuda.is_available() and args.use_cuda:
+        devices, accelerator = -1,  "auto"
+    else:
+        devices, accelerator = 1, "cpu"
+
+    # Setup logger
+    logger: bool | Logger
+    if args.dry_run:
+        logger = False
+        args.small_ds = True
+    elif not args.use_logger:
+        logger = False
+    else:
+        logger = CSVLogger(save_dir=os.path.join("lightning_logs", ds), name=save_name)
+
+    # Setup model
+    net = TorchModel(config["model"])
+    model = LitModel(net, config["training"])
+
+    # Load checkpoint into model
+    ckpt_dict = torch.load(ckpt)
+    model.load_state_dict(ckpt_dict["state_dict"], strict=False)
+
+    # Setup datamodule
+    if args.root:
+        config["datamodule"]["root"] = args.root
+    datamodule = LitDataModule(config["datamodule"], args)
+
+    # Setup trainer
+    trainer = Trainer(accelerator=accelerator, devices=devices, logger=logger)
+
+    # Start testing
+    trainer.test(model, datamodule=datamodule, verbose=True)
+
+
+if __name__ == "__main__":
+    seed_everything(args.seed, workers=True)
+
+    ds_name = config["dataset"]
+    mdl_name = config["model"]["class"]
+    add_name = f"-{args.add_name}" if args.add_name else ""
+
+    full_save_name = f"{mdl_name}{add_name}-{ds_name}"
+
+    print('----------------------------------------------------')
+    print(f'\nGetting ready to test model: {full_save_name} \n')
+    print('----------------------------------------------------')
+
+    main(full_save_name)

train.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import os
 import time
 import pathlib
 import warnings
@@ -42,7 +42,7 @@
 
 def main(save_name: str) -> None:
     ds = config["dataset"]
-    ckpt_path = pathlib.Path(f"saved_models/{ds}/{save_name}.ckpt")
+    ckpt_path = pathlib.Path(os.path.join("saved_models", ds, f"{save_name}.ckpt"))
 
     # Check if checkpoint exists and the overwrite flag is not set
     if ckpt_path.exists() and not args.overwrite:
@@ -88,15 +88,16 @@ def main(save_name: str) -> None:
         run_name = f"{save_name}_{time.strftime('%d-%m_%H:%M:%S')}"
         logger = WandbLogger(project="dronalize", name=run_name)
 
-    # Setup model, datamodule and trainer
+    # Setup model
     net = TorchModel(config["model"])
     model = LitModel(net, config["training"])
 
+    # Setup datamodule
     if args.root:
         config["datamodule"]["root"] = args.root
-
     datamodule = LitDataModule(config["datamodule"], args)
 
+    # Setup trainer
     trainer = Trainer(max_epochs=config["training"]["epochs"],
                       logger=logger,
                       devices=devices,
@@ -113,8 +114,15 @@ def main(save_name: str) -> None:
 if __name__ == "__main__":
     seed_everything(args.seed, workers=True)
 
+    mdl_name = config["model"]["class"]
     ds_name = config["dataset"]
-    full_save_name = f"Example{args.add_name}-{ds_name}"
+    add_name = f"-{args.add_name}" if args.add_name else ""
+
+    full_save_name = f"{mdl_name}{add_name}-{ds_name}"
+
+    if args.dry_run:
+        full_save_name += "-DEBUG"
+
     print('----------------------------------------------------')
     print(f'\nGetting ready to train model: {full_save_name} \n')
     print('----------------------------------------------------')