Added command line interface

Signed-off-by: Jim Martens <github@2martens.de>

setup.py

@@ -34,7 +34,9 @@ setup(
     packages=find_packages('src', exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     package_dir={'': 'src'},
     package_data={},
-    entry_points={},
+    entry_points={
+        "console_scripts": ["tm-masterthesis = twomartens.masterthesis.main:main"]
+    },
     python_requires="~=3.6",
     install_requires=["tensorflow-gpu", "Pillow", "h5py", "numpy", "opencv-python", "scikit-learn", "tqdm",
                       "beautifulsoup4", "matplotlib"],

src/twomartens/masterthesis/main.py

@@ -0,0 +1,112 @@
+#  -*- coding: utf-8 -*-
+#
+#  Copyright 2019 Jim Martens
+#
+#  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.
+
+"""
+Provides entry point into the application.
+
+Functions:
+    main(...): provides command line interface
+"""
+import argparse
+
+
+def main() -> None:
+    """
+    Provides command line interface.
+    """
+    parser = argparse.ArgumentParser(
+        description="Train, test, and use SSD with novelty detection.",
+    )
+    
+    parser.add_argument("--verbose", action="store_true", help="provide to get extra output")
+    parser.add_argument('--version', action='version', version='2martens Masterthesis 0.1.0')
+    sub_parsers = parser.add_subparsers(dest="action")
+    sub_parsers.required = True
+    
+    train_parser = sub_parsers.add_parser("train", help="Train a network")
+    test_parser = sub_parsers.add_parser("test", help="Test a network")
+    use_parser = sub_parsers.add_parser("use", help="Use a network")
+    
+    # build sub parsers
+    _build_train(train_parser)
+    
+    args = parser.parse_args()
+    
+    if args.action == "train":
+        _train(args)
+    elif args.action == "test":
+        _test(args)
+    elif args.action == "use":
+        _use(args)
+
+
+def _build_train(parser: argparse.ArgumentParser) -> None:
+    sub_parsers = parser.add_subparsers(dest="network")
+    sub_parsers.required = True
+    
+    # ssd_bayesian_parser = sub_parsers.add_parser("bayesian_ssd", help="SSD with dropout layers")
+    auto_encoder_parser = sub_parsers.add_parser("auto_encoder", help="Auto-encoder network")
+    
+    # build sub parsers
+    # _build_bayesian_ssd(ssd_bayesian_parser)
+    _build_auto_encoder(auto_encoder_parser)
+
+
+def _build_auto_encoder(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument("--coco_path", type=str, help="the path to the COCO data set")
+    parser.add_argument("--weights_path", type=str, help="path to the weights directory")
+    parser.add_argument("category", type=int, help="the COCO category to use")
+    parser.add_argument("num_epochs", type=int, help="the number of epochs to train", default=80)
+    parser.add_argument("iteration", type=int, help="the training iteration")
+
+
+def _build_bayesian_ssd(parser: argparse.ArgumentParser) -> None:
+    raise NotImplementedError
+
+
+def _train(args: argparse.Namespace) -> None:
+    if args.network == "bayesian_ssd":
+        _bayesian_ssd_train(args)
+    elif args.network == "auto_encoder":
+        _auto_encoder_train(args)
+
+
+def _test(args: argparse.Namespace) -> None:
+    raise NotImplementedError
+
+
+def _use(args: argparse.Namespace) -> None:
+    raise NotImplementedError
+
+
+def _auto_encoder_train(args: argparse.Namespace) -> None:
+    from twomartens.masterthesis import data
+    from twomartens.masterthesis.aae import train
+    
+    coco_path = args.coco_path
+    category = args.category
+    batch_size = 32
+    coco_data = data.load_coco(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size)
+    train.train_simple(coco_data, iteration=args.iteration, weights_prefix=args.weights_path,
+                       channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
+
+
+def _bayesian_ssd_train(args: argparse.Namespace) -> None:
+    raise NotImplementedError
+
+
+if __name__ == "__main__":
+    main()