Merge pull request #75 from opengeokube/dev

Update

.github/workflows/build_mkdocs.yml

@@ -0,0 +1,34 @@
+name: Deploy MkDocs site to GitHub Pages
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout the repository
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install mkdocs mkdocs-material
+
+    - name: Build MkDocs site
+      run: mkdocs build
+
+    - name: Deploy to GitHub Pages
+      uses: peaceiris/actions-gh-pages@v3
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: .

kit4dl/cli/app.py

@@ -69,8 +69,8 @@ def _is_test_allowed(trainer: Trainer) -> bool:
 @_app.command()
 def init(
     name: Annotated[
-        str, typer.Option(help="The name of your new project")
-    ] = "new_kit4dl_project"
+        str, typer.Option(default=..., help="The name of your new project")
+    ] = "new_kit4dl_project",
 ) -> None:
     """Create a new Kit4DL project.
 
@@ -93,8 +93,8 @@ def init(
 @_app.command()
 def resume(
     checkpoint: Annotated[
-        str, typer.Option(help="Path to the checkpoint file")
-    ]
+        str, typer.Option(default=..., help="Path to the checkpoint file")
+    ] = "./checkpoint.ckpt",
 ):
     """Resume learning from the checkpoint.
 
@@ -109,7 +109,8 @@ def resume(
 @_app.command()
 def test(
     conf: Annotated[
-        str, typer.Option(help="Path to the configuration TOML file")
+        str,
+        typer.Option(default=..., help="Path to the configuration TOML file"),
     ] = get_default_conf_path(),
 ):
     """Test using the configuration file.
@@ -135,16 +136,20 @@ def test(
 @_app.command()
 def train(
     conf: Annotated[
-        str, typer.Option(help="Path to the configuration TOML file")
+        str,
+        typer.Option(default=..., help="Path to the configuration TOML file"),
     ] = get_default_conf_path(),
     skiptest: Annotated[
         bool,
-        typer.Option(help="If testing (using best weights) should be skipped"),
+        typer.Option(
+            default=...,
+            help="If testing (using best weights) should be skipped",
+        ),
     ] = False,
     overwrite: Annotated[
         Optional[str],
         typer.Option(
-            ...,
+            default=...,
             callback=parse_overwriting_options,
             help="Comma-separated key-value pairs (KEY=VALUE)",
         ),

pyproject.toml

@@ -48,6 +48,12 @@ dependencies = [
     "toml; python_version<'3.11'"
 ]
 
+[project.urls]
+"Homepage" = "https://opengeokube.github.io/kit4dl/"
+"Documentation" = "https://opengeokube.github.io/kit4dl/"
+"Source Code" = "https://github.com/opengeokube/kit4dl"
+"Bug Tracker" = "https://github.com/opengeokube/kit4dl/issues"
+
 [tool.setuptools.dynamic]
 version = {attr = "kit4dl._version.__version__"}
 

tests/fixtures.py

@@ -118,10 +118,13 @@ def base_conf_txt():
 
 @pytest.fixture
 def base_conf_txt_full(base_conf_txt):
-    return base_conf_txt + """
+    return (
+        base_conf_txt
+        + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes = 10}
     """
+    )
 
 
 @pytest.fixture

tests/nn/test_confmodels.py

@@ -376,41 +376,53 @@ def test_define_val_train_test_predict_none_on_trainval_defined(self):
 class TestConf:
     @pytest.fixture
     def full_conf_dict(self, base_conf_txt):
-        entries = base_conf_txt + """
+        entries = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes=10}
         """
+        )
         yield toml.loads(entries)
 
     def test_conf_parse(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes = 10}
         FBetaScore = {target = "torchmetrics::Recall", task = "multiclass", num_classes = 10, beta = 0.1}        
         """
+        )
         _ = Conf(**toml.loads(load))
 
     @pytest.mark.skipif(
         sys.version_info < (3, 11), reason="test for Python < 3.11"
     )
     def test_fail_on_duplicated_key_name(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes = 10}
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes = 10}
         """
+        )
         with pytest.raises(ValueError, match="Duplicate keys!"):
             _ = Conf(**toml.loads(load))
 
     @pytest.mark.skipif(
         sys.version_info > (3, 10), reason="test for Python > 3.10"
     )
     def test_fail_on_duplicated_key_name(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes=10}
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes=10}
         """
+        )
         with pytest.raises(ValueError, match="Cannot overwrite a value.*"):
             _ = Conf(**toml.loads(load))
 
@@ -421,10 +433,13 @@ def test_fail_on_duplicated_key_name(self, base_conf_txt):
         )
     )
     def test_conf_custom_metric(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         test.dummy_module.CustomMetric = {}
         """
+        )
         conf = Conf(**toml.loads(load))
 
     @pytest.mark.skip(
@@ -434,18 +449,24 @@ def test_conf_custom_metric(self, base_conf_txt):
         )
     )
     def test_conf_custom_metric_fail_on_wrong_parentclass(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         teset.dummy_module.CustomMetricWrong = {}
         """
+        )
         with pytest.raises(ValidationError, match="duplicate"):
             _ = Conf(**toml.loads(load))
 
     def test_conf_fail_on_nonexisting_metric(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         MyMetric = {target = "torchmetrics::NonExistingMetric"}
         """
+        )
         with pytest.raises(
             AttributeError,
             match=(
@@ -455,10 +476,13 @@ def test_conf_fail_on_nonexisting_metric(self, base_conf_txt):
             _ = Conf(**toml.loads(load))
 
     def test_conf_fail_on_monitoring_undefined_metric(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision"}
         """
+        )
         load_dict = toml.loads(load)
         load_dict["training"]["checkpoint"]["monitor"] = {
             "metric": "Recall",
@@ -468,21 +492,27 @@ def test_conf_fail_on_monitoring_undefined_metric(self, base_conf_txt):
             _ = Conf(**load_dict)
 
     def test_conf_get_metric_obj_failed_on_missing_target(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {}
         """
+        )
         with pytest.raises(
             ValidationError,
             match=r".*`target` is not defined for some metric.*",
         ):
             conf = Conf(**toml.loads(load))
 
     def test_conf_get_metric_obj_failed_on_missing_task(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision"}
         """
+        )
         conf = Conf(**toml.loads(load))
         with pytest.raises(
             TypeError,
@@ -494,10 +524,13 @@ def test_conf_get_metric_obj_failed_on_missing_task(self, base_conf_txt):
             conf.metrics_obj
 
     def test_conf_get_metric_obj(self, base_conf_txt):
-        load = base_conf_txt + """
+        load = (
+            base_conf_txt
+            + """
         [metrics]
         Precision = {target = "torchmetrics::Precision", task = "multiclass", num_classes = 10}
         """
+        )
         conf = Conf(**toml.loads(load))
         metrics = conf.metrics_obj
         assert "precision" in metrics
@@ -547,10 +580,13 @@ def test_conf_schedulers_double_preconfigured_schedulers_classes(
         )
 
     def test_use_base_exp_name_for_metric_logging(self, base_conf_txt_full):
-        load = base_conf_txt_full + """
+        load = (
+            base_conf_txt_full
+            + """
         [logging]
         type = "csv"
         """
+        )
         conf = Conf(**toml.loads(load))
         assert "name" in conf.logging.arguments
         assert (
@@ -564,11 +600,14 @@ def test_use_base_exp_name_for_metric_logging(self, base_conf_txt_full):
     def test_dont_override_exp_name_with_base_if_provided(
         self, base_conf_txt_full
     ):
-        load = base_conf_txt_full + """
+        load = (
+            base_conf_txt_full
+            + """
         [logging]
         type = "csv"
         name = "logging_exp_name"
         """
+        )
         conf = Conf(**toml.loads(load))
         assert "name" in conf.logging.arguments
         assert conf.logging.arguments["name"] == "logging_exp_name"