[mednet.scripts] Update usage examples

src/mednet/libs/classification/scripts/config.py

@@ -27,7 +27,7 @@ def config():
 
      .. code:: sh
 
-        mednet config list
+        mednet classification config list
 
 
 \b
@@ -36,7 +36,7 @@ def config():
 
      .. code:: sh
 
-        mednet config list -v
+        mednet classification config list -v
 
 """,
 )
@@ -54,7 +54,7 @@ def list_(verbose) -> None:  # numpydoc ignore=PR01
 
      .. code:: sh
 
-        mednet config describe montgomery
+        mednet classification config describe montgomery
 
 
 \b
@@ -63,7 +63,7 @@ def list_(verbose) -> None:  # numpydoc ignore=PR01
 
      .. code:: sh
 
-        mednet config describe montgomery -v
+        mednet classification config describe montgomery -v
 
 """,
 )
@@ -87,7 +87,7 @@ def describe(name, verbose) -> None:  # numpydoc ignore=PR01
 
      .. code:: sh
 
-        $ mednet config copy montgomery -vvv newdataset.py
+        $ mednet classification config copy montgomery -vvv newdataset.py
 
 """,
 )

src/mednet/libs/classification/scripts/database.py

@@ -89,7 +89,7 @@ def database() -> None:
 
        .. code:: sh
 
-          $ mednet database list
+          $ mednet classification database list
 
 """,
 )
@@ -108,7 +108,7 @@ def list_():
 
        .. code:: sh
 
-          mednet datamodule check -vv montgomery-f0
+          mednet classification datamodule check -vv montgomery-f0
 
 """,
 )

src/mednet/libs/classification/scripts/evaluate.py

@@ -24,13 +24,13 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
 
    .. code:: sh
 
-      mednet evaluate -vv --predictions=path/to/predictions.json
+      mednet classification evaluate -vv --predictions=path/to/predictions.json
 
 2. Run evaluation on an existing prediction output, tune threshold a priori on the `validation` set:
 
    .. code:: sh
 
-      mednet evaluate -vv --predictions=path/to/predictions.json --threshold=validation
+      mednet classification evaluate -vv --predictions=path/to/predictions.json --threshold=validation
 """,
 )
 @click.option(

src/mednet/libs/classification/scripts/experiment.py

@@ -28,7 +28,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
 
      .. code:: sh
 
-        $ mednet experiment -vv pasa montgomery --epochs=2
+        $ mednet classification experiment -vv pasa montgomery --epochs=2
 """,
 )
 @training_options
@@ -60,7 +60,6 @@ def experiment(
     .. code::
 
        └─ <output-folder>/
-          ├── command.sh
           ├── model/  # the generated model will be here
           ├── predictions.json  # the prediction outputs for the sets
           └── evaluation/  # the outputs of the evaluations for the sets

src/mednet/libs/classification/scripts/predict.py

@@ -21,13 +21,13 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
 
    .. code:: sh
 
-      mednet predict -vv pasa montgomery --weight=path/to/model.ckpt --output=path/to/predictions.json
+      mednet classification predict -vv pasa montgomery --weight=path/to/model.ckpt --output=path/to/predictions.json
 
 2. Enable multi-processing data loading with 6 processes:
 
    .. code:: sh
 
-      mednet predict -vv pasa montgomery --parallel=6 --weight=path/to/model.ckpt --output=path/to/predictions.json
+      mednet classification predict -vv pasa montgomery --parallel=6 --weight=path/to/model.ckpt --output=path/to/predictions.json
 
 """,
 )

src/mednet/libs/classification/scripts/train.py

@@ -23,7 +23,7 @@ logger = setup("mednet", format="%(levelname)s: %(message)s")
 
    .. code:: sh
 
-      mednet train -vv pasa elastic montgomery --batch-size=4 --device="cuda:0"
+      mednet classification train -vv pasa elastic montgomery --batch-size=4 --device="cuda:0"
 """,
 )
 @reusable_options

src/mednet/libs/segmentation/scripts/config.py

@@ -23,11 +23,11 @@ def config():
     epilog="""Examples:
 
 \b
-  1. Lists all configuration resources (type: mednet.libs.classification.config) installed:
+  1. Lists all configuration resources (type: mednet.libs.segmentation.config) installed:
 
      .. code:: sh
 
-        mednet config list
+        mednet segmentation config list
 
 
 \b
@@ -36,7 +36,7 @@ def config():
 
      .. code:: sh
 
-        mednet config list -v
+        mednet segmentation config list -v
 
 """,
 )
@@ -50,20 +50,20 @@ def list_(verbose) -> None:  # numpydoc ignore=PR01
     epilog="""Examples:
 
 \b
-  1. Describe the Montgomery dataset configuration:
+  1. Describe the Drive dataset configuration:
 
      .. code:: sh
 
-        mednet config describe montgomery
+        mednet segmentation config describe drive
 
 
 \b
-  2. Describe the Montgomery dataset configuration and lists its
+  2. Describe the Drive dataset configuration and lists its
      contents:
 
      .. code:: sh
 
-        mednet config describe montgomery -v
+        mednet segmentation config describe deive -v
 
 """,
 )
@@ -87,7 +87,7 @@ def describe(name, verbose) -> None:  # numpydoc ignore=PR01
 
      .. code:: sh
 
-        $ mednet config copy montgomery -vvv newdataset.py
+        $ mednet segmentation config copy drive -vvv newdataset.py
 
 """,
 )

src/mednet/libs/segmentation/scripts/database.py

@@ -21,7 +21,7 @@ def _get_raw_databases() -> dict[str, dict[str, str]]:
         containing two string keys:
 
         * ``module``: the full Pythonic module name (e.g.
-        ``mednet.libs.classification.data.montgomery``).
+        ``mednet.libs.segmentation.data.drive``).
         * ``datadir``: points to the user-configured data directory for the
         current dataset, if set, or ``None`` otherwise.
     """
@@ -71,14 +71,14 @@ def database() -> None:
 
 \b
     1. To install a database, set up its data directory ("datadir").  For
-       example, to setup access to Montgomery files you downloaded locally at
-       the directory "/path/to/montgomery/files", edit the RC file (typically
-       ``$HOME/.config/mednet.libs.classification.toml``), and add a line like the following:
+       example, to setup access to Drive files you downloaded locally at
+       the directory "/path/to/drive/files", edit the RC file (typically
+       ``$HOME/.config/mednet.toml``), and add a line like the following:
 
        .. code:: toml
 
           [datadir]
-          montgomery = "/path/to/montgomery/files"
+          drive = "/path/to/drive/files"
 
        .. note::
 
@@ -89,7 +89,7 @@ def database() -> None:
 
        .. code:: sh
 
-          $ mednet database list
+          $ mednet segmentation database list
 
 """,
 )
@@ -102,12 +102,12 @@ def list_():
 @database.command(
     epilog="""Examples:
 
-    1. Check if all files from the fold 'montgomery-f0' of the Montgomery
+    1. Check if all files from the fold 'default' of the Drive
        database can be loaded:
 
        .. code:: sh
 
-          mednet datamodule check -vv montgomery-f0
+          mednet segmentation database check -vv drive
 
 """,
 )

src/mednet/libs/segmentation/scripts/experiment.py

@@ -23,13 +23,13 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
     epilog="""Examples:
 
 \b
-  1. Train a pasa model with montgomery dataset, on the CPU, for only two
+  1. Train a lwnet model with drive dataset, on the CPU, for only two
      epochs, then runs inference and evaluation on stock datasets, report
      performance as a table and figures:
 
      .. code:: sh
 
-        $ mednet experiment -vv pasa montgomery --epochs=2
+        $ mednet segmentation experiment -vv lwnet drive --epochs=2
 """,
 )
 @training_options
@@ -59,9 +59,8 @@ def experiment(
 
         \b
        └─ <output-folder>/
-          ├── command.sh
           ├── model/  # the generated model will be here
-          ├── predictions.json  # the prediction outputs for the sets
+          ├── predictions  # the prediction outputs for the sets
           └── evaluation/  # the outputs of the evaluations for the sets
     """
 

src/mednet/libs/segmentation/scripts/predict.py

@@ -69,13 +69,13 @@ def _save_hdf5(
 
    .. code:: sh
 
-      mednet predict -vv pasa montgomery --weight=path/to/model.ckpt --output=path/to/predictions.json
+      mednet segmentation predict -vv lwnet drive --weight=path/to/model.ckpt --output=path/to/predictions.json
 
 2. Enable multi-processing data loading with 6 processes:
 
    .. code:: sh
 
-      mednet predict -vv pasa montgomery --parallel=6 --weight=path/to/model.ckpt --output=path/to/predictions.json
+      mednet segmentation predict -vv lwnet drive --parallel=6 --weight=path/to/model.ckpt --output=path/to/predictions.json
 
 """,
 )

src/mednet/libs/segmentation/scripts/train.py

@@ -18,11 +18,11 @@ logger = setup("mednet", format="%(levelname)s: %(message)s")
     cls=ConfigCommand,
     epilog="""Examples:
 
-1. Train a pasa model with the montgomery dataset, on a GPU (``cuda:0``):
+1. Train a lwnet model with the frive dataset, on a GPU (``cuda:0``):
 
    .. code:: sh
 
-      mednet train -vv pasa montgomery --batch-size=4 --device="cuda:0"
+      mednet segmentation train -vv lwnet drive --batch-size=4 --device="cuda:0"
 """,
 )
 @reusable_options
@@ -44,7 +44,7 @@ def train(
     augmentations,
     **_,
 ) -> None:  # numpydoc ignore=PR01
-    """Train an CNN to perform image classification.
+    """Train an CNN to perform image segmentation.
 
     Training is performed for a configurable number of epochs, and
     generates checkpoints.  Checkpoints are model files with a .ckpt