[train-analysis] Fix input parameter issue

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

@@ -101,7 +101,7 @@ def experiment(
     ctx.invoke(
         train_analysis,
         logdir=train_output_folder / "logs",
-        output=output_folder / "trainlog.pdf",
+        output_folder=output_folder / "trainlog.pdf",
     )
 
     logger.info("Ended train analysis")

src/mednet/libs/common/scripts/train_analysis.py

@@ -9,8 +9,6 @@ import click
 from clapper.click import ResourceOption, verbosity_option
 from clapper.logging import setup
 
-from .click import ConfigCommand
-
 # avoids X11/graphical desktop requirement when creating plots
 __import__("matplotlib").use("agg")
 
@@ -186,7 +184,6 @@ def _create_figures(
 
 
 @click.command(
-    cls=ConfigCommand,
     epilog="""Examples:
 
 \b
@@ -194,7 +191,7 @@ def _create_figures(
 
        .. code:: sh
 
-          mednet train-analysis -vv --log-dir=results/logs --output=trainlog.pdf
+          mednet train-analysis -vv results/logs
 """,
 )
 @click.option(
@@ -203,19 +200,15 @@ def _create_figures(
     help="Path to the directory containing the Tensorboard training logs",
     required=True,
     type=click.Path(dir_okay=True, exists=True, path_type=pathlib.Path),
-    default="results/logs",
-    cls=ResourceOption,
 )
 @click.option(
-    "--output",
+    "--output-folder",
     "-o",
-    help="""Path to a PDF file in which to store results. (leading
-    directories are created if they do not exist).""",
+    help="Directory in which to store results (created if does not exist)",
     required=True,
-    cls=ResourceOption,
     type=click.Path(
-        file_okay=True,
-        dir_okay=False,
+        file_okay=False,
+        dir_okay=True,
         writable=True,
         path_type=pathlib.Path,
     ),
@@ -224,19 +217,21 @@ def _create_figures(
 @verbosity_option(logger=logger, expose_value=False)
 def train_analysis(
     logdir: pathlib.Path,
-    output: pathlib.Path,
-    **_,
+    output_folder: pathlib.Path,
 ) -> None:  # numpydoc ignore=PR01
     """Create a plot for each metric in the training logs and saves them in a .pdf file."""
     import matplotlib.pyplot as plt
     from matplotlib.backends.backend_pdf import PdfPages
     from mednet.libs.common.utils.tensorboard import scalars_to_dict
 
+    train_log_filename = "trainlog.pdf"
+    train_log_file = pathlib.Path(output_folder) / train_log_filename
+
     data = scalars_to_dict(logdir)
 
-    output.parent.mkdir(parents=True, exist_ok=True)
+    train_log_file.parent.mkdir(parents=True, exist_ok=True)
 
-    with PdfPages(output) as pdf:
+    with PdfPages(train_log_file) as pdf:
         for figure in _create_figures(data):
             pdf.savefig(figure)
             plt.close(figure)

src/mednet/libs/segmentation/models/typing.py

@@ -3,14 +3,11 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 """Defines most common types used in code."""
 
+import pathlib
 import typing
 
-import torch
-
 Checkpoint: typing.TypeAlias = typing.MutableMapping[str, typing.Any]
 """Definition of a lightning checkpoint."""
 
-SegmentationPrediction: typing.TypeAlias = tuple[
-    str, torch.Tensor, torch.Tensor, torch.Tensor
-]
+SegmentationPrediction: typing.TypeAlias = tuple[pathlib.Path, pathlib.Path]
 """The sample name, the target, and the predicted value."""