Make square centre-padding a model transform

This MR fixes issues #23 (closed) and #39 (closed), by:

• Removing centre-cropping from all databases raw-data loaders
• Creating a SquareCenterPad transform that operates on tensors
• Adding that transform to all relevant models (Pasa, Alexnet, Densenet)

tests/test_cli.py

@@ -530,110 +530,3 @@ def test_experiment(temporary_basedir):
         )
         == 58
     )
-
-
-# This script does not work anymore, either fix or remove the script + this test
-# def test_evaluatevis(temporary_basedir):
-#     import pandas as pd
-
-#     from mednet.scripts.evaluatevis import evaluatevis
-
-#     runner = CliRunner()
-
-#     # Create a sample directory structure and CSV files
-#     input_folder = temporary_basedir / "camutils_cli" / "gradcam"
-#     input_folder.mkdir(parents=True, exist_ok=True)
-#     class1_dir = input_folder / "class1"
-#     class1_dir.mkdir(parents=True, exist_ok=True)
-#     class2_dir = input_folder / "class2"
-#     class2_dir.mkdir(parents=True, exist_ok=True)
-
-#     data = {
-#         "MoRF": [1, 2, 3],
-#         "LeRF": [2, 4, 6],
-#         "Combined Score ((LeRF-MoRF) / 2)": [1.5, 3, 4.5],
-#         "IoU": [1, 2, 3],
-#         "IoDA": [2, 4, 6],
-#         "propEnergy": [1.5, 3, 4.5],
-#         "ASF": [1, 2, 3],
-#     }
-#     df = pd.DataFrame(data)
-#     df.to_csv(class1_dir / "file1.csv", index=False)
-#     df.to_csv(class2_dir / "file1.csv", index=False)
-#     df.to_csv(class1_dir / "file2.csv", index=False)
-#     df.to_csv(class2_dir / "file2.csv", index=False)
-
-#     result = runner.invoke(evaluatevis, ["-vv", "-i", str(input_folder)])
-
-#     assert result.exit_code == 0
-
-#     assert (input_folder / "file1_summary.csv").exists()
-#     assert (input_folder / "file2_summary.csv").exists()
-
-
-# Not enough RAM available to do this test
-# @pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
-# def test_predict_densenetrs_montgomery(temporary_basedir, datadir):
-
-#    from mednet.scripts.predict import predict
-
-#    runner = CliRunner()
-
-#    with stdout_logging() as buf:
-
-#        output_folder = str(temporary_basedir / "predictions")
-#        result = runner.invoke(
-#            predict,
-#            [
-#                "densenet_rs",
-#                "montgomery_f0_rgb",
-#                "-vv",
-#                "--batch-size=1",
-#                f"--weight={str(datadir / 'lfs' / 'models' / 'densenetrs.pth')}",
-#                f"--output-folder={output_folder}",
-#                "--grad-cams"
-#            ],
-#        )
-#        _assert_exit_0(result)
-
-#        # check predictions are there
-#        predictions_file1 = os.path.join(output_folder, "train/predictions.csv")
-#        predictions_file2 = os.path.join(output_folder, "validation/predictions.csv")
-#        predictions_file3 = os.path.join(output_folder, "test/predictions.csv")
-#        assert os.path.exists(predictions_file1)
-#        assert os.path.exists(predictions_file2)
-#        assert os.path.exists(predictions_file3)
-#        # check some grad cams are there
-#        cam1 = os.path.join(output_folder, "train/cams/MCUCXR_0002_0_cam.png")
-#        cam2 = os.path.join(output_folder, "train/cams/MCUCXR_0126_1_cam.png")
-#        cam3 = os.path.join(output_folder, "train/cams/MCUCXR_0275_1_cam.png")
-#        cam4 = os.path.join(output_folder, "validation/cams/MCUCXR_0399_1_cam.png")
-#        cam5 = os.path.join(output_folder, "validation/cams/MCUCXR_0113_1_cam.png")
-#        cam6 = os.path.join(output_folder, "validation/cams/MCUCXR_0013_0_cam.png")
-#        cam7 = os.path.join(output_folder, "test/cams/MCUCXR_0027_0_cam.png")
-#        cam8 = os.path.join(output_folder, "test/cams/MCUCXR_0094_0_cam.png")
-#        cam9 = os.path.join(output_folder, "test/cams/MCUCXR_0375_1_cam.png")
-#        assert os.path.exists(cam1)
-#        assert os.path.exists(cam2)
-#        assert os.path.exists(cam3)
-#        assert os.path.exists(cam4)
-#        assert os.path.exists(cam5)
-#        assert os.path.exists(cam6)
-#        assert os.path.exists(cam7)
-#        assert os.path.exists(cam8)
-#        assert os.path.exists(cam9)
-
-#        keywords = {
-#            r"^Loading checkpoint from.*$": 1,
-#            r"^Total time:.*$": 3,
-#            r"^Grad cams folder:.*$": 3,
-#        }
-#        buf.seek(0)
-#        logging_output = buf.read()
-
-#        for k, v in keywords.items():
-#            assert _str_counter(k, logging_output) == v, (
-#                f"Count for string '{k}' appeared "
-#                f"({_str_counter(k, logging_output)}) "
-#                f"instead of the expected {v}:\nOutput:\n{logging_output}"
-#            )