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Make square centre-padding a model transform

Merged Make square centre-padding a model transform
issue-23-and-39 into main
Merged André Anjos requested to merge issue-23-and-39 into main
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tests/test_cli.py
+ 94
− 105
@@ -436,108 +436,97 @@ def test_evaluate_pasa_montgomery(temporary_basedir):
)
# 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}"
# )
@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
def test_experiment(temporary_basedir):
from mednet.scripts.experiment import experiment
runner = CliRunner()
output_folder = str(temporary_basedir / "experiment")
num_epochs = 2
result = runner.invoke(
experiment,
[
"-vv",
"pasa",
"montgomery",
f"--epochs={num_epochs}",
f"--output-folder={output_folder}",
],
)
_assert_exit_0(result)
assert os.path.exists(os.path.join(output_folder, "command.sh"))
assert os.path.exists(os.path.join(output_folder, "predictions.json"))
assert os.path.exists(os.path.join(output_folder, "model", "command.sh"))
assert os.path.exists(os.path.join(output_folder, "model", "constants.csv"))
assert os.path.exists(
os.path.join(
output_folder, "model", f"model-at-epoch={num_epochs-1}.ckpt"
)
)
# Need to glob because we cannot be sure of the checkpoint with lowest validation loss
assert (
len(
glob.glob(
os.path.join(
output_folder,
"model",
"model-at-lowest-validation-loss-epoch=*.ckpt",
)
)
)
== 1
)
assert os.path.exists(
os.path.join(output_folder, "model", "model-summary.txt")
)
assert os.path.exists(os.path.join(output_folder, "model", "trainlog.pdf"))
assert (
len(
glob.glob(
os.path.join(
output_folder, "model", "logs", "events.out.tfevents.*"
)
)
)
== 1
)
assert os.path.exists(
os.path.join(output_folder, "evaluation", "plots.pdf")
)
assert os.path.exists(
os.path.join(output_folder, "evaluation", "summary.rst")
)
assert os.path.exists(os.path.join(output_folder, "gradcam", "saliencies"))
assert (
len(
glob.glob(
os.path.join(
output_folder,
"gradcam",
"saliencies",
"CXR_png",
"MCUCXR_*.npy",
)
)
)
== 138
)
assert os.path.exists(
os.path.join(output_folder, "gradcam", "visualizations")
)
assert (
len(
glob.glob(
os.path.join(
output_folder,
"gradcam",
"visualizations",
"CXR_png",
"MCUCXR_*.png",
)
)
)
== 58
)