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Fix supervised-training CLI and add test #153

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Merged
constantinpape merged 1 commit into from
Jan 27, 2026
+102 −4
Merged

Fix supervised-training CLI and add test #153

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10 changes: 6 additions & 4 deletions synapse_net/training/supervised_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,7 +267,7 @@ def supervised_training(
model = get_2d_model(out_channels=out_channels, in_channels=in_channels)
else:
model = get_3d_model(out_channels=out_channels, in_channels=in_channels)

if checkpoint_path:
model = torch_em.util.load_model(checkpoint=checkpoint_path)

Expand Down Expand Up @@ -362,8 +362,8 @@ def _parse_input_files(args):
else:
if args.val_label_folder is None:
raise ValueError("You have passed a val_folder, but not a val_label_folder.")
val_image_paths = _parse_input_folder(args.val_image_folder, args.image_file_pattern, raw_key)
val_label_paths = _parse_input_folder(args.val_label_folder, args.label_file_pattern, label_key)
val_image_paths, _ = _parse_input_folder(args.val_folder, args.image_file_pattern, raw_key)
val_label_paths, _ = _parse_input_folder(args.val_label_folder, args.label_file_pattern, label_key)

return train_image_paths, train_label_paths, val_image_paths, val_label_paths, raw_key, label_key

Expand Down Expand Up @@ -410,6 +410,8 @@ def main():
parser.add_argument("--n_samples_val", type=int, help="The number of samples per epoch for validation. If not given will be derived from the data size.") # noqa
parser.add_argument("--val_fraction", type=float, default=0.15, help="The fraction of the data to use for validation. This has no effect if 'val_folder' and 'val_label_folder' were passed.") # noqa
parser.add_argument("--check", action="store_true", help="Visualize samples from the data loaders to ensure correct data instead of running training.") # noqa
parser.add_argument("--n_iterations", type=int, default=int(1e5), help="The maximal number of iterations to train for.") # noqa
parser.add_argument("--save_root", help="Root path for saving the checkpoint and log dir.")
args = parser.parse_args()

train_image_paths, train_label_paths, val_image_paths, val_label_paths, raw_key, label_key =\
Expand All @@ -420,5 +422,5 @@ def main():
train_label_paths=train_label_paths, val_label_paths=val_label_paths,
raw_key=raw_key, label_key=label_key, patch_shape=args.patch_shape, batch_size=args.batch_size,
n_samples_train=args.n_samples_train, n_samples_val=args.n_samples_val,
check=args.check,
check=args.check, n_iterations=args.n_iterations, save_root=args.save_root,
)
96 changes: 96 additions & 0 deletions test/training/test_training_cli.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,96 @@
import os
import unittest
from shutil import rmtree
from subprocess import run

from skimage.data import binary_blobs
from skimage.measure import label


class TestTrainignCLI(unittest.TestCase):
tmp_folder = "./tmp_data"

# Create test folder and sample data.
def setUp(self):
n_train = 5
n_val = 2

self.train_data = [
binary_blobs(length=128, n_dim=3, volume_fraction=0.15).astype("uint8") for _ in range(n_train)
]
self.val_data = [
binary_blobs(length=128, n_dim=3, volume_fraction=0.15).astype("uint8") for _ in range(n_val)
]

self.train_labels = [label(data).astype("uint16") for data in self.train_data]
self.val_labels = [label(data).astype("uint16") for data in self.val_data]

os.makedirs(self.tmp_folder, exist_ok=True)

def tearDown(self):
try:
rmtree(self.tmp_folder)
except OSError:
pass

def _test_supervised_training(
self,
train_image_folder,
train_label_folder,
val_image_folder,
val_label_folder,
file_pattern,
):
name = "test-model"
cmd = [
"synapse_net.run_supervised_training",
"-n", name,
"--train_folder", train_image_folder,
"--image_file_pattern", file_pattern,
"--label_folder", train_label_folder,
"--label_file_pattern", file_pattern,
"--val_folder", val_image_folder,
"--val_label_folder", val_label_folder,
"--patch_shape", "64", "64", "64",
"--batch_size", "1",
"--n_samples_train", "5",
"--n_samples_val", "1",
"--n_iterations", "6",
"--save_root", self.tmp_folder,
]
run(cmd)

# Check that the checkpoint exists.
ckpt_path = os.path.join(self.tmp_folder, "checkpoints", name, "latest.pt")
self.assertTrue(os.path.exists(ckpt_path))

def test_supervised_training_mrc(self):
import mrcfile

# Create MRC train and val data.
def write_data(data, labels, out_root):
data_out, label_out = os.path.join(out_root, "volumes"), os.path.join(out_root, "labels")
os.makedirs(data_out, exist_ok=True)
os.makedirs(label_out, exist_ok=True)
for i, (data, labels) in enumerate(zip(data, labels)):
fname = f"tomo-{i}.mrc"
with mrcfile.new(os.path.join(data_out, fname), overwrite=True) as f:
f.set_data(data)
with mrcfile.new(os.path.join(label_out, fname), overwrite=True) as f:
f.set_data(labels)
return data_out, label_out

train_image_folder, train_label_folder = write_data(
self.train_data, self.train_labels, os.path.join(self.tmp_folder, "train")
)
val_image_folder, val_label_folder = write_data(
self.val_data, self.val_labels, os.path.join(self.tmp_folder, "val")
)

self._test_supervised_training(
train_image_folder, train_label_folder, val_image_folder, val_label_folder, file_pattern="*.mrc",
)


if __name__ == "__main__":
unittest.main()