Merge pull request #86 from JoHof/fix/logging_config

package logger and no logging propagation

lungmask/__main__.py

@@ -1,5 +1,4 @@
 import argparse
-import logging
 import os
 import sys
 
@@ -8,6 +7,7 @@
 import SimpleITK as sitk
 
 from lungmask import LMInferer, utils
+from lungmask.logger import logger
 
 
 def path(string):
@@ -86,18 +86,18 @@ def main():
     args = parser.parse_args(argsin)
 
     if args.classes is not None:
-        logging.warn(
+        logger.warn(
             "!!! Warning: The `classes` parameter is deprecated and will be removed in the next version !!!"
         )
 
     batchsize = args.batchsize
     if args.cpu:
         batchsize = 1
 
-    logging.info("Load model")
+    logger.info("Load model")
 
     input_image = utils.load_input_image(args.input, disable_tqdm=args.noprogress)
-    logging.info("Infer lungmask")
+    logger.info("Infer lungmask")
     if args.modelname == "LTRCLobes_R231":
         assert (
             args.modelpath is None
@@ -132,7 +132,7 @@ def main():
 
     result_out = sitk.GetImageFromArray(result)
     result_out.CopyInformation(input_image)
-    logging.info(f"Save result to: {args.output}")
+    logger.info(f"Save result to: {args.output}")
     sitk.WriteImage(result_out, args.output)
 
 

lungmask/logger.py

@@ -0,0 +1,13 @@
+import logging
+import sys
+
+logger = logging.getLogger("lungmask")
+logger.setLevel(logging.INFO)
+logger.propagate = False
+formatter = logging.Formatter(
+    fmt="lungmask %(asctime)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
+)
+handler = logging.StreamHandler(sys.stdout)
+handler.setFormatter(formatter)
+handler.setLevel(logging.INFO)
+logger.addHandler(handler)

lungmask/mask.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import sys
 import warnings
@@ -12,16 +11,10 @@
 from tqdm import tqdm
 
 from lungmask import utils
+from lungmask.logger import logger
 
 from .resunet import UNet
 
-logging.basicConfig(
-    stream=sys.stdout,
-    format="lungmask %(asctime)s %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=logging.INFO,
-)
-
 warnings.filterwarnings("ignore", category=UserWarning)
 
 
@@ -130,7 +123,7 @@ def __init__(
             if torch.cuda.is_available():
                 self.device = torch.device("cuda")
             else:
-                logging.info("No GPU found, using CPU instead")
+                logger.info("No GPU found, using CPU instead")
         self.model.to(self.device)
 
         self.fillmodelm = None
@@ -241,14 +234,14 @@ def apply(self, image: Union[sitk.Image, np.ndarray]) -> np.ndarray:
         if self.fillmodel is None:
             return self._inference(image, self.model)
         else:
-            logging.info(f"Apply: {self.modelname}")
+            logger.info(f"Apply: {self.modelname}")
             res_l = self._inference(image, self.model)
-            logging.info(f"Apply: {self.fillmodel}")
+            logger.info(f"Apply: {self.fillmodel}")
             res_r = self._inference(image, self.fillmodelm)
             spare_value = res_l.max() + 1
             res_l[np.logical_and(res_l == 0, res_r > 0)] = spare_value
             res_l[res_r == 0] = 0
-            logging.info("Fusing results... this may take up to several minutes!")
+            logger.info("Fusing results... this may take up to several minutes!")
             return utils.postprocessing(res_l, spare=[spare_value])
 
 

lungmask/utils.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import sys
 from typing import Tuple
@@ -13,6 +12,8 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
+from lungmask.logger import logger
+
 
 def preprocess(
     img: np.ndarray, resolution: list = [192, 192]
@@ -169,8 +170,8 @@ def read_dicoms(path, primary=True, original=True, disable_tqdm=False):
                                 dcm_header_info.append(h_info)
 
             except Exception as e:
-                logging.error("Unexpected error:", e)
-                logging.warning("Doesn't seem to be DICOM, will be skipped: ", fname)
+                logger.error("Unexpected error:", e)
+                logger.warning("Doesn't seem to be DICOM, will be skipped: ", fname)
 
     conc = [x[1] for x in dcm_header_info]
     sidx = np.argsort(conc)
@@ -179,9 +180,9 @@ def read_dicoms(path, primary=True, original=True, disable_tqdm=False):
     vol_unique = np.unique(conc, return_index=1, return_inverse=1)  # unique volumes
     n_vol = len(vol_unique[1])
     if n_vol == 1:
-        logging.info("There is " + str(n_vol) + " volume in the study")
+        logger.info("There is " + str(n_vol) + " volume in the study")
     else:
-        logging.info("There are " + str(n_vol) + " volumes in the study")
+        logger.info("There are " + str(n_vol) + " volumes in the study")
 
     relevant_series = []
     relevant_volumes = []
@@ -215,17 +216,17 @@ def load_input_image(path: str, disable_tqdm=False) -> sitk.Image:
         sitk.Image: Loaded image
     """
     if os.path.isfile(path):
-        logging.info(f"Read input: {path}")
+        logger.info(f"Read input: {path}")
         input_image = sitk.ReadImage(path)
     else:
-        logging.info(f"Looking for dicoms in {path}")
+        logger.info(f"Looking for dicoms in {path}")
         dicom_vols = read_dicoms(
             path, original=False, primary=False, disable_tqdm=disable_tqdm
         )
         if len(dicom_vols) < 1:
             sys.exit("No dicoms found!")
         if len(dicom_vols) > 1:
-            logging.warning(
+            logger.warning(
                 "There are more than one volume in the path, will take the largest one"
             )
         input_image = dicom_vols[
@@ -252,7 +253,7 @@ def postprocessing(
     Returns:
         np.ndarray: Postprocessed volume
     """
-    logging.info("Postprocessing")
+    logger.info("Postprocessing")
 
     # CC analysis
     regionmask = skimage.measure.label(label_image)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="lungmask",
-    version="0.2.15",
+    version="0.2.16",
     author="Johannes Hofmanninger",
     author_email="johannes.hofmanninger@gmail.com",
     description="Package for automated lung segmentation in CT",