Use a configured logger instead of print statement

Add find-links section to setup.cfg for easier install
Change LOGLEVEL env variable to LOG_LEVEL (in concordance with ark)

scripts/inference.py

@@ -5,6 +5,7 @@
 import os
 import pickle
 
+import sybil.utils.logging_utils
 from sybil import Serie, Sybil, visualize_attentions
 
 script_directory = os.path.dirname(os.path.abspath(__file__))
@@ -52,29 +53,20 @@ def _get_parser():
         help="Name of the model to use for prediction. Default: sybil_ensemble",
     )
 
-    parser.add_argument("-l", "--log", "--loglevel", default="INFO", dest="loglevel")
+    parser.add_argument("-l", "--log", "--loglevel", "--log-level",
+                        default="INFO", dest="loglevel")
 
     return parser
 
 
-def logging_basic_config(args):
-    info_fmt = "[%(asctime)s] - %(message)s"
-    debug_fmt = "[%(asctime)s] [%(filename)s:%(lineno)d] %(levelname)s - %(message)s"
-    fmt = debug_fmt if args.loglevel.upper() == "DEBUG" else info_fmt
-
-    logging.basicConfig(
-        format=fmt, datefmt="%Y-%m-%d %H:%M:%S", level=args.loglevel.upper()
-    )
-
-
 def inference(
     image_dir,
     output_dir,
     model_name="sybil_ensemble",
     return_attentions=False,
     file_type="auto",
 ):
-    logger = logging.getLogger("inference")
+    logger = sybil.utils.logging_utils.get_logger()
 
     input_files = os.listdir(image_dir)
     input_files = [os.path.join(image_dir, x) for x in input_files if not x.startswith(".")]
@@ -94,11 +86,11 @@ def inference(
 
     num_files = len(input_files)
 
+    logger.debug(f"Beginning prediction using {num_files} {file_type} files from {image_dir}")
+
     # Load a trained model
     model = Sybil(model_name)
 
-    logger.debug(f"Beginning prediction using {num_files} {file_type} files from {image_dir}")
-
     # Get risk scores
     serie = Serie(input_files, file_type=file_type)
     series = [serie]
@@ -130,7 +122,7 @@ def inference(
 
 def main():
     args = _get_parser().parse_args()
-    logging_basic_config(args)
+    sybil.utils.logging_utils.configure_logger(args.loglevel)
 
     os.makedirs(args.output_dir, exist_ok=True)
 

scripts/run_inference_demo.sh

@@ -10,11 +10,11 @@ demo_scan_dir=sybil_demo_data
 if [ ! -d "$demo_scan_dir" ]; then
   # Download example data
   curl -L -o sybil_example.zip "https://www.dropbox.com/scl/fi/covbvo6f547kak4em3cjd/sybil_example.zip?rlkey=7a13nhlc9uwga9x7pmtk1cf1c&dl=1"
-  tar -xf sybil_example.zip
+  unzip -q sybil_example.zip
 fi
 
 python3 scripts/inference.py \
 --loglevel DEBUG \
 --output-dir demo_prediction \
 --return-attentions \
-$demo_scan_dir
+$demo_scan_dir

setup.cfg

@@ -23,6 +23,10 @@ platforms = any
 classifiers =
     Programming Language :: Python
 
+[easy_install]
+find_links =
+    https://download.pytorch.org/whl/cu117/torch_stable.html
+
 [options]
 zip_safe = False
 packages = find:
@@ -32,7 +36,6 @@ python_requires = >=3.8
 # Version specifiers like >=2.2,<3.0 avoid problems due to API changes in
 # new major versions. This works if the required packages follow Semantic Versioning.
 # For more information, check out https://semver.org/.
-# Use --find-links https://download.pytorch.org/whl/cu117/torch_stable.html for torch libraries
 install_requires =
     importlib-metadata; python_version>="3.8"
     numpy==1.24.1

sybil/__init__.py

@@ -20,5 +20,6 @@
 from sybil.model import Sybil
 from sybil.serie import Serie
 from sybil.utils.visualization import visualize_attentions
+import sybil.utils.logging_utils
 
 __all__ = ["Sybil", "Serie", "visualize_attentions"]

sybil/model.py

@@ -12,6 +12,8 @@
 
 from sybil.serie import Serie
 from sybil.models.sybil import SybilNet
+from sybil.utils.logging_utils import get_logger
+from sybil.utils.device_utils import get_default_device
 from sybil.utils.metrics import get_survival_metrics
 
 
@@ -182,6 +184,7 @@ def __init__(
             By default uses GPU, if available.
 
         """
+        self._logger = get_logger()
         # Download if needed
         if isinstance(name_or_path, str) and (name_or_path in NAME_TO_FILE):
             name_or_path, calibrator_path = download_sybil(name_or_path, cache)
@@ -240,7 +243,7 @@ def load_model(self, path):
 
         # Set eval
         model.eval()
-        print(f"Loaded model from {path}")
+        self._logger.info(f"Loaded model from {path}")
         return model
 
     def _calibrate(self, scores: np.ndarray) -> np.ndarray:

sybil/utils/logging_utils.py

@@ -0,0 +1,68 @@
+import logging
+import os
+
+
+LOGGER_NAME = "sybil"
+LOGLEVEL_KEY = "LOG_LEVEL"
+
+
+def _get_formatter(loglevel="INFO"):
+    warn_fmt = "[%(asctime)s] %(levelname)s -%(message)s"
+    debug_fmt = "[%(asctime)s] [%(filename)s:%(lineno)d] %(levelname)s - %(message)s"
+    fmt = debug_fmt if loglevel.upper() in {"DEBUG"} else warn_fmt
+    return logging.Formatter(
+        fmt=fmt,
+        datefmt="%Y-%b-%d %H:%M:%S %Z",
+    )
+
+
+def remove_all_handlers(logger):
+    while logger.hasHandlers():
+        logger.removeHandler(logger.handlers[0])
+
+
+def configure_logger(loglevel=None, logger_name=LOGGER_NAME, logfile=None):
+    """Do basic logger configuration and set our main logger"""
+
+    # Set as environment variable so other processes can retrieve it
+    if loglevel is None:
+        loglevel = os.environ.get(LOGLEVEL_KEY, "WARNING")
+    else:
+        os.environ[LOGLEVEL_KEY] = loglevel
+
+    logger = logging.getLogger(logger_name)
+    logger.setLevel(loglevel)
+    remove_all_handlers(logger)
+    logger.propagate = False
+
+    formatter = _get_formatter(loglevel)
+    def _prep_handler(handler):
+        for ex_handler in logger.handlers:
+            if type(ex_handler) == type(handler):
+                # Remove old handler, don't want to double-handle
+                logger.removeHandler(ex_handler)
+        handler.setLevel(loglevel)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+    sh = logging.StreamHandler()
+    _prep_handler(sh)
+
+    if logfile is not None:
+        fh = logging.FileHandler(logfile, mode="a")
+        _prep_handler(fh)
+
+    return logger
+
+
+def get_logger(base_name=LOGGER_NAME):
+    """
+    Return a logger.
+    Use a different logger in each subprocess, though they should all have the same log level.
+    """
+    pid = os.getpid()
+    logger_name = f"{base_name}-process-{pid}"
+    logger = logging.getLogger(logger_name)
+    if not logger.hasHandlers():
+        configure_logger(logger_name=logger_name)
+    return logger