check TODOs

src/model_server.py

@@ -5,6 +5,10 @@
 
 import logging
 
+from config import load_pipeline_config
+config = load_pipeline_config()
+
+
 
 # Load the TensorFlow model
 logging.info("Loading model...")
@@ -33,7 +37,7 @@ def predict():
         # Run inference
         results = score_fn(
             waveform=waveform_exp, # waveform_exp,
-            context_step_samples=10_000  # TODO load from config
+            context_step_samples=config.classify.model_sample_rate
         )["scores"][0] # NOTE currently only support batch size 1
         logging.info(f"results.shape = {results.shape}")
         logging.debug("results = ", results)

src/stages/sift.py

@@ -104,9 +104,8 @@ def _plot_signal_detections(self, pcoll, min_max_detections, all_detections, par
             plot_name=key
         )
 
-        # TODO make dirs cleaner
-        if not os.path.isdir(os.path.sep.join(plot_path.split(os.path.sep)[:-1])):
-            os.makedirs(os.path.sep.join(plot_path.split(os.path.sep)[:-1]))
+        if not beam.io.filesystems.FileSystems.exists(plot_path):
+            beam.io.filesystems.FileSystems.mkdirs(plot_path)
 
         # normalize and center
         signal = signal / np.max(signal)    # normalize