fixed missing get_default_db_config function

batdetect2/evaluate/evaluate_models.py

@@ -7,6 +7,7 @@ import copy
 import json
 import os
 
+import torch
 import numpy as np
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
@@ -739,7 +740,7 @@ if __name__ == "__main__":
     #
     if args["bd_model_path"] != "":
         # load model
-        bd_args = du.get_default_run_config()
+        bd_args = du.get_default_bd_args()
         model, params_bd = du.load_model(args["bd_model_path"])
 
         # check if the class names are the same
@@ -754,11 +755,13 @@ if __name__ == "__main__":
         }
 
         preds_bd = []
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         for ii, gg in enumerate(gt_test):
             pred = du.process_file(
                 gg["file_path"],
                 model,
                 run_config,
+                device,
             )
             preds_bd.append(pred)
 

batdetect2/utils/detector_utils.py

@@ -34,9 +34,26 @@ __all__ = [
     "process_spectrogram",
     "process_audio_array",
     "process_file",
+    "get_default_bd_args",
 ]
 
 
+def get_default_bd_args():
+    args = {}
+    args["detection_threshold"] = 0.001
+    args["time_expansion_factor"] = 1
+    args["audio_dir"] = ""
+    args["ann_dir"] = ""
+    args["spec_slices"] = False
+    args["chunk_size"] = 3
+    args["spec_features"] = False
+    args["cnn_features"] = False
+    args["quiet"] = True
+    args["save_preds_if_empty"] = True
+    args["ann_dir"] = os.path.join(args["ann_dir"], "")
+    return args
+
+
 def list_audio_files(ip_dir: str) -> List[str]:
     """Get all audio files in directory.
 

scripts/gen_spec_image.py

@@ -12,6 +12,7 @@ import json
 import os
 import sys
 
+import torch
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -85,7 +86,7 @@ if __name__ == "__main__":
     args_cmd = vars(parser.parse_args())
 
     # load the model
-    bd_args = du.get_default_run_config()
+    bd_args = du.get_default_bd_args()
     model, params_bd = du.load_model(args_cmd["model_path"])
     bd_args["detection_threshold"] = args_cmd["detection_threshold"]
     bd_args["time_expansion_factor"] = args_cmd["time_expansion_factor"]
@@ -141,7 +142,8 @@ if __name__ == "__main__":
     }
 
     # run model and filter detections so only keep ones in relevant time range
-    results = du.process_file(args_cmd["audio_file"], model, run_config)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    results = du.process_file(args_cmd["audio_file"], model, run_config, device)
     pred_anns = filter_anns(
         results["pred_dict"]["annotation"],
         args_cmd["start_time"],

scripts/gen_spec_video.py

@@ -15,6 +15,7 @@ import sys
 
 import matplotlib.pyplot as plt
 import numpy as np
+import torch
 from scipy.io import wavfile
 
 import batdetect2.detector.parameters as parameters
@@ -23,7 +24,6 @@ import batdetect2.utils.detector_utils as du
 import batdetect2.utils.plot_utils as viz
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument("audio_file", type=str, help="Path to input audio file")
     parser.add_argument(
@@ -72,7 +72,7 @@ if __name__ == "__main__":
         sys.exit()
 
     if not os.path.isfile(args_cmd["model_path"]):
-        print("Model not found: ", model_path)
+        print("Model not found: ", args_cmd["model_path"])
         sys.exit()
 
     start_time = 0.0
@@ -88,7 +88,7 @@ if __name__ == "__main__":
         os.makedirs(op_dir)
 
     params = parameters.get_params(False)
-    args = du.get_default_run_config()
+    args = du.get_default_bd_args()
     args["time_expansion_factor"] = args_cmd["time_expansion_factor"]
     args["detection_threshold"] = args_cmd["detection_threshold"]
 
@@ -118,6 +118,8 @@ if __name__ == "__main__":
     max_val = spec.max() * 1.1
 
     if not args_cmd["no_detector"]:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
         print("  Loading model and running detector on entire file ...")
         model, det_params = du.load_model(args_cmd["model_path"])
         det_params["detection_threshold"] = args["detection_threshold"]
@@ -126,7 +128,12 @@ if __name__ == "__main__":
             **det_params,
             **args,
         }
-        results = du.process_file(audio_file, model, run_config)
+        results = du.process_file(
+            audio_file,
+            model,
+            run_config,
+            device,
+        )
 
         print("  Processing detections and plotting ...")
         detections = []