adding typing and documentation to load_device function

bat_detect/utils/detector_utils.py

@@ -1,6 +1,6 @@
 import json
 import os
-import sys
+from typing import List, Tuple
 
 import numpy as np
 import pandas as pd
@@ -12,6 +12,20 @@ import bat_detect.detector.post_process as pp
 import bat_detect.utils.audio_utils as au
 from bat_detect.detector import models
 
+try:
+    from typing import TypedDict
+except ImportError:
+    from typing_extensions import TypedDict
+
+
+DEFAULT_MODEL_PATH = os.path.join(
+    os.path.dirname(os.path.dirname(__file__)),
+    "models",
+    "model.pth",
+)
+
+__all__ = ["load_model", "DEFAULT_MODEL_PATH"]
+
 
 def get_default_bd_args():
     args = {}
@@ -29,25 +43,64 @@ def get_default_bd_args():
     return args
 
 
-def get_audio_files(ip_dir):
+def get_audio_files(ip_dir: str) -> List[str]:
+    """Get all audio files in directory.
 
+    Args:
+        ip_dir (str): Input directory.
+
+    Returns:
+        list: List of audio files. Only .wav files are returned. Paths are
+        relative to ip_dir.
+
+    Raises:
+        FileNotFoundError: Input directory not found.
+
+    """
     matches = []
-    for root, dirnames, filenames in os.walk(ip_dir):
+    for root, _, filenames in os.walk(ip_dir):
         for filename in filenames:
             if filename.lower().endswith(".wav"):
                 matches.append(os.path.join(root, filename))
     return matches
 
 
-def load_model(model_path, load_weights=True):
+class ModelParameters(TypedDict):
+    """Model parameters."""
+    model_name: str
+    num_filters: int
+    emb_dim: int
+    ip_height: int
+    resize_factor: int
+    class_names: List[str]
+    device: torch.device
+
+
+def load_model(
+    model_path: str=DEFAULT_MODEL_PATH,
+    load_weights: bool=True
+) -> Tuple[torch.nn.Module, ModelParameters]:
+    """Load model from file.
+
+    Args:
+        model_path (str): Path to model file. Defaults to DEFAULT_MODEL_PATH.
+        load_weights (bool, optional): Load weights. Defaults to True.
+
+    Returns:
+        model, params: Model and parameters.
+
+    Raises:
+        FileNotFoundError: Model file not found.
+        ValueError: Unknown model.
+    """
 
     # load model
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if os.path.isfile(model_path):
+
-        net_params = torch.load(model_path, map_location=device)
+    if not os.path.isfile(model_path):
-    else:
+        raise FileNotFoundError("Model file not found.")
-        print("Error: model not found.")
+
-        sys.exit(1)
+    net_params = torch.load(model_path, map_location=device)
 
     params = net_params["params"]
     params["device"] = device
@@ -77,7 +130,7 @@ def load_model(model_path, load_weights=True):
             resize_factor=params["resize_factor"],
         )
     else:
-        print("Error: unknown model.")
+        raise ValueError("Unknown model.")
 
     if load_weights:
         model.load_state_dict(net_params["state_dict"])
@@ -215,7 +268,6 @@ def save_results_to_file(results, op_path):
 
 
 def compute_spectrogram(audio, sampling_rate, params, return_np=False):
-
     # pad audio so it is evenly divisible by downsampling factors
     duration = audio.shape[0] / float(sampling_rate)
     audio = au.pad_audio(