Changed bat_detect to batdetect2

app.py

@@ -3,9 +3,9 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
-import bat_detect.utils.audio_utils as au
-import bat_detect.utils.detector_utils as du
-import bat_detect.utils.plot_utils as viz
+import batdetect2.utils.audio_utils as au
+import batdetect2.utils.detector_utils as du
+import batdetect2.utils.plot_utils as viz
 
 # setup the arguments
 args = {}

batdetect2/api.py

@@ -1,6 +1,6 @@
-"""Python API for bat_detect.
+"""Python API for batdetect2.
 
-This module provides a Python API for bat_detect. It can be used to
+This module provides a Python API for batdetect2. It can be used to
 process audio files or spectrograms with the default model or a custom
 model.
 
@@ -8,7 +8,7 @@ Example
 -------
 You can use the default model to process audio files. To process a single
 file, use the `process_file` function.
->>> import bat_detect.api as api
+>>> import batdetect2.api as api
 >>> # Process audio file
 >>> results = api.process_file("audio_file.wav")
 
@@ -16,7 +16,7 @@ To process multiple files, use the `list_audio_files` function to get a list
 of audio files in a directory. Then use the `process_file` function to
 process each file.
 
->>> import bat_detect.api as api
+>>> import batdetect2.api as api
 >>> # Get list of audio files
 >>> audio_files = api.list_audio_files("audio_directory")
 >>> # Process audio files
@@ -44,7 +44,7 @@ array directly, or `process_spectrogram` to process spectrograms. This
 allows you to do other preprocessing steps before running the model for
 predictions.
 
->>> import bat_detect.api as api
+>>> import batdetect2.api as api
 >>> # Load audio
 >>> audio = api.load_audio("audio_file.wav")
 >>> # Process the audio array
@@ -73,7 +73,7 @@ following:
 If you wish to interact directly with the model, you can use the `model`
 attribute to get the default model.
 
->>> import bat_detect.api as api
+>>> import batdetect2.api as api
 >>> # Get the default model
 >>> model = api.model
 >>> # Process the spectrogram
@@ -84,7 +84,7 @@ model outputs are a collection of raw tensors. The `postprocess`
 function can be used to convert the model outputs into a list of
 detections and a list of CNN features.
 
->>> import bat_detect.api as api
+>>> import batdetect2.api as api
 >>> # Get the default model
 >>> model = api.model
 >>> # Process the spectrogram
@@ -102,22 +102,22 @@ from typing import List, Optional, Tuple
 import numpy as np
 import torch
 
-import bat_detect.utils.audio_utils as au
-import bat_detect.utils.detector_utils as du
-from bat_detect.detector.parameters import (
+import batdetect2.utils.audio_utils as au
+import batdetect2.utils.detector_utils as du
+from batdetect2.detector.parameters import (
     DEFAULT_MODEL_PATH,
     DEFAULT_PROCESSING_CONFIGURATIONS,
     DEFAULT_SPECTROGRAM_PARAMETERS,
     TARGET_SAMPLERATE_HZ,
 )
-from bat_detect.types import (
+from batdetect2.types import (
     Annotation,
     DetectionModel,
     ModelOutput,
     ProcessingConfiguration,
     SpectrogramParameters,
 )
-from bat_detect.utils.detector_utils import list_audio_files, load_model
+from batdetect2.utils.detector_utils import list_audio_files, load_model
 
 # Remove warnings from torch
 warnings.filterwarnings("ignore", category=UserWarning, module="torch")

batdetect2/cli.py

@@ -3,9 +3,9 @@ import os
 
 import click
 
-from bat_detect import api
-from bat_detect.detector.parameters import DEFAULT_MODEL_PATH
-from bat_detect.utils.detector_utils import save_results_to_file
+from batdetect2 import api
+from batdetect2.detector.parameters import DEFAULT_MODEL_PATH
+from batdetect2.utils.detector_utils import save_results_to_file
 
 CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
 

batdetect2/detector/models.py

@@ -3,14 +3,14 @@ import torch.fft
 import torch.nn.functional as F
 from torch import nn
 
-from bat_detect.detector.model_helpers import (
+from batdetect2.detector.model_helpers import (
     ConvBlockDownCoordF,
     ConvBlockDownStandard,
     ConvBlockUpF,
     ConvBlockUpStandard,
     SelfAttention,
 )
-from bat_detect.types import ModelOutput
+from batdetect2.types import ModelOutput
 
 __all__ = [
     "Net2DFast",
@@ -104,7 +104,6 @@ class Net2DFast(nn.Module):
             )
 
     def forward(self, ip, return_feats=False) -> ModelOutput:
-
         # encoder
         x1 = self.conv_dn_0(ip)
         x2 = self.conv_dn_1(x1)
@@ -326,7 +325,6 @@ class Net2DFastNoCoordConv(nn.Module):
             )
 
     def forward(self, ip, return_feats=False) -> ModelOutput:
-
         x1 = self.conv_dn_0(ip)
         x2 = self.conv_dn_1(x1)
         x3 = self.conv_dn_2(x2)
@@ -344,11 +342,12 @@ class Net2DFastNoCoordConv(nn.Module):
         cls = self.conv_classes_op(x)
         comb = torch.softmax(cls, 1)
 
+        pred_emb = (self.conv_emb(x) if self.emb_dim > 0 else None,)
+
         return ModelOutput(
             pred_det=comb[:, :-1, :, :].sum(1).unsqueeze(1),
             pred_size=F.relu(self.conv_size_op(x), inplace=True),
             pred_class=comb,
             pred_class_un_norm=cls,
-            pred_emb=self.conv_emb(x) if self.emb_dim > 0 else None,
             features=x,
         )

batdetect2/detector/parameters.py

@@ -1,10 +1,7 @@
 import datetime
 import os
 
-from bat_detect.types import (
-    ProcessingConfiguration,
-    SpectrogramParameters,
-)
+from batdetect2.types import ProcessingConfiguration, SpectrogramParameters
 
 TARGET_SAMPLERATE_HZ = 256000
 FFT_WIN_LENGTH_S = 512 / 256000.0

batdetect2/detector/post_process.py

@@ -5,8 +5,8 @@ import numpy as np
 import torch
 from torch import nn
 
-from bat_detect.detector.models import ModelOutput
-from bat_detect.types import NonMaximumSuppressionConfig, PredictionResults
+from batdetect2.detector.models import ModelOutput
+from batdetect2.types import NonMaximumSuppressionConfig, PredictionResults
 
 np.seterr(divide="ignore", invalid="ignore")
 

batdetect2/evaluate/evaluate_models.py

@@ -11,11 +11,11 @@ import numpy as np
 import pandas as pd
 from sklearn.ensemble import RandomForestClassifier
 
-from bat_detect.detector import parameters
-import bat_detect.train.evaluate as evl
-import bat_detect.train.train_utils as tu
-import bat_detect.utils.detector_utils as du
-import bat_detect.utils.plot_utils as pu
+from batdetect2.detector import parameters
+import batdetect2.train.evaluate as evl
+import batdetect2.train.train_utils as tu
+import batdetect2.utils.detector_utils as du
+import batdetect2.utils.plot_utils as pu
 
 
 def get_blank_annotation(ip_str):

batdetect2/evaluate/readme.md

@@ -1,4 +1,8 @@
 # Evaluating BatDetect2
+
+> **Warning**
+> This code in currently broken. Will fix soon, stay tuned.
+
 This script evaluates a trained model and outputs several plots summarizing the performance. It is used as follows:  
 `python path_to_store_images/ path_to_audio_files/ path_to_annotation_file/ path_to_trained_model/`
 

batdetect2/finetune/finetune_model.py

@@ -10,20 +10,18 @@ import torch
 import torch.nn.functional as F
 from torch.optim.lr_scheduler import CosineAnnealingLR
 
-sys.path.append(os.path.join("..", ".."))
-import bat_detect.detector.models as models
-import bat_detect.detector.parameters as parameters
-import bat_detect.detector.post_process as pp
-import bat_detect.train.audio_dataloader as adl
-import bat_detect.train.evaluate as evl
-import bat_detect.train.losses as losses
-import bat_detect.train.train_model as tm
-import bat_detect.train.train_utils as tu
-import bat_detect.utils.detector_utils as du
-import bat_detect.utils.plot_utils as pu
+import batdetect2.detector.models as models
+import batdetect2.detector.parameters as parameters
+import batdetect2.detector.post_process as pp
+import batdetect2.train.audio_dataloader as adl
+import batdetect2.train.evaluate as evl
+import batdetect2.train.losses as losses
+import batdetect2.train.train_model as tm
+import batdetect2.train.train_utils as tu
+import batdetect2.utils.detector_utils as du
+import batdetect2.utils.plot_utils as pu
 
 if __name__ == "__main__":
-
     info_str = "\nBatDetect - Finetune Model\n"
 
     print(info_str)
@@ -272,7 +270,6 @@ if __name__ == "__main__":
 
     # main train loop
     for epoch in range(0, params["num_epochs"] + 1):
-
         train_loss = tm.train(
             model,
             epoch,

batdetect2/finetune/prep_data_finetune.py

@@ -1,16 +1,13 @@
 import argparse
 import json
 import os
-import sys
 
 import numpy as np
 
-sys.path.append(os.path.join("..", ".."))
-import bat_detect.train.train_utils as tu
+import batdetect2.train.train_utils as tu
 
 
 def print_dataset_stats(data, split_name, classes_to_ignore):
-
     print("\nSplit:", split_name)
     print("Num files:", len(data))
 
@@ -37,7 +34,6 @@ def print_dataset_stats(data, split_name, classes_to_ignore):
 
 
 def load_file_names(file_name):
-
     if os.path.isfile(file_name):
         with open(file_name) as da:
             files = [line.rstrip() for line in da.readlines()]
@@ -53,7 +49,6 @@ def load_file_names(file_name):
 
 
 if __name__ == "__main__":
-
     info_str = "\nBatDetect - Prepare Data for Finetuning\n"
 
     print(info_str)

batdetect2/finetune/readme.md

@@ -1,5 +1,9 @@
-
 # Finetuning the BatDetet2 model on your own data  
+
+| :warning: WARNING          |
+|:---------------------------|
+| This is not currently working, but we are working on fixing this code |
+
 Main steps:  
 1. Annotate your data using the annotation GUI.
 2. Run `prep_data_finetune.py` to create a training and validation split for your data.  

batdetect2/train/audio_dataloader.py

@@ -7,8 +7,8 @@ import torch
 import torch.nn.functional as F
 import torchaudio
 
-import bat_detect.utils.audio_utils as au
-from bat_detect.types import AnnotationGroup, HeatmapParameters
+import batdetect2.utils.audio_utils as au
+from batdetect2.types import AnnotationGroup, HeatmapParameters
 
 
 def generate_gt_heatmaps(

batdetect2/train/train_model.py

@@ -7,15 +7,15 @@ import numpy as np
 import torch
 from torch.optim.lr_scheduler import CosineAnnealingLR
 
-from bat_detect.detector import models
-from bat_detect.detector import parameters
-from bat_detect.train import losses
-import bat_detect.detector.post_process as pp
-import bat_detect.train.audio_dataloader as adl
-import bat_detect.train.evaluate as evl
-import bat_detect.train.train_split as ts
-import bat_detect.train.train_utils as tu
-import bat_detect.utils.plot_utils as pu
+from batdetect2.detector import models
+from batdetect2.detector import parameters
+from batdetect2.train import losses
+import batdetect2.detector.post_process as pp
+import batdetect2.train.audio_dataloader as adl
+import batdetect2.train.evaluate as evl
+import batdetect2.train.train_split as ts
+import batdetect2.train.train_utils as tu
+import batdetect2.utils.plot_utils as pu
 
 warnings.filterwarnings("ignore", category=UserWarning)
 

batdetect2/train/train_split.py

@@ -26,7 +26,7 @@ def split_diff(ann_dir, wav_dir, load_extra=True):
                 "is_binary": True,  # just a bat / not bat dataset ie no classes
                 "ann_path": ann_dir
                 + "train_set_bulgaria_batdetective_with_bbs.json",
-                "wav_path": wav_dir + "bat_detective/audio/",
+                "wav_path": wav_dir + "batdetect2ive/audio/",
             }
         )
         train_sets.append(
@@ -154,7 +154,7 @@ def split_same(ann_dir, wav_dir, load_extra=True):
                 "is_binary": True,
                 "ann_path": ann_dir
                 + "train_set_bulgaria_batdetective_with_bbs.json",
-                "wav_path": wav_dir + "bat_detective/audio/",
+                "wav_path": wav_dir + "batdetect2ive/audio/",
             }
         )
         train_sets.append(

batdetect2/utils/audio_utils.py

@@ -6,36 +6,12 @@ import librosa.core.spectrum
 import numpy as np
 import torch
 
-from bat_detect.detector.parameters import (
-    DENOISE_SPEC_AVG,
-    DETECTION_THRESHOLD,
-    FFT_OVERLAP,
-    FFT_WIN_LENGTH_S,
-    MAX_FREQ_HZ,
-    MAX_SCALE_SPEC,
-    MIN_FREQ_HZ,
-    NMS_KERNEL_SIZE,
-    NMS_TOP_K_PER_SEC,
-    RESIZE_FACTOR,
-    SCALE_RAW_AUDIO,
-    SPEC_DIVIDE_FACTOR,
-    SPEC_HEIGHT,
-    SPEC_SCALE,
-)
-
 from . import wavfile
 
-try:
-    from typing import TypedDict
-except ImportError:
-    from typing_extensions import TypedDict
-
 __all__ = [
     "load_audio",
     "generate_spectrogram",
     "pad_audio",
-    "SpectrogramParameters",
-    "DEFAULT_SPECTROGRAM_PARAMETERS",
 ]
 
 
@@ -60,7 +36,6 @@ def generate_spectrogram(
     return_spec_for_viz=False,
     check_spec_size=True,
 ):
-
     # generate spectrogram
     spec = gen_mag_spectrogram(
         audio,

batdetect2/utils/detector_utils.py

@@ -7,12 +7,12 @@ import pandas as pd
 import torch
 import torch.nn.functional as F
 
-import bat_detect.detector.compute_features as feats
-import bat_detect.detector.post_process as pp
-import bat_detect.utils.audio_utils as au
-from bat_detect.detector import models
-from bat_detect.detector.parameters import DEFAULT_MODEL_PATH
-from bat_detect.types import (
+import batdetect2.detector.compute_features as feats
+import batdetect2.detector.post_process as pp
+import batdetect2.utils.audio_utils as au
+from batdetect2.detector import models
+from batdetect2.detector.parameters import DEFAULT_MODEL_PATH
+from batdetect2.types import (
     Annotation,
     DetectionModel,
     FileAnnotations,

batdetect2/utils/plot_utils.py

@@ -217,7 +217,6 @@ def plot_spec(
     plot_boxes=True,
     fixed_aspect=True,
 ):
-
     if fixed_aspect:
         # ouptut image will be this width irrespective of the duration of the audio file
         width = 12

run_batdetect.py

@@ -1,5 +1,5 @@
-"""Run bat_detect.command.main() from the command line."""
-from bat_detect.cli import detect
+"""Run batdetect2.command.main() from the command line."""
+from batdetect2.cli import detect
 
 if __name__ == "__main__":
     detect()

scripts/gen_dataset_summary_image.py

@@ -5,17 +5,15 @@ is the mean spectrogram for each class.
 
 import argparse
 import os
-import sys
 
 import matplotlib.pyplot as plt
 import numpy as np
 import viz_helpers as vz
 
-sys.path.append(os.path.join(".."))
-import bat_detect.detector.parameters as parameters
-import bat_detect.train.train_split as ts
-import bat_detect.train.train_utils as tu
-import bat_detect.utils.audio_utils as au
+import batdetect2.detector.parameters as parameters
+import batdetect2.train.train_split as ts
+import batdetect2.train.train_utils as tu
+import batdetect2.utils.audio_utils as au
 
 if __name__ == "__main__":
 

scripts/gen_spec_image.py

@@ -15,11 +15,10 @@ import sys
 import matplotlib.pyplot as plt
 import numpy as np
 
-sys.path.append(os.path.join(".."))
-import bat_detect.evaluate.evaluate_models as evlm
-import bat_detect.utils.audio_utils as au
-import bat_detect.utils.detector_utils as du
-import bat_detect.utils.plot_utils as viz
+import batdetect2.evaluate.evaluate_models as evlm
+import batdetect2.utils.audio_utils as au
+import batdetect2.utils.detector_utils as du
+import batdetect2.utils.plot_utils as viz
 
 
 def filter_anns(anns, start_time, stop_time):

scripts/gen_spec_video.py

@@ -17,11 +17,10 @@ import matplotlib.pyplot as plt
 import numpy as np
 from scipy.io import wavfile
 
-sys.path.append(os.path.join(".."))
-import bat_detect.detector.parameters as parameters
-import bat_detect.utils.audio_utils as au
-import bat_detect.utils.detector_utils as du
-import bat_detect.utils.plot_utils as viz
+import batdetect2.detector.parameters as parameters
+import batdetect2.utils.audio_utils as au
+import batdetect2.utils.detector_utils as du
+import batdetect2.utils.plot_utils as viz
 
 if __name__ == "__main__":
 

scripts/viz_helpers.py

@@ -7,7 +7,7 @@ from scipy import ndimage
 
 sys.path.append(os.path.join(".."))
 
-import bat_detect.utils.audio_utils as au
+import batdetect2.utils.audio_utils as au
 
 
 def generate_spectrogram_data(

tests/test_api.py

@@ -7,7 +7,7 @@ import numpy as np
 import torch
 from torch import nn
 
-from bat_detect import api
+from batdetect2 import api
 
 PKG_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 TEST_DATA_DIR = os.path.join(PKG_DIR, "example_data", "audio")

tests/test_cli.py

@@ -1,10 +1,11 @@
 """Test the command line interface."""
 from click.testing import CliRunner
 
-from bat_detect.cli import cli
+from batdetect2.cli import cli
 
 
 def test_cli_base_command():
+    """Test the base command."""
     runner = CliRunner()
     result = runner.invoke(cli, ["--help"])
     assert result.exit_code == 0
@@ -12,6 +13,7 @@ def test_cli_base_command():
 
 
 def test_cli_detect_command_help():
+    """Test the detect command help."""
     runner = CliRunner()
     result = runner.invoke(cli, ["detect", "--help"])
     assert result.exit_code == 0
@@ -19,6 +21,7 @@ def test_cli_detect_command_help():
 
 
 def test_cli_detect_command_on_test_audio(tmp_path):
+    """Test the detect command on test audio."""
     results_dir = tmp_path / "results"
 
     # Remove results dir if it exists