Format code

batdetect2/detector/compute_features.py

@@ -1,4 +1,5 @@
 """Functions to compute features from predictions."""
+
 from typing import Dict, List, Optional
 
 import numpy as np
@@ -219,7 +220,6 @@ def compute_call_interval(
     return round(prediction["start_time"] - previous["end_time"], 5)
 
 
-
 # NOTE: The order of the features in this dictionary is important. The
 # features are extracted in this order and the order of the columns in the
 # output csv file is determined by this order. In order to avoid breaking

batdetect2/detector/models.py

@@ -206,7 +206,10 @@ class Net2DFastNoAttn(nn.Module):
             num_filts // 4, 2, kernel_size=1, padding=0
         )
         self.conv_classes_op = nn.Conv2d(
-            num_filts // 4, self.num_classes + 1, kernel_size=1, padding=0,
+            num_filts // 4,
+            self.num_classes + 1,
+            kernel_size=1,
+            padding=0,
         )
 
         if self.emb_dim > 0:

batdetect2/detector/parameters.py

@@ -5,7 +5,10 @@ from typing import List, Optional, Union
 
 from pydantic import BaseModel, Field, computed_field
 
-from batdetect2.train.train_utils import get_genus_mapping, get_short_class_names
+from batdetect2.train.train_utils import (
+    get_genus_mapping,
+    get_short_class_names,
+)
 from batdetect2.types import ProcessingConfiguration, SpectrogramParameters
 
 TARGET_SAMPLERATE_HZ = 256000

batdetect2/detector/post_process.py

@@ -1,4 +1,5 @@
 """Post-processing of the output of the model."""
+
 from typing import List, Tuple, Union
 
 import numpy as np

batdetect2/evaluate/evaluate_models.py

@@ -20,7 +20,6 @@ from batdetect2.detector import parameters
 
 
 def get_blank_annotation(ip_str):
-
     res = {}
     res["class_name"] = ""
     res["duration"] = -1
@@ -77,7 +76,6 @@ def create_genus_mapping(gt_test, preds, class_names):
 
 
 def load_tadarida_pred(ip_dir, dataset, file_of_interest):
-
     res, ann = get_blank_annotation("Generated by Tadarida")
 
     # create the annotations in the correct format
@@ -120,7 +118,6 @@ def load_sonobat_meta(
     class_names,
     only_accepted_species=True,
 ):
-
     sp_dict = {}
     for ss in class_names:
         sp_key = ss.split(" ")[0][:3] + ss.split(" ")[1][:3]
@@ -182,7 +179,6 @@ def load_sonobat_meta(
 
 
 def load_sonobat_preds(dataset, id, sb_meta, set_class_name=None):
-
     # create the annotations in the correct format
     res, ann = get_blank_annotation("Generated by Sonobat")
     res_c = copy.deepcopy(res)
@@ -221,7 +217,6 @@ def load_sonobat_preds(dataset, id, sb_meta, set_class_name=None):
 
 
 def bb_overlap(bb_g_in, bb_p_in):
-
     freq_scale = 10000000.0  # ensure that both axis are roughly the same range
     bb_g = [
         bb_g_in["start_time"],
@@ -465,7 +460,6 @@ def check_classes_in_train(gt_list, class_names):
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "op_dir",

batdetect2/models/decoder.py

@@ -13,5 +13,3 @@ else:
     def pairwise(iterable: Sequence) -> Iterable:
         for x, y in zip(iterable[:-1], iterable[1:]):
             yield x, y
-
-

batdetect2/models/encoder.py

@@ -13,5 +13,3 @@ else:
     def pairwise(iterable: Sequence) -> Iterable:
         for x, y in zip(iterable[:-1], iterable[1:]):
             yield x, y
-
-

batdetect2/post_process.py

@@ -1,6 +1,6 @@
 """Module for postprocessing model outputs."""
 
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -30,6 +30,16 @@ class PostprocessConfig(BaseModel):
     top_k_per_sec: int = Field(default=TOP_K_PER_SEC, gt=0)
 
 
+class RawPrediction(BaseModel):
+    start_time: float
+    end_time: float
+    low_freq: float
+    high_freq: float
+    detection_score: float
+    class_scores: Dict[str, float]
+    features: np.ndarray
+
+
 def postprocess_model_outputs(
     outputs: ModelOutput,
     clips: List[data.Clip],
@@ -125,6 +135,88 @@ def postprocess_model_outputs(
     return predictions
 
 
+def compute_predictions_from_outputs(
+    start: float,
+    end: float,
+    scores: torch.Tensor,
+    y_pos: torch.Tensor,
+    x_pos: torch.Tensor,
+    size_preds: torch.Tensor,
+    class_probs: torch.Tensor,
+    features: torch.Tensor,
+    classes: List[str],
+    min_freq: int = 10000,
+    max_freq: int = 120000,
+    detection_threshold: float = DETECTION_THRESHOLD,
+) -> List[RawPrediction]:
+    _, freq_bins, time_bins = size_preds.shape
+
+    sorted_indices = torch.argsort(x_pos)
+    valid_indices = sorted_indices[
+        scores[sorted_indices] > detection_threshold
+    ]
+
+    scores = scores[valid_indices]
+    x_pos = x_pos[valid_indices]
+    y_pos = y_pos[valid_indices]
+
+    predictions: List[RawPrediction] = []
+    for score, x, y in zip(scores, x_pos, y_pos):
+        width, height = size_preds[:, y, x]
+        class_prob = class_probs[:, y, x].detach().numpy()
+        feats = features[:, y, x].detach().numpy()
+
+        start_time = np.interp(
+            x.item(),
+            [0, time_bins],
+            [start, end],
+        )
+
+        end_time = np.interp(
+            x.item() + width.item(),
+            [0, time_bins],
+            [start, end],
+        )
+
+        low_freq = np.interp(
+            y.item(),
+            [0, freq_bins],
+            [max_freq, min_freq],
+        )
+
+        high_freq = np.interp(
+            y.item() - height.item(),
+            [0, freq_bins],
+            [max_freq, min_freq],
+        )
+
+        start_time, end_time = sorted([float(start_time), float(end_time)])
+        low_freq, high_freq = sorted([float(low_freq), float(high_freq)])
+
+        predictions.append(
+            RawPrediction(
+                start_time=start_time,
+                end_time=end_time,
+                low_freq=low_freq,
+                high_freq=high_freq,
+                detection_score=score.item(),
+                features=feats,
+                class_scores={
+                    class_name: prob
+                    for class_name, prob in zip(classes, class_prob)
+                },
+            )
+        )
+
+    return predictions
+
+
+def convert_raw_prediction_to_soundevent(
+    prediction: RawPrediction,
+) -> data.SoundEventPrediction:
+    pass
+
+
 def compute_sound_events_from_outputs(
     clip: data.Clip,
     scores: torch.Tensor,

batdetect2/train/evaluate.py

@@ -1,10 +1,13 @@
 from typing import List
 
 import numpy as np
+import pandas as pd
 from sklearn.metrics import auc, roc_curve
 from soundevent import data
 from soundevent.evaluation import match_geometries
 
+from batdetect2.train.targets import build_encoder, get_class_names
+
 
 def match_predictions_and_annotations(
     clip_annotation: data.ClipAnnotation,
@@ -48,6 +51,13 @@ def match_predictions_and_annotations(
     return matches
 
 
+def build_evaluation_dataframe(matches: List[data.Match]) -> pd.DataFrame:
+    ret = []
+
+    for match in matches:
+        pass
+
+
 def compute_error_auc(op_str, gt, pred, prob):
     # classification error
     pred_int = (pred > prob).astype(np.int32)

batdetect2/train/modules.py

@@ -1,8 +1,10 @@
+from pathlib import Path
 from typing import Optional
 
 import pytorch_lightning as L
 import torch
 from pydantic import Field
+from soundevent import data
 from torch.optim.adam import Adam
 from torch.optim.lr_scheduler import CosineAnnealingLR
 from torch.utils.data import DataLoader
@@ -19,7 +21,7 @@ from batdetect2.post_process import (
     PostprocessConfig,
     postprocess_model_outputs,
 )
-from batdetect2.preprocess import PreprocessingConfig
+from batdetect2.preprocess import PreprocessingConfig, preprocess_audio_clip
 from batdetect2.train.dataset import LabeledDataset, TrainExample
 from batdetect2.train.evaluate import match_predictions_and_annotations
 from batdetect2.train.losses import LossConfig, compute_loss
@@ -146,12 +148,14 @@ class DetectorModel(L.LightningModule):
             config=self.config.postprocessing,
         )[0]
 
-        self.validation_predictions.extend(
-            match_predictions_and_annotations(clip_annotation, clip_prediction)
+        matches = match_predictions_and_annotations(
+            clip_annotation,
+            clip_prediction,
         )
 
+        self.validation_predictions.extend(matches)
+
     def on_validation_epoch_end(self) -> None:
-        print(len(self.validation_predictions))
         self.validation_predictions.clear()
 
     def configure_optimizers(self):
@@ -159,3 +163,23 @@ class DetectorModel(L.LightningModule):
         optimizer = Adam(self.parameters(), lr=conf.learning_rate)
         scheduler = CosineAnnealingLR(optimizer, T_max=conf.t_max)
         return [optimizer], [scheduler]
+
+    def process_clip(
+        self,
+        clip: data.Clip,
+        audio_dir: Optional[Path] = None,
+    ) -> data.ClipPrediction:
+        spec = preprocess_audio_clip(
+            clip,
+            config=self.config.preprocessing,
+            audio_dir=audio_dir,
+        )
+        tensor = torch.from_numpy(spec.data).unsqueeze(0).unsqueeze(0)
+        outputs = self.forward(tensor)
+        return postprocess_model_outputs(
+            outputs,
+            clips=[clip],
+            classes=self.class_names,
+            decoder=self.decoder,
+            config=self.config.postprocessing,
+        )[0]

batdetect2/train/train_split.py

@@ -16,7 +16,6 @@ def get_train_test_data(ann_dir, wav_dir, split_name, load_extra=True):
 
 
 def split_diff(ann_dir, wav_dir, load_extra=True):
-
     train_sets = []
     if load_extra:
         train_sets.append(
@@ -144,7 +143,6 @@ def split_diff(ann_dir, wav_dir, load_extra=True):
 
 
 def split_same(ann_dir, wav_dir, load_extra=True):
-
     train_sets = []
     if load_extra:
         train_sets.append(

batdetect2/train/train_utils.py

@@ -69,7 +69,8 @@ def get_genus_mapping(class_names: List[str]) -> Tuple[List[str], List[int]]:
 
 
 def standardize_low_freq(
-    data: List[types.FileAnnotation], class_of_interest: str,
+    data: List[types.FileAnnotation],
+    class_of_interest: str,
 ) -> List[types.FileAnnotation]:
     # address the issue of highly variable low frequency annotations
     # this often happens for contstant frequency calls

batdetect2/types.py

@@ -1,4 +1,5 @@
 """Types used in the code base."""
+
 from typing import Any, List, NamedTuple, Optional
 
 
@@ -594,8 +595,7 @@ class FeatureExtractor(Protocol):
         self,
         prediction: Prediction,
         **kwargs: Any,
-    ) -> float:
-        ...
+    ) -> float: ...
 
 
 class DatasetDict(TypedDict):

batdetect2/utils/audio_utils.py

@@ -100,7 +100,7 @@ def generate_spectrogram(
         # log_scaling = (1.0 / sampling_rate)*10e4
         spec = np.log1p(log_scaling * spec)
     elif params["spec_scale"] == "pcen":
-        spec = pcen(spec , sampling_rate)
+        spec = pcen(spec, sampling_rate)
 
     elif params["spec_scale"] == "none":
         pass

batdetect2/utils/plot_utils.py

@@ -417,7 +417,9 @@ def plot_confusion_matrix(
     cm_norm = cm.sum(1)
 
     valid_inds = np.where(cm_norm > 0)[0]
-    cm[valid_inds, :] = cm[valid_inds, :] / cm_norm[valid_inds][..., np.newaxis]
+    cm[valid_inds, :] = (
+        cm[valid_inds, :] / cm_norm[valid_inds][..., np.newaxis]
+    )
     cm[np.where(cm_norm == -0)[0], :] = np.nan
 
     if verbose:

batdetect2/utils/visualize.py

@@ -155,9 +155,9 @@ class InteractivePlotter:
 
                 # draw bounding box around call
                 self.ax[1].patches[0].remove()
-                spec_width_orig = self.spec_slices[self.current_id].shape[1] / (
-                    1.0 + 2.0 * self.spec_pad
-                )
+                spec_width_orig = self.spec_slices[self.current_id].shape[
+                    1
+                ] / (1.0 + 2.0 * self.spec_pad)
                 xx = w_diff + self.spec_pad * spec_width_orig
                 ww = spec_width_orig
                 yy = self.call_info[self.current_id]["low_freq"] / 1000
@@ -183,7 +183,9 @@ class InteractivePlotter:
                         round(self.call_info[self.current_id]["start_time"], 3)
                     )
                     + ", prob="
-                    + str(round(self.call_info[self.current_id]["det_prob"], 3))
+                    + str(
+                        round(self.call_info[self.current_id]["det_prob"], 3)
+                    )
                 )
                 self.ax[0].set_xlabel(info_str)
 

batdetect2/utils/wavfile.py

@@ -8,6 +8,7 @@ Functions
 `write`: Write a numpy array as a WAV file.
 
 """
+
 from __future__ import absolute_import, division, print_function
 
 import os
@@ -156,7 +157,6 @@ def read(filename, mmap=False):
         fid = open(filename, "rb")
 
     try:
-
         # some files seem to have the size recorded in the header greater than
         # the actual file size.
         fid.seek(0, os.SEEK_END)

scripts/gen_dataset_summary_image.py

@@ -16,7 +16,6 @@ import batdetect2.train.train_utils as tu
 import batdetect2.utils.audio_utils as au
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "audio_path", type=str, help="Input directory for audio"
@@ -65,7 +64,9 @@ if __name__ == "__main__":
     else:
         # load uk data - special case
         print("\nLoading:", args["uk_split"], "\n")
-        dataset_name = "uk_" + args["uk_split"]  # should be uk_diff, or uk_same
+        dataset_name = (
+            "uk_" + args["uk_split"]
+        )  # should be uk_diff, or uk_same
         datasets, _ = ts.get_train_test_data(
             args["ann_file"],
             args["audio_path"],

scripts/gen_spec_image.py

@@ -33,7 +33,6 @@ def filter_anns(anns, start_time, stop_time):
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser()
     parser.add_argument("audio_file", type=str, help="Path to audio file")
     parser.add_argument("model_path", type=str, help="Path to BatDetect model")
@@ -143,7 +142,9 @@ if __name__ == "__main__":
 
     # run model and filter detections so only keep ones in relevant time range
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    results = du.process_file(args_cmd["audio_file"], model, run_config, device)
+    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

@@ -25,7 +25,9 @@ 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(
+        "audio_file", type=str, help="Path to input audio file"
+    )
     parser.add_argument(
         "model_path", type=str, help="Path to trained BatDetect model"
     )

scripts/viz_helpers.py

@@ -198,7 +198,6 @@ def save_summary_image(
     )
     ii = 0
     for row in ax:
-
         if type(row) != np.ndarray:
             row = np.array([row])
 
@@ -215,7 +214,9 @@ def save_summary_image(
                 )
                 col.grid(color="w", alpha=0.3, linewidth=0.3)
                 col.set_xticks([])
-                col.title.set_text(str(ii + 1) + " " + species_names[order[ii]])
+                col.title.set_text(
+                    str(ii + 1) + " " + species_names[order[ii]]
+                )
                 col.tick_params(axis="both", which="major", labelsize=7)
                 ii += 1