Evaluating BatDetect2

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/

e.g.
python evaluate_models.py ../../plots/results_compare_yuc/ /data1/bat_data/data/yucatan/audio/ /data1/bat_data/annotations/anns_finetune/ ../../experiments/2021_12_17__15_58_43/2021_12_17__15_58_43.pth.tar

By default this will just evaluate the set of test files that are already specified in the model at training time. However, you can also specify a single set of annotations to evaluate using the --test_file flag. These must be stored in one annotation file, containing a list of the individual files.

You can also specify if the plots are saved as a .png or .pdf using --file_type and you can set title text for a plot using --title_text, e.g. --file_type pdf --title_text "My Dataset Name"

Comparing to Tadarida-D

It is also possible to compare to Tadarida-D. For Tadarida-D the following steps are performed:

Matches Tadarida's detections to manually annotated calls
Trains a RandomForest classifier using Tadarida call features
Evaluate the classifier on a held out set

Uses precomputed binaries for Tadarida-D from:
https://github.com/YvesBas/Tadarida-D/archive/master.zip

Needs to be run using the following arguments:
./TadaridaD -t 4 -x 1 ip_dir/
-t 4 means 4 threads
-x 1 means time expansions of 1

This will generate a folder called txt which contains a corresponding .ta file for each input audio file. Example usage is as follows:
python evaluate_models.py ../../plots/results_compare_yuc/ /data1/bat_data/data/yucatan/audio/ /data1/bat_data/annotations/anns_finetune/ ../../experiments/2021_12_17__15_58_43/2021_12_17__15_58_43.pth.tar --td_ip_dir /data1/bat_data/baselines/tadarida_D/