original_code/classify.py

import torch
import supervised as s
import numpy as np
import data384 as data
import time
import os
import sys

def do_classification(wavlist_file, modelfile, speciesfile, logit_threshold, outfile):
    species = data.read_filelist(speciesfile)
    nspecies = len(species)
    filelist = data.read_filelist(wavlist_file)
    n = len(filelist)
    device=torch.device('cpu')
    model=s.Net(nclasses=nspecies)
    model.load_state_dict(torch.load(modelfile, map_location=device))

    # summarize detections in each wavfile by number of species detections
    
    with open(outfile, 'w') as fd:
        for wavfile in filelist:
            print(f'wavfile {wavfile}')
            dat = data.wav2spectrograms(wavfile)
            oname = os.path.basename(wavfile)
            # if dat is long, use s.classify1_cpu instead of s.classify_cpu so that memory is not exceeded
            logits = s.classify1_cpu(dat,model,nspecies)
            # logits = s.classify_cpu(dat,model)
            detected = [];
            for i in range(nspecies):
                m=sum(logits[:,i] > logit_threshold)
                if m>0:
                    detected.append(species[i] + ',' + str(m))
            line = ','.join(detected)
            fd.write(wavfile + '\t' + line + '\n')
    return n    
        
def main(args):
    wavlist_file = args[0]
    modelfile = args[1]
    speciesfile = args[2]
    outfile = args[3]
    logit_threshold = 0.0
    
    starttime=time.time()
    n = do_classification(wavlist_file, modelfile, speciesfile, logit_threshold, outfile)
    endtime=time.time()
    print(f'Classification of {n} wavfiles done. Runtime {endtime-starttime:.1f} seconds')   
    return 0

if __name__=="__main__":
    main(sys.argv[1:])