sedinet_train.py

## Written by Daniel Buscombe,
## MARDA Science
## daniel@mardascience.com

##> Release v1.3 (July 2020)

###===================================================
# import libraries

import sys, getopt, json, os

# set to False if you wish to use cpu (not recommended)
##True or False
USE_GPU = True
# PREDICT = False
#
# ##OS
# if PREDICT == True:
#    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
if USE_GPU == True:
   ##use the first available GPU
   os.environ['CUDA_VISIBLE_DEVICES'] = '0'
else:
   ## to use the CPU (not recommended):
   os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

from numpy import any as npany
from sedinet_infer import *

#==============================================================
if __name__ == '__main__':

    argv = sys.argv[1:]
    try:
        opts, args = getopt.getopt(argv,"h:c:")
    except getopt.GetoptError:
        print('python sedinet_train.py -c configfile.json')
        sys.exit(2)
    for opt, arg in opts:
        if opt == '-h':
            print(
            'Example usage: python sedinet_train.py -c config/config_9percentiles.json'
            )
            sys.exit()
        elif opt in ("-c"):
            configfile = arg

    # load the user configs
    with open(os.getcwd()+os.sep+configfile) as f:
        config = json.load(f)

    ###===================================================
    ## user defined variables: proportion of data to use for training (a.k.a. the "train/test split")
    train_csvfile = config["train_csvfile"]
    #csvfile containing image names and class values
    test_csvfile = config["test_csvfile"]
    #csvfile containing image names and class values
    res_folder = config["res_folder"]
    #folder containing csv file and that will contain model outputs
    name = config["name"]
    #name prefix for output files
    #convert imagery to greyscale or not
    dropout = config["dropout"]
    #dropout factor
    scale = config["scale"] #do scaling on variable
    greyscale = config['greyscale']

    try:
       numclass = config['numclass']
    except:
       numclass = 0

    try:
       greyscale = config['greyscale']
    except:
       greyscale = True

    #output variables
    vars = [k for k in config.keys() if not npany([k.startswith('base'),
            k.startswith('MIN_LR'), k.startswith('DO_AUG'), k.startswith('SHALLOW'), k.startswith('MAX_LR'),
            k.startswith('res_folder'), k.startswith('train_csvfile'), k.startswith('csvfile'),
            k.startswith('test_csvfile'), k.startswith('name'),
            k.startswith('greyscale'), k.startswith('aux_in'),
            k.startswith('dropout'), k.startswith('N'),
            k.startswith('scale'), k.startswith('numclass')])]
    vars = sorted(vars)

    auxin = [k for k in config.keys() if k.startswith('aux_in')]

    if len(auxin) > 0:
       auxin = config[auxin[0]]
       ##at least for now, just one 'auxilliary'
       ## (numerical/categorical) input in addition to imagery
       if len(vars) ==1:
          mode = 'miso'
       elif len(vars) >1:
          mode = 'mimo'
    else:
       if len(vars) ==1:
          mode = 'siso'
       elif len(vars) >1:
          mode = 'simo'

    print("Mode: %s" % (mode))
    ###==================================================

    train_csvfile = res_folder+os.sep+train_csvfile
    test_csvfile = res_folder+os.sep+test_csvfile

    if (mode=='siso' or mode=='simo'):
       run_training_siso_simo(vars, train_csvfile, test_csvfile,
                              name, res_folder, mode, greyscale,
                              dropout, numclass, scale)

    # if (mode=='miso' or mode=='mimo'):
    #    run_training_miso_mimo(vars, train_csvfile, test_csvfile,
    #                           name, res_folder, mode, greyscale,
    #                           auxin, dropout, numclass, scale)