train.py

#! /usr/bin/env python

import argparse
import os
import numpy as np
from preprocessing import parse_annotation
from frontend import YOLO
import json

os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3" #4 GPU

argparser = argparse.ArgumentParser(
    description='Train and validate YOLO_v2 model on any dataset')

argparser.add_argument(
    '-c',
    '--conf',
    help='path to configuration file')

def _main_(args):

    config_path = args.conf

    with open(config_path) as config_buffer:    
        config = json.loads(config_buffer.read())

    os.environ["CUDA_VISIBLE_DEVICES"]=config['env']['gpu']    
    gpus = max(1, len(config['env']['gpu'].split(",")))

    #print("{}").format(config)
    ###############################
    #   Parse the annotations 
    ###############################

    # parse annotations of the training set
    train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'], 
                                                config['train']['train_image_folder'], 
                                                config['model']['labels'], config['train']['image_ext_name'], config['train']['image_prefix'])
   
    # parse annotations of the validation set, if any, otherwise split the training set
    if os.path.exists(config['valid']['valid_annot_folder']):
        valid_imgs, valid_labels = parse_annotation(config['valid']['valid_annot_folder'], 
                                                    config['valid']['valid_image_folder'], 
                                                    config['model']['labels'], config['train']['image_ext_name'], config['train']['image_prefix'])
    else:
        train_valid_split = int(0.8*len(train_imgs))
        np.random.shuffle(train_imgs)

        valid_imgs = train_imgs[train_valid_split:]
        train_imgs = train_imgs[:train_valid_split]

    
    overlap_labels = set(config['model']['labels']).intersection(set(train_labels.keys()))

    print ('Seen labels:{}'.format(train_labels))
    print ('Given labels:{}'.format(config['model']['labels']))
    print ('Overlap labels:{}'.format(overlap_labels))    

    if len(overlap_labels) < len(config['model']['labels']):
        print ('Some labels have no images! Please revise the list of labels in the config.json file!')
        return
        
    ###############################
    #   Construct the model 
    ###############################

    yolo = YOLO(architecture        = config['model']['architecture'],
                input_size          = config['model']['input_size'], 
                labels              = config['model']['labels'], 
                max_box_per_image   = config['model']['max_box_per_image'],
                anchors             = config['model']['anchors']
                gpus                = gpus)

    ###############################
    #   Load the pretrained weights (if any) 
    ###############################    

    if os.path.exists(config['train']['pretrained_weights']):
        print ("Loading pre-trained weights in {}".format(config['train']['pretrained_weights']))
        yolo.load_weights(config['train']['pretrained_weights'])

    ###############################
    #   Start the training process 
    ###############################

    yolo.train(train_imgs         = train_imgs,
               valid_imgs         = valid_imgs,
               train_times        = config['train']['train_times'],
               valid_times        = config['valid']['valid_times'],
               nb_epoch           = config['train']['nb_epoch'], 
               learning_rate      = config['train']['learning_rate'], 
               batch_size         = config['train']['batch_size'],
               warmup_bs          = config['train']['warmup_batches'],
               object_scale       = config['train']['object_scale'],
               no_object_scale    = config['train']['no_object_scale'],
               coord_scale        = config['train']['coord_scale'],
               class_scale        = config['train']['class_scale'],
               saved_weights_name = config['train']['saved_weights_name'],
               debug              = config['train']['debug'])

if __name__ == '__main__':
    args = argparser.parse_args()
    _main_(args)