| import argparse |
| import mxnet as mx |
| |
| from rcnn.logger import logger |
| from rcnn.config import config, default, generate_config |
| from rcnn.tools.train_rpn import train_rpn |
| from rcnn.tools.test_rpn import test_rpn |
| from rcnn.tools.train_rcnn import train_rcnn |
| from rcnn.utils.combine_model import combine_model |
| |
| |
| def alternate_train(args, ctx, pretrained, epoch, |
| rpn_epoch, rpn_lr, rpn_lr_step, |
| rcnn_epoch, rcnn_lr, rcnn_lr_step): |
| # basic config |
| begin_epoch = 0 |
| config.TRAIN.BG_THRESH_LO = 0.0 |
| |
| logger.info('########## TRAIN RPN WITH IMAGENET INIT') |
| train_rpn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path, |
| args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume, |
| ctx, pretrained, epoch, 'model/rpn1', begin_epoch, rpn_epoch, |
| train_shared=False, lr=rpn_lr, lr_step=rpn_lr_step) |
| |
| logger.info('########## GENERATE RPN DETECTION') |
| image_sets = [iset for iset in args.image_set.split('+')] |
| for image_set in image_sets: |
| test_rpn(args.network, args.dataset, image_set, args.root_path, args.dataset_path, |
| ctx[0], 'model/rpn1', rpn_epoch, |
| vis=False, shuffle=False, thresh=0) |
| |
| logger.info('########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION') |
| train_rcnn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path, |
| args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume, |
| ctx, pretrained, epoch, 'model/rcnn1', begin_epoch, rcnn_epoch, |
| train_shared=False, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn') |
| |
| logger.info('########## TRAIN RPN WITH RCNN INIT') |
| train_rpn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path, |
| args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume, |
| ctx, 'model/rcnn1', rcnn_epoch, 'model/rpn2', begin_epoch, rpn_epoch, |
| train_shared=True, lr=rpn_lr, lr_step=rpn_lr_step) |
| |
| logger.info('########## GENERATE RPN DETECTION') |
| image_sets = [iset for iset in args.image_set.split('+')] |
| for image_set in image_sets: |
| test_rpn(args.network, args.dataset, image_set, args.root_path, args.dataset_path, |
| ctx[0], 'model/rpn2', rpn_epoch, |
| vis=False, shuffle=False, thresh=0) |
| |
| logger.info('########## COMBINE RPN2 WITH RCNN1') |
| combine_model('model/rpn2', rpn_epoch, 'model/rcnn1', rcnn_epoch, 'model/rcnn2', 0) |
| |
| logger.info('########## TRAIN RCNN WITH RPN INIT AND DETECTION') |
| train_rcnn(args.network, args.dataset, args.image_set, args.root_path, args.dataset_path, |
| args.frequent, args.kvstore, args.work_load_list, args.no_flip, args.no_shuffle, args.resume, |
| ctx, 'model/rcnn2', 0, 'model/rcnn2', begin_epoch, rcnn_epoch, |
| train_shared=True, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn') |
| |
| logger.info('########## COMBINE RPN2 WITH RCNN2') |
| combine_model('model/rpn2', rpn_epoch, 'model/rcnn2', rcnn_epoch, 'model/final', 0) |
| |
| |
| def parse_args(): |
| parser = argparse.ArgumentParser(description='Train Faster R-CNN Network') |
| # general |
| parser.add_argument('--network', help='network name', default=default.network, type=str) |
| parser.add_argument('--dataset', help='dataset name', default=default.dataset, type=str) |
| args, rest = parser.parse_known_args() |
| generate_config(args.network, args.dataset) |
| parser.add_argument('--image_set', help='image_set name', default=default.image_set, type=str) |
| parser.add_argument('--root_path', help='output data folder', default=default.root_path, type=str) |
| parser.add_argument('--dataset_path', help='dataset path', default=default.dataset_path, type=str) |
| # training |
| parser.add_argument('--frequent', help='frequency of logging', default=default.frequent, type=int) |
| parser.add_argument('--kvstore', help='the kv-store type', default=default.kvstore, type=str) |
| parser.add_argument('--work_load_list', help='work load for different devices', default=None, type=list) |
| parser.add_argument('--no_flip', help='disable flip images', action='store_true') |
| parser.add_argument('--no_shuffle', help='disable random shuffle', action='store_true') |
| parser.add_argument('--resume', help='continue training', action='store_true') |
| # alternate |
| parser.add_argument('--gpus', help='GPU device to train with', default='0', type=str) |
| parser.add_argument('--pretrained', help='pretrained model prefix', default=default.pretrained, type=str) |
| parser.add_argument('--pretrained_epoch', help='pretrained model epoch', default=default.pretrained_epoch, type=int) |
| parser.add_argument('--rpn_epoch', help='end epoch of rpn training', default=default.rpn_epoch, type=int) |
| parser.add_argument('--rpn_lr', help='base learning rate', default=default.rpn_lr, type=float) |
| parser.add_argument('--rpn_lr_step', help='learning rate steps (in epoch)', default=default.rpn_lr_step, type=str) |
| parser.add_argument('--rcnn_epoch', help='end epoch of rcnn training', default=default.rcnn_epoch, type=int) |
| parser.add_argument('--rcnn_lr', help='base learning rate', default=default.rcnn_lr, type=float) |
| parser.add_argument('--rcnn_lr_step', help='learning rate steps (in epoch)', default=default.rcnn_lr_step, type=str) |
| args = parser.parse_args() |
| return args |
| |
| |
| def main(): |
| args = parse_args() |
| logger.info('Called with argument: %s' % args) |
| ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')] |
| alternate_train(args, ctx, args.pretrained, args.pretrained_epoch, |
| args.rpn_epoch, args.rpn_lr, args.rpn_lr_step, |
| args.rcnn_epoch, args.rcnn_lr, args.rcnn_lr_step) |
| |
| if __name__ == '__main__': |
| main() |