roidb = rpn_train.get_training_roidb(imdb)
output_dir = rpn_config.get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
### mix anothor dataset
if args.mix_imdb_name != None:
imdb_mix = get_imdb(args.mix_imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb_mix.name)
roidb_mix = rpn_train.get_training_roidb(imdb_mix)
roidb.extend(roidb_mix)
###
stage1_model = rpn_train.train_net(
train_cfg.stage1.solver,
roidb,
anchordb,
output_dir,
final_name=imdb.name,
pretrained_model=train_cfg.common.pretrained_model,
max_iters=train_cfg.stage1.max_iters)
print 'Stage-1: training rpn finished!'
print 're-store stage-1 final model...'
dest_path = train_cfg.stage1.model_path.format(imdb.name)
os.system('cp {:s} {:s}'.format(stage1_model, dest_path))
print 'done!'
print 'stage-1 done!'
# =============== stage-2 training fast-rcnn with proposals generated by rpn
if current_stage <= 2:
print 'start stage-2...'
imdb = get_imdb(args.imdb_name)
roidb = rpn_train.get_training_roidb(imdb)
output_dir = rpn_config.get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
### mix anothor dataset
if args.mix_imdb_name != None:
imdb_mix = get_imdb(args.mix_imdb_name)
roidb_mix = rpn_train.get_training_roidb(imdb_mix)
roidb.extend(roidb_mix)
###
rpn_model = rpn_train.train_net(args.solver,
roidb,
anchordb,
output_dir,
final_name=imdb.name,
pretrained_model=args.pretrained_model,
max_iters=args.max_iters)
print 'training rpn finished!'
print 're-store rpn final model...'
# dest_path = train_cfg.stage1.model_path.format(imdb.name)
# os.system('cp {:s} {:s}'.format(rpn_model, dest_path))
print 'done!'
# =============== stage-2 testing
final_test_imdbname = args.test_imdb
if not final_test_imdbname:
print 'have no image set to test!\nexit!'
sys.exit()
imdb = get_imdb(args.imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
roidb = rpn_train.get_training_roidb(imdb)
output_dir = rpn_config.get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
### mix anothor dataset
if args.mix_imdb_name != None:
imdb_mix = get_imdb(args.mix_imdb_name)
roidb_mix = rpn_train.get_training_roidb(imdb_mix)
roidb.extend(roidb_mix)
###
rpn_model = rpn_train.train_net(args.solver,
roidb, anchordb, output_dir, final_name=imdb.name,
pretrained_model=args.pretrained_model,
max_iters=args.max_iters)
print 'training rpn finished!'
print 're-store rpn final model...'
# dest_path = train_cfg.stage1.model_path.format(imdb.name)
# os.system('cp {:s} {:s}'.format(rpn_model, dest_path))
print 'done!'
# =============== stage-2 testing
final_test_imdbname = args.test_imdb
if not final_test_imdbname:
print 'have no image set to test!\nexit!'
sys.exit()
print 'start test...'
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
roidb = rpn_train.get_training_roidb(imdb)
output_dir = rpn_config.get_output_dir(imdb, None)
print 'Output will be saved to `{:s}`'.format(output_dir)
### mix anothor dataset
if args.mix_imdb_name != None:
imdb_mix = get_imdb(args.mix_imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb_mix.name)
roidb_mix = rpn_train.get_training_roidb(imdb_mix)
roidb.extend(roidb_mix)
###
stage1_model = rpn_train.train_net(train_cfg.stage1.solver,
roidb, anchordb, output_dir, final_name=imdb.name,
pretrained_model=train_cfg.common.pretrained_model,
max_iters=train_cfg.stage1.max_iters)
print 'Stage-1: training rpn finished!'
print 're-store stage-1 final model...'
dest_path = train_cfg.stage1.model_path.format(imdb.name)
os.system('cp {:s} {:s}'.format(stage1_model, dest_path))
print 'done!'
print 'stage-1 done!'
# =============== stage-2 training fast-rcnn with proposals generated by rpn
if current_stage <= 2:
print 'start stage-2...'
imdb = get_imdb(args.imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
