def alternate_train(args, ctx, pretrained, epoch, rpn_epoch, rcnn_epoch):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# basic config
begin_epoch = 0
config.TRAIN.BG_THRESH_LO = 0.0
logging.info('########## TRAIN RPN WITH IMAGENET INIT')
train_rpn(args, ctx, pretrained, epoch, 'model/rpn1', begin_epoch, rpn_epoch)
logging.info('########## GENERATE RPN DETECTION')
test_rpn(args, ctx[0], 'model/rpn1', rpn_epoch)
logging.info('########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION')
train_rcnn(args, ctx, pretrained, epoch, 'model/rcnn1', begin_epoch, rcnn_epoch)
logging.info('########## TRAIN RPN WITH RCNN INIT')
train_rpn(args, ctx, 'model/rcnn1', rcnn_epoch, 'model/rpn2', begin_epoch, rpn_epoch,
finetune=True)
logging.info('########## GENERATE RPN DETECTION')
test_rpn(args, ctx[0], 'model/rpn2', rpn_epoch)
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, ctx, 'model/rcnn2', 0, 'model/rcnn2', begin_epoch, rcnn_epoch,
finetune=True)
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model('model/rpn2', rpn_epoch, 'model/rcnn2', rcnn_epoch, 'model/final', 0)
def alternate_train(args, ctx, pretrained, epoch,
rpn_epoch, rpn_lr, rpn_lr_step,
rcnn_epoch, rcnn_lr, rcnn_lr_step):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# basic config
begin_epoch = 0
config.TRAIN.BG_THRESH_LO = 0.0
logging.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)
#logging.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)
logging.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')
logging.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)
logging.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 alternate_train(args, ctx, pretrained, epoch,
rpn_epoch, rpn_lr, rpn_lr_step,
rcnn_epoch, rcnn_lr, rcnn_lr_step):
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# basic config
begin_epoch = 0
config.TRAIN.BG_THRESH_LO = 0.0
logging.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)
logging.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)
logging.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')
logging.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)
logging.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)