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 = 8
config.TRAIN.BG_THRESH_LO = 0.0
# model path
model_path = args.prefix
'''
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_path+'/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_path+'/rpn1', rpn_epoch,
vis=False, shuffle=False, thresh=0)
'''
'''
logging.info('########## TRAIN RCNN WITH IMAGENET INIT AND RPN DETECTION')
train_maskrcnn(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_path+'/rcnn1', begin_epoch, rcnn_epoch,
train_shared=False, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn', maskrcnn_stage='rcnn1')
'''
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_path+'/rcnn1', rcnn_epoch, model_path+'/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_path+'/rpn2', rpn_epoch,
vis=False, shuffle=False, thresh=0)
logger.info('########## COMBINE RPN2 WITH RCNN1')
combine_model(model_path+'/rpn2', rpn_epoch, model_path+'/rcnn1', rcnn_epoch, model_path+'/rcnn2', 0)
logger.info('########## TRAIN RCNN WITH RPN INIT AND DETECTION')
train_maskrcnn(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_path+'/rcnn2', 0, model_path+'/rcnn2', begin_epoch, rcnn_epoch,
train_shared=True, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn', maskrcnn_stage='rcnn2')
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model(model_path+'/rpn2', rpn_epoch, model_path+'/rcnn2', rcnn_epoch, model_path+'/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(filename="mask_rcnn_alternate_train_%d.log" % int(time.time()))
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# basic config
begin_epoch = 0
config.TRAIN.BG_THRESH_LO = 0.0
# model path
model_path = args.prefix
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_path + '/rcnn1',
rcnn_epoch,
model_path + '/rpn2',
begin_epoch,
rpn_epoch,
train_shared=True,
lr=rpn_lr,
lr_step=rpn_lr_step)
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '1'
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,
model_path + '/rpn2',
rpn_epoch,
vis=False,
shuffle=False,
thresh=0)
logger.info('########## COMBINE RPN2 WITH RCNN1')
combine_model(model_path + '/rpn2', rpn_epoch, model_path + '/rcnn1',
rcnn_epoch, model_path + '/rcnn2', 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):
# 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/blouse', 0,
vis=True, shuffle=False, thresh=0.97)
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 alternate_combine(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
# model path
model_path = args.prefix
try:
logger.info('########## COMBINE RPN1 WITH RCNN1')
logger.info('rpn_epoch %d, rcnn_epoch %d' % (rpn_epoch, rcnn_epoch))
combine_model(model_path + '/rpn1', rpn_epoch, model_path + '/rcnn1',
rcnn_epoch, model_path + '/pre', 0)
logger.info('########## COMBINE RPN2 WITH RCNN1')
combine_model(model_path + '/rpn2', rpn_epoch, model_path + '/rcnn1',
rcnn_epoch, model_path + '/rcnn2', 0)
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model(model_path + '/rpn1', rpn_epoch, model_path + '/rcnn1',
rcnn_epoch, model_path + '/final', 0)
except:
print("Didn't finish training")
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(filename="mask_rcnn_alternate_train_%d.log" % int(time.time()))
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# basic config
begin_epoch = 0
config.TRAIN.BG_THRESH_LO = 0.0
# model path
model_path = args.prefix
logger.info('########## TRAIN RCNN WITH RPN INIT AND DETECTION')
train_maskrcnn(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_path + '/rcnn2', 0, model_path + '/rcnn2', begin_epoch, rcnn_epoch,
train_shared=True, lr=rcnn_lr, lr_step=rcnn_lr_step, proposal='rpn', maskrcnn_stage='rcnn2')
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model(model_path + '/rpn2', rpn_epoch, model_path + '/rcnn2', rcnn_epoch, model_path + '/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,
use_data_augmentation=args.use_data_augmentation)
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',
use_data_augmentation=args.use_data_augmentation,
use_global_context=args.use_global_context,
use_roi_align=args.use_roi_align)
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,
use_data_augmentation=args.use_data_augmentation)
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',
use_data_augmentation=args.use_data_augmentation,
use_global_context=args.use_global_context,
use_roi_align=args.use_roi_align)
logger.info('########## COMBINE RPN2 WITH RCNN2')
combine_model('model/rpn2', rpn_epoch, 'model/rcnn2', rcnn_epoch,
'model/final', 0)
