def main():
print('Called with argument:', args)
ctx = [mx.gpu(int(i)) for i in config.gpus.split(',')]
logger, output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'),
output_path)
prefix = os.path.join(output_path, 'rfcn')
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
train_rcnn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
config.network.pretrained,
config.network.pretrained_epoch,
prefix,
config.TRAIN.begin_epoch,
config.TRAIN.end_epoch,
train_shared=False,
lr=config.TRAIN.lr,
lr_step=config.TRAIN.lr_step,
proposal=config.dataset.proposal,
logger=logger)
def main():
print ('Called with argument:', args)
ctx = [mx.gpu(int(i)) for i in config.gpus.split(',')]
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
shutil.copy2(os.path.join(curr_path, 'symbols', config.symbol + '.py'), output_path)
prefix = os.path.join(output_path, 'rfcn')
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
train_rcnn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, config.network.pretrained, config.network.pretrained_epoch, prefix, config.TRAIN.begin_epoch,
config.TRAIN.end_epoch, train_shared=False, lr=config.TRAIN.lr, lr_step=config.TRAIN.lr_step,
proposal=config.dataset.proposal, logger=logger)
def alternate_train(args, ctx, pretrained, epoch):
# set up logger
logger, output_path = create_logger(config.output_path, args.cfg,
config.dataset.image_set)
# basic config
begin_epoch = 0
# logging.info('########## TRAIN RPN WITH IMAGENET INIT')
rpn1_prefix = os.path.join(output_path, 'rpn1')
if not os.path.exists(rpn1_prefix):
os.makedirs(rpn1_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
pretrained,
epoch,
rpn1_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rpn1_epoch,
train_shared=False,
lr=config.TRAIN.ALTERNATE.rpn1_lr,
lr_step=config.TRAIN.ALTERNATE.rpn1_lr_step,
logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
image_sets.extend(
[iset for iset in config.dataset.test_image_set.split('+')])
for image_set in image_sets:
test_rpn(config,
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
ctx,
rpn1_prefix,
config.TRAIN.ALTERNATE.rpn1_epoch,
vis=False,
shuffle=False,
thresh=0,
logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
rfcn1_prefix = os.path.join(output_path, 'rfcn1')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
pretrained,
epoch,
rfcn1_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rfcn1_epoch,
train_shared=False,
lr=config.TRAIN.ALTERNATE.rfcn1_lr,
lr_step=config.TRAIN.ALTERNATE.rfcn1_lr_step,
proposal='rpn',
logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN RPN WITH rfcn INIT')
rpn2_prefix = os.path.join(output_path, 'rpn2')
if not os.path.exists(rpn2_prefix):
os.makedirs(rpn2_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
rfcn1_prefix,
config.TRAIN.ALTERNATE.rpn2_epoch,
rpn2_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rpn2_epoch,
train_shared=True,
lr=config.TRAIN.ALTERNATE.rpn2_lr,
lr_step=config.TRAIN.ALTERNATE.rpn2_lr_step,
logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN FIXED_PARAMS_SHAREDDETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
for image_set in image_sets:
test_rpn(config,
config.dataset.dataset,
image_set,
config.dataset.root_path,
config.dataset.dataset_path,
ctx,
rpn2_prefix,
config.TRAIN.ALTERNATE.rpn2_epoch,
vis=False,
shuffle=False,
thresh=0,
logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn1')
rfcn2_prefix = os.path.join(output_path, 'rfcn2')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn1_prefix,
config.TRAIN.ALTERNATE.rfcn1_epoch, rfcn2_prefix, 0)
logger.info('########## TRAIN rfcn WITH RPN INIT AND DETECTION')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config,
config.dataset.dataset,
config.dataset.image_set,
config.dataset.root_path,
config.dataset.dataset_path,
args.frequent,
config.default.kvstore,
config.TRAIN.FLIP,
config.TRAIN.SHUFFLE,
config.TRAIN.RESUME,
ctx,
rfcn2_prefix,
0,
rfcn2_prefix,
begin_epoch,
config.TRAIN.ALTERNATE.rfcn2_epoch,
train_shared=True,
lr=config.TRAIN.ALTERNATE.rfcn2_lr,
lr_step=config.TRAIN.ALTERNATE.rfcn2_lr_step,
proposal='rpn',
logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn2')
final_prefix = os.path.join(output_path, 'final')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn2_prefix,
config.TRAIN.ALTERNATE.rfcn2_epoch, final_prefix, 0)
def alternate_train(args, ctx, pretrained, epoch):
# set up logger
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
# basic config
begin_epoch = 0
# logging.info('########## TRAIN RPN WITH IMAGENET INIT')
rpn1_prefix = os.path.join(output_path, 'rpn1')
if not os.path.exists(rpn1_prefix):
os.makedirs(rpn1_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, pretrained, epoch, rpn1_prefix, begin_epoch, config.TRAIN.ALTERNATE.rpn1_epoch, train_shared=False,
lr=config.TRAIN.ALTERNATE.rpn1_lr, lr_step=config.TRAIN.ALTERNATE.rpn1_lr_step, logger=logger, output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
image_sets.extend([iset for iset in config.dataset.test_image_set.split('+')])
for image_set in image_sets:
test_rpn(config, config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
ctx, rpn1_prefix, config.TRAIN.ALTERNATE.rpn1_epoch, vis=False, shuffle=False, thresh=0, logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN rfcn WITH IMAGENET INIT AND RPN DETECTION')
rfcn1_prefix = os.path.join(output_path, 'rfcn1')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, pretrained, epoch, rfcn1_prefix, begin_epoch, config.TRAIN.ALTERNATE.rfcn1_epoch, train_shared=False,
lr=config.TRAIN.ALTERNATE.rfcn1_lr, lr_step=config.TRAIN.ALTERNATE.rfcn1_lr_step, proposal='rpn', logger=logger,
output_path=rpn1_prefix)
logging.info('########## TRAIN RPN WITH rfcn INIT')
rpn2_prefix = os.path.join(output_path, 'rpn2')
if not os.path.exists(rpn2_prefix):
os.makedirs(rpn2_prefix)
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RPN_BATCH_IMAGES
train_rpn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, rfcn1_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rpn2_prefix, begin_epoch, config.TRAIN.ALTERNATE.rpn2_epoch,
train_shared=True, lr=config.TRAIN.ALTERNATE.rpn2_lr, lr_step=config.TRAIN.ALTERNATE.rpn2_lr_step, logger=logger,
output_path=output_path)
logging.info('########## GENERATE RPN DETECTION')
image_sets = [iset for iset in config.dataset.image_set.split('+')]
for image_set in image_sets:
test_rpn(config, config.dataset.dataset, image_set, config.dataset.root_path, config.dataset.dataset_path,
ctx, rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, vis=False, shuffle=False, thresh=0, logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn1')
rfcn2_prefix = os.path.join(output_path, 'rfcn2')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn1_prefix, config.TRAIN.ALTERNATE.rfcn1_epoch, rfcn2_prefix, 0)
logger.info('########## TRAIN rfcn WITH RPN INIT AND DETECTION')
config.TRAIN.BATCH_IMAGES = config.TRAIN.ALTERNATE.RCNN_BATCH_IMAGES
train_rcnn(config, config.dataset.dataset, config.dataset.image_set, config.dataset.root_path, config.dataset.dataset_path,
args.frequent, config.default.kvstore, config.TRAIN.FLIP, config.TRAIN.SHUFFLE, config.TRAIN.RESUME,
ctx, rfcn2_prefix, 0, rfcn2_prefix, begin_epoch, config.TRAIN.ALTERNATE.rfcn2_epoch, train_shared=True,
lr=config.TRAIN.ALTERNATE.rfcn2_lr, lr_step=config.TRAIN.ALTERNATE.rfcn2_lr_step, proposal='rpn', logger=logger,
output_path=rpn2_prefix)
logger.info('########## COMBINE RPN2 WITH rfcn2')
final_prefix = os.path.join(output_path, 'final')
combine_model(rpn2_prefix, config.TRAIN.ALTERNATE.rpn2_epoch, rfcn2_prefix, config.TRAIN.ALTERNATE.rfcn2_epoch, final_prefix, 0)
